00:00 The Journey of an Innovator

06:27 The Birth of a Smartphone ECG

11:29 Overcoming Resistance in Digital Health

16:20 The Evolution of ECG Technology

23:43 The Importance of Early Detection in Cardiac Care

26:20 Innovations in 12-Lead ECG Technology

29:00 AI and Machine Learning in Cardiac Diagnostics

34:23 Remote Monitoring and Patient Empowerment

38:34 Navigating AI Diagnostics: Sensitivity vs Specificity

41:26 Consumer Wearables vs. Medical Devices

43:14 Future of AI in Cardiology and Personal Health Awareness

00:00 The Origin Story of Arrive Health

06:04 Rebranding and Evolving Mission

11:50 Real-Time Patient-Specific Drug Costs

18:08 Tackling Prior Authorization Challenges

22:56 Leveraging AI for Healthcare Efficiency

25:36 Understanding Scale and Impact

28:04 Collaboration with Payers and PBMs

30:27 Leveraging AI for Prior Authorization

32:43 Enhancing Access to Medications

36:27 Expanding Beyond Medications

38:19 Reframing Access to Care

40:51 Future Directions and Innovations

44:55 Wisdom for Innovators in Healthcare

01:06 The founding story of Retina AI

03:56 Why ophthalmology is uniquely suited for AI solutions

06:27 Recent Series B funding and what it means for the company

08:31 How AI integrates into real-world clinical workflows

12:04 AI alerts and notifications for clinical decision support

16:36 Working with pharmaceutical companies on clinical trials

19:15 Predicting disease progression for untreated patients

25:42 The transition from traditional AI to newer approaches

29:28 Expanding beyond eye care with the Ikerian rebrand

32:06 Using the eye as a window to detect other diseases

37:44 How physicians have responded to AI diagnostic tools

41:09 Predictions for AI in healthcare and eye care by 2028

00:00 Introduction and Company Updates

02:49 The Digital Transformation of Ultrasound Imaging

06:00 Advancements in Technology and Market Growth

09:03 AI Integration in Medical Imaging

14:50 Impact on Global Health and Humanitarian Efforts

20:55 Challenges in Mainstream Adoption of Handheld Ultrasound

29:43 Strategic Sales Approaches in Medical Devices

34:11 Finding Product-Market Fit

36:12 Simplicity in Medical Technology

39:26 Unexpected Use Cases and Market Adoption

45:24 Future Innovations in Handheld Ultrasound

51:58 The Importance of Patient Data Ownership

09:08 The Technology Behind Carry Heart

18:00 Clinical Implications and Risk Assessment

27:27 Actionable Steps for Patients

30:34 Optimizing Cardiovascular Drug Dosing

32:31 AI in Cardiovascular Medicine

33:50 Leveraging Historical Data for Risk Prediction

36:25 AI's Role in Molecular Pathway Analysis

39:03 GLP-1 and Cardiovascular Outcomes

41:56 Targeted Therapies in Cardiovascular Treatment

42:45 Building Trust in New Technologies

49:16 Regulatory Approvals and Future Prospects

54:15 Expanding Applications Beyond Cardiology

57:16 Looking Ahead: The Future of Caristo Diagnostics

The JP Morgan Healthcare Conference indicated a positive outlook for the industry.

AI is becoming a central theme in healthcare discussions.

Concert AI is developing a comprehensive data ecosystem for oncology.

The introduction of agentic AI models is set to revolutionize data processing.

Collaboration with NVIDIA is enhancing Concert AI's capabilities.

Kera is a significant advancement in AI-driven healthcare solutions.

The future of drug development will rely heavily on AI and data analytics.

Healthcare organizations must adapt to the rapid pace of technological change.

Building partnerships is crucial for addressing healthcare fragmentation.

The integration of AI in clinical trials can significantly reduce timelines.

Chapters

00:00 The Annual Healthcare Pilgrimage

03:19 Optimism in the Pharma Industry

06:07 The Rise of AI in Healthcare

10:03 Concert AI's Evolution and Innovations

15:10 2024: A Standout Year for Concert AI

18:55 Balancing Growth and Innovation

22:33 Scaling Across Therapeutic Areas

26:26 The Future of Collaborations in Healthcare

28:37 Integrating Immune Status and Data Collaboration

31:04 Introducing Kera AI: The Future of Data Management

35:24 Innovations in Clinical Trials and Data Solutions

40:16 Rethinking Drug Development: Digital Twins and AI

42:36 Navigating Market Shifts and Talent Challenges

54:02 The Future of Concert AI and Healthcare Solutions

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify! 

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One thing that’s for sure is that generative AI isn’t magic. You can’t just  sprinkle it like pixie dust over an existing project or dataset and expect wonderful things to happen automatically. In fact, just to use the data you already have, you have to you may have to invest a lot in the new infrastructure and tools needed to train a generative model. And that’s the part of the puzzle Harry focuses on in today's interview with David Buniatyan. He’s the founder of a company called ActiveLoop, which is trying to address the need for infrastructure capable of handling large-scale data for AI applications. He has a background in neuroscience from Princeton University, where he was part of a team working on reconstructing neural connectivity in mouse brains using petabyte-scale imaging data. At ActiveLoop, David has led the development of Deep Lake, a database optimized for AI and deep learning models trained on equally large datasets.

Deep Lake manages data in a tensor-native format, allowing for faster iterations when training generative models. David says the company’s goal is to take over the boring stuff. That means removing the burden of data management from scientists and engineers, so they can focus on the bigger questions—like making sure their models are training on the right data—and ultimately innovate faster.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify! 

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Caristo’s test is being used on an experimental basis in the UK, and it hasn’t yet been approved for use in the US. But it’s a leading example of the way AI, put together with fundamental advances in our understanding of human biology, is really beginning to change the practice of medicine. Cheng and Channon say Caristo’s test isn’t intended to put cardiologists or radiologists out of work—it’s designed to help them be more effective. And given that cardiovascular disease is the number one cause of death around the world, any technology that can help catch signs of coronary artery disease earlier could save a lot of lives.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify! 

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For example, you might be convinced that the most important thing is to understand the physics of protein-protein interactions, at an atomic level. And so you would put your money into atomic-scale simulations that show how proteins fold or unfold to form different shapes under different conditions. Or you might think that it’s more important to model proteins at the molecular scale, to make predictions about whether and how a particular drug molecule might dock with a target protein. Or you might think that it’s smarter to try to model whole cells and see how different molecular pathways interact to affect different functions of the cell. Or you might not care about the details of physics- or chemistry-based models at all. In that case could just take a big generative AI model, similar to a large language model, and train it on huge amounts of unlabeled data about genes and proteins in diseases cells and healthy cells to see what kinds of predictions it comes up with.

It’s too early to say which of these computational approaches—and which level or scale of focus—is going to be the most fruitful. But maybe you don’t have to choose. Maybe you can bet on all of these different ideas, all at once. Harry's guests this week are the CEO and CSO of a startup that’s taking an all-of-the-above approach. It’s called Deep Origin, and it was formed last year from the merger of two companies founded by theoretical chemist Garegin Papoian and software builder Michael Antonov. 

Antonov helped to found the virtual reality hardware company Oculus. After Facebook acquired Oculus, he got curious about longevity and how software could help untangle the trillions of gene-protein interactions that mediate health and disease. He founded a company called Formic Labs to dig into that problem, and last year the company changed its name to Deep Origin. Papoian, meanwhile, is a former academic scientist who’s who also took the helm as CEO of his startup AI and who’s interested in how to use software to model molecular dynamics and quantum chemistry. 

Recently Antonov and Papoian decided to join forces, and Biosim AI merged into Deep Origin. They say the company’s philosophy is that physics-based modeling by itself won’t be enough to build a powerful drug discovery engine. But neither will generative AI, which requires more training data than lab scientists will ever be able to provide. They think the only reasonable approach today is to combine the two, and use both physics and AI to try to get better at predicting which molecules could become effective drugs.

Exactly how Antonov and Papoian came to their conclusion, and how that integration is playing out, was the main theme of this week's conversation. It’s important stuff, because if Deep Origin is right, then a lot of other more specialized biotech and techbio startups could be going down the wrong path. 

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify! 

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The company’s CEO, Jeff Elton, first spoke with Harry back in July of 2021. At that time, the company was already one of the leaders in gathering and analyzing broad collections of data about cancer patients involved in clinical trials for new treatments. Its specialty was, and is, going beyond the very specific endpoints measured in clinical trials and looking to electronic medical records, genome sequencing data, insurance claims data, and other sources in order to build a more comprehensive picture of cancer patients and their journeys through the healthcare system. That kind of data can be very useful to companies trying to track the performance of their drugs after they’ve reached the market, and to researchers planning new clinical trials. 

And since that first conversation, the company has grown by leaps and bounds. It’s taken over management of more data sources, including the massive CancerLinq database formerly maintained by the American Society of Clinical Oncology. It’s struck up partnerships with some of the leading technology startups, research centers, and drug companies working to beat cancer. And it’s leaning hard into the new wave of deep-learning AI tools and their potential to help find patterns in vast amounts of data about patients. It’s probably safe to say that ConcertAI has gathered up more data about cancer patients than any other company on the planet. And investors have been rushing to pour money into the company, on the conviction that data is going to be the key to getting more and better cancer drugs to market. That’s certainly Jeff Elton's conviction too, as you’ll hear in today's interview.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify! 

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For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts or Spotify! 

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For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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The microbiome has been getting more and more attention from researchers and doctors now that we’re starting to have the tools we need to identify and measure all those microbes and see what they’re up to. Harry's guest this week is serial healthcare and AI entrepreneur Leo Grady, whose company Jona is on a mission is to help patients and physicians keep up with the skyrocketing amount of scientific literature about the microbiome and try to translate it into real steps people can take to improve their health.

If you’re a Jona customer, you start by sending in a fecal sample. Then the company uses a large-scale gene sequencing technique called shotgun metagenomics to get a profile of all the microbes in your GI tract. Since everyone’s microbiome contains a different mix of microbes, the next step is to use large language models to sift through the published science about the microbiome and find the studies that relate to the specific bugs in your microbiome. Then the company gives patients and their doctors a report that parses out whether their microbiome makeup might be contributing to their health problems, and whether there might be any health or nutritional interventions that would help. It’s all in the early stages. And right now Jona’s test is mostly available through concierge medical services, executive health clinics, and other offices that do a lot of cash-pay tests. But Grady thinks that over the long term the service has the potential to turn the microbiome from a former black box into something closer to what he calls an “organ of data"—meaning a part of the body that doctors can, in a sense, visualize and analyze in the same way we can use MRI and other forms of imaging to scan our other organs.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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That’s the market opening that Harry's guest Taylor Chartier says she saw back in 2020, during the coronavirus pandemic. Chartier watched the stories about the Baltimore company Emergent BioSolutions, which was manufacturing vaccines for Johnson & Johnson and AstraZeneca and had to throw out millions of doses of both vaccines due to suspected cross-contamination, and thought: there has to be a better way. So she started her own company. And today her startup Modicus Prime is partnering with top pharma companies to use new machine vision and AI capabilities to catch drug manufacturing problems faster.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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That's it! Thanks so much.

That’s why Harry wanted to have Nassib Chamoun on the show. Chamoun is the founder and CEO of Health Data Analytics Institute (HDAI), which has been working with a major healthcare system, Houston Methodist, to test out a working platform called HealthVision. It's a collection of AI-driven models that use huge amounts of data, both from Medicare and from Houston’s own electronic health record system, to make predictions that help doctors and administrators spend less time poring over records and data, and more time interacting with actual patients and making good clinical and management decisions.

Nassib has a way of talking about HDAI and HealthVision that leaves out the hype and focuses on the real-world problems AI can solve for doctors and administrators—like how to identify the patients discharged from hospitals to their homes or to skilled nursing facilities who are at the highest risk of complications, and which interventions could help keep them alive and prevent readmission. Nassib tells Harry that “AI is not magic" and points out that even the most famous large language models, like ChatGPT, are just massive statistical representations of data created, collected, or curated by humans. And while these models are powerful, Nassib argues they’ll need guardrails around them to guarantee transparency and explainability and to prevent bias, before they can be useful in high-stakes fields like healthcare.

HDAI has raised tens of millions of dollars of capital and spent seven years developing HealthVision, and now the company is getting ready to grow beyond Houston Methodist and deploy the system at other big healthcare institutions like the Cleveland Clinic and the Dana-Farber Cancer Institute—so more providers will get a chance to test whether AI can keep patients healthier and make healthcare delivery more efficient.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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That's it! Thanks so much.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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That's it! Thanks so much.

Scott works at Google Cloud, where he’s the director of Applied AI in the Office of the CTO. He and his team work with Google’s big corporate customers, including a variety of customers in healthcare and pharmaceutical R&D, to help them solve business problems that require large-scale computing and deep learning. 

Scott compares Google’s cloud computing capabilities to a racecar that can be adapted to any type of race—whether that’s a customer like Ginkgo Bioworks that leans on computation to reprogram bacterial cells to pump out pharmaceuticals and other products, or a giant health network like Anthem that uses AI to deliver personalized services to members. Because Scott helps set up these partnerships, and because he gets the first look at the Google’s emerging products and services, he has a unique picture of how computing is changing the everyday practice of doing R&D and running a healthcare company. As he himself puts it, he’s in the catbird seat. So listen along as Scott and Harry geek out about how far things have come in AI's transformation of healthcare, and how much more is just around the corner. 

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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That's it! Thanks so much.

Recently Harry learned about a company called Vytal that’s building eye-tracking technology to measure brain health, and he knew he wanted to have the co-founders on the show. Not just because the technology is interesting, but because CEO Rohan Kalahasty and the CTO Sai Mattapali are both 18 years old, and both entering their senior years at Thomas Jefferson High School of Science and Technology in Fairfax County, Virginia. 

Very few teenagers have ten employees and over a million dollars in seed capital. But that's exactly where Rohan and Sai are right now. Some of the challenges they’ve faced have been absolutely typical—like how to build a network of partners and how to meet government standards for new medical devices. And others have been a little unusual, like how to get time off from school to meet with investors and how to convince their parents that the business won’t take too much time away from their studies. Listen in to hear their whole startup story.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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That doesn’t mean machines are making humans obsolete. But it does mean that organizations that deal in information need to figure out how to equip their people to use the new generative AI tools effectively.  If they don’t, they’re going to get outperformed by competitors that do that better. And in Harry's view, professionals in drug discovery, drug development, and healthcare don’t quite understand the scale of the change that’s coming. They need to get up to speed right now if they want to incorporate generative AI into their work in a way that’s effective and safe.

Fortunately there are plenty of people in the life sciences industry thinking about how to help with that. And one of them is Harry's guest this week, Lana Feng. She’s the CEO and co-founder of Huma.ai, and under her leadership the company has been working with OpenAI to find ways to adapt large language models for use inside biotech and pharmaceutical companies. GPT-4 and competing models are extremely powerful. But for a bunch of reasons that Lana explains in this episode, it wouldn’t be smart to apply them directly to the kinds of data gathering and data analysis that go on in the biopharma world. Huma.ai is working on that problem. They’re building on top of GPT-4 to make the model more private, more secure, more reliable, and more transparent, so that companies in drug development can really trust it with their data and not get tripped up by issues like the hallucination problem. Anybody who wants to understand how generative AI could change practices in the drug industry needs to know what the company is up to.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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That's it! Thanks so much.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

1. Open the Podcasts app on your iPhone, iPad, or Mac. 

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For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

1. Open the Podcasts app on your iPhone, iPad, or Mac. 

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EEG is a non-invasive way to measure electrical activity in the brain, and it’s been a common medical tool for almost 100 years. But takes a lot of training for a human doctor to interpret an EEG correctly. It’s slow, it’s expensive, and it’s a bit of a dark art—all of which makes it the perfect candidate for machine learning analysis. Donoghue says the goal at Beacon Biosignals is to use computation to get more value out of existing EEG data. By peering deeper into the data, he thinks it should be possible to identify subtypes of problems like epilepsy or Alzheimer’s, and help neurologists understand which patients will respond best to which therapies. On top of that, better EEG measurements could also give drug developers and regulators more clinical endpoints to measure when they’re trying to evaluate the safety and efficacy of new drugs for CNS diseases. If Beacon’s vision comes true, the precision medicine revolution might finally start to reach the brain.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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Inscripta had previously focused on a benchtop "bio-foundry" machine called Onyx that that makes programmed edits to bacterial or yeast cells at thousands of different points in their genome in parallel. Now it's pivoting away from selling the machine and instead focusing on becoming a power user of its own technology. Its ultimate plan is market multiple biomanufactured products, starting with a synthetic form of bakuchiol, an alternative to the anti-aging compound retinol.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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The company doesn’t hunt for just small-molecule drugs or just protein therapies. It explores both. It doesn’t utilize just one form of neural networking or machine learning. It uses whatever model produces the best science for a given problem. It doesn’t hunt for drugs using just wet lab data or just computational simulations. It does both. It isn’t just assembling its own pipeline of drugs or just partnering with larger pharma companies. It’s working on both. Jen wasn’t even dead set on being an entrepreneur—she had to be talked into applying to the Y Combinator startup incubator and into accepting her Series A investment from Microsoft’s venture fun.

She says the way 1910 thinks about drug discovery is to start with the desired output -- say, a new molecule to block pain -- then figure out what sorts of data inputs exist. Then they find or create all the data they need to analyze the problem. Then they transform that data using whatever AI tools work best, until they get some decent drug candidates. She calls it Input, Transform, Output. It’s never that simple, of course. But at a time when AI and machine learning focused drug discovery companies are sprouting up faster than dandelions—each one touting some specific reason why its model is better than all the others—1910 Genetics is has a more inclusive approach to solving classic problems in pharmacology, and it’s one that should spread to other parts of the life science business. 

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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Harry's guests Anna Daily and Omid Moghadam are from a startup called Namida Lab that’s the first company to market a lab test using tears to predict cancer risk. Specifically, Namida’s test assesses the short-term risk that a patient might have breast cancer, as a way of helping them decide how soon to go in for a mammogram. 

"Namida" is actually the Japanese word for tears, and beyond breast cancer, the company aims to build a whole business around risk assessment and diagnostics, using just the biomarkers in tears. Eventually it could be possible to collect a sample of your tears on a small strip of absorbent paper, send it in to Namida Lab, and find out whether you have colon cancer, pancreatic cancer, prostate cancer, or ovarian cancer. Namida’s big vision, as Moghadam and Daily tell it, is to use tear testing to make precision medicine and diagnostics more accessible and affordable, including to patients who might live far away from tertiary care centers.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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Since at least 2016, Zhavoronkov has been publishing papers about the power of a class of AI algorithms called generative adversarial networks or GANs to help with drug discovery. One of the main selling points for GANs in pharma research is that they can generate lots of possible designs for molecules that could carry out specified functions in the body, such as binding to a defective protein to stop it from working. Drug hunters still have to sort through all the possible molecules identified by GANs to see which ones will actually work in vitro or in vivo, but at least their pool of starting points can be bigger and possibly more specific.

Zhavoronkov says that when Insilico first started touting this approach back in the mid-2010s, few people in the drug business believed it would work. So to persuade investors and partners of the technology's power, the company decided to take a drug designed by its own algorithms all the way to clinical trials. And it’s now done that. This February the FDA granted orphan drug designation to a small-molecule drug Insilico is testing as a treatment for a form of lung scarring called idiopathic pulmonary fibrosis. Both the target for the compound, and the design of the molecule itself, were generated by Insilico’s AI. 

The designation was a big milestone for the company and for the overall idea of using generative models in drug discovery. In this week's interview, Zhavoronkov talks about how Insilico got to this point; why he thinks the company will survive the shakeout happening in the biotech industry right now; and how its suite of generative algorithms and other technologies such as robotic wet labs could change the way the pharmaceutical industry operates.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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RNA has been in the news a lot lately too. That's in part because some of the vaccines that helped us beat back the coronavirus pandemic were made from messenger RNA, a form of the molecule that instructs cells how to build proteins (in that case, antibodies to the virus). But RNA has many other functions in the body, and if we knew how to design small-molecule drugs to attach to binding pockets on any given RNA to interrupt or modulate its functions, it could open up a whole new realm of medical treatments. The problem is, if all you know about an RNA molecule is its nucleotide sequence, it’s very hard to predict where those binding pockets might be and what kind of drug might fit into them. 

As a PhD student at Stanford, Townshend designed a deep learning model to tackle that problem. The model, called ARES, started with a proposed structure for an RNA molecule with a known nucleotide sequence, and predict how that proposal would compare to real-world data. ARES turned out to be stunningly accurate, and it acquired its skills by studying a remarkably small training set: just 18 examples of RNAs with known structures. So in a way, it was using the power of small data, together with a bit of physics. Now Atomic AI is building on that original model to create an engine for discovering new small-molecule drugs that could potentially interrupt any disease where RNA is a player.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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If you wanted to find a comparably successful crossover between medicine and comedy, you’d probably have to go all the way back to TV shows like M*A*S*H and Scrubs. But as funny as Will and Kristin’s comedy work can be, it comes from a pretty serious place. Will’s been on the patient side of medical care. He survived two bouts of testicular cancer. And in May of 2020, after WIll went into cardiac arrest, Kristin saved his life by administering CPR until emergency medical technicians could arrive and rush him to the hospital, where surgeons implanted a defibrillator. It was a nightmare experience. But Flanary’s collision after the surgery with the health insurance bureaucracy may have been even worse. All of it became grist for his comedy sketches, and today the Glaucomfleckens videos and podcast range across topics like what goes on behind the scenes in emergency rooms, how oncologists deliver bad news, or why doctors in different specialties sometimes have a hard time communicating. The basic insight behind Will and Kristin’s work is that in a country where the healthcare system often feels so broken and so full of crazy personalities, sometimes you just have to laugh.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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Kingsmore earned his medical degrees in Northern Ireland, trained in internal medicine and rheumatology at Duke, and studied genomic medicine at Children’s Mercy Hospital in Kansas City. And he’s now the president and CEO of the Institute for Genomic Medicine at Rady Children’s Hospital in San Diego. There, he’s been leading an aggressive push to prove that rapid whole-genome sequencing and diagnosis can not only save the lives of newborns, but save the healthcare system a lot of money by making hospital stays shorter and therapies more directed. He’s been able to use that argument to get Medicaid agencies in California and five other states, as well as a handful of private insurance companies, to cover whole-genome sequencing as the new standard of care for babies who end up in intensive care with unexplained illnesses. And if his newest project, BeginNGS, succeeds, it could lead to universal screening of all newborns for hundreds or even thousands of rare genetic disorders. 

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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At the time, Arterys had recently won FDA clearance for a cloud-based software platform that used deep learning to help radiologists automatically locate the contours of the ventricles of the heart. The company would go on to apply similar technology to MRI and CT images of all sorts of tissue, including the breast, chest, brain, and lungs. What made the platform doubly unique was that doctors could access it over the web, so hospitals didn’t have to maintain expensive on-premise software or hardware. 

Today it would be hard to find a health tech company that isn’t using AI and cloud computing in some way, but it's easy to forget how recent those developments are; Arterys was the very first company to obtain FDA clearance for a cloud-based radiology platform. In light of the acquisition news, we decided to dip into the show’s archives and bring that you that original interview with Fabien Beckers, who's now head of digital pathology for the Alphabet company Verily Life Sciences.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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But that might finally be changing. Harry's guest this week is Ryan Field, chief technology officer at Kernel. The vision of the L.A.-based company is to develop a consumer device that would work like a pulse oximeter, but for your brain. The first version, Kernel Flow, is shaped like a bicycle helmet, and it contains more than 50 low-power lasers that beam light through your scalp into your skull, into the outermost layers of your brain. Hundreds of detectors built in the helmet collect the light that’s scattered back to determine oxygen levels in the brain’s blood supply, which is an indirect measure of neural activity.

Field says the company isn't yet targeting specific consumer applications for the Kernel Flow. But it's already using the device in early studies designed to measure a user’s level of focus on a specific task, or how their brain activity changes in response to pain therapy or psychedelic drugs. Field says what Kernel has done is sort of like building the very first iPhone -- but if the only app the device came with was Maps. Now it's up to developers to figure out what else to do with it.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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This week Harry interviews Grail's president, Dr. Josh Ofman. He says that the company is working to bring down the price of the test, and that if multi-cancer early detection tests like Galleri are eventually approved for population-level screening, it could help avert 100,000 deaths per year.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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OCT uses near-infrared light that penetrates just a couple of millimeters into a tissue such as an artery wall or the retina of the eye. By collecting the light that scatters back, OCT can produce an incredibly high-resolution cross section or even a 3D reconstruction of the tissue. Ophthalmology is one of the fields putting OCT to use most aggressively, partly because it’s perfect for showing cross-sections of the retina, the iris, the cornea, or the lens on the scale of micrometers.

But as you can imagine, every time an ophthalmologist or optometrist uses an OCT scanner, the procedure generates a huge amount of digital data. Harry's guest, Carlos Ciller, started a company called RetinAI whose mission is to help eye doctors, eye surgeons, and scientists studying the eye manage and analyze all that information. And not just information from OCT, but from other types of eye imaging like fundus photography and fluorescent angiography.

At one level, RetinAI is just doing its part to cure a huge headache we’ve talked about again and again on the show, which is the lack of standards and interoperability in the healthcare IT world. They want to make it possible to store and analyze digital images of the eye no matter what technology or device was used to capture it. But more intriguingly, once that data is stored in a structured way, it’s possible to use machine learning and other forms of artificial intelligence to sort through image data and identify pathologies or double-check the judgments of human physicians. RetinAI is developing algorithms that could make it easier to diagnose and treat common conditions like age-related macular degeneration—a form of damage to the retina that causes vision loss in almost 200 million people around the world. Ciller told me he started out his career as a telecom engineer and never thought he’d wind up running a 40-person company that works to help people with vision problems. But at a time when there’s so much new data available to diagnose disease rand identify the best treatments, journey’s like his—from the computer lab to the clinic—are becoming more and more common.  

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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January.ai is one such company, and co-founder and CEO Noosheen Hashemi joined Harry on the show back in July of 2021. It turns out that the same foods can have different effects on the blood glucose levels of different individuals, and January’s app starts off using live CGM data to study those patterns using machine learning algorithms. Then it can start making predictions about a user's future blood glucose levels, even after they stop wearing a CGM. That can help them make smarter decisions about what, when, or how much to eat, or how much they need to exercise after eating.

January’s main goal is not to treat diabetes but actually to prevent it from arising in the first place in the tens of millions of people who have signs of pre-diabetes. Now Hashemi has helped to launch a second business, Eden's, that helps with that goal by promoting better gut health. The company makes a nutritional supplement that provides a blend of polyphenols, probiotics, and prebiotics to help improve the function of the bacteria that call your large intestine home. 

Probiotics, which live organisms introduced to change the makeup of your gut microbiome. Prebiotics are non-digestible substances that are fermented by beneficial bacteria like bifidobacteria and lactobacillus, breaking them down into useful nutrients like short-chain fatty acids. Altogether, the Eden’s blend is designed to keep your gut microbiomes happy, which can have the useful side effect of helping to keep your blood glucose steady. Harry talked with Hashemi to talk about why that’s so important, and about the work January has been doing this year to update its glucose monitoring app—and how the app works in concert with the Eden's supplements. 

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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And that’s exactly what Harry's guest Mylene Yao, the co-founder of Univfy, is doing. Univfy helps patients with two aspects of the IVF process. The first is using machine learning to provide patients with a more accurate assessment of  the odds of success, before they decide whether to invest in one or more IVF cycles, which can cost up to $30,000 per cycle. The second is financing. Univfy works with a bank called Lightstream to provide up to $100,000 in financing for up to three rounds of IVF, with a large refund as part of the deal if the treatments don’t result in a baby. 

Harry talks with Dr. Yao about the prospects for far broader access to IVF, now that the field is finally adopting more ideas from the worlds of technology and finance. 

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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This fall, Illumina announced that it’s coming out a new gene sequencing machine called the NovaSeq X that can sequence a genome more than twice as fast as Illumina’s previous top-of-the-line machine, and at a lower cost. That’s bound to speed up progress all across the field of genetic medicine, drug discovery, and life science research. And that’s where Harry starts his interview with Febbo.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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As you’ll hear in this week's interview, Sable thinks most IVF labs today still operate almost like artisanal kitchens, with way too much riding on the judgment of individual doctors and technicians. He thinks machine learning algorithms could supplement human expertise at many points in the process, and turn what’s essentially a craft into a truly automated and predictable industry. His central argument is that IVF won’t truly be democratized until providers have “engineered the hell out of” the procedure, to increase success rates and lower the chances that patients will have to pay for more than one cycle of the treatment. At the same time, he says the concept of value-based care needs to make its way into the IVF world, so that patients and their insurers or their employers only pay when the procedure works, not when it fails. 

Stay tuned to future episodes for more discussion about the role of AI in IVF.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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But these labs are surrendering. And Harry's guest this week, Erwin Seinen, is helping to accelerate that shift. He’s the founder and CEO of a company called eLabNext, whose core product is a Web-based software platform called eLabJournal that includes tools for inventory and sample tracking, managing experimental protocols and procedures, and recording experimental results.

Seinen spent years building e-commerce tools before he went back to school and got his degree in medical genetics. So he knew how to write software, and to streamline his dissertation work, he built his own electronic lab notebook tool. He says his lab colleagues were so jealous that he realized every lab researcher needs a similar tool. And that’s how eLabNext was born.

But when absolutely everything goes digital, there’s the danger of losing the special connection between mind, pen, and paper that goes with making old-fashioned handwritten notes. Harry talked with Seinen about that, as well as his vision of how an electronic lab notebook can fit together with other lab tools in an era where there’s just too much data to print out everything on paper. If companies and universities manage this transition right, they can benefit from all the latest digital tools—without sacrificing any of the spontaneity, curiosity, or creativity that good science is all about.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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But there has been one important change during that decade. Thanks to new technologies, ranging from wearables like the Apple Watch to sophisticated deep brain implants from companies like Medtronic, we’re now able to gather a lot more data about what’s happening in the daily lives of patients with Parkinson’s, and how the disease is affecting their brain function and their physical movement. Which means there’s now the potential to make much smarter and more timely decisions about how to dose the drugs patients are taking, or whether they should think about joining a clinical trials.

Gathering and analyzing that information and feeding it back to patients and their doctors in a user-friendly form is the mission of Rune Labs, where Pepin is CEO. He says we’re on the edge of a new era of “precision neurology,” where data gives doctors the power to predict the course of a disease and muster a meaningful clinical response. And he wants Rune Labs to be at the leading edge of that change. 

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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Philadelpha-based Proscia is working to change that—and open up pathology to the power of remote work and automated image analysis—by building a cloud-based infrastructure for storing and sharing scanned pathology images. Harry's guest today is Proscia CEO David West, who says there are still strong cultural barriers to the adoption of digital pathology, but "the community is realizing this can be really great for them and their discipline." West says easier scanning, higher resolution, faster image delivery, and the ability to review images from anywhere and tap the power of artificial intelligence are powerful advantages driving adoption of Proscia's platform.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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That’s a 10-year project designed to gather medical data from more than a million people around the United States to help doctors make more customized health recommendations based on a patient’s environment, lifestyle, family history, and genetic makeup. If you’re going to try to recruit a million people into your research study and keep tabs on their health, and if those people are going to be from diverse backgrounds, and if they’re going to be distributed around the country, then there’s only one practical way to reach them, and that’s on their smartphones. NIH asked Vibrent to build a mobile app and an online portal that would become the communications backbone and the central data gathering repository for the whole project. And now that NIH is six or seven years into the All of Us project, it’s clear that in some ways the project, and Vibrent's front end, have leapfrogged over the rest of the US healthcare ecosystem. 

The app provides an easy way to gather and manage data from patients in the study, and to monitor and interact with them, while still protecting their privacy.  As Jain puts it, it meets All of Us participants "where they are" – meaning, on their phones. Technology like that still isn’t part of the offering at most big health plans or hospital networks. But Vibrent is working to change that by partnering with health systems, academic health centers, pharmaceutical companies, public health organizations, and research organizations to get its mobile apps distributed more widely. If you believe that our phones are going to be a key element of personalized and precision medicine for everyone, then the work Vibrent is doing with NIH and its other customers is worth watching.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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Our guest this week, Artificial CEO David Fuller, believes that life sciences labs will always revolve around manual labor, but thinks there’s a way to orchestrate the process more precisely. Artificial makes software that allows lab managers to create what he calls a digital twin of their entire laboratory. Inside this digital twin, data structures track what’s happening with each piece of lab equipment and keep them in sync, even if they’re from different manufacturers. The software provides what Fuller calls “a single pane of glass that makes it easier to see the state of the equipment and the science as it's running in your lab”…meaning what’s happening, why it’s happening, and what errors may be cropping up. Humans will always stay in the loop, but Fuller says the benefit for companies who orchestrate their labs in this way is that the data and the products coming out of the lab will be more consistent. Which will be even more important as laboratories start to act more like factories, where a lot of the actual production of biologic drugs or other materials happens.

For a full transcript of this episode, please visit our show page at http://www.glorikian.com/podcast

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Rigby is the head of a new non-profit organization called Rare-X that’s trying to tackle a systematic problem that affects everyone with a rare disease: Data. In the rare disease world, Rigby says, data collection is so inconsistent that each effort to understand and treat a specific disease feels like reinventing the wheel. For longtime listeners of the show, that’s a familiar story. Time and again, Harry has talked with people who point out the harms of storing patient data in separate formats in separate silos, and who have new ideas for ways to break down the walls between these silos. Rare-X is trying to do exactly that for the rare disease world, by building what Rigby calls a federated, cloud-based, cross-disorder data sharing platform. The basic idea is to take the burden of data management off of rare disease patients and their families and create a single central repository that can help accelerate drug development.

Harry talked with Rigby about the challenges involved in that work, how it gets funded, how soon it might start to benefit patients, and what it might mean in a near-future world where every child’s genome is screened at birth for potential mutations that could lead to the discovery of rare medical disorders.

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

For people with common health problems like diabetes or high blood pressure or high cholesterol, pharmaceuticals has worked wonders and extended lifespans enormously.

But there’s another category of people who tend to get overlooked by the drug industry.

And that’s patients with rare genetic disorders.

By definition, rare diseases are rare, meaning they might only affect one in a thousand or one in two thousand people. 

But here’s the thing. If you add up all the different rare genetic disorders known to medicine, it’s a very large number.

My guest today, Charlene Son Rigby, says there may be as many as 10,000 separate disorders affecting small populations.

And if you count everyone who has these conditions, it may add up to as many as 30 million people in the United States and 350 million people worldwide.

That’s a lot of people who are being underserved by the medical establishment.

And Rigby is the head of a new non-profit organization called Rare-X that’s trying to fix that.

Now, there are a lot of rare disease organizations that are looking for a cure for a specific condition.

Rigby actually came to Rare-X from one of those, the STXBP1 Foundation, which is searching for a treatment for a rare neurological condition that affects Rigby’s own daughter Juno.

But Rare-X is a little different. 

It’s trying to tackle a systematic problem that affects everyone with a rare disease. 

The problem is data.

Rigby says that in the rare disease world, data collection is so inconsistent that each effort to understand and treat a specific disease feels like reinventing the wheel. 

For longtime listeners, that’ll be a very familiar story.

Time and again I’ve talked with people who point out the harms of storing patient data in separate formats in separate silos, and who have new ideas for ways to break down the walls between these silos. 

Rare-X is trying to do exactly that for the rare disease world, by building what Rigby calls a federated, cloud-based, cross-disorder data sharing platform.

The basic idea is to take the burden of data management off of rare disease patients and their families and create a single central repository that can help accelerate drug development.

I talked with Rigby about the challenges involved in that work, how it gets funded, how soon it might start to benefit patients, and what it might mean in a near-future world where every child’s genome is screened at birth for potential mutations that could lead to the discovery of rare medical disorders.

Here’s our full conversation.

Harry Glorikian: Charlene, welcome to the show.

Charlene Son Rigby: Thanks. Nice to be here, Harry.

Harry Glorikian: So I've been reading about what you guys are doing. I mean, all of it sounds super exciting. I'm, you know, been looking into this space for a long time from a rare disease, but many different angles of it. But can you just start off, tell us a little bit about yourself and how you got active in this world of rare disease research?

Charlene Son Rigby: Yeah, thanks for that question. So I've spent most of my career building scalable software solutions for analyzing big data, and that's been both in health care as well as enterprise software. And so I'm now the CEO at Rare-X where we're building a platform to analyze rare disease data cross-disorder. And prior to being at Rare-X, I was the chief business officer at a company called Fabric Genomics, where we developed artificial intelligence approaches to speed diagnosis of patients through genomics. We had a considerable focus on rare disease and contributed to projects like the 100,000 Genomes Project and also the work that Stephen Kingsmore is doing at Rady Children's with diagnosing critically ill newborns in the NICU. And so when I started at Fabric, my daughter Juno was ten weeks old. She's my second child. And it was kind of a fortuitous timing, in some ways kismet, because at when I started at Fabric, I didn't know that she was going to start experiencing issues with her development. But at around four months she started missing milestones. And that started us on a three and a half year journey to find an answer to what was going on with her. And so during that time, we went through many, many tests, including genetic tests, MRIs, all kinds of all kinds of things, and everything kept coming back as negative or inconclusive. And so I was working at a genomics company, and so I kept pushing for whole exome testing, which at that time was still not, not readily available clinically and by insurance was still considered experimental. So we were denied three times, until we finally were able to get authorization in 2015. And so in early 2016, we got my daughter's diagnosis and she has a mutation in a gene that's involved in communication between neurons and the genes called STXBP1.

Charlene Son Rigby: And so it's very rare. Thirteen kids born a day somewhere in the world. So thinking about Juno and thinking about this from a science standpoint, that it was pretty interesting that when she was diagnosed because she didn't have a classic phenotype for STXBP1. So most kids, 90% of the kids have seizures. And she has more of the symptoms around developmental delay, low muscle tone, cognitive issues and delayed walking and motor issues. And, you know, this this kind of challenge around these atypical phenotypes, I think, is actually becoming much more common in disease generally, so in rare disease and also more broadly in more common conditions as we're really starting to understand kind of the true breadth of patients. So in terms of your original question about my journey through rare disease, so I went on to co-found the STXBP1 Foundation to accelerate the development of therapies for kids like my daughter. And then coming to Rare-X was really a kind of joining of my software background with my passion for rare disease and really wanting to do something more broadly for the rare disease community.

Harry Glorikian: I have to tell you, like what you said, three and a half years, I'm like, oh, my God. Like, I would be I have so many stories. And like when I was at Applied Biosystems and, you know, we're doing all this work. It just boggles the mind that some of these things are not really readily available to help get over that diagnostic odyssey for especially parents, because you're going to do anything to help your child. I'm glad it's actually moving theoretically faster these days. I'm not sure if insurance has actually kept up, but we're, on the technology side, I know we're everybody's pushing the envelope now. But when we talk about rare disease and you did some of the numbers but we hear about these rare diseases, I think a lot of people think of like there's an n of 1, right? They assume that this disease only affects a tiny number of people. Right. Maybe just one or a handful worldwide. But I mean, the fact is, if you add up all these different rare genetic diseases that exist in the human population, the number of people is actually pretty big. I mean, can you sort of. Put that into some sort of scale for us in what you've seen.

Charlene Son Rigby: Yeah, you're absolutely right. You know, rare disease is by definition rare. And so it's easy in some ways to be dismissive of a rare disease because, oh, it's only affecting a few people. And it's true that a single rare disease can affect a very small number of people, even down to the n of 1 case that you talked about. From a definition standpoint, so, in the US, rare disease is defined as a disease affecting fewer than 200,000 Americans. And in Europe, in the EU, it's defined as affecting no more than one in 2,000 people. So we even though for ultra rare or n of 1 diseases, we can be talking about a small number of people, or like in my daughter's disorder, we can be talking about low thousands, there are still thousands of rare diseases and the traditional number that we hear a lot is 7,000. So 7,000 rare diseases. Rare-X is about to come out with some research that indicates that there are over 10,000 individual rare diseases, and this is really due to our growing understanding of genetics. So previously we might have grouped together a set of disorders based on what the symptoms were like. But now we understand that those actually are due to a different genetic etiology or different cause at a genetic level. And so if you aggregate all of those people up, across those 10,000 rare diseases, you know, what we're looking at is one in ten, potentially one in ten people in the world. And so in the US that's about 30 million people and in total 350 million people worldwide. So it's really a huge number of people. And from an impact standpoint, it's staggering when you look at the impact from a health care standpoint and from an economic standpoint.

Harry Glorikian: Yeah, I mean, if you can diagnose, I mean, if there is a way to treat someone, then you get to it faster. And the economic impact is huge and unfortunately, if there isn't, maybe it spurs a pharmaceutical company to, you know, start working on it or figure out a way to treat that patient better. But at least you, I always tell people, the better the diagnosis, the better the next step. I see people sometimes, it seems like they're throwing a dart, you know, and they're it's an educated guess, but it's not, you know, the accurate diagnosis that you'd like to have. So. But how and where, when was sort of Rare-X born and what are you trying to do with the organization? What do you want to fix?

Charlene Son Rigby: Yeah. So Rare-X was a pandemic baby. The organization was started in early 2020 and I just joined the organization last year. But, you know, it's really been quite a journey being able to have the, launch the platform during COVID. And I know we can talk about that in a little bit, but the unsolved problem that we are working to address is really around collecting data for rare disease. And one might ask, well, why is this an issue? I'll give an example. From the early days of the STXBP1 Foundation. W e assembled our scientific advisory board and we got together for our first scientific meeting. And we were going to develop our roadmap so that that would guide our priorities in terms of scientific development. And we were all very focused on therapies. So my expectation going into the meeting was we were going to talk about all the mice models we were going to build. What did we need to do in the lab? How are we going to get to that first therapeutic candidate? And the number one priority that came out of that meeting was to build a prospective regulatory-compliant natural history study. And so it was a huge learning for me because if you look at the kind of canonical steps in terms of drug development, it's always preclinical and then you move into clinical. And what I think that kind of simple model misses is this foundational layer around the data that you need and the real kind of understanding of the symptoms and the disease progression that is critical to building effective therapies, developing effective therapies.

Charlene Son Rigby: And so that's really what Rare-X was started to do, was to enable the gathering of this data, the structuring of this data and enable it to be shared and to do this at scale. So, cross-disorder. And there are several problems today that that make this challenging. And so maybe I can talk a little bit about that. There are three or four of these significant challenges. So today some of this data does exist, but it's often kind of trapped in data silos. So it was generated in an individual project that might have happened in academia or industry. And then the data is often really only accessible to the group that collected it. And in rare disease where we don't have that many patients, it really makes it challenging to create a kind of more comprehensive understanding and picture of the patients if that data is trapped in these individual silos. 

Charlene Son Rigby: Another challenge that that we've seen is the lack of usable data. So individual studies may not include the key data that's needed to drive drug development forward. So some of these data repositories, they might either be a symptom specific. So they're looking at a specific organ system that might have been of interest to that researcher. So they're an incomplete picture. Or some of these repositories or these registries were started by passionate parents. You talked about that, the urgency that one feels as a parent, that I feel as a parent. And the registry may have been structured or the questions may have been structured in a way that isn't necessarily immediately usable by researchers because of the fact that it was started by a parent who, like you, you might not have had a statistical analysis background, you might not have had a survey methodology background. And we so those can be challenges in terms of having the data be robust and usable later. 

Charlene Son Rigby: And then the other thing that can be challenging and probably is often the most challenging is, is especially in these very, very new diseases, there's no data, and it takes quite a bit of funding to start data collection. Often, often passionate parents are going around trying to get researchers interested in their disorder. But it's often that you have to have a little bit of data to get a researcher interested. And so this is a huge challenge in terms of implementing data collection. And the other thing that kind of underlies this is that patients often are not empowered in this process. And so that was a fundamental piece of the way that we've structured Rare-X and the way that we collect data and the way that we enable patients to participate in the process to power data collection.

Harry Glorikian: Yeah. I mean, it's, you know, they make movies out of this, right? People trying to push this boulder up a hill. So, what are the new ideas that say Rare-X is bringing to the table? I mean, your organization has called for like, you know, the largest data collection and federated data system and analysis platform in rare disease. So, I think unpacking that statement because it's a big statement, right, of, you know, what are you doing to improve data collection? What do you mean by federated, for those people that are listening? And why is it important? A  nd how will the platform enable better analysis of this rare disease data?

Charlene Son Rigby: Yeah. Great question. From a design perspective, the one of the things that we wanted to do was make sure that the platform was cross-disorder. So a lot of registries are started for an individual disorder. And what we really wanted to be able to do was given that number of 10,000 diseases, how do we scale to support so many disorders to accelerate therapies? And so a fundamental design principle was to do that cross- disorder. The other piece of this is that we are focused on patient-reported data. So typically a participant will join the research program, create an account on the platform and they are either a patient or a caregiver of a patient and providing information on their symptoms. There is a lot of other data out there in the ecosystem that could come from other related registries, or it could come from clinical data, it could come from many different types of studies. And so we really want to enable the aggregation of or federation of that data. So you asked me to define that term. It really means bringing together multiple different data sets in a way that enables those data sets to be analyzed together. And I think, again, going back to this theme that for any individual rare disorder, there aren't that many patients. And so analyzing that data, kind of individually, we are really missing the opportunity to maximally use the data that's been contributed by rare disease patients. And I would even argue that it's a moral imperative for us to do that as a rare disease community, because we urgently need to move these understanding of these disorders forward in development of therapies as well.

Harry Glorikian: I almost wish I could take all the companies I know doing this and put them there so the n goes up for everybody. But I know that there's all sorts of reasons that that doesn't happen. But, you know, when you were saying we're pulling in patient-reported data, you know, the first thing, and we talk a lot about this from different groups on the show is, you know, would a wearable or one of these other devices that are now available give you more granular, real- time information that might be valuable to this sort of study. And have you guys considered things like that?

Charlene Son Rigby: T he short answer is yes, because the our desire is to really continue to expand the types of data that are collected. And the I think that the nice thing about mobile, mobile devices, wearables, is that it makes it very easy to collect that data. And so we have a partnership with Huma. They do work in the mobile space. And we're definitely continuing to evaluate where we can develop partnerships there. I mean, our goal overall is to de- burden patients and so that the, if we can do that in a way that additive to an overall body of research, then we're huge proponents of it. And I think that it's also important that we're really trying to create an open system. So our partnership model is a very, very open partnership model in terms of who we can work with.

Harry Glorikian: Yeah, I had a really extensive conversation with the head of data sciences at WHOOP yesterday and you know, they're pulling in somewhere between 50 and 100 megabytes of data per patient per day. I shouldn't say patient -- per individual per day. Right. I was like, that's a lot of data. And she was, you know, the kid in a candy store because they're she's like, we can really see what's happening with people. And you can ask questions at a scale that you couldn't ask before. Like she was saying, you know, the last one of the things that we're working on publishing is 300,000 people. You couldn't imagine that in the world of, say, a clinical trial of 300,000 people are just going to, you know, and you have all the data, almost 24/7 on this person that's delivered by this device, which is sort of interesting, you know, place to be. So, you know, I know that you don't have 300,000 people in one in one area, but it'd be interesting to have that sort of 24/7 data available from these kids if you could, you know, get a device that would lend itself to that. But what stage is the company at in building the platform and you know, I guess the killer question is, when will drug developers or other researchers be able to start using it? If they already are, do you have any early success stories you can share?

Charlene Son Rigby: Yeah, yeah. It's really a very exciting time at Rare-X. So the platform launched last summer and we have over 25 communities on the platform. And those encompass several hundred participants already. So we're really starting to see some exciting numbers in terms of in terms of participants. So we are launching our researcher portal at the end of Q2. So very soon. And at that point, any researcher, so academic researchers, pharma researchers, will be able to access the data and be able to utilize analytical tools to really interrogate the data. I'm excited that we also have launched our first sponsored program, and that's with Travere. They're supporting the homocystinuria community to start data collection, to start a registry. And we just launched that at the end of February.

Harry Glorikian: So I want to. Jump back, like just talking through some of the biggest technical challenges along the way. I mean I know one of your goals is like interconnecting all these disparate data sources. But one of the issues that always comes up is how do you clean up that that existing data so that you can store it all the same way. And then obviously that enables somebody to then do the analytics right after that. But the biggest issue that I hear from a lot of people is, man, it takes a lot of effort to make sure that that data is cleaned up and put in the right place.

Charlene Son Rigby: Yes, the data munging. Yeah. I mean, I think that that is really the, a significant challenge, because creating research-ready data and then harmonizing data sets is a huge amount of upfront work that has to happen before you can actually do any of the analysis and the data mining. So what we have done with the core data that's being generated within Rare-X is that we have mapped it to data standards. So we utilize standards like the human phenotype ontology, OMIM, HL7, so that the data that we're producing already is mapped to all of these generally utilized standards. And then we would if we were working on a federation project, the same thing would need to happen with these other data sets to really enable that type of integrated that type of integrated analysis. And you're right, it's it can be a very brute force effort in terms of doing it accurately. And that's why I think that it's really important from a from an industry perspective to really start adopting these standards and putting them into the base model, you know, for assuming just making the assumption up front that the data is going to be federated and utilized downstream. I think that kind of traditional studies, a lot of the scope was more really looked at in terms of what are we doing with the data today? And we need to be really thinking about from a lifetime perspective, how is this data going to be used?

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. 

It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: Now if we go one step before like getting that data, I mean. I have to imagine there's a huge political, bureaucratic or organizational challenge when it comes to who controls that data. And I think I have to assume,  part of your job is convincing them to share it, right, despite its potential as intellectual property. Right. So how do you get on the phone and say, “Why don't you press send and shoot that over to me and so that we can take the next steps with it?”

Charlene Son Rigby: Yeah, well, this is a really significant challenge, and I think that we're in a time of change in terms of attitudes around this. And part of it is what's been happening in terms of national programs to collect data. And people are starting to see the benefit of being able to share and really utilize these larger data sets. But the reality today is that in terms of the status quo, researchers control the data, and that's because the data was generated in a specific project that might have happened in academia or in industry. And there's a challenge with alignment of incentives. So on the academic side, I think that if one were to ask a researcher, do they want to hoard data, they don't want to hoard data. But the reality is, is that we still have this challenge with academic tenure and needing to publish or perish in that environment. And so researchers are still rightly concerned because of that paradigm that they have to write their paper and get their paper in before they can feel comfortable with allowing others to access the data. And so something really needs to happen there to that incentive system. 

Charlene Son Rigby: And in pharma, interestingly, I think that that's also an area where there has been a feeling that data is almost akin to intellectual property. But I think that especially in rare disease, there has been a growing understanding that accessing natural history data is not going to, at the end of the day, enable pharma to win because they're going to win on the quality of their therapeutic pipeline and how quickly they can get those therapies through to a successful market approval. And so what we've been really working to do is position natural history data as pre-competitive and for rare disease, frankly, it's too expensive to build these data sets, you know, alone. They're, as we scale to all of these disorders it's going to become untenable to for each company to build their own data set. The thing that we need to do and what Rare-X has been working to do with our collaborators is to transform the way that research has been done and initiated and break down these barriers and just show that the model of building these pre-competitive collaborations can work, both from a how does the business model work and then how is the data shared? And so I think that Rare-X being a nonprofit and a kind of neutral third party is really additive in terms of building those relationships so that this, this kind of public-private partnership model can really serve as a way to drive this type of change.

Harry Glorikian: Now. Okay. So we've talked about industry sharing data, but I always I mean, especially in the last maybe 5 to 10 years, I keep thinking about, you know, how much of this comes directly or will come directly from patients, right? If they have control or access to their data, they have the ability, theoretically, the ability to then share that data. Right. And it could be just for the research in general as opposed to, not specifically to find a cure for a specific disease. So how do you get that data or convince patients to share it?

Charlene Son Rigby: Yeah, well, I think that in in rare disease patients are typically highly motivated. You know, there are many rare diseases that can be pretty devastating in terms of the symptoms and the disease progression. And so overall, there is a a good portion of the rare disease population that is motivated to provide their data. And so what we do there and I think that that your points about the paradigms and thinking about it, that the patients are sharing their data, is really important. Because I think that that gets lost a lot. You know, a patient, and we've all signed up for some research study in our lives. You go and you fill out a survey or you contribute a blood sample or something, but oftentimes the patient contributions get forgotten because it becomes part of the researcher’s data set. And so the what we're really trying to do is turn around that kind of paradigm with a core principle that patients are the ones who own their data and they're contributing their data. And so we enable them to, through an innovative consent process, we enable them to basically say that, yes, they're willing to share their data for these types of projects, and they can change that at any time. And we really feel that that changes the paradigm and allows them to have a real seat at the table. And then I wanted to also talk about, because obviously not everyone is — there is this proportion of folks who are motivated and trust and that's part of the reason that they will be willing to share their data — but there is also a portion of the population that might not be as motivated. And so it's important for us to be able to reach those populations and to build trust in the approach that we're taking and the value of it in terms of really being able to drive research. And so patient education is an important component of our model patient education, patient engagement. So we work directly with patient advocacy organizations and patient advocates to educate their communities on the value of data collection, how it really spurs and supports research. I think that that's a critical component to this as well.

Harry Glorikian: Well, hopefully people will listen to this podcast worldwide and me that may spur someone to search you guys up on the web. But I noticed that another principle of the company is you don't sell patient data, right? Does that mean you're giving it away? And if that is true, what's the criteria of doing that? And do your data partners that you're giving it to have to meet certain standards?

Charlene Son Rigby: Yeah, this is a great question because monetization models around data are very, very common today. Some companies have built significant valuations around data monetization. And for from a Rare-X standpoint, and this is part of the reason why we were started, is that the question was asked like, is that the right thing to do, especially for diseases where we're in the very early stages of understanding a disorder, and so I talked about this a little bit earlier, that if you have no data, getting any researcher interested is already then a huge challenge. And so we're here really to break down barriers to advancing rare disease research and encourage that research. And so I say sometimes that it's really important that we free the data. So we don't sell data at Rare-X. And we have an open access model for researchers to access the data. 

Charlene Son Rigby: And so there it is not, “we open the doors and anybody can come, come and access the data.” It's done in a responsible way. So one of the key things is that the data is de-identified. And so it is it is critical to do that, because we want the data to be utilized for research. It doesn't need to have identifiable information in it to drive that research forward. You know, the second thing is, is that researchers need to submit information on their project, and then that's reviewed by a data access committee. And the idea behind this data access committee is not to slow down things. It's a streamlined and efficient process. But the idea is that there is a review process. The researchers need to specify whether there's an IRB with whether that protocol has gone through an institutional review board review, and patients can opt to only have their data. As an example, patients can opt to only have their data shared with projects that have gone through IRB review. So there's really kind of a, since this is in many ways a two sided platform, there's really a way that patients can actively engage in terms of who's accessing their data. And then the researchers also in terms of the types of projects that they're that they're going to put forward.

Harry Glorikian: Okay. So now you're giving away the data. Remember, I'm a venture capitalist, so you're giving away the data, right? First question somebody like me asks is, how do you pay for the operations? I mean, you're building this fairly sophisticated system that is, you know, you've got to clean the data, you've got to make it available. You're trying to talk to all these people. I mean, are you funded by let's say, I mean the typical stuff, grants? Is it member donations? Is it major gifts from individuals? You know, those are all the questions that that would cross my mind.

Charlene Son Rigby: Yeah, absolutely. So frankly, it took me some time to get my arms around this, because my whole career has been in tech and venture backed companies. And so so I took some time to really think about this and think about this scalable model from a scalability standpoint before joining. So we get our funding largely through pharma and industry, as well as some grants. And the way that that funding happens is, it's basically platform investment. And I think that this is a really key thing from my perspective of, of thinking about the, the platform as something that is, if you will, a social good. Because they're investing in expanding the platform. They might invest, like Travere did, additionally to help to onboard specific groups or expand our capabilities in terms of being able to gather data in a particular disease area. But the funding that they're providing is to make the platform and the research program more robust. The data at the back end will be open in the way that we've we have talked about it. We have a unique ability to do that and create that kind of model as a nonprofit. And you're right that what we're doing, we're kind of blending this health tech company with this this nonprofit  tmodel. But I think that there are some good examples out there of public private partnerships that have been very successful in the long term in doing this. And that's the model that we're really pursuing.

Harry Glorikian: This area is small. I feel like I've been in and around it for a long time because of, you know, being in and around genomics. But there's a small but sort of growing infrastructure of support for rare disease, you know, patients in the world, sort of nonprofits, NGOs, patient advocacy group. Tthere's Global Genes, right? There's the Rare and Undiagnosed Network, RUN. There's the Undiagnosed Disease Network Foundation, and then there's the n-Lorem Foundation. And so many others that I don't want to leave out, right, the long list. But how does your, or, does your group overlap with these? I mean, I was reading a press release that this summer you guys will launch a collaboration with RUN and the Undiagnosed Disease Network Foundation to launch something called the Undiagnosed Data Collection Program. I mean, if you could sort of talk about what that project is about. Is your real ambition to be the data infrastructure sharing platform for the entire community of rare disease patients and families?

Charlene Son Rigby: Yeah, well, I love that you call it infrastructure because I think this is critical from a concept standpoint. Rare disease should not be a model where each rare disease is doing it on its own. That was one thing that really struck me, thinking again about my daughter's disorder, where we were looking at ways to ladder up to that prospective natural history study. And we were trying to do something. I talked to a few other genetic neurodevelopmental conditions that were kind of our cohort, if you will, and we were all doing it in different ways. And it's such an opportunity cost to be figuring out the model new each time. And so these groups like Global Genes, amazing organization, actually, the Rare-X founder, Nicole Boyce, was also the founder of Global Genes. And we were, the STXBP1 Foundation used every single resource possible that came out of Global Genes. You know, that there's this broad this really broad education and enablement that needs to happen for people who want to become rare disease advocates. And that Global Genes has really done that in a tremendous way for so many organizations and so many individuals. And so we partner with them in terms of, and are very complementary, in terms of providing that infrastructure where Rare-X is focused on this area of how do you accelerate research through data collection, and then we use that.

Charlene Son Rigby: It's great that you saw the announcement on the work that we're doing with RUN and the UDNF. I'm particularly excited about this because Rare-X, we talked earlier about ultra rare diseases, about n of 1 diseases. The reason why Rare-X is able to collect data across all of these disorders is that we have a fundamental assumption in the way that we collect data, which is that we don't assume that anybody does or does not have any symptoms. So we start out with a very high level, head to toe type of set of questions that if you say yes to any of them, it leads into a more detailed set of questions to collect data on particular symptoms. And so this is really ideally suited to situations where there isn't a lot of characterization around or understanding of the symptoms in a disorder and where you don't have a diagnosis. Because then what we're really enabling an individual to do is to gather robust data about their individual symptoms and disease progression that then can be utilized for research. And so we're very excited about being able to work with and support RUN and UDNF in in that effort. 

Charlene Son Rigby: And so do we have, you asked about ambition? You know, do we have a goal of being the only data sharing platform? I would say that our goal is to be an incredibly robust comprehensive cross- disorder platform. We believe that the way that we are approaching things really is enabling us to support all rare diseases. And we're really focused on de- burdening patients. So we're enabling patient communities to get started very quickly. And they don't have to become experts in protocol development, they don't have to become experts in creating clinical outcome assessments, etc. At the same time, the world is large and that they're going to be groups who decide that they need specific solutions. They may want to take on the role of being a principal investigator, as an example. And so I think that that's also the reason why federation is an important component of what we're really bringing forward as a as a way to bring all of that data together.

Harry Glorikian: So again, you know, being on the venture side, right. You can lead a horse to water, but you can't make them drink, right? So you can do a lot. You can improve clinical trial readiness. You can make sure the data is better about rare disease patients, and that it's available. But you can't force the drug discovery companies or the drug makers to sort of develop a cure for a specific disease. Right. How do you think about that as part of a rare disease problem? Is that is that part of the work that Rare-X is,are you making it less risky so that they are willing to take that next leap?

Charlene Son Rigby: You're right that pharma is going to be making, I would say, rational business decisions based on commercial drivers. And the challenge with a lot of rare diseases is that no one knows about that individual rare disease, and there isn't much data on it. And so anything that can be done to de-risk that process for a pharma company  is huge in terms of increasing their interest or generating interest for them and then increasing their interest. And those things can include knowing that there's an activated community, you know, because if you have a clinical trial and nobody wants to participate in the clinical trial, that's going to be a huge problem in terms of being able to get that drug through an approval process. And so Rare-X, by building a very robust data set, is able to de-risk that process in terms of that investment, of trying to understand what the disorder is and also trying rto understand disease progression. And going back to that point about activation of the community, we're also able to help to demonstrate the activation of the community because of the number of people participating in the in the data collection.

Harry Glorikian: I know it's not science fiction. I think it's right around the corner, hopefully, but I think, isn't an ideal future where we do either whole-exome or preferably whole genome on every newborn and scan for these genetic changes that are associated with rare diseases. I mean, I'm assuming that would really push this area much farther along. And if that is true, if that statement is true, how long do you think it'll take for us to get there?

Charlene Son Rigby: Wow. You're reminding me of the Gattaca movie, but hopefully that's not the real future for us, you know. Winding things back. So my daughter was born, my daughter Juno was born in 2013. So that's nine years ago. And it took three years for us to get a diagnosis. And, you know, that's like an entire other podcast. But I think that the really, if we fast forward to 2022, we have groups like Stephen Kingsmore's group at Rady Children's where they're diagnosing newborns who are in the NICU, in less than 24 hours. And even standard exome testing, which it took us three months to get our results, the standard exome testing results are now returned in less than two weeks. You can also get it faster if you have an urgent testing and we have the tech. Illumina has long been dominant and continues to be dominant in the clinical area. But you have these new entrants with Oxford Nanopore, Element, Singular, and there are others that are entering now. And so these costs are coming down and this is really going to be a transformative in terms of becoming, I do think that this is going to become standard of care and it's closer than we think. I think that it's probably going to be in the next ten years, less than ten years.

Charlene Son Rigby: We already have some analogs to this in terms of or precursors, I should say, in terms of newborn screening. And so what I think is going to happen is that genomic sequencing is is going to become a core newborn screening tool. And the interesting thing is that there are applications, not just in rare disease, but also in common conditions and the value of genomic sequencing. So today, 5% of rare diseases have a therapy, but there are right now hundreds of gene therapies that are currently in preclinical and clinical pipeline. So this picture is going to change enormously in the next five years. And so because the value of is going to grow, because there are therapies, the other important thing is therapeutic windows. So therapeutic windows are when we can intervene to have the most impact on a disorder. And so that's often when someone's young before the symptoms present or start or very early in that process. And so I think that this is going to become a reality in the next decade. And frankly, I think it's a very exciting time. I have always been a big believer that knowledge is power. And this is this is one of those great situations where we have the ability to do something because we know.

Harry Glorikian: Yeah, I talk about some of this in my book and there's some, you know, interesting stories and it's a fascinating time. And when I think back, you know, to when we first started sequencing and people would say, why would you want to sequence anything? And now it's the complete opposite. And the price is coming down. It's becoming easier and faster. And I mean, at some point, I think the price is going to be low enough between the actual sequencing and then the analysis, that as my friend says, it's going to be a nothingburger. I mean, it's just going to be like, yeah, we should just do that because it gives us the information we need for the next step, which is sort of going to be interesting.

Charlene Son Rigby: Yeah, absolutely. I think that the that is the challenges that I talked about, cost of sequencing. But you're right that, you know, the analysis is still quite expensive today. And that's something that we're also going to need to need to improve. I mean, AI and the growing knowledge bases is really going to help to address that. Yeah. And but that's a huge component of it as well today. Absolutely.

Harry Glorikian: Yeah. I'm looking at a company that in this particular area of oncology, they've gotten the whole genome analytics down to about $60. So it's, you know, it's coming to a point where you're like, why wouldn't you do that? Like, what's stopping you from doing that? So it's been great having you. Great conversation. I wish you guys incredible success. A nd I'd love to keep up on how things are going with the organization.

Charlene Son Rigby: That'd be great, Harry. Really enjoyed it today. Thanks.

Harry Glorikian: Thank you.

Harry Glorikian: That’s it for this week’s episode. 

You can find a full transcript of this episode as well as the full archive of episodes of The Harry Glorikian Show and MoneyBall Medicine at our website. Just go to glorikian.com and click on the tab Podcasts.

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Thanks for listening, stay healthy, and be sure to tune in two weeks from now for our next interview.

Harry's guest this week is Emily Capodilupo, the senior vice president of data science and research at Boston-based WHOOP, which is based here in Boston. To explain why the company focuses on measuring what it calls strain, rather than counting steps or calories, she reaches all the way back to the beginning of the company in 2012. That’s when founder and CEO Will Ahmed had just finished college at Harvard and was looking back at his experiences on the varsity squash team. Ahmed realized that had often underperformed because he had overtrained, neglecting to give his body time to recover between workouts or between matches. To this day, WHOOP designs the WHOOP band and its accompanying smartphone software around measuring the physical quantities that best predict athletic performance, and giving users feedback that can help them decide how much to push or not push on a given day.

Capodilupo calls the WHOOP band “the first wearable that tells you to do less.” But it’s really all about designing a safe and effective training program and helping users make smarter decisions. Meanwhile, the WHOOP band collects so many different forms of data that it can also help to detect conditions like atrial fibrillation, or even predict whether you’re about to be diagnosed with Covid-19. It’s not a medical device, but Capodilupo acknowledges that the line between wellness and diagnostics is shifting all the time.  And with the rise of telemedicine, which is spreading even faster thanks to the pandemic, she predicts that more patients and more doctors will want access to the kinds of health data that the WHOOP band and other trackers collect 24/7. 

The conversation touched on a very different way of thinking about fitness and health, and on the relationship between big data and quality of life—which is, after all, the main theme of the show.

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

If you’re a gadget lover and data aficionado like me, you’ve probably tried a lot of different fitness monitors and other wearable devices, like a Fitbit, or an Oura ring, or an Apple Watch.

We’ve talked about a lot of these devices on the show. Usually they come with a smartphone app, or they run their own apps. 

And the job of the apps is to track your fitness progress and encourage you to get out there every day and “close your rings” or “do your 10,000 steps.”

But there’s one activity tracker that’s a little different. It’s the WHOOP band. 

The WHOOP is not designed to tell you when to work out. It’s designed to tell you when to stop.

My guest today is Emily Capodilupo. She’s the senior vice president of data science and research at WHOOP, which is based here in Boston. 

And to explain why the company focuses on measuring what it calls strain, rather than counting steps or calories, she reaches all the way back to the beginning of the company in 2012.

That’s when founder and CEO Will Ahmed had just finished college at Harvard and was looking back at his experiences on the varsity squash team.

I’ll let Emily tell the whole story, but basically Will realized that had often underperformed because he had overtrained, neglecting to give his body time to recover between workouts or between matches.

To this day, WHOOP designs its signature WHOOP band and its accompanying smartphone software around measuring the physical quantities that best predict athletic performance, and giving users feedback that can help them decide how much to push or not push on a given day.

Emily calls the WHOOP band “the first wearable that tells you to do less.”

But it’s really all about designing a safe and effective training program and helping users make smarter decisions.

Meanwhile, the WHOOP band collects so many different forms of data that it can also help to detect conditions like atrial fibrillation, or even predict whether you’re about to be diagnosed with Covid-19.

But it’s not a medical device.

But Emily acknowledges that the line between wellness and diagnostics is shifting all the time. 

And with the rise of telemedicine, which is spreading even faster thanks to the pandemic, she predicts that more patients and more doctors will want access to the kinds of health data that the WHOOP band and other trackers collect 24/7. 

It was a fascinating conversation that touched on a very different way of thinking about fitness and health, and on the relationship between big data and quality of life, which is, after all, the main theme of this show.

So I want to play the whole interview for you now.

Harry Glorikian: Emily, welcome to the show.

Emily Capodilupo: Thanks so much for having me.

Harry Glorikian: Yeah, I have to tell you, I was reading your background and I'm like, oh, my God, I'm so excited. She comes from like, you know, like real training in sleep. And we're going to talk about these devices. And it's one of the things I use them all for, as you can tell, like I'm I'm sort of geared up and I've got all of them and I and I cross correlate and I can tell when somebody has updated something and the algorithm, like I can see like all of a sudden they start moving apart from each other or being different from each other. But, you know, for those people who aren't, say, up to speed on the world of fitness monitors, I'd love for you to start, you know, by explaining you WHOOP's mission, and then maybe talk about different parts of your system, you know, like the band, the sensors, you know, the basic capabilities, that sort of stuff.

Emily Capodilupo: Sure. So WHOOP's mission is to unlock human performance. And in a lot of ways it started out at the beginning. You really focus on athletic performance. Our origin story is very much in preventing overtraining. But as we started to do more and more research, we started to discover that the things that predict athletic performance at the sort of root physiological level are actually the same things that predict all kinds of performance. So we've seen them predict things like cognitive performance. We've seen them predict like emotional intelligence and, you know, like how short you are with people, stuff like that, you know, as well as like how people feel like they're performing at work or in their jobs, in their relationship, stuff like that. So while ...physical performance is, where a lot of those algorithms and sort of like our research started, we started to realize that without tweaking any of the algorithms at all, they started to be really good predictors of other elements of performance as well. So we've really broadened our mission. It's all about unlocking human performance in the broadest sense possible, and we do that with this device. Some of the things that we think are really important about our design as it compares to some of the other wearables, is that as you'll see, it's screenless. And we really think about the device just as this itty bitty little bit that slides out from the fabric.

Emily Capodilupo: And so it's actually capable of being worn almost anywhere on your body. So we have clothing that totally hides it. You can wear it in your underwear, on your bra, on a t shirt, anything like that, as well as sort of the traditional wearable locations like on your wrist or bicep. And one of the reasons why we wanted that form factor is we really wanted to collect 24/7 data and be able to get this complete picture of your body. It actually charges wirelessly so you don't even have to take it off to charge it. And that allows us to get the most complete picture of what's going on. And so we don't miss like the 2 hours when you take it off to charge or you don't charge it overnight and then miss the sleep or anything like that. So it gives us this like really incredible picture. Kind of one of the other important differentiators just in the hardware itself is because we're not powering a screen, we're able to put 100% of the battery into driving the sensors and getting the most accurate signal. And so when you start with the most accurate signal, the most accurate raw data, you're then able to power better feedback, better coaching, because you're starting with something more reliable. And so we've done a lot on the coaching side and the algorithms side that other wearables just haven't been able to do.

Harry Glorikian: Interesting. So Will Ahmed and John...and I'm going to try to pronounce it. 

Emily Capodilupo: Capodilupo.

Harry Glorikian: Thank you. Started WHOOP in 2012, right? While John was at Harvard and Will had just graduated. Right. So, you know, I mean, maybe a little bit about the company's origin story or. I don't. God, that was you know, if I go back that far, the fitness monitoring market was like in its nascency.

Emily Capodilupo: Yeah it was, the Jawbone Up had just come out, the original Fitbits had just come out. And not too long after that the Nike FuelBand started, which no longer exists, of course. And, you know, if you look at what wearables were doing at the time. Oh, and then, of course, there was this other class of wearables that had been around for a little bit, which were like the Garmin running watches. So it kind of GPS watches that you put on for the run or for a bike ride or whatever it is. It would capture all the GPS data, give you information about your pace, and then you take it off when the run was over. And so you kind of had those like two classes of wearables. We had these like 24-ish/7 step counters, and then you had the like more intense while you were working out data, but nobody was really bridging those things. But the sort of theme across all wearables, both of those different categories at the time, was this like push harder, more is more, faster is better, just do it, right. All of those kinds of messaging. And we weren't really seeing, at least with the like kind of step counter class of wearables, we weren't seeing any kind of adoption in like elite athletes or even like collegiate athletes because they didn't really need to be told do more.

Emily Capodilupo: And actually what happened is, sort of the WHOOP origin story is, Will was captain of the Harvard squash team. And when he got named captain, he sort of committed that “I'm the captain. I should work harder than everybody else. That's what a leader does.” And he worked so, so hard that he overtrained, really burnt himself out and like did really poorly. And he had this moment of like, you know, I'm in a Division I school and I'm like the fanciest, you know, squash programs that there is. How come nobody knew I was overtraining and like, told me to stop. And like, who knew that this was a thing? Like, I always thought that if I worked harder, I'd get better. And actually, you can work too hard and working too hard is bad. And he found that like everybody on his team was really motivated to work hard and sort of motivating each other to work harder. And they didn't have that balancing voice of like, Oh, I should take a rest day and like sit out, even though like my teammates are practicing. That would have felt like very uncomfortable and like not being a team player or something like that. But he started digging into the data and it really did show that like actually when you need a rest day, you will be stronger for having taken the rest day, than you will be for like manning up and pushing through.

Emily Capodilupo: And so he really set out to create the first wearable that was going to tell you to do less. It was very countercultural in that moment. But he was trying to address kind of the highly motivated market that needed almost like permission to pull back and to be told what their limits were. And so from day one, we were really focused on like, how can we create a recovery score that's going to tell you, like, you're better off resting today than you are like doing this program or that, like, a coach could use and see the data and say, okay, these four players, they're going to do an extra set or an extra drill or whatever it is. And these four players, they're actually going to stop 20 minutes early and, you know, go sit in the sauna or stretch or whatever it is. And by modulating people's training in response to their bodies, readiness to respond to that training, actually create like safer and more effective training programs. And that was where we started and then kind of evolved into the product we are right now. But a lot of that is very, very much, that philosophy is still kind of at the core of what we're doing.

Harry Glorikian: Yeah, I definitely have questions. We definitely have to talk about the recovery score and sleep apnea, because I have a vested interest in understanding this better. Actually, it's funny, I try to talk about this with my doctor and he's like, “Man, you know more than I do about this.” But so, you know, thinking about how the company is evolving. It's been moving forward. I've been watching it. I mean, what is the company's sort of larger philosophy about like the role of technology in fitness and health. I mean, do you feel like we're headed towards a future where everybody is going to rely on their mobile and wearable devices for health advice?

Emily Capodilupo: I think so. And I think that, you know, there's a big asterisk to that answer, which is I don't think that wearables are ever going to replace doctors, and I don't think that we're trying to do that either. But we do have a lot of information that doctors don't have. And there's a really, I think, exciting opportunity if the medical community were more open to it. And they're definitely shifting in that direction. And that's been accelerated by the pandemic and the rise of telemedicine, where there really is an opportunity. I mean, if you think about it, just like the really simple basic stuff like telemedicine appointments skyrocketed during the pandemic.

Harry Glorikian: Right.

Emily Capodilupo: Every other in-person doctor's appointment I've ever been to, the first thing they do is they take your vital signs right, often before you even get to see the doctor. They've taken your vital signs, or if you've a telemedicine appointment, they just totally skip it, right? And so it's like, well, you know, my wearable can tell you what my resting heart rate is, could tell you not just what it was this morning, but what it's been all month and all that kind of stuff. It also can tell you what my blood oxygen level is, my temperature. And that's a lot of information that's like, you know, is a lot better than having nothing. Which is what telemedicine has right now. And so it's not like let's throw out all the EKG machines and all of that.

Emily Capodilupo: But, you know, there are a lot of situations where remote monitoring can add a lot of value. And then there's other places where even if the doctor was there to take your vital signs, sometimes vital signs in context have a lot more information than an isolated reading. So like we published a paper about a little over a year ago now where we were looking at respiratory rate in response to COVID-19 infections. And what we found was about three days before or up to three days before reported symptom onset, people's respiratory rates were starting to climb. And we would see this like because daily your respiratory rate when you're healthy, it doesn't change at all from night to night, it’s super flat. And so it will be like the exact same thing night after night. And then all of a sudden you'd see this spike like two, three days before COVID-19 symptom onset. It would stay up or keep climbing. And then three days later, people would say, like, Oh, I don't feel well, whatever. They go get a COVID test, and lo and behold, it would be positive. And so it was this like interesting early warning sign. But what was really, really interesting about that study is that oftentimes people's respiratory rates were only going up like one or two breaths, which didn't make them like clinically like high respiratory rates, like clinically significant.

Emily Capodilupo: It was only significant in how it was compared to your baseline. And so that's a case where like if I had gone to my doctor and they measured my respiratory rate, they would have said, this is a normal human respiratory rate, you know, between 12 and 20 breaths per minute, which is sort of normal. But like my baseline is about 14. So if it went up to 18, that's a huge, huge rise for me, but it's still technically clinically normal, so they would have completely missed that. But by having a wearable that's like passively monitoring my respiratory rate every single night, you could see like something's going on, and that can be a huge red flag that something's going on with your respiratory system. Right. And of course, COVID-19 is a lower respiratory tract infection primarily. So it's going to show up there. But we would expect to see similar things with somebody who had pneumonia or certain strains of the flu. And so these kind of like early warning signs that can show up in your vital signs before symptoms. You're not going to have a fever yet. You're not going to be complaining about not feeling well or have any other indication that you might have COVID. And so I think that's like an example of where a wearable paired with a doctor can provide information that like a doctor in their office wouldn't be able to provide alone.

Harry Glorikian: Well, I mean, I think, you know, if you took respiratory rate plus a slow change in temperature, right now you have two biomarkers that you can use to show something is physiologically off.

Emily Capodilupo: Yeah. What we were seeing was that respiratory rate was climbing before temperature was climbing, which was interesting.

Harry Glorikian: Interesting. Okay. You know, another story. It's funny because I was talking to a friend of mine and he has A-fib [atrial fibrillation] and he knew he was going into A-fib and then he got together with his doctor and his doctor was actually digging into the data from the WHOOP to sort of see like when he was going into A-fib and sort of, you know, using the technology, because he wasn't wearing a Holter monitor or anything like that. This, this sort of acted as a way for him to peer into when it started, how long it lasted and things like that. So I think when a doctor wants to, it's interesting because some of these wearables like yours have that data available for them to, you know, interrogate.

Emily Capodilupo: Mm hmm. Yeah. And I think A-fib is such an interesting example there because, like, people who have paroxysmal A-fib can go into A-fib for just, like a couple of minutes a month. And so your typical like seven-day or 48-hour Holter monitor reading could easily miss it. But A-fib puts you at risk of all kinds of things like stroke that you might want to be treating, and so like having 24/7 data collection over months and months and months can give you a better picture versus I don't really know too many people who are going to be willing to like or Holter monitor for a year.

Harry Glorikian: Yeah. So I mean, I'm going back to your 24/7 and the wearable and the fact that you're driving all the power to the sensors, I mean, you guys collect, I think I saw the number, 50 to 100 megabytes of data per day, per user, which is a gigantic amount of data compared to maybe like a Fitbit or an Apple Watch. I mean. Why collect that much data? I mean, what do you do with it? I mean...

Emily Capodilupo: Yeah, great question. You know, we keep all of the data because it has tremendous research value in addition to being able to power the features that we're providing today. You know, there's all kinds of fascinating early research, you know, different things like the shape that your pulse makes. So if you look at not just how fast your heart is beating, but literally, you know what that raw, we called PPG, photoplethysmography signal, looks like, you can actually tell a lot about the health of a cardiovascular system. And we published a paper a couple of years ago now where we're looking at age as a function of this like cardiovascular pulse shape. And we haven't productized that research yet, but stuff that we're exploring down the road and there's just there's so much, so much you can answer with large data sets that traditional academic research just hasn't been able to answer because they haven't had access to data like this. And so by keeping it all around, we're able to do a lot of research and move the field forward as well as create really, really feature rich experiences for our members.

Harry Glorikian: Can I suggest, you know, custom consulting for guys like me who actually would love to dig into the data as as a service that that people would be willing to pay for. But correct me if I'm wrong -- the WHOOP doesn't really detect when I'm exercising. Right. I've got to tell it, no, I'm exercising.

Emily Capodilupo: We detect when you're working out.

Harry Glorikian: Because it seems like it's more accurate when I push the button first and it starts rather than wait for it to like if I'm about to start a weightlifting session, it's more accurate when I push the button, then when I wait for it to tell I'm doing something.

Emily Capodilupo: Yeah. Well, with certain activities it's hard to get the exact start times right. And different people have different attitudes about things like warm ups and downs and if they should be included. So if you do have a strong preference about whether or not you want those included, we do give people the opportunity to manually trim the bounds of their workouts or to just start and stop them manually. But we do detect any activity with a strain above an eight that lasts at least 15 minutes will get automatically detected.

Harry Glorikian: Okay. And by the way, I love the fact that you guys integrated with the Apple Watch because, like, because when I go on my treadmill, it automatically connects to the watch and then tracks the whole thing and then ports the info. That's great. That is fantastic. As a as an opportunity. But, you know, how do you think about WHOOP versus any of the competitive technologies? And I'll tell you why I say that when people say, well, what do you see is the difference? I'm like, you know, the Apple Watch is more of what what I think of as a data aggregation device in a sense, because it's sort of taking all sorts of stuff. You know, the WHOOP I think of almost like a coach in a sense, as opposed to it's pulling in data and pushing it out to different apps and I can do different things with it. So I don't want to misrepresent how you might frame it, but that's sort of how I think about it.

Emily Capodilupo: No, I think that's totally spot on. I think that we have a very strong stance around not showing or generating data that we can't tell you what to do with it. And so we really want to be like your coach or your trainer or at a minimum like your workout buddy kind of thing, where it's somebody that or something you can kind of look to, to understand, you know, am I reaching my goals? What are the things that are helping and hurting me and sort of how do I then make changes to go forward? I think one of the biggest examples here is, we've been very much like countercultural in not counting steps and we've been asked a lot by our members, like, why don't you count steps? It's not actually that hard. It's not because we can't figure out how to do it. It's that we actually don't think that they're valuable. Steps count the same if you run them or walk them. If you walk them upstairs or flat. You don't get any steps if you swim for a mile and you certainly don't get any steps if you're wheelchair bound. And we didn't like any of those constraints, they didn't really make sense to us as a metric. And we also really didn't like this kind of arbitrary, like everybody needs 10,000 steps. Well, is that true if I'm 90 versus 19, is that true f I ran a marathon yesterday, should I still be trying to get 10,000 steps today? Is it different if I've been sitting on the couch for three days? And so we came up with this metric of strain where instead of being an external metric, like steps are sort of something that you did and you can count them and it's objective, we wanted an internal metric where it's like, How did your body respond to that thing that you did and how much flow did you take as a function of what you're capable of? And so sort of what strain does, it's very much like in opposition to what steps does, is they’re internally normalized to reflect like if I ran versus walk to those steps, if I ran versus my brother ran and he's more fit than I am, or if I do a two mile run this weekend and then I train a whole bunch and get more fit and then do the same two mile run six months from now, I should actually get a lower strain when I do it, when I'm more fit than I did when I got did it this weekend. Like all of a sudden, strain becomes this very rich thing because it has this, like, natural comparison where like a higher strain actually mean something objectively, both within and across people, than a lower strain does. Whereas that that's not really true with steps. Right? I could walk fewer steps than you, but have done them up a mountain. And so I've actually put a lot more strain on my body than if I'd done the same number as you, but like flat pacing around my kitchen, eating snacks and making dinner or something like that.

Harry Glorikian: Yeah, well, actually there was an interesting paper that it was a sort of a study that brought in all sorts of studies to show that, you know, at an older age, you actually, you know, you need less steps, and it has a difference in mortality. And, you know, if you're younger, then you want a higher level of steps. And, you know, so it was a good paper. I'll actually I'll send you the reference later. But you know, the interesting thing about strain is and this is the good part about the body and the bad part about the body, in a sense, is that it optimizes itself. Right. And so if you want to get the same strain goal and if you're fit, you really have to…I mean, at some point, I'm like I look at if I had an incredible night, which is rare and it's really in the green, I'm like, I'm never going to hit that. Like, I'm going to have to run ten miles to hit that, that goal. So, I mean, I try to like get out and lift that day and maybe get a run in, then get a walk in. And I'm still you know, when you can't hit that high mark, if you're actually in shape. When you're not in shape, sort of, you can get there a little bit easier because your body is has optimized itself in a sense. Which is great, I guess. But when you're when you're holding yourself up to that number, you're like, Oh, my God, I'm never going to hit that number.

Emily Capodilupo: Yeah. I mean, it's super interesting how the human body works, right? There's almost like this weird kindness in how we work where it's like easier and more fun to make progress when you're brand new and starting out and it's harder to make progress the better you are.

Harry Glorikian: I mean, it's an efficient machine. It has to optimize itself. Right. So, again, you were saying no display, no interface. All the information happens on the associated device, the phone. I mean, you mentioned some of the pros and cons, but are there any other that I haven't asked or I know that at some point it pings me and says like. You need to connect because it's been some time between connections. So is there an offloading time frame that it needs to...

Emily Capodilupo: No, it can store up to three days of data on the device itself.

Harry Glorikian: Oh, interesting. Okay.

Emily Capodilupo: Yeah. So if you like went camping for the weekend or something and didn't have internet, we would just store the data locally and then transmit it all when you got back. But it tries to transmit the data more or less consistently, constantly throughout the day. What it's pinging you about is not that you're in any way in danger of losing the data, but just that you're behind. And so you might be missing any kind of analysis or getting credit for your strains. We want to make sure you're up to date so that if you want to look at your data from the day, you would have access to it.

Harry Glorikian: Here's a question. Would it ever make sense to make a WHOOP app for the Apple Watch? Or is the device sort of inextricably linked to the app?

Emily Capodilupo: Yeah. I mean, there's a lot of good reasons to think about something like that, right? You can make it a lot more affordable if you didn't tie it to hardware. Right now, we believe that we have the best hardware on the market, but there's sort of valid pushback that some people are willing to settle for something less than best in order to only wear one thing. And they want to wear their Apple Watch because they like the phone call notifications and the texting and email and all that kind of stuff. There's a lot of great features that Apple has that we don't. I'm certainly not trying to hate on the competitors at all. But I think like the way we kind of think about what we've done is like if Apple Watch does a lot of little things, you know, at like a relatively shallow depth, so it's like a lot of coverage, we do a small subset of those things, but we do them very, very, very well. And so by not doing things like putting on a screen and letting you text and all of those things, we're able to have all of the power of the device drive towards getting the most accurate signal data. And so we are sampling the heart rate more frequently than Apple is, and the device is more purpose built around optimizing both internally and externally for the sensors. So there's even little things like electrical coupling on the circuit board. When you try and shove too much functionality into something small, they kind of like run into each other. And, you know, so we're not trying to make room for a GPS chip or make room for a screen or like all of those things. And so it lets us lay out the hardware very specifically for this purpose. And so we believe that in data to support that, we're getting more and more accurate like metric data.

[musical interlude]

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Harry Glorikian: So switching to sort of business model, because you sort of touched on that, is like it's a subscription model. You don't buy the device. If I'm not mistaken. The service starts at say 30 bucks a month and the package actually includes the WHOOP band. They'll just ship it to you like I'm wearing mine. Right. And so what was the rationale behind subscription versus just selling the device. If you have insight into, how did they pick 30 bucks? You know, I just wonder, like, you know, did they, is that something you guys felt reaches the broadest market sort of thing?

Emily Capodilupo: Yeah, pretty much. So when we actually first launched, it was sold more like a traditional hardware product. So it was $500, one time fee, sort of use it as long as you want. And then we switched over to the subscription model in 2018. A nd we chose the price of $30. It was sort of designed to make the product accessible and lower the barrier of entry. $500 up front is a lot of money, especially for younger athletes. We want to make sure that people in college could afford it and stuff like that. And so we found just by market testing, that $30 was an approachable price point. And so after a couple of different market tests, that was what we landed with and more or less where we've been. We occasionally discount it and different things like that, and you can get a lower rate if you commit to more months upfront.

Harry Glorikian: Yeah, I think I signed up for the maximum, which then brought it down to I think it was $18. Yeah. So here's a, you know, because this show is, you know, supposed to focus on AI and health care and things like that, I'm just sort of imagining in the back of my mind with that much data, you really have the opportunity to build some really cool analytics on top of it. You know, what role, if any, like does machine learning or other forms of AI play in you know how you analyze the data and then how do you, do you actually use that to personalize it back to the individual using it.

Emily Capodilupo: Yeah, I mean, that's pretty much all my team is doing is machine learning. No, it plays a huge role in what we're doing, from like very traditional ML approaches, so like if you think about how we're doing our sleep staging, we have polysomnography is like the gold standard for getting sleep truth data. So that's like the stages when we know we're in REM sleep or slow-wave sleep. So we sent thousands and thousands of people into a clinical sleep lab with two straps on and they underwent a clinical sleep study. And then we took all of the data from the sleep study, lined it up with the WHOOP data, and then used all kinds of different traditional ML approaches in order to figure out how to get from a strap the same sleep staging information that we're able to get from this gold standard approach. Obviously the sort of gold standard sleep study uses a lot of sensors that we don't have right things. EEGs, which you need to be on someone's head to use. You can't get EEG from the wrist. EOGs, which you have to measure eye movement. So you need a little sensor there. And then we were able to find good proxies from the data that we can get at the wrist for all of those different signals and reconstruct the same sleep stage information.

Emily Capodilupo: So that's a super fun ML problem. We also do things like when we detect a workout, we can figure out what, which sport or exercise modality you're using. And so the ability to classify those workouts is kind of again like a traditional ML like time series classification problem where you can tell the difference just from the heart rate and accelerometer signals. Are you doing basketball or CrossFit or running or anything like that? And then so those are kind of more traditional ML approaches. And then we've also done a lot around trying to understand behavioral impacts and how your body responds to different things. And then we're doing things like much, much more personalized. So we have a feature called The Journal where every day you fill out this little diary and you answer a bunch of questions about what you've done in the last 24 hours and can self report things like when you were eating, if you did different like kind of wellness activities like, meditate, journal. You know.

Harry Glorikian: How much alcohol you had. I always wonder, like how honestly somebody answers that question.

Emily Capodilupo: Any of those kinds of things. And then we look at the sort of signals in your data and try and separate out which of the things are helping you, which are hurting you, so that we can then recommend the things that are good for you, and for the things that are less good for you, maybe help you quantify the cost of those things that you can deploy them strategically. We certainly don't expect everybody to become like a teetotaller and never drink again, even though we're going to tell you it's bad for you, because it's pretty much always what shows up in the data. But we do want to help people make those informed decisions because a lot of people think like, Oh, I can have two drinks and it won't affect me tomorrow. And like, okay, here's the effect. And if tomorrow's not that important, go for it. And you have that really important meeting tomorrow, maybe don't. Y rou know, we're not trying to kill all the fun by any means, but we do want to make sure that people are empowered by data to know understand what they're doing to their body and then make decisions accordingly.

Harry Glorikian: So I'm throwing in sort of like something important to me, right? Which is, you know, I have sleep apnea. Right. And it's funny because my wife diagnosed me, but then, you know, all the devices at some point, my Apple Watch actually asked me once, you know, have you ever been diagnosed with sleep apnea, which was interesting. But I've noticed like, the recovery number, if don't wear my CPAP, my recovery number tends to be much higher than if I do wear my CPAP. And I always wonder, does the positive air pressure cause a difference in how much your heart actually rests or not? Because it is pushing, it is positive air pressure on you all the time. So even in between apneas, you don't really maybe not rest as much. And I'm wondering if you have any insight on that.

Emily Capodilupo: Yeah, we, we haven't specifically dug into why, but we have seen that as an unexpected pattern. You're not the only person to report that. It's on the to do list to better understand what's going on there. I think your theory is a valid one. We haven't verified or ruled it out yet, but I think there's a lot to be learned there. And I think one of the things that's exciting about the data that we're collecting is that if you wear a CPAP is one of the things you can report in our journals. We do have a tremendous amount of data on that and therefore the ability to kind of tease that apart and get insights that haven't been made available yet by traditional academic research.

Harry Glorikian: Oh, I didn't know I could add CPAP in there. I have to go back and and check. But yeah, because my strain score ends up, my recovery score ends up lower. So it's like, you know, then of course, I always exceed on the strain side because I'm going to go work out the next day. And you know, it is what it is. But the other thing that you guys offer is like WHOOP for teams. And I don't know if you mean sports teams. You mean organizations. I'm not 100% sure because obviously I don't use that. I'm using it as an individual. Can you explain the additional value that provides when a group of people are using it together?

Emily Capodilupo: Yeah. So all the above, we do it corporate teams as well as athletic teams, and there's a couple of different layers of the added value. So sometimes it's just accountability. I'm on a team with my family and it's just kind of fun, make fun of each other when our recovery scores are poor and, you know, cheer each other on when we have particularly good strain scores. And, you know, there's a lot of data to support that when you have a workout buddy or an accountability buddy or anything like that, that you tend to stick with things longer. And so creating just like a really friendly way for people to compete and cheer for each other just helps with the accountability and motivation keeping people on track. And deeper and more importantly, we do have a lot of people who create teams around different kinds of research initiatives or trying to understand a certain life stage. Like we create teams for people based on the month that their babies are due. So pregnant women can join a team of all the women on WHOOP who are expecting a baby in June 2022 can join this team together and pregnancy is this like very foreign weird moment in your body where everything's changing all the time and it just creates, like, a way for people to connect and be, like, this weird thing that's happening to me, is it normal? Like, who else is sleeping funny? And I think it's just very comforting to know that, like, all these weird things happening to your body aren't so weird. And then with like the sports teams and different things like that, what we're seeing is that the coaches are using the information to make better training or like decisions because now they actually have information that they didn't have access to before.

Emily Capodilupo: So we've done a lot of work with different like collegiate programs and professional programs where they do things like if you're red, they will have you do a lighter version of the practice or skip a section of the practice in order to give your body a chance to recover. And if you're green, they might have you push a little bit harder. And so by modulating the training to where your body is today, we've actually shown in a project we completed a little over two years ago that you can reduce injury without reducing performance gains over the course of like an eight week training period. And so by reducing your training, when you're red, so your recovery score is below 33%, you actually like you will reduce injury without reducing performance gains. We've shown this. And so there's like literally zero value for those coaches to like push the athletes to complete the program or the day’s rtraining. And so we've seen a lot of coaches make those different training plans as well as game day decisions about who should start. You know, somebody might be your best player ordinarily, but if they're red, they're not all that primed on game day to perform. And so being able to make those kinds of different decisions. And then on the corporate side, people have used it in order to triage different access to supportive resources. So we've seen people offer like breaks to people who have been red for a number of different days in a row or things like that suggest that somebody might be burning out or overwhelmed or something like that.

Harry Glorikian: Okay, so. Everywhere it states that it is not a medical device, is not intended to diagnose, monitor any disease or medical condition. Right. What's the line in your mind between, say, a fitness monitor and a medical device, because I think I always think that line is getting….because you guys and others like you guys have so much data, the level of insight that I've seen when I've gone into some of these is crazy. So. What what is that line in your mind?

Emily Capodilupo: Yeah. I mean, I think that there's you know, it's always been the case that technology moves faster than the law. And so, like, you know, I think a lot of these things are going to shift as the technology is going to force them to shift. But, you know, like you said, we have a lot of data that's quite similar. The official line is what the FDA says is the line. And the FDA has carved out this like space that they've you know, they've called this wellness devices. They've sort of reserved the right to change their mind at any time, and we very much expect them to. But WHOOP falls into their definition of what a wellness device is, not a medical device, which is why we can say things like, this is your heart rate, but we can't say, because then you would cross into a medical device, like “Your heart rate is healthy, your heart rate is unhealthy,” right? You can't give those kinds of any kind of diagnoses or any kind of, like, you will prevent a heart attack if you do these things or something like that. So we have to keep the recommendations a bit more general, a little bit more vague in order to not cross over into that regulated health space. One of the things that we're seeing that's interesting, is that there's been a movement in wearables to get these like SAMD clearances, Software as a Medical Device, where pieces of wearables need different features or different algorithms do end up going through an FDA process and getting clearance to make certain claims in different settings.

Emily Capodilupo: And I think that that's going to really accelerate over the next couple of years. These are very long processes, and then the lines are going to get more and more blurry because you're going to have this like hybrid consumer medical device, which is something that until a couple of years ago we really didn't have. There was like step counters and GPS watches and they were over here and then there was like medical stuff that didn't look cool and wasn't comfortable or easy to use and was very, very expensive. And it was all over here. And now we're seeing them kind of come into the middle where more and more the medical stuff cares about being like all the human factors like that's comfortable to use and that people want to wear it and they can get good compliance. And the wellness devices are finding more and more applications for their data in the health care space. So I think a lot of it's going to come down to what doctors end up getting trained on. If they're willing to look at this data, if they have any clue how to use it, sort of by being in the medical world and science training their whole lives, a lot of them just don't have the education and training to understand big data and to understand technology in that way. So they're not being trained on how to make use of the data or how to apply it. And I think that that's something that might change in the next couple of decades.

Harry Glorikian: Well, it's interesting, right, because I always tell people I'm like, this is a medical device. Like I you know, I mean, you know, you may think it's not, but it really has certain capabilities that allow it to get FDA clearance in a particular area. Right. And they're picking their space one by one. But the amount of data that you guys pick up on all of these devices, I mean, you know, we've seen atrial fibrillation. I'm sure that tachycardia shows up on there. You know, there's different things that they, because it's 24/7, it's looking, right and it's monitoring and it's got multiple sensors which you can now cross-correlate. There's so much insight that comes from this that I would almost like love to encourage the companies to think about moving down this road because I think it would be so helpful to patients. But, you know, jumping to a different thing. So. How do you guys define success for WHOOP? If you hit all your product and sales goals and for the next, say, 2 to 5 years, what does success look like for the organization?

Emily Capodilupo: Yeah. I mean, I'll let the finance team worry about the sales goals and things, but I mean, for me in my team, like what success really comes down to is like, can we help people make actually better decisions? I think like a lot of the first generation of wearables, like it was this stream of fun facts. And we're all obsessed with ourselves, right? Like humans are sort of naturally narcissists, at least to a certain extent. And so it's like fun to be like, ooh, I slept for 7 hours or like, ooh, I ran a mile. But it's like kind of you maybe already knew that, right? And I think, like, what we're trying to do and like where we see a lot of success is, can we tell you something that you don't know? And can we convince you that you should do something about it? And then can we make you, like, realize, like, oh, wow, this, like, incredible thing happened and I feel so much better. And the features that we get the most excited about are like the sort of user stories are not, like, “Wow, it's so much fun to see my sleep data” or like, “This was fun.” But like when we released our paper showing that this respiratory rate spike sort of predicted or often preceded COVID symptom onset and therefore COVID infection, the paper came out like right before Thanksgiving and we saw so many people tell us that like because they had a respiratory rate spike, they didn't go home for Thanksgiving or they didn't travel and then like they tested positive a few days later and they were like, my grandma was at Thanksgiving or like my uncle who's in his eighties or stuff like that.

Emily Capodilupo: And you know, those kind of moments where it's like, we educated you, we showed you this vital sign that like, you never would have felt anything. You didn't know you were sick, you weren't feeling bad. It's not like you went to go get a test because you weren't feeling good, like you just saw this in your WHOOP data and you're like, You know what? I'm going to stay home and not risk like seeing grandma because WHOOP said so, right? And then like, who knows how many COVID infections didn't happen and like what kind of role we played there. And like, it was probably like the most meaningful thing we did that year. And we did a lot of other cool stuff, but to think that by helping people notice that pattern, potentially they saved a relative's life and all the like crappy things that would happen if you thought you were responsible for killing your grandma and how much that ruins your own life as well? I think like we just get really excited about that. And one of the features that we released is last year was we were looking at how your reproductive hormones is part of your menstrual cycle affect your ability to respond to training. And I was an athlete my whole life. I was a gymnast, like before I could walk, and like nobody asked me a single time when my last period was or anything like that. That was just totally not part of like the coach-athlete relationship. But we know that like your ability to put on muscle and your ability to recover from training is totally different during the follicular phase, the first half of your menstrual cycle, than it is during the luteal phase, which is the second half. And if we modulate your training so that you're training more during the first half of the cycle than the second half, you can way more efficiently build muscle and strength, have fewer injuries, make more efficient gains. And if we now we do coach, in our product, women to do this, and we've gotten this incredible feedback of like people saying they feel so much better and like they're, well, you know, their training is going more smoothly and they feel like their body so much less random, it feels more predictable and they kind of understand what's going on. Nobody ever told them that reproductive hormones were relevant beyond their role in reproduction, but they actually affect everything we do. Like when progesterone is elevated in the back half of our menstrual cycle during the luteal phase, we sweat more and we lose a lot of salt by doing that. And so we need to eat more salty foods and we need to be more careful about hydrating, which is really important if you're an athlete, but nobody's telling us this. And so like we can connect these by looking at big data because we are tracking your menstrual cycle around the clock or around the month.

Emily Capodilupo: We can put that into the product and then we see people are making better training decisions, understanding their body, feeling like things are less random. Right. And that's so empowering. And I think like female athletes in particular have been so underrepresented in research. There's a paper that came out eight months ago that said that just 6% of athletic performance research focused on women, 6%. And it was looking at all research between 2014 and 2020. And it was trending down, not up. So it was worse in like 2018, '19 and '20 than it had been like earlier in the twenty-teens. And so it's like completely neglected. And there is all this data that like wearables and WHOOP are sitting on and we're able to create features around that and just help people understand their bodies in a way that nobody else is doing right now. And so those are the features that, like I really define as like big successes. If we made our sleep staging accuracy 1% more accurate or we caught one more workout, like those are obviously like from a pure data science perspective, they can feel like wins. But what we really care about is like, am I helping you, cheesily going back to our mission, am I helping you unlock your performance in some way by helping you understand your body and making a better decision? Like, are you better off for having been on WHOOP? That's what, internally, those are the KPIs that we track the most closely.

Harry Glorikian: Yeah. And I mean I would encourage you as well as all the other companies to, you know, peer reviewed papers, get them out there. Right. I mean, just when I search the space or peer reviewed journals for things utilizing the technologies, I mean, there's not a whole lot out there. And then the other thing is, is sometimes I read the devices they're using, I'm like, whoa, what is that? I've never heard of that device. And if I haven't heard about it, it must be on the fringe sort of thing. So I would highly encourage it because, you know, people like me would love to be looking at that sort of data. Because I'm constantly investing in the space, constantly working with the different technologies, you know, constantly talking to people through the podcast or writing a book, you know. So that information is incredibly useful to someone like me as, as, as well as the average person. So if you could send a message back through time to yourself in 2013 when you joined the company, you know. What would you say? What have you learned about the wearables and fitness market that you know you wish you knew then?

Emily Capodilupo: Oh, what a fun question. You know, I think, like. It's hard to know what I wish I knew earlier because like in so many ways and I feel so lucky that this is true, like the vision that Will pitched me on when I met him, like when he was like, “Come join WHOOP, this is why it's super cool,” is exactly what we're doing. And so, like, I did trust him. I guess my message in a lot of ways would be trust him that like this is for real. I think the space has been so exciting and just there's so much opportunity. I came from doing academic sleep research and I would work on these papers where we had like 14 subjects and it was like, “Oh, that's a, that's a good size sleep study. Like that'll get into a good journal.” And everyone was like excited. And then it's like, you know, I just, I'm working on a paper right now and we have 300,000 people's data in it. We're looking at like a year of data at a time. So we've got just like millions and millions of sleeps and workouts in this data set that we're combing through. When we did this project, which was published in the British Medical Journal last year, where we were looking at the menstrual cycle phases and how they affected your training, we looked at 14,000 menstrual cycles, like just the orders of magnitude more data than what you can do in traditional academic research. And that's what I got really excited about. It's why I became a data scientist because I realized that like the most interesting questions that there are to answer about how humans work are going to require larger datasets than we've had access to before.

Harry Glorikian: So I'm putting in a plug for sleep apnea, man, if you get a chance, I'd love to see a study on that one.

Emily Capodilupo: No, sleep apnea, it's definitely on the list. About 80% of sleep apnea is believed to be undiagnosed. And it does have tremendous effects on long term health when it goes undiagnosed, especially in later stages. And so anything we can do around helping people realize that they might have sleep apnea and then helping them treat it once they do and better understand the disease progression. And all of that has a huge quality of life implications down the road.

Harry Glorikian: I will happily volunteer. So great to speak to you. Very insightful discussion. I'm going to tell my wife about the whole menstrual cycle thing and working out and this is exactly why she eats salty food like at certain times. But this is great. I'm so glad to have you on the show and I look forward to seeing the progress of the company and the technology.

Emily Capodilupo: Awesome. Well, thank you so much for having me. This is such a fun conversation.

Harry Glorikian: Thank you.

Harry Glorikian: That’s it for this week’s episode. 

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

If you live in the United States and you’ve ever had your doctor prescribe a new medication, you’ve probably had the following experience.

You drive from the doctor’s office to the pharmacy.

And when you get there, you find out that the pharmacy doesn’t carry that particular drug. 

Or that they do carry it, but your insurance provider doesn’t cover it. 

Or your insurance does cover it, but they require prior authorization. 

Which means you have to get back in touch with your doctor and ask them to tell the insurance company that you really do need the medicine.

Or you already have prior authorization, but you haven’t met your annual deductible yet, so your out-of-pocket cost is much more than you expected.

If any one of these problems crops up, the chances that you’ll actually get your prescription filled on the day you need it go way down.

And it’s not uncommon for several of these snafus to happen all at once.

Fundamentallythat’s because the electronic health record systems and the electronic prescribing systems at your doctor’s office don’t include price and benefit information for prescription drugs.

All of that information lives on separate systems at your insurance company and your health plan’s pharmacy benefit manager, or PBM.

And that’s the gap that a company called RxRevu is trying to fix.

My guest on today’s show is the CEO of RxRevu, Kyle Kiser.

We talked about the software they’ve built to make drug cost and coverage information available within the major EHR systems

When doctors can see in real time which drugs are covered, at what price, for a specific patient, it    obviously solves a huge pain point for patients, because it means they’re more likely to get the drugs they need at an affordable price.

But it also solves a big problem for doctors. Because, fairly or not, they’re the ones who usually shoulder the blame when it turns out the medication they just prescribed is too expensive or isn’t available.

The kind of information RxRevu provides is going to be more and more important as the U.S. enters into an era of far greater price transparency, as mandated by the federal No Surprises Act, which went into effect on January 1 of this year.

RxRevu is based in Denver, Colorado, and I reached Kyle Kiser at his home in Seattle, Washington. Here’s our full conversation.

Harry Glorikian: Kyle, welcome to the show.

Kyle Kiser: Thanks, Harry. Happy to be here.

Harry Glorikian: So, you know, we were just talking. You're in Seattle and I'm in Boston. I don't think we could be much farther apart when it comes to this particular country. So but let's start with a little bit of background, right. So. You're the CEO of RxRevu. And can you tell us a little bit about sort of the origin story about how you got started here? I mean, I understand your co-founder, Dr. Kevin O'Brien, had an interesting experience trying to get prescriptions filled for his mother, Lucy, but. What's the rest of that story? What did that story reveal to you about what's broken or missing in the way that doctors prescribes medicines or, you know, where the way that maybe payers approve prescription?

Kyle Kiser: Yeah, absolutely. So a little background on Kevin's story. Kevin was initially inspired to do this because he wanted to solve a problem for his mom. She had an outsized out-of-pocket spend for meds. Like any good son, he wanted to help solve a problem for his mom. He used his expertise to find sort of ways to save on those medications, and that inspired him to start doing that in his clinic for his patients more comprehensively. So he was, you know, way ahead of his time and putting in all of this extra effort to really help find prescription options for patients that they could afford more easily. And that was the initial inspiration for what we've done today, which is connecting the point of care and clinical decision making with costs and coverage information that's real time and patient specific and location specific and moment in time specific, because all those things matter as inputs into a price.

Kyle Kiser: So, you know, really the challenge we've been focused on is, is largely that, you know, the clinical decision making process has been pretty, pretty much disconnected, right, from marketplace information. So, you know, anything that impacts the purchasing of that care. And that was okay in a world where deductibles were low, formularies were relatively inexpensive and simple. But that world has changed dramatically over the last 10 to 20 years, right, as consumer driven healthcare has become the way of the world. And first dollar risk is now at the feet of the patient. It's that patients are now demanding that providers can consider not just what's best from a clinical perspective, but also set expectations around costs, set expectations around any restrictions that exist, and be an advocate for access to care. And the problem we're solving. We're building an access network. And within that access network, we help drive affordability and speed to care for patients. And we're doing that with a number of stakeholders. But at a high level, that's what we're trying to accomplish.

Harry Glorikian: Well, you know, it's interesting, right? You know, entrepreneurship 101, solve a real need, right? So that there's a market there because everybody wants it. But so, I mean, look, I think everyone in the United States has probably had experiences similar to Dr. O'Brien's mom. I mean, you get to the pharmacy, you find out that the medication your doctor prescribed isn't covered by your plan, or you find out that the co-pay is outrageously high. But behind their personal experiences, I bet most people don't have a concept of how big and widespread this problem is. You know, you have any maybe some statistics that might illustrate the scale of the problem or how much money is wasted in the medical system because of these disconnects. I mean, I'm wondering how many prescriptions get abandoned or how many patients don't get the meds they need.

Kyle Kiser: Yeah, I mean, at a. A macro level, you know, the prescription drug market makes just over makes up, you know, just over a half a trillion. Right. And, you know, estimates are that a third, even as much as half of that is waste and waste in the form of, you know, medications that aren't taken as prescribed or aren't delivering the right outcomes. I don't it's hard to find actually a a stakeholder in the supply chain that's delivered more value than meds themselves. I mean, if you think about, you know, the innovation in that world over the last 30 years, it's second to none. But the, you know, the supply chain within which they exist is complicated and it's hard to navigate. And the consequences of that is waste. And, you know, a ton of administrivia and friction. And frankly, patients bear the brunt of that. Ultimately, it's health plans and PBMs and risk bearing entities making rules on one end. It's providers and care teams making clinical decisions on the other end. And both of those processes are largely disconnected. And the only way that that gets harmonized in any way is a patient advocating for themselves. And we just fundamentally don't believe it should happen that way. What we're building is the connectivity between those stakeholders so that whether it's a provider at the point of care making the decision, whether it's a care team member trying to help you overcome a prior, or whether it's a patient trying to advocate for themselves using their own technology, we want to put real time, patient-specific, moment in time specific information in their hands to drive affordability and speed to care for that patient, no matter where they are in the care continuum.

Harry Glorikian: Yeah. I mean, so this lack of prescription cost data, I mean at the point of care feels like a real canonical example of deep systemic problems with the with origins that are buried like deep in at least three of these complex organizations. Providers, payers and EHR makers. I mean, once you guys decided what the problem you wanted to fix was, how the hell did you figure out where to like -- okay, let's start here and let's move forward, right? Because.

Kyle Kiser: Yeah.

Harry Glorikian: Not trivial.

Kyle Kiser: No, it's exactly the right observation because ultimately what we're building is a multi-sided network. And what's difficult about building a multi-sided network is, you know, users on one end, in this case, providers, aren't going to engage if it doesn't have the appropriate information in it. And the data sources, the ability to capture that appropriate information, they don't want to provide that data to you unless you have the appropriate users. So you get stuck in this chicken or the egg problem. And that's job one in growing this business, is to overcome that chicken or the egg problem. And the way we went about that was we worked really closely with health systems, with provider organizations, primarily because that's where the trust exists, is that ultimately patients seek out their provider and their care team to answer these questions. And so we worked closely with them as strategic partners and brought some of them in as investors in the company and aggregated a group of meaningful collaborators on the health system side, which then helped us bring PBMs and payers to the table to say, how do we solve these problems together? And that's that's sort of how we got out of the gate.

Harry Glorikian: So I mean, tell me if we could dig a little into I think the product is called SwiftRx, if I remember correctly, but at a high level. You know, if you could describe for listeners, what is it? How does it work? And. Where does it fit in relation to the overall system?

Kyle Kiser: Sure. Yes. So SwiftRx Direct is the product you're describing. What it provides is, is that real time, patient specific, location specific, moment in time specific information in the provider's native ordering workflow. So we are a data network that's powering a native feature inside the EMR that provides that insight while providers are selecting medications. So a typical flow would look like, a provider selects a medication. They then place that into a pending status in the software that they use. When that happens, we're able to gain visibility to that choice. We send that transaction out to our network of data sources, payers, PBMs, etc.. And what we get back is the price that is patient specific. We have formulary insights, so prior auth, quantity, limit, step therapy, those sorts of things. Those are also patient specific. And then most importantly, we get back alternatives. And those alternatives come in two forms. They're either a lower cost medication or a lower cost pharmacy where the patient can fulfill that medication. And that's sort of the core information that we then render back into the e-prescribing workflow. And we only interrupt those providers' workflows, or we and our partners only interrupt those providers workflows, when there's relevant information to consider. Because as I'm sure, you know, being deep in this world, provider engagement stuff -- you really have to be thoughtful about when, when is the appropriate time to intervene and when, when do we want to sort of get out of the way and make sure that when we are intervening, it's meaningful and understood to be meaningful?

Harry Glorikian: Yeah. So I'm going to I mean, I heard a lot of what you said. I'm I want to maybe summarize all the. A few of these areas that people run into problems. But to try to understand sort of what are the big problems you had to solve to get it to really work? Because I'm just trying to get my head around the magnitude of the data headache here. Right. So if if you'll allow me, I'll just try to break it down into parts and then you can tell me how you're bridging all of these. So for one thing, there's the patient specific data about what kind of insurance each patient has and what level of benefits they have. And none of that is stored in the EHR at the clinic. As far as I know, typically the EHR would only list the patient's group number, subscription number or maybe the RxBin number. And then separate from all that, every insurance has a formula of drugs that will cover and sometimes a, you know, a schedule of different copay amounts for those drugs. And those formularies change every year and even more often. Right. And then there's a patient's actual prescription data which may live in their EMR or may live in a different system at the pharmacy. And then on top of that, there's this obscure black box system of prior authorization criteria that insurers may use to deny a prescription if they don't feel like paying for it. So the fact that the system is so fragmented is a familiar story to anybody who listens to this show. But tell me, you know, how on earth you were able to sort of get all this data under one roof, so to speak? You know, is there a specific architecture of the Swift system that makes you good at collecting all of this changing data and presenting it to the providers in real time?

Kyle Kiser: Yeah. The only other element I'd add to your complexity salad is also benefit design, right. Is that yeah, the, the out-of-pocket cost can be and is dramatically different based on where you are in your coverage. If you're a commercial member with a high deductible, you're bearing the, you know, the in-network negotiated rate inside that deductible. And that changes pretty dramatically once you reach a deductible. Or if you're a Medicare member, there's the donut hole. And all of those things are also inputs and complexity to add to this. So to answer your question, it's really working closely with the stakeholders that control those, that are the source of that data. Right. You really can't get to an accurate price without working with those with those data sources specifically. So we work closely with the PBM, with the payer, and we do more or less a mock adjudication. So the same type of adjudication activity that happens on their end when a patient arrives at the point of sale is happening when a provider is making a prescribing decision in this case.

Harry Glorikian: I mean, I can tell you, like the last time I had to sit and choose an insurer, and you would think that I'd be better at this than most, I remember having to take two Tylenol, because when I got done, because I thought my head was going to explode. And I could honestly not say to you I made the best choice. It was at the end, it was almost like a Hail Mary, I guess with all the complexity. And the other thing that I keep thinking about is when I used to watch, I think if you have kids, you've watched The Incredibles and there's a point in the show where the manager says they're penetrating inside of our systems to understand how to get how to get the system to pay them or whatever. It feels like it's that level of complexity. And you really need a sophisticated system to sort of bring all that information together to make sense of it all.

Kyle Kiser: Yeah, that's true. And it is it is dynamic, it is highly variable and it's very different from administrator to administrator. Right. And a specific example of that, right, is that responses we get back are not across the board consistent, that here's an error and here's what that error means. And that error message is consistent from health plan to health plan. That's just not the way the world works, right. The error messages are specific to those claim systems because ultimately on the other side of the fence, these are mainframe systems in some cases that were designed decades ago that they've then created a layer to expose to the outside world, in this case us. And, you know, it's not simple work for us or for them. So I think the thing also to point out here is that there's a lot of effort from the payer- rrPBM community to make this accessible and to sort of change the way they're doing business and to change the way their technology works to enable some of these things, which is which is progress and should be commended for sure.

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. 

It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: Interesting. So if I'm not mistaken, both Epic and Cerner have made it possible for providers to embed SwiftRx into their EHR. So if I understand it correctly, it even comes as a standard part of Cerner now. So those are two of the biggest EHR providers in the US.

Kyle Kiser: And Athena.

Harry Glorikian: And Athena, so question: how did you make that happen?

Kyle Kiser: Well, you know, we've got a great team and the team executed ultimately. We worked really hard on those relationships. And I think it's both working with the right customers in small ways in the early days that leads to working with these types of partners and bigger ways. And frankly, some of the open programs at some of these places led to this. So early days, we were working in kind of the more open developer type programs with these EMR partners. We were working closely with some of their customers. Banner was one of our first customers. UC Health was one of our first customers, both a Cerner and an Epic user respectively, and, you know, is working in small ways to solve these problems together with those health systems that led us both to interacting with PBMs and ultimately building these enterprise level relationships with the EMR. It's, you know, it's, it's earning the trust, it's delivering for these customers and then earning the right to do this at scale. And we're to a point now where we'll do almost 100 million of these transactions this year. And it's you know, it's grown fast.

Harry Glorikian: Yeah, that's a lot of that's a lot of data flowing back and forth. But so let's ask the money question, like, what's the business model? Who ends up paying you? Is it the provider buying SwiftRx as an add on to the existing EHR or how does that work?

Kyle Kiser: It's the risk bearing entity ultimately. So think about that as payer and PBM. In most cases, there are cases where we work with health systems and there are some things we do that that are either channel related or related to specific needs that they have when they're that risk bearing entity. But at a high level, we follow the risk and we want to work with the customer that is bearing that risk because ultimately they're the ones that stand to benefit from an optimized prescription choice.

Harry Glorikian: Okay. So that everybody gets a clear idea of like, can you give me a before and after picture at a clinic that brings SwiftRx into their EHR?

Kyle Kiser: Sure. Yeah. So. You know, this is probably an experience many of many of the listeners have had. Right. Is that. Before such acts you interact with your physician, they diagnose you with whatever condition they've perceived. They select a medication. They route it to the pharmacy. You go to the pharmacy and cross your fingers that all of the requirements have been met. And that is at a price that you can afford if there is a prior or if that's too expensive. When you arrive at that site of fulfillment, you discover that, right, if there's a prior that's not been completed, then you've got to go through that prior authorization process and you're not picking up that prescription today. If it's a price you can't afford, you got to figure out how to pay for it. And there's a variety of ways that that happens. But ultimately, it's up to the patient to figure those things out. In a world where SwiftRx is installed, the difference is, as that prescription decision is happening, we notify the prescriber of the patient's out-of-pocket cost. In some cases, even the plan cost associated with that choice. Any restrictions that exist like prior or quantity limit or step therapy. And we also notify them of any lower cost alternatives. So in many cases, simple changes make big differences in in the out-of-pocket cost. And it might even be something as simple as, time release metformin can be hundreds if not $1,000, and regular old metformin is four bucks and has been four bucks for decades.

Kyle Kiser: So it's some of those almost unintentional, I hesitate to call them errors on the provider side. It's just they're making choices based on their own sort of clinical expertise. But they don't they don't know these things, right? They don't know how a time release metformin might be reimbursed for one of the ten or 12 payers that they may see in a given clinic day. So it's just providing that insight upfront so that they can make those decisions and understand the trade offs. Is time release really important or is this patient going to be fine? And is that out-of-pocket costs for a med going to prevent them from being able to actually take that medication? And as a result, they're not going to receive any of the clinical benefit. So ultimately, the $4 option is probably better. So it's really connecting that clinical decision making process with all of the complexity that exists on the payer and PBM end so that we can get the decision right the first time. And when the patient shows up at the pharmacy, they know how much is going to cost, they feel comfortable that they can pay for it and they're either aware of the prior auth and have already completed the requirements or have some, some level of expectations set to how to complete those requirements.

Harry Glorikian: So for all the reasons we've been discussing, doctors traditionally have been able to stay somewhat separated or maybe called it shielded from discussions about drug prices. I mean, they just prescribe a drug, leave it to their office staff or the patient or their pharmacy to figure out whether it's covered. But now, for organizations that are using your system that are built into their EHR, a clinical encounter, it can involve essentially going shopping in real time for the best drug at the best price. I mean, in your experience, how do doctors like being pulled into these decisions? I mean, I can see how it be great for patients, but I wonder if doctors are equally excited.

Kyle Kiser: You know, one of the things that's been the most surprising to us around this subject, specifically patient out-of-pocket cost, is one of the most requested pieces of information in a primary care clinic, because it's so complex and it creates so many callbacks and it creates so much patient dissatisfaction. Because ultimately the patient's going to, at some level, hold that prescriber accountable for that decision. And if it's really expensive med there's an assumption that the provider knew that already or should have known that, whether that's true or not. And so what that's resulted in is primary care providers want this information, they want it. They want to have this at their fingertips when they're making decisions. It's the world certainly changed in that way. So I think, you know, it's becoming a part of the standard of care being able to consider cost. Because to the point earlier, the only medication that works is the one the patient can afford. And so you really have to consider those things because of the way our sort of health care payment infrastructure exists. Right. There's just, patients are bearing a dramatic portion of that cost these days and got to consider that as a part of the way you deliver care.

Harry Glorikian: I mean, I almost feel like your company is is pushing. These providers and payers and to fix the prescription benefit system or making them more efficient or compatible.

Kyle Kiser: Yeah. I think there is a, I maybe describe it as rationalizing. R I don't think that a clinical team and a PBM and PNT committee at a health system have dramatically different opinions on what medications should be prescribed, for what conditions. The friction exists in that they're making those decisions in isolation of one another. So I think I see our role as a connector to help, you know, in a value based world, the incentives start to align between risk bearing entity and health system. And many times the health system becomes the risk bearing entity fully. And so our goal is to empower providers to understand those things in real time, to manage the complexity for them, only engage them with the information that makes a difference in the decision they're trying to make and ultimately create a better experience for the patient, a better outcome for the patient, and a less burdensome process for the provider organization.

Harry Glorikian: So as we all know, I mean, the American medical system is famous for sending patients surprise bills after clinical encounter or an emergency room visit, right. Where a bandage or an aspirin can carry some crazy prices that I've seen. And I'm trying to project onto where you are as a company and where you want to go. I mean, now that you've tackled the rrtransparency in drug pricing, which I would honestly like to see everywhere, because I think I've heard my wife complain all the time when she encounters some astronomical price. Right. Can you imagine trying to tackle or bring greater transparency to other medical costs, such as maybe a surgical procedure or hospital supplies. I mean, is there anything that you've learned about prescription benefits that's transferable to all these other types of care?

Kyle Kiser: Absolutely. Yeah. We're already moving beyond prescriptions today and focused on labs, radiology services, generally. And see the dynamics of the payer-PBM end of the market five or six years ago as it relates to pharmacy real time benefit shaping up much in the same way around medical benefits. That payers are thinking about these problems in the same ways and are showing initiative and prioritizing putting this information at the point of care for for all of the reasons that we just described on the drug side are true in many ways on the medical side. So, yes, absolutely. That's where we're headed. And the regulatory tailwinds are there in a new way. Right. If you think about in the last 12 months, there's been more price transparency legislation than in the last 30 years. And that, combined with the no surprise billing legislation, really creates this this kind of pre EOB requirement for each of the stakeholders and they got to solve that problem. And we see ourselves as really well positioned to be a part of that solution.

Harry Glorikian: Yeah. I mean, you know, it there was no way. I mean, the Affordable Care Act got put into place and there were certain things in there that just there was no way that you were going to be able to do that without some level of transparency and understanding what's going on.

Kyle Kiser: Yeah. Yeah, that's right. But even further, right, before the end of last year there were price transparency regulations for health systems, for providers, for payers. And then the no surprise billing legislation has in it a component that says, you know, before you deliver care, you got to be able to give an estimation of cost. And so all of those things sort of work together from a regulatory perspective to start to drive the market in that direction. So absolutely, it's coming everywhere. It's going to be, it's going to be a part of the way that every health care decision is made in the future. And it's just a matter of time before that's the case.

Harry Glorikian: Yeah. It's interesting because I have lots of conversations with, you know, lots of different people. And they I don't think they understand that. If you don't have that level of transparency, you truly don't have a competitive environment, right? You can't make choices because you don't have the information to be able to make that choice.

Kyle Kiser: That's exactly right. Without it, there is no marketplace. Right. That's probably overstated. It's without it, it's a dysfunctional marketplace. And with transparency, we will start to see real competitive dynamics emerge. And I'm hopeful for that. Sunlight's the greatest antiseptic.

Harry Glorikian: Oh, I totally agree. I mean, for me, it's always been like a walled garden. Like, you know, either you're here or, you know, you're out of luck, right? Because you don't have any information so you can go across the street. So. So. I guess I should be asking. I've probably reached the limit of my knowledge on the subject matter, but like, is. Is there anything I haven't asked you or anything, you know, that you would want to add to the conversation that would be enlightening to the people that are listening?

Kyle Kiser: Yeah, well, the only thing I would sort of make sure we reframe a little bit is that this isn't necessarily about price transparency. Price transparency is a component of providing access to care for patients, and that's ultimately what we continually focus on inside of our company, that price is an input. Affordability is an input. Convenience is an input. The ability to actually receive the prescription is an input. We're ultimately trying to make sure that affordability and speed to care lead to better outcomes. And that's an access story, not just a price transparency story. And so that's the only sort of reframe that I'd offer is that ultimately this has to lead to better health, people getting healthier, getting the care they need, being able to afford the medications that they need. And that's the work. And we're going to stop at nothing to make sure that that happens.

Harry Glorikian: Excellent. Well, it was great talking to you, Kyle. I wish you great success because, I mean, whenever I talk to anybody, I'm like, I know I could be benefiting from all of this, so I want everybody to be successful.

Kyle Kiser: We appreciate the well-wishes and we'll be working hard to ensure that that's the case.

Harry Glorikian: Excellent. Thank you so much.

Kyle Kiser: All right. Thanks, Harry.

Harry Glorikian: Bye bye.

Harry Glorikian: That’s it for this week’s episode. 

You can find a full transcript of this episode as well as the full archive of episodes of The Harry Glorikian Show and MoneyBall Medicine at our website. Just go to glorikian.com and click on the tab Podcasts.

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Thanks for listening, stay healthy, and be sure to tune in two weeks from now for our next interview.

Longtime listeners know that data interoperability in healthcare, or more often the lack of interoperability, is a repeating theme of the show. In fields from drug development to frontline medical care, we’ve got petabytes of data to work with, in the form of electronic medical records, genomic and proteomic data, and clinical trial data. That data could be the fuel for machine learning and other kinds of computation that could help us make develop drugs faster and make smarter decisions about care. The problem is, it’s all stored in different databases and formats that can’t be safely merged without a nightmarish amount of work. So when someone like Daimler says they have a way to use math to bring heterogeneous data together without compromising that data’s integrity – well, it's time to pay attention. That's why on today’s show, we’re all going back to school for an introductory class in category theory.

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

My guest today is Eric Daimler, a serial software entrepreneur and a former Presidential Innovation Fellow in the Obama Administration.

And he has an interesting argument about math. 

Daimler says if you’re a young person today trying to decide which math course you’re going to take, or maybe an old person who just wants to brush up, you shouldn’t bother with trigonometry or calculus.

Instead he says you should study category theory.

That’s a field that isn’t even part of the curriculum at most high schools. 

But it’s increasingly important in computer science.

Category theory is about the relationships between sets or structures. 

It can be used to prove that different structures are consistent or compatible with one another, and to prove that the relationships in a dataset are still intact even after you’ve transformed that data in some way.

Together with two former MIT mathematicians, Daimler co-founded a company called Conexus that uses category theory to tackle the problem of data interoperability.

Now…longtime listeners of the show know that data interoperability in healthcare, or more often the lack of interoperability, is one of my biggest hobby horses. 

In fields from drug development to frontline medical care, we’ve got petabytes of data to work with, in the form of electronic medical records, genomic and proteomic data, and clinical trial data.

That data could be the fuel for machine learning and other kinds of computation that could help us make develop drugs faster and make smarter decisions about care. 

The problem is, it’s all stored in different databases and formats that can’t be safely merged without a nightmarish amount of work.

So when someone like Daimler says they have a way to use math to bring heterogeneous data together without compromising that data’s integrity – well, I pay attention.

So on today’s show, we’re all going back to school for an introductory class in category theory from Conexus CEO Eric Daimler.

Harry Glorikian: Eric, welcome to the show.

Eric Daimler: It's great to be here.

Harry Glorikian: So I was reading your varied background. I mean, you've worked in so many different kinds of organizations. I'm not sure that there is a compact way or even an accurate way to describe you. So can you describe yourself? You know, what do you do and what are your main interest areas?

Eric Daimler: Yeah, I mean, the easiest way to describe me might come from my mother. Well, where, you know, somebody asked her, is that the doctor? And she says, Well, yes, but he's not the type that helps people. So I you know, I've been doing research around artificial intelligence and I from a lot of different perspectives around my research in graph theory and machine learning and computational linguistics. I've been a venture capitalist on Sand Hill Road. I've done entrepreneurship, done entrepreneurship, and I started a couple of businesses which I'm doing now. And most notably I was doing policy in Washington, D.C. is part of the Obama administration for a time. So I am often known for that last part. But my background really is rare, if not unique, for having the exposure to AI from all of those angles, from business, academia and policy.

Harry Glorikian: Yeah. I mean, I was looking at the obviously the like you said, the one thing that jumped out to me was the you were a Presidential Innovation Fellow in the Obama administration in 2016. Can you can you give listeners an idea of what is what is the Presidential Innovation Fellowship Program? You know, who are the types of people that are fellows and what kind of things do they do?

Eric Daimler: Sure, it was I guess with that sort of question, it's helpful then to give a broader picture, even how it started. There was a a program started during the Nixon administration that's colloquially known as the Science Advisers to the President, you know, a bipartisan group to give science advice to the president that that's called the OSTP, Office of Science and Technology Policy. There are experts within that group that know know everything from space to cancer, to be super specific to, in my domain, computer security. And I was the authority that was the sole authority during my time in artificial intelligence. So there are other people with other expertise there. There are people in different capacities. You know, I had the particular capacity, I had the particular title that I had that was a one year term. The staffing for these things goes up and down, depending on the administration in ways that you might be able to predict and guess. The people with those titles also also find themselves in different parts of the the executive branch. So they will do a variety of things that are not predicted by the the title of the fellow. My particular role that I happened to be doing was in helping to coordinate on behalf of the President, humbly, on behalf of the President, their research agenda across the executive branch. There are some very able people with whom I had the good fortune of working during my time during my time there, some of which are now in the in the Biden administration. And again, it's to be a nonpartisan effort around artificial intelligence. Both sides should really be advocates for having our research agenda in government be most effective. But my role was coordinating such things as, really this is helpful, the definition of robotics, which you might be surprised by as a reflex but but quickly find to be useful when you're thinking that the Defense Department's definition and use, therefore, of robotics is really fundamentally different than that of health and human services use and a definition of robotics and the VA and Department of Energy and State and and so forth.

Eric Daimler: So that is we find to be useful, to be coordinated by the Office of the President and experts speaking on behalf. It was started really this additional impulse was started after the effects of, I'll generously call them, of healthcare.gov and the trip-ups there where President Obama, to his great credit, realized that we needed to attract more technologists into government, that we had a lot of lawyers to be sure we had, we had a ton of academics, but we didn't have a lot of business people, practical technologists. So he created a way to get people like me motivated to come into government for short, short periods of time. The the idea was that you could sit around a cabinet, a cabinet meeting, and you could you would never be able to raise your hand saying, oh, I don't know anything about economics or I don't know anything about foreign policy, but you could raise your hand and say, Oh, I don't know anything about technology. That needs to be a thing of the past. President Obama saw that and created a program starting with Todd. Todd Park, the chief technologist, the second chief technology officer of the United States, is fantastic to to start to start some programs to bring in people like me.

Harry Glorikian: Oh, yeah. And believe me, in health care, we need we need more technologists, which I always preach. I'm like, don't go to Facebook. Come here. You know, you can get double whammy. You can make money and you can affect people's lives. So I'm always preaching that to everybody. But so if I'm not mistaken, in early 2021, you wrote an open letter to the brand new Biden administration calling for sort of a big federal effort to improve national data infrastructure. Like, can you summarize for everybody the argument in that piece and. Do you see them doing any of the items that you're suggesting?

Eric Daimler: Right. The the idea is that despite us making some real good efforts during the Obama administration with solidifying our, I'll say, our view on artificial intelligence across the executive, and this continuing actually into the Trump administration with the establishment of an AI office inside the OSTP. So credit where credit is due. That extended into the the Biden administration, where some very well-meaning people can be focusing on different parts of the the conundrum of AI expressions, having various distortions. You know, the popular one we will read about is this distortion of bias that can express itself in really ugly ways, as you know, as individuals, especially for underrepresented groups. The point of the article was to help others be reminded of of some of the easy, low hanging fruit that we can that we can work on around AI. So, you know, bias comes in a lot of different ways, the same way we all have cognitive distortions, you know, cognitive biases. There are some like 50 of them, right. You know, bias can happen around gender and ethnicity and age, sexual orientation and so forth. You know, it all can also can come from absence of data. There's a type of bias that's present just by being in a developed, rich country in collecting, for example, with Conexus's customers, my company Conexus's customers, where they are trying to report on their good efforts for economic and social good and around clean, renewable energies, they find that there's a bias in being able to collect data in rich countries versus developing countries.

Eric Daimler: That's another type of bias. So that was that was the point of me writing that open letter, to prioritize, these letters. It's just to distinguish what the low hanging fruit was versus some of the hard problems. The, some of tthe low hanging fruit, I think is available, I can say, In three easy parts that people can remember. One is circuit breakers. So we we can have circuit breakers in a lot of different parts of these automated systems. You know, automated car rolling down a road is, is the easiest example where, you know, at some point a driver needs to take over control to determine to make a judgment about that shadow being a person or a tumbleweed on the crosswalk, that's a type of circuit breaker. We can have those circuit breakers in a lot of different automated systems. Another one is an audit. And the way I mean is audit is having people like me or just generally people that are experts in the craft being able to distinguish the data or the biases can become possible from the data model algorithms where biases also can become possible. Right. And we get a lot of efficiency from these automated systems, these learning algorithms. I think we can afford a little bit taken off to audit the degree to which these data models are doing what we intend.

Eric Daimler: And an example of a data model is that Delta Airlines, you know, they know my age or my height, and I fly to San Francisco, to New York or some such thing. The data model would be their own proprietary algorithm to determine whether or not I am deserving of an upgrade to first class, for example. That's a data model. We can have other data models. A famous one that we all are part of is FICO scores, credit scores, and those don't have to be disclosed. None of us actually know what Experian or any of the credit agencies used to determine our credit scores. But they they use these type of things called zero knowledge proofs, where we just send through enough data, enough times that we can get to a sense of what those data models are. So that's an exposure of a data model. A declarative exposure would be maybe a next best thing, a next step, and that's a type of audit.

Eric Daimler: And then the third low hanging fruit, I'd say, around regulation, and I think these are just coming towards eventualities, is demanding lineage or demanding provenance. You know, you'll see a lot of news reports, often on less credible sites, but sometimes on on shockingly credible sites where claims are made that you need to then search yourself and, you know, people in a hurry just won't do it, when these become very large systems and very large systems of information, alert systems of automation, I want to know: How were these conclusions given? So, you know, an example in health care would be if my clinician gave me a diagnosis of, let's say, some sort of cancer. And then to say, you know, here's a drug, by the way, and there's a five chance, 5 percent chance of there being some awful side effects. You know, that's a connection of causation or a connection of of conclusions that I'm really not comfortable with. You know, I want to know, like, every step is like, wait, wait. So, so what type of cancer? So what's the probability of my cancer? You know, where is it? And so what drug, you know, how did you make that decision? You know, I want to know every little step of the way. It's fine that they give me that conclusion, but I want to be able to back that up. You know, a similar example, just in everyday parlance for people would be if I did suddenly to say I want a house, and then houses are presented to me. I don't quite want that. Although that looks like good for a Hollywood narrative. Right? I want to say, oh, wait, what's my income? Or what's my cash? You know, how much? And then what's my credit? Like, how much can I afford? Oh, these are houses you can kind of afford. Like, I want those little steps or at least want to back out how those decisions were made available. That's a lineage. So those three things, circuit breaker, audit, lineage, those are three pieces of low hanging fruit that I think the European Union, the State of New York and other other government entities would be well served to prioritize.

Harry Glorikian: I would love all of them, especially, you know, the health care example, although I'm not holding my breath because I might not come back to life by how long I'd have to hold my breath on that one. But we're hoping for the best and we talk about that on the show all the time. But you mentioned Conexus. You're one of three co founders, I believe. If I'm not mistaken, Conexus is the first ever commercial spin out from MIT's math department. The company is in the area of large scale data integration, building on insights that come out of the field of mathematics that's called category algebra, categorical algebra, or something called enterprise category theory. And to be quite honest, I did have to Wikipedia to sort of look that up, was not familiar with it. So can you explain category algebra in terms of a non mathematician and maybe give us an example that someone can wrap their mind around.

Eric Daimler: Yeah. Yeah. And it's important to get into because even though what my company does is, Conexus does a software expression of categorical algebra, it's really beginning to permeate our world. You know, the the way I tell my my nieces and nephews is, what do quantum computers, smart contracts and Minecraft all have in common? And the answer is composability. You know, they are actually all composable. And what composable is, is it's kind of related to modularity, but it's modularity without regard to scale. So the the easy analogy is in trains where, yeah, you can swap out a boxcar in a train, but mostly trains can only get to be a couple of miles long. Swap in and out boxcars, but the train is really limited in scale. Whereas the train system, the system of a train can be infinitely large, infinitely complex. At every point in the track you can have another track. That is the difference between modularity and composability. So Minecraft is infinitely self referential where you have a whole 'nother universe that exists in and around Minecraft. In smart contracts is actually not enabled without the ability to prove the efficacy, which is then enabled by categorical algebra or its sister in math, type theory. They're kind of adjacent. And that's similar to quantum computing. So quantum computing is very sexy. It gets in the press quite frequently with forks and all, all that. If it you wouldn't be able to prove the efficacy of a quantum compiler, you wouldn't actually. Humans can't actually say whether it's true or not without type theory or categorical algebra.

Eric Daimler: How you think of kind categorical algebra you can think of as a little bit related to graph theory. Graph theory is those things that you see, they look like spider webs. If you see the visualizations of graph theories are graphs. Category theory is a little bit related, you might say, to graph theory, but with more structure or more semantics or richness. So in each point, each node and each edge, in the vernacular, you can you can put an infinite amount of information. That's really what a categorical algebra allows. This, the discovery, this was invented to be translating math between different domains of math. The discovery in 2011 from one of my co-founders, who was faculty at MIT's Math Department, was that we could apply that to databases. And it's in that the whole world opens up. This solves the problem that that bedeviled the good folks trying to work on healthcare.gov. It allows for a good explanation of how we can prevent the next 737 Max disaster, where individual systems certainly can be formally verified. But the whole plane doesn't have a mechanism of being formally verified with classic approaches. And it also has application in drug discovery, where we have a way of bringing together hundreds of thousands of databases in a formal way without risk of data being misinterpreted, which is a big deal when you have a 10-year time horizon for FDA trials and you have multiple teams coming in and out of data sets and and human instinct to hoard data and a concern about it ever becoming corrupted. This math and the software expression built upon it opens up just a fantastically rich new world of opportunity for for drug discovery and for clinicians and for health care delivery. And the list is quite, quite deep.

Harry Glorikian: So. What does Conexus provide its clients? Is it a service? Is it a technology? Is it both? Can you give us an example of it?

Eric Daimler: Yeah. So Conexus is software. Conexus is enterprise software. It's an enterprise software platform that works generally with very large organizations that have generally very large complex data data infrastructures. You know the example, I can start in health care and then I can I can move to an even bigger one, was with a hospital group that we work with in New York City. I didn't even know health care groups could really have this problem. But it's endemic to really the world's data, where one group within the same hospital had a particular way that they represented diabetes. Now to a layman, layman in a health care sense, I would think, well, there's a definition of diabetes. I can just look it up in the Oxford English Dictionary. But this particular domain found diabetes to just be easily represented as yes, no. Do they have it? Do they not? Another group within the same hospital group thought that they would represent it as diabetes, ow are we treating it? A third group would be representing it as diabetes, how long ago. And then a fourth group had some well-meaning clinicians that would characterize it as, they had it and they have less now or, you know, type one, type two, you know, with a more more nuanced view.

Eric Daimler: The traditional way of capturing that data, whether it's for drug discovery or whether it's for delivery, is to normalize it, which would then squash the fidelity of the data collected within those groups. Or they most likely to actually just wouldn't do it. They wouldn't collect the data, they wouldn't bring the data together because it's just too hard, it's too expensive. They would use these processes called ETL, extract, transform, load, that have been around for 30 years but are often slow, expensive, fragile. They could take six months to year, cost $1,000,000, deploy 50 to 100 people generally from Accenture or Deloitte or Tata or Wipro. You know, that's a burden. It's a burden, you know, so the data wasn't available and that would then impair the researchers and their ability to to share data. And it would impair clinicians in their view of patient care. And it also impaired the people in operations where they would work on billing. So we work with one company right now that that works on 1.4 trillion records a year. And they just have trouble with that volume and the number of databases and the heterogeneous data infrastructure, bringing together that data to give them one view that then can facilitate health care delivery. 

Eric Daimler: The big example is, we work with Uber where they they have a very smart team, as smart as one might think. They also have an effectively infinite balance sheet with which they could fund an ideal IT infrastructure. But despite that, you know, Uber grew up like every other organization optimizing for the delivery of their service or product and, and that doesn't entail optimizing for that infrastructure. So what they found, just like this hospital group with different definitions of diabetes, they found they happen to have grown up around service areas. So in this case cities, more or less. So when then the time came to do analysis -- we're just passing Super Bowl weekend, how will the Super Bowl affect the the supply of drivers or the demand from riders? They had to do it for the city of San Francisco, separate than the city of San Jose or the city of Oakland. They couldn't do the whole San Francisco Bay Area region, let alone the whole of the state or the whole of the country or what have you. And that repeated itself for every business question, every organizational question that they would want to have. This is the same in drug discovery. This is the same in patient care delivery or in billing. These operational questions are hard, shockingly hard.

Eric Daimler: We had another one in logistics where we had a logistics company that had 100,000 employees. I didn't even know some of these companies could be so big, and they actually had a client with 100,000 employees. That client had 1000 ships, each one of which had 10,000 containers. And I didn't even know like how big these systems were really. I hadn't thought about it. But I mean, they're enormous. And the question was, hey, where's our personal protective equipment? Where is the PPE? And that's actually a hard question to ask. You know, we are thinking about maybe our FedEx tracking numbers from an Amazon order. But if you're looking at the PPE and where it is on a container or inside of a ship, you know, inside this large company, it's actually a hard question to ask. That's this question that all of these organizations have. 

Eric Daimler: In our case, Uber, where they they they had a friction in time and in money and in accuracy, asking every one of these business questions. They went then to find, how do I solve this problem? Do I use these old tools of ETL from the '80s? Do I use these more modern tools from the 2000s? They're called RDF or OWL? Or is there something else? They discovered that they needed a more foundational system, this categorical algebra that that's now expressing itself in smart contracts and quantum computers and other places. And they just then they found, oh, who are the leaders in the enterprise software expression of that math? And it's us. We happen to be 40 miles north of them. Which is fortunate. We worked with Uber to to solve that problem in bringing together their heterogeneous data infrastructure to solve their problems. And to have them tell it they save $10 million plus a year in in the efficiency and speed gains from the solution we helped provide for them.

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. 

It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: So your website says that your software can map data sources to each other so that the perfect data model is discovered, not designed. And so what does that mean? I mean, does that imply that there's some machine learning or other form of artificial intelligence involved, sort of saying here are the right pieces to put together as opposed to let me design this just for you. I'm trying to piece it together.

Eric Daimler: Yeah. You know, the way we might come at this is just reminding ourselves about the structure of artificial intelligence. You know, in the public discourse, we will often find news, I'm sure you can find it today, on deep learning. You know, whatever's going on in deep learning because it's sexy, it's fun. You know, DeepMind really made a name for themselves and got them acquired at a pretty valuation because of their their Hollywood-esque challenge to Go, and solving of that game. But that particular domain of AI, deep learning, deep neural nets is a itself just a subset of machine learning. I say just not not not to minimize it. It's a fantastically powerful algorithm. But but just to place it, it is a subset of machine learning. And then machine learning itself is a subset of artificial intelligence. That's a probabilistic subset. So we all know probabilities are, those are good and bad. Fine when the context is digital advertising, less fine when it's the safety of a commercial jet. There is another part of artificial intelligence called deterministic artificial intelligence. They often get expressed as expert systems. Those generally got a bad name with the the flops of the early '80s. Right. They flopped because of scale, by the way. And then the flops in the early 2000s and 2010s from IBM's ill fated Watson experiment, the promise did not meet the the reality.

Eric Daimler: It's in that deterministic A.I. that that magic is to be found, especially when deployed in conjunction with the probabilistic AI. That's that's where really the future is. There's some people have a religious view of, oh, it's only going to be a probabilistic world but there's many people like myself and not to bring up fancy names, but Andrew Ng, who's a brilliant AI researcher and investor, who also also shares this view, that it's a mix of probabilistic and deterministic AI. What deterministic AI does is, to put it simply, it searches the landscape of all possible connections. Actually it's difference between bottoms up and tops down. So the traditional way of, well, say, integrating things is looking at, for example, that hospital network and saying, oh, wow, we have four definitions of diabetes. Let me go solve this problem and create the one that works for our hospital network. Well, then pretty soon you have five standards, right? That's the traditional way that that goes. That's what a top down looks that looks like.

Eric Daimler: It's called a Golden Record often, and it rarely works because pretty soon what happens is the organizations will find again their own need for their own definition of diabetes. In most all cases, that's top down approach rarely works. The bottoms up approach says, Let's discover the connections between these and we'll discover the relationships. We don't discover it organically like we depend on people because it's deterministic. I, we, we discover it through a massive, you know, non intuitive in some cases, it's just kind of infeasible for us to explore a trillion connections. But what the AI does is it explores a factorial number actually is a technical, the technical equation for it, a factorial number of of possible paths that then determine the map of relationships between between entities. So imagine just discovering the US highway system. If you did that as a person, it's going to take a bit. If you had some infinitely fast crawlers that robot's discovering the highway system infinitely fast, remember, then that's a much more effective way of doing it that gives you some degree of power. That's the difference between bottoms up and tops down. That's the difference between deterministic, really, we might say, and probabilistic in some simple way.

Harry Glorikian: Yeah, I'm a firm believer of the two coming together and again, I just look at them as like a box. I always tell people like, it's a box of tools. I need to know the problem, and then we can sort of reach in and pick out which set of tools that are going to come together to solve this issue, as opposed to this damn word called AI that everybody thinks is one thing that they're sort of throwing at the wall to solve a problem.

Harry Glorikian: But you're trying to solve, I'm going to say, data interoperability. And on this show I've had a lot of people talk about interoperability in health care, which I actually believe is, you could break the system because things aren't working right or I can't see what I need to see across the two hospitals that I need information from. But you published an essay on Medium about Haven, the health care collaboration between Amazon, JPMorgan, Berkshire Hathaway. Their goal was to use big data to guide patients to the best performing clinicians and the most affordable medicines. They originally were going to serve these first three founding companies. I think knowing the people that started it, their vision was bigger than that. There was a huge, you know, to-do when it came out. Fireworks and everything. Launched in 2018. They hired Atul Gawande, famous author, surgeon. But then Gawande left in 2020. And, you know, the company was sort of quietly, you know, pushed off into the sunset. Your essay argued that Haven likely failed due to data interoperability challenges. I mean. How so? What what specific challenges do you imagine Haven ran into?

Eric Daimler: You know, it's funny, I say in the article very gently that I imagine this is what happened. And it's because I hedge it that that the Harvard Business Review said, "Oh, well, you're just guessing." Actually, I wasn't guessing. No, I know. I know the people that were doing it. I know the challenges there. But but I'm not going to quote them and get them in trouble. And, you know, they're not authorized to speak on it. So I perhaps was a little too modest in my framing of the conclusion. So this actually is what happened. What happens is in the same way that we had the difficulty with healthcare.gov, in the same way that I described these banks having difficulty. Heterogeneous databases don't like to talk to one another. In a variety of different ways. You know, the diabetes example is true, but it's just one of many, many, many, many, many, many cases of data just being collected differently for their own use. It can be as prosaic as first name, last name or "F.last name." Right? It's just that simple, you know? And how do I bring those together? Well, those are those are called entity resolutions. Those are somewhat straightforward, but not often 100 percent solvable. You know, this is just a pain. It's a pain. And, you know, so what what Haven gets into is they're saying, well, we're massive. We got like Uber, we got an effectively infinite balance sheet. We got some very smart people. We'll solve this problem. And, you know, this is some of the problem with getting ahead of yourself. You know, I won't call it arrogance, but getting ahead of yourself, is that, you think, oh, I'll just be able to solve that problem.

Eric Daimler: You know, credit where credit is due to Uber, you know, they looked both deeper saying, oh, this can't be solved at the level of computer science. And they looked outside, which is often a really hard organizational exercise. That just didn't happen at Haven. They thought they thought they could they could solve it themselves and they just didn't. The databases, not only could they have had, did have, their own structure, but they also were stored in different formats or by different vendors. So you have an SAP database, you have an Oracle database. That's another layer of complication. And when I say that these these take $1,000,000 to connect, that's not $1,000,000 one way. It's actually $2 million if you want to connect it both ways. Right. And then when you start adding five, let alone 50, you take 50 factorial. That's a very big number already. You multiply that times a million and 6 to 12 months for each and a hundred or two hundred people each. And you just pretty soon it's an infeasible budget. It doesn't work. You know, the budget for us solving solving Uber's problem in the traditional way was something on the order of $2 trillion. You know, you do that. You know, we had a bank in the U.S. and the budget for their vision was was a couple of billion. Like, it doesn't work. Right. That's that's what happened Haven. They'll get around to it, but but they're slow, like all organizations, big organizations are. They'll get around to solving this at a deeper level. We hope that we will remain leaders in database integration when they finally realize that the solution is at a deeper level than their than the existing tools.

Harry Glorikian: So I mean, this is not I mean, there's a lot of people trying to solve this problem. It's one of those areas where if we don't solve it, I don't think we're going to get health care to the next level, to sort of manage the information and manage people and get them what they need more efficiently and drive down costs.

Eric Daimler: Yeah.

Harry Glorikian: And I do believe that EMRs are. I don't want to call them junk. Maybe I'm going too far, but I really think that they you know, if you had decided that you were going to design something to manage patients, that is not the software you would have written to start. Hands down. Which I worry about because these places won't, they spent so much putting them in that trying to get them to rip them out and put something in that actually works is challenging. You guys were actually doing something in COVID-19, too, if I'm not mistaken. Well, how is that project going? I don't know if it's over, but what are you learning about COVID-19 and the capabilities of your software, let's say?

Eric Daimler: Yeah. You know, this is an important point that for anybody that's ever used Excel, we know what it means to get frustrated enough to secretly hard code a cell, you know, not keeping a formula in a cell. Yeah, that's what happened in a lot of these systems. So we will continue with electronic medical records to to bring these together, but they will end up being fragile, besides slow and expensive to construct. They will end up being fragile, because they were at some point hardcoded. And how that gets expressed is that the next time some other database standard appears inside of that organization's ecosystem from an acquisition or a divestiture or a different technical standard, even emerging, and then the whole process starts all over again. You know, we just experience this with a large company that that spent $100 million in about five years. And then they came to us and like, yeah, we know it works now, but we know like a year from now we're going to have to say we're going to go through it again. And, it's not like, oh, we'll just have a marginal difference. No, it's again, that factorial issue, that one database connected to the other 50 that already exist, creates this same problem all over again at a couple of orders of magnitude. So what we discover is these systems, these systems in the organization, they will continue to exist.

Eric Daimler: These fragile systems will continue to exist. They'll continue to scale. They'll continue to grow in different parts of the life sciences domain, whether it's for clinicians, whether it's for operations, whether it's for drug discovery. Those will continue to exist. They'll continue to expand, and they will begin to approach the type of compositional systems that I'm describing from quantum computers or Minecraft or smart contracts, where you then need the the discovery and math that Conexus expresses in software for databases. When you need that is when you then need to prove the efficacy or otherwise demonstrate the lack of fragility or the integrity of the semantics. Conexus can with, it's a law of nature and it's in math, with 100 percent accuracy, prove the integrity of a database integration. And that matters in high consequence context when you're doing something as critical as drug side effects for different populations. We don't want your data to be misinterpreted. You can't afford lives to be lost or you can't, in regulation, you can't afford data to be leaking. That's where you'll ultimately need the categorical algebra. You'll need a provable compositional system. You can continue to construct these ones that will begin to approach compositionality, but when you need the math is when you need to prove it for either the high consequence context of lives, of money or related to that, of regulation.

Harry Glorikian: Yeah, well, I keep telling my kids, make sure you're proficient in math because you're going to be using it for the rest of your life and finance. I always remind them about finance because I think both go together. But you've got a new book coming out. It's called "The Future is Formal" and not tuxedo like formal, but like you're, using the word formal. And I think you have a very specific meaning in mind. And I do want you to talk about, but I think what you're referring to is how we want automated systems to behave, meaning everything from advertising algorithms to self-driving trucks. And you can tell me if that my assumption is correct or not.

Eric Daimler: Though it's a great segue, actually, from the math. You know, what I'm trying to do is bring in people that are not programmers or research technology, information technology researchers day to day into the conversation around automated digital systems. That's my motivation. And my motivation is, powered by the belief that we will bring out the best of the technology with more people engaged. And with more people engaged, we have a chance to embrace it and not resist it. You know, my greatest fear, I will say, selfishly, is that we come up with technology that people just reject, they just veto it because they don't understand it as a citizen. That also presents a danger because I think that companies' commercial expressions naturally will grow towards where their technology is needed. So this is actually to some extent a threat to Western security relative to Chinese competition, that we embrace the technology in the way that we want it to be expressed in our society. So trying to bring people into this conversation, even if they're not programmers, the connection to math is that there are 18 million computer programmers in the world. We don't need 18 million and one, you know. But what we do need is we do need people to be thinking, I say in a formal way, but also just be thinking about the values that are going to be represented in these digital infrastructures.

Eric Daimler: You know, somewhere as a society, we will have to have a conversation with ourselves to determine the car driving to the crosswalk, braking or rolling or slowing or stopping completely. And then who's liable if it doesn't? Is it the driver or is it the manufacturer? Is it the the programmer that somehow put a bug in their code? You know, we're entering an age where we're going to start experiencing what some person calls double bugs. There's the bug in maybe one's expression in code. This often could be the semantics. Or in English. Like your English doesn't make sense. Right? Right. Or or was it actually an error in your thinking? You know, did you leave a gap in your thinking? This is often where where some of the bugs in Ethereum and smart contracts have been expressed where, you know, there's an old programming rule where you don't want to say something equals true. You always want to be saying true equals something. If you get if you do the former, not the latter, you can have to actually create bugs that can create security breaches.

Eric Daimler: Just a small little error in thinking. That's not an error in semantics. That level of thinking, you don't need to know calculus for, or category theory for that matter. You just need to be thinking in a formal way. You know, often, often lawyers, accountants, engineers, you know, anybody with scientific training can, can more quickly get this idea, where those that are educated in liberal arts can contribute is in reminding themselves of the broader context that wants to be expressed, because often engineers can be overly reductionist. So there's really a there's a push and pull or, you know, an interplay between those two sensibilities that then we want to express in rules. Then that's ultimately what I mean by formal, formal rules. Tell me exactly what you mean. Tell me exactly how that is going to work. You know, physicians would understand this when they think about drug effects and drug side effects. They know exactly what it's going to be supposed to be doing, you know, with some degree of probability. But they can be very clear, very clear about it. It's that clear thinking that all of us will need to exercise as we think about the development and deployment of modern automated digital systems.

Harry Glorikian: Yeah, you know, it's funny because that's the other thing I tell people, like when they say, What should my kid take? I'm like, have him take a, you know, basic programming, not because they're going to do it for a living, but they'll understand how this thing is structured and they can get wrap their mind around how it is. And, you know, I see how my nephew thinks who's from the computer science world and how I think, and sometimes, you know, it's funny watching him think. Or one of the CTOs of one of our companies how he looks at the world. And I'm like you. You got to back up a little bit and look at the bigger picture. Right. And so it's the two of us coming together that make more magic than one or the other by themselves.

Harry Glorikian: So, you know, I want to jump back sort of to the different roles you've had in your career. Like like you said, you've been a technology investor, a serial startup founder, a university professor, an academic administrator, an entrepreneur, a management instructor, Presidential Innovation Fellow. I don't think I've missed anything, but I may have. You're also a speaker, a commentator, an author. Which one of those is most rewarding?

Eric Daimler: Oh, that's an interesting question. Which one of those is most rewarding? I'm not sure. I find it to be rewarding with my friends and family. So it's rewarding to be with people. I find that to be rewarding in those particular expressions. My motivation is to be, you know, just bringing people in to have a conversation about what we want our world to look like, to the degree to which the technologies that I work with every day are closer to the dystopia of Hollywood narratives or closer to our hopes around the utopia that's possible, that where this is in that spectrum is up to us in our conversation around what these things want to look like. We have some glimpses of both extremes, but I'd like people, and I find it to be rewarding, to just be helping facilitate the helping catalyze that conversation. So the catalyst of that conversation and whatever form it takes is where I enjoy being.

Harry Glorikian: Yeah, because I was thinking about like, you know, what can, what can you do as an individual that shapes the future. Does any of these roles stand out as more impactful than others, let's say?

Eric Daimler: I think the future is in this notion of composability. I feel strongly about that and I want to enroll people into this paradigm as a framework from which to see many of the activities going around us. Why have NFTs come on the public, in the public media, so quickly? Why does crypto, cryptocurrency capture our imagination? Those And TikTok and the metaverse. And those are all expressions of this quick reconfiguration of patterns in different contexts that themselves are going to become easier and easier to express. The future is going to be owned by people that that take the special knowledge that they've acquired and then put it into short business expressions. I'm going to call them rules that then can be recontextualized and redeployed. This is my version of, or my abstraction of what people call the the future being just all TikTok. It's not literally that we're all going to be doing short dance videos. It's that TikTok is is an expression of people creating short bits of content and then having those be reconfigured and redistributed. That can be in medicine or clinical practice or in drugs, but it can be in any range of expertise, expertise or knowledge. And what's changed? What's changed and what is changing is the different technologies that are being brought to bear to capture that knowledge so that it can be scalable, so it can be compositional. Yeah, that's what's changing. That's what's going to be changing over the next 10 to 20 years. The more you study that, I think the better off we will be. And I'd say, you know, for my way of thinking about math, you might say the more math, the better. But if I were to choose for my children, I would say I would replace trig and geometry and even calculus, some people would be happy to know, with categorical algebra, category theory and with probability and statistics. So I would replace calculus, which I think is really the math of the 20th century, with something more appropriate to our digital age, which is categorical algebra.

Harry Glorikian: I will tell my son because I'm sure he'll be very excited to to if I told him that not calculus, but he's not going to be happy when I say go to this other area, because I think he'd like to get out of it altogether.

Eric Daimler: It's easier than calculus. Yeah.

Harry Glorikian: So, you know, it was great having you on the show. I feel like we could talk for another hour on all these different aspects. You know, I'm hoping that your company is truly successful and that you help us solve this interoperability problem, which is, I've been I've been talking about it forever. It seems like I feel like, you know, the last 15 or 20 years. And I still worry if we're any closer to solving that problem, but I'm hopeful, and I wish you great success on the launch of your new book. It sounds exciting. I'm going to have to get myself a copy.

Eric Daimler: Thank you very much. It's been fun. It's good to be with you.

Harry Glorikian: Thank you.

Harry Glorikian: That’s it for this week’s episode. 

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

My guest Rohit Nimbasan comes from the worlds of biotech and data science. 

And during our interview he made an interesting point.

These days you can use an app like GrubHub to order a pizza for twenty or twenty-five bucks, and the app will give you a real-time, minute by minute accounting of where the pizza is and when it’s going to arrive at your door.

But Nimbasan points out that if you’re a pharmaceutical company running a clinical trial for a new drug, you can spend anywhere from $3 million to $300 million and still have absolutely no idea when the trial will finish or whether your drug will turn out to be effective.

The problem is, there’s just no infrastructure for analyzing clinical trial data in midstream or spotting trouble before it arrives.

As a result, according to Nimbasan, twenty to thirty percent of the money drug makers spend on clinical trials goes down the drain, because of studies that continue long after they should have been canceled, or good data that gets thrown out because of some minor procedural flaw.

Nimbasan is the CEO of a company called Lokavant that wants to change all that.

The company is building a data platform that allows drug makers and clinical research organizations to harmonize the results coming in from study sites, compare it to data from other trials, and discover important signals in the data before it’s too late.

For example, a company might discover that it’s not enrolling patients fast enough to complete a trial on schedule, or that the researchers administering the study aren’t following the exact protocols laid out in advance.

All of those problems can increase the cost of a trial.
They can even lead regulators to deny approval for a drug that might have proved effective if it had been property tested.

For an average healthcare consumer, these kinds of headaches might sound abstract and remote, like something only clinical trial managers would ever have to worry about. 

But the fact is poor data management slows down the whole drug development process, which means fewer beneficial new drugs make it to market ever year.

So I think we should all be cheering companies like Lokavant who are trying to fix the process.

Here’s my full interview with Rohit.

Harry Glorikian: Rohit, welcome to the show.

Rohit Nambisan: Thanks, Harry, for having me.

Harry Glorikian: You know, you and I sort of talk off and on all the time about the space and what's going on, but, you know, having it on the show, I have to step back and sort of forget everything I know about the company and start from scratch. So, you know, can you explain to people Lokavant's business in a way that would make sense to someone, say, outside of the pharmaceutical industry. In other words, you know, what are the big problems you're solving for organizations that, say, are running a clinical trial, and how are you solving them?

Rohit Nambisan: Sure, I can do that. I think it bears noting that we should probably step back a little bit and talk about the industry as a whole and where it's been going, and then I can clarify where Lokavant comes in. So I think as many folks know and for those who don't, I'll fill in the blanks. I know you know this area, but in the last, I'd say 15 to 20 years, we've been moving in pharmaceutical development away from blockbuster medications, things like diabetes type 2. Right, developing therapies for that and getting each drug developer trying to find a smaller piece of market and larger pie to specialized, niche therapeutic indications. Right. So the way I could probably better started with the diabetes example is it's no longer diabetes type 2. It's let's develop the compounds or therapies for diabetes type 2 patients that are comorbid with that have also chronic kidney disease and are metformin naive, meaning they haven't taken a particular therapy known as metformin. Right. So it's a more complex filter criteria, so to speak. Right. And so what happens when the industry moves in that that direction is that when you get into these very niche therapeutic areas, you need to collect particular niche, commensurately niche types of data to validate your hypothesis whether or not this therapy is safe and efficacious through clinical trials. Right. 

Rohit Nambisan: And in doing that, you now increased the complexity of the trial greatly, not only in terms of the different types of data collecting, but the amount of different types of data you're collecting. So now each trial becomes a lot more specialized. Not just specialized therapeutics, but each trial becomes more specialized. Right? And so for that reason, we've seen a big challenge as we as we moved across that space. And actually, it's been really beneficial for patients because now we're going after, as an industry, we're going after really niche unmet clinical needs that previously there were no therapies for or being developed for. So it's a really good thing for a patient perspective, but from the perspective of development, it makes it that much harder. Not only is there a smaller market opportunity, there's less patients to treat, right, but the complexity, the actual costs of the trial and the complexity of trial has gotten exponentially that much greater. So what Lokavant came out of was we were actually a, shall we say, an internal initiative within Roivant Sciences, which is a company that launches a number of different biotechnology companies and tech companies as well. But better known for biotechnology companies. And we saw a great need to be able to develop therapies for niche indications much faster, much more efficient, much more cost effectively, and also meet the complexities of that trial better through novel data and tech.

Rohit Nambisan: And so what Lokavant is essentially, is a data platform that allows drug developers, pharma, therapy developers, to be able to choose which data sources they need, data types they need for a trial. And we can ingest any of those data sources, we can analyze any of those data sources in a holistic manner and expose patterns or signals that could be beneficial or detrimental to the study on an ongoing basis. And when I say ongoing basis, I mean you're not waiting until the end of the study. And I guess the best way I can align this is just like my kids do sometimes. You're not waiting until the last day before your term paper is due, before the project's due to finish your work, you're actually assessing, doing bits of it along the way to assess where there may be challenges, which gives you, really, the time to correct issues to manage your trial better. And frankly, each one of these trials now, there are between, what, $2 million and $300 million we're investing in these single trials at this point. So it's egregious to me that we do not have the toolset to be able to even identify, pull in that data effectively on an ongoing basis to detect these signals so we can plan effectively to do something about it.

Harry Glorikian: Anybody who's done a clinical trial knows that there's a lot of risk. Right. So, you know, can you talk about some of the types of risks you're trying to help make sure drug developers diminish, for the most part.

Rohit Nambisan: Yeah. So I think the way we start with that is always at the highest level, time, cost and quality, right? So when we talk about time, it's really important to understand that you're going to be able to achieve less. For example, I'll give you a few instances. Target participant accrual, right? Obviously for you to run a trial effectively, you need to have particular types of participants or patients, if it's a sick population. In a vaccine population, they weren't necessarily sick. So that's why I use participants as the term. But you need to make sure that you have path to randomized screening and randomizing these patients for your trial in a given time period. Right. And if that's if your enrollment is is not on track for the countries and the sites you've decided to actually activate the study in, you could, your timeline for your study could be exceptionally extended. Right. So that's that's one type of one example of a thing we look at to understand how the timeline looking for the study. Another area on timeline for example and similarly is discontinuation. So you can you could enroll patients fine. But if you've high volumes of discontinuation of participants in your study, then what ends up happening is you actually don't have as many evaluable subjects in your study of some evaluable participants. So you have to recruit or enroll more subjects, right? So that could extend the timeline as well. One aspect of the timeline that really affects the overall market opportunity is oftentimes these therapies are only in under patient for a certain amount of time. So the faster you can get them to market, the faster you can get recoup your return on investment. But also on the patient side, the faster we can get these therapies out through the market to address unmet clinical needs. That's just one flavor.

Rohit Nambisan: Then we have subsequent types of flavors. When we talk about data quality, making sure the data is actually collected in the way that you stated you wanted it to be collected in the plan and the protocol at the outset of the study, as well as cost implications. Right. So we look at cost implications as well, which is how, what will this, what will the extension of enrollment or bad data quality data do to the overall budget that you had planned for this study? But then when you drill down on the level further, you can get into risk categories, is something we look at quite a bit when we look at things like protocol, adherence, when you're when you're collecting this data, as I mentioned, it has to be done per a very prescriptive method that is specified a priori before starting the trial in a protocol. And if it's not collected in that manner, it can be discounted. So we are tracking the risk to protocol deviations and understanding trends and not only understanding trends within that study, but we're looking at similar types of studies in this particular therapy area, neurology or say, psychiatry or gastrointestinal type studies and saying, what has been the protocol adherence in studies like yours? And therefore, can we glean some insights about how you are doing in your study based on your comparators in the study as well? But that's just a small flavor. I could probably wax on for quite some time on this question.

Harry Glorikian: Well, that that brings us to the question -- I mean, everything you just said, it brings to the question like, from what I know, the company sort of predicts how clinical trials will go by comparing it against a proprietary data set of, I think I was reading, 2000 past trials, right? So I guess the question becomes, so you're comparing one to the past of things that are similar, but you know, for everybody who's listening sort of, you know, where does that data come from in one sense, is it truly proprietary? I mean, that's what I'm you know, that's my set of questions at the moment.

Rohit Nambisan: Sure. So I worked for a while, before coming to the life sciences, in the R&D space and the life sciences commercial space. And I think that data sets, are there are proprietary datasets in that space? Very much so. But there is a third party market for that data a little bit more. So then we find life sciences data. It's really hard to get access to R&D data and as you can imagine, that makes a lot of sense, right? If you're a drug developer or a pharmaceutical developer that successfully completed a trial, you never want to share that data. Thereafter, you spent millions of dollars investing in the study, if you want. If there are potentially unknown issues that you haven't identified, would you want to put that at risk? If you are similarly, if you are a therapeutics developer that didn't meet your endpoints, do you want that information to get out and maybe potentially things that issues that that you should have should not overlook, right, getting out in public, etc.? There's just a lot of business risk. There's also IP risk, right? There's a number of different risks associated with getting that data out. So it's been not a very straightforward journey to aggregate data in life sciences, R&D. That being said, I think how we approached this was we've developed models that are both used for benchmarking, as I mentioned before, comparing against similar trials for particular performance KPIs, so to speak, as well as predictive model generation and machine learning models that require a fair amount of data to train on to actually deliver value.

Rohit Nambisan: And in that model, we've talked to a lot of our partners or let's say folks that leads them before their partners. And we talk to them. We say we have a growing dataset. There's precedent for this because we've done this with other partners, number one. Number two, we've worked with them to leverage their data combined with our data, write their enterprise data with our data, because it's a common, it's not just one entity's data that's going to provide that value. Your performance, your processes, the way you run trials is inherent in your data. And if we don't leverage that data to train our model to retrain some parts of our models against, we're not providing you the most value we could be with our predictive models or benchmarking. So with that approach, we've been able to do comparative analysis of our data set versus other people's datasets and then anonymize their data upon having a partnership with them to grow our data assets in a very risk-tolerant manner. Right. All the information about CROs or sponsors or other entities, people running trials is removed from the data and we only leverage that data for the purposes of analytics or generating a benchmark. So none of that data is ever shared. So through that process, over the last, I'd say two years, maybe a little two years and change since we started, we've been able to continuously grow this asset and provide greater and greater value with our descriptive diagnostic predictive analytics as well as our benchmarking.

Harry Glorikian: How much money, if you had to guess just to give people like an idea, how much money do you think gets poured down the drain preventably every year, and you could save all this money if you just ran smarter, if you did smarter clinical trial management, if I had to frame it that way.

Rohit Nambisan: Oh, at least I would say we've done some back calculations on this and happy to digress into the details of them if warranted, but at least somewhere between 20 to 30 percent of the trial costs right now and depending on the phase and depending on the therapeutic area, again, that could be anywhere from 20 to 30 percent of $3 million to $300 million per study.

Harry Glorikian: Yeah. I mean it's you know, that's got to be, I don't know how many billions that is. I can't I don't know exactly how much is being spent annually off the top of my head.

Rohit Nambisan: We believe we've done some back of the envelope calculations to show that it is in the billions for sure. Across the across the global pharmaceutical market, we're looking just just the value proposition and the signal detection we're bringing to bear is somewhere around $18 to $20 billion, in terms of market opportunity.

Harry Glorikian: I mean, how would you guys run or help a team run a clinical trial in practice? Can you sort of give me a real-world example, maybe de-identified, where you helped the client avoid or mitigate some kind of risk, whether it has to do with patient enrollment or site compliance or safety issues during a trial, any one of those will do.

Rohit Nambisan: Sure. So I think one example that I can bring to bear is working with a large CRO. And with this large CRO, they had a sizable data asset that was not harmonized, so to speak. It was still living in the transactional exports from the source data systems or CSPs. Et cetera. All around. So it was they had a bunch of different hypotheses about where they were proficient, where they were deficient, but nothing validated. So we spent some time with them trying to understand what all their data assets looked like. And we started collecting these different representations of former trials and ongoing trials, and we collected them and we harmonized them. In fact, as I mentioned before, one of our major differentiators is this is creation of a single source of truth. And we take that upon ourselves, too. It's not like a service, it's part of our offering, right? Our platform offering. And so what we did was we brought that data together and we it was about, I think 400 to 500 studies worth of data at that point. We harmonized it into what we call our local and canonical data format, which is a single representation for multiple different domains of data, scientific data, operational data, enrollment data, etc. And then we compared that against similar studies in our repository, our growing repository, and said, okay, we can tell you comparatively that you are deficient in these particular areas and you're very proficient at the various--for example, in this case they were very proficient in achieving first patient in on the timeline that they expected to actually, scratch that, that they were very they were very proficient in actually accruing the subjects by last patient in in the time they were expected to write so they could hit their accrual when they wanted to.

Rohit Nambisan: But when we looked deeper into the data and looked at across like first patient in, the 50 percent enrollment mark for the study and then last patient in for the study, we were able to identify that there was actually a slowdown and a major overcorrection to make up for that. So they were actually hitting what they needed to hit. But as we all probably know, at least in the clinical research phase and any or any budgeting process, being over your budgeting process is bad. Being under your budgeting process is bad, right? So in this case, it's again the same. They were burning resource and cash and resources to rapidly overcorrect for for a milestone they were not hitting reliably earlier in their studies. And so we realized in that accrual situation we said, okay, what you need is, we've identified an error, you're potentially deficient. What you need is an enrollment forecasting application that brings in the data in real time from your study. Right. And it also combines historical data from our repository in your historical data to seed some prior knowledge about the study. So and it's automated, fully automated. So every day you can understand where you are in relation to where you need to be. Right? And it's not a naive straight line kind of curve. It's basically it's based on looking at thousands of historical studies in this space and understanding what the curvature of the actual model should look like.

Rohit Nambisan: So we generated that and we were able to actually, in the proof of concept, and this is just one particular example of an application we've been able to generate from our clinical trial intelligence platform, we generated that and we were able to, on a study, predict two years out within one month when they would actually really hit the accrual and it was within one month accurate. Now while that was valuable in terms of understanding at the end state, what really the value was of this closed loop model, so to speak, right, is that it is closed loop. It allows them in silica to say, what happens if I open some sites here? What happens if I close some sites? So what happens if I close this country here? How will that affect my plan before I put that into action in the real world, which oftentimes is very, very, first of all, it's very risky. But second of all, it can yield a number of unknown consequences if you don't try it before in silico. So I think the approach here was we were able to not only predict these things better and also predict the impact of change orders on the study, that might actually affect the timeline of the study. But we were able to actually provide them an application, an interface by which they could test it all their hypotheses in a virtualized manner before they implemented them. And we're growing like crazy with that, with that partner right now at that point.

Harry Glorikian: Yeah. And I mean, I mean, you know, in some ways it sounds like, you know, I didn't get it done and I'm pulling all nighters, like at some point so that I can get it done. Right. So there's a whole staffing model. And how do you bring this to the attention of everybody so that they don't drop the ball? Right, because there's a million other things that might be coming at them at that moment.

Rohit Nambisan: That's exactly right. Actually, one thing I'll add to that, given you mentioned the staffing model around it, is that we were born within small biotech. Right. And small biotech is very resource-constrained in its ability to manage and oversee a study. That's fairly well known. So our approach has always been what I'd like to call machine-assisted human intelligence. We have experts that are human experts that know the space, but they need to be augmented. They need to be able to look at more complex streams of information and have a machine pick out particular salient insights, salient information, and provide that to them so they can process it, reducing degrees of freedom for them to process it.

Harry Glorikian: So just I mean, there are a lot of statistical tools out there now that that for managing risks in clinical trials. So how is the approach that you guys are taking either different or better or both.

Rohit Nambisan: It's a good question. One way we've been able to address this question is that statistical approaches generally require certain amounts of data points to be collected before you can warrant using statistical parameters or assumptions, etc. And so there's two things at play here. On top of that, I just mentioned, we're moving into more specialized therapeutic areas, right? So patients per study are smaller, right. And on top of that, when you're starting out a study which is usually the riskiest points in the study, when you're early in the study to mid-stage in a study, you cross them with the fact that you have less patients and there are more niche studies, it's hard to find those patients. Now, your early phase, your riskiest phase, is going to be extended as compared to when you were developing against blockbuster indications. So for a long time in the study, you can't really reliably use statistical parameters to identify an outlier or identify something as aberrant. And then you need to focus on so the way we've done it, we've done it in a slightly different way. There's two approaches. One is we've actually developed a pretty complex risk score system that's based on a set of very different metrics. Think of it as like an array of different KPIs, right? Those KPIs will affect risk differently depending on the type of study you're in. And they'll have different weights to those risks of time, cost and quality depending on the study you're in. So we look at the given study, we're going to deploy and we say, okay, what are the features that characterize the study? Let's look in our historical repository against those same features, pull similar, we call look alike studies and we'll understand how to set those weightings to say protocol deviations at this point in the study are going to impact the overall quality of time. That's much more for this type of study. So we can basically, for lack of a better term, I guess the simplistic way of saying is we can augment the data that we have coming in from a study, which is small at the outset of the study, with lookalike data to increase the power. Right? So that's another way to look at this. So we can actually, we have much better power to be able to detect these issues earlier on and reliably confer that to clinical operators and clinical developers who can do something about it.

Harry Glorikian: It would be nice if you had enough data at some point to almost run the whole trial in silico, in a sense. But I think we need a lot more data get there. But, just for everybody that's listening, sort of as a philosophical point, the reason we put drugs through clinical trials in humans is we simply don't know whether they'll work or what the unexpected side effect they might have once you start them on a much larger population. So in that sense, it's expected, even normal for some drugs, maybe even a lot of drugs, to fail at some point in phase one, phase two or phase three. And as an investor, you know, you don't want it to fail in phase three. You want it to fail early. So is Lokavant's goal to reduce the failures or simply help drug developers get to yes or no faster, safer, more cheaply?

Rohit Nambisan: Yeah. So our approach has been initially get yes or no faster, safer, more cheaply, more efficiently, right. As part of that process and actually related to some of the work we have done in the last few months on monitoring scientific risk. Right. You have to be careful about these efficacy analyses because they can unblind the study, especially when you have single or double blind blinded studies. So you have to be careful about this point. But in some circumstances we can actually leverage our analysis on blinded endpoint analysis and understand how particular endpoints are collaborating or not collaborating or trending, to understand if there is any effect whatsoever that's being generated in the study. So this is early days for us. But to your to your point about the first use case, we are starting to think about that as an opportunity as well, because we found a way to effectively blind the information and still assess the information content to understand if there is any form of efficacy signal being produced. So I think that that is a really valuable way for us to approach the market in the near future. I think the other point here is that if you are cleaning the data, if you are identifying those data quality issues on a more real time basis, you should be able to reduce the time to do an interim analysis. Right. We should be able to -- you mentioned fail fast. Right. Failing fast requires you to also assess the data, to understand if there's an efficacy signal, there's a safety issue. And if we have these long cycle times before we can actually do an interim analysis. And much of the data indicates that those long cycle times are due to not knowing where the issues are and finding those issues then cleansing them. If we can do that faster, we should be able to do interim analysis much more frequently. Therefore, being able to generate a fail fast scenario.

Harry Glorikian: You could almost, you should be able to set up the system to almost be running it and sort of move the bar on where it is on, “Looks successful,” or “It's moving down towards failure.” There's got to be some sort of almost real-time indicator as data is coming in to. You just don't want humans to jump the gun on that. The interesting thing is, I was looking at one of the blogs you have and you sort of say that one of the main reasons clinical trials are so costly and inefficient is bad data management and a lack of interoperability across data repositories. And, you know, it's funny because anybody who listens to this show knows that just comes up over. And it doesn't matter who you are in health care. It is a recurrent theme that for some reason people are not willing to step up and solve. I mean, it has to be a party like yours that comes in and helps clean it up from the outside as opposed to it being cleaned from the inside the way that you would ideally like it to be.

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

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It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: So on this show we talk about, you know, how does analytics play into this? So, how do—and I've got to start finding new words—but AI and ML come into this picture. What types of tools in the AI toolbox is Lokavant using? What special powers does AI give you to extract predictions from your data set that other people don't?

Rohit Nambisan: Yeah, I think I think the first piece is, and it's going to sound interesting in relation to what folks usually talk about in terms of AI and ML, but it's a harmonized data model, right? When I was working as a data scientist a number of years back, nobody told me all the work that you have to do with data governance and data harmonization. And then when you think about fast forward today where a lot of the actual models themselves are function calls, right? You realize that a lot of the work is actually making sure that data is ready to be analyzed for this particular use case. Right. So it's not to say that we don't do a number of different, try different approaches to gradient boosted descent or support vector machines or neural nets, which is actually my background in terms of grad school and research. But we spend a lot of time thinking through how we need to harmonize, create validated data pipelines to harmonize data for use. In this case. And even in that case, a lot of the work we do is a kind of intelligence or artificial intelligence. So when we're harmonizing the data, we're looking for views on leveraging multivariate clustering algorithms to actually figure out which particular types of data attributes should be mapped to one particular field.

Rohit Nambisan: So it's not to say that the data harmonization is devoid of intelligent approaches, it is full of intelligent approaches, but it is an absolute necessity to have the integrity of the data that you need to run those sophisticated front end models, which we run a ton of. But I just I want to call attention to the fact that that is a core asset for Lokavant from the get-go, that Lokavant's canonical data model and the processes we use to harmonize data to get it into that state has been a core focus because if you can do that—and that is the same model you're providing to your data science and analytics teams, your product development teams—then you really have that flywheel that you can generate a number of different analyses. For example, I just mentioned that predictive enrollment forecast model that comes off of in our our Lokavant canonical data model. That is something that is a predictive model, leveraging historical data and ongoing study data in an automated model that indexes towards the historical data early in the trial, indexes towards prediction indexes towards ongoing study data as it comes in. And we have more confidence that input over the trial, that's like an emergent benefit of having the harmonized data harmonize.

Harry Glorikian: So, you know, one has to ask in the age of the coronavirus, right, how has the business of running clinical trials changed since the pandemic? I mean. And how do you guys...is that an advantage or disadvantage? I'm trying to, you know, place where you guys are in the whole realm of how things have hopefully changed for the better.

Rohit Nambisan: Yeah, it's been quite a tailwind for us actually. And I would say that, number one, it's been it's been beneficial to us because there's just been a lot more scrutiny and interest in clinical research. Not to say there wasn't before, especially for niche therapeutic areas, but and the fact that we were able to develop and get novel COVID vaccines out pretty rapidly. But there was also a lot of challenges along the way in getting to that point. And also delays and trials and challenges in therapeutics development to address COVID as well. So there's just been a lot of scrutiny in the last 24 to 30 months on how efficient and how fast and how effective clinical research can be. So just that alone has been beneficial. Now let's take the next step there and say that all associated with the pandemic, there's been a great impact to clinical trials across the board, not just COVID trials or therapeutic trials. Patients, participants couldn't get to sites for site data collection, right. Site staff couldn't get in there, too, for data entry or site management or site oversight activities. Right. So in general, it's been a huge boon to those technology groups that have developed, decentralized or direct-to-patient data capture methodologies, thereby lowering the patient burden and the site burden for clinical trials to continue in a pandemic fueled environment. What's interesting about that as well, when we think about ourselves as both a data type agnostic platform for clinical research as well as an analytics engine, a platform on top of that, you see this huge movement to another type of data, another data, for example, decentralized trial data as another data source.

Rohit Nambisan: And what we've seen also is that while there's been a shift to a lot of decentralized trial collection on most studies, at least 90 percent of studies and above, they're hybrid, they're not fully decentralized. So you have to have some site data collection and you have some decentralized data collection. And that makes sense for those things that may make the most sense to lower patient and site burden to administer. Let the patient let the participant be at home. For those that require like biopsies, etc., that require a participant oftentimes to come into the site, let that be the site. The challenge there is now you have these two different complex data streams that are not necessarily harmonized and aggregated. So this is, again, I think that's been an area where we've been able to come in and say we'll just as a matter of course, you're doing business, this is another data set to us. We need to bring these two in and we have to also enable comparative analysis against decentralized and traditional site based data collection, because otherwise you're going to miss insights. You're going to miss information that are critical to your study.

Harry Glorikian: Yeah, a part of me was just thinking, you know, you guys should buy somebody, like Unlearn AI and go at it together where you can have, you know virtualized patients that you can put into the trial, but that's… we won't go there. So let's step back for though, for a second. So let's talk about the company’s origin story. Lokavant is one of many companies launch by Roivant, as you mentioned earlier. A Lot of the companies end up with the word “vant.” So can you explain so that people understand: What is Roivant, how it operates, what are vants and and why was Lokavant born. And how did you become president and CEO?

Rohit Nambisan: Sure. So Roivant started about seven years ago. And I should mention Roivant is our parent company. We were founded out of Roivant and spun out as a technology company itself. So Roivant initially started as a company that launched "vants" -- nimble, entrepreneurial biotech companies and now health tech companies as well. When I joined Roivant three and a half years ago, Roivant had about 15 different biotech companies. And what was really interesting about their approach is it was therapy agnostic, so it was not that there was a strategic focus or oncology or strategic focus on immunology. There was a focus around identifying compounds that may have been deprioritized in larger pharma companies, which says pharma companies that had a lot of potential and had could address critically unmet clinical needs. And so Roivant would in-license those therapies and start a therapy therapeutically oriented vant. So at the time Axavant it was the new neurological oriented, neurological disease oriented vant. Myovant was the human reproductive oriented, disease oriented vant. Et Cetera. And so now when you think about somebody like myself who comes from the tech world and life sciences, health care technology world, brought into Roivant three and a half years ago, the premise behind Roivant at the time was we can more efficiently develop these therapeutics and have more favorable outcomes leveraging innovative ways of addressing human talent as well as technology. And that latter piece is where obviously I came in and we were starting to look at in my team what are some of the most significant challenges and frequent challenges amongst the vants themselves in running these clinical trials? And then does that map against some of the more significant frequent challenges we see outside in the market? And not surprisingly, there were quite a few particular areas of resonance.

Rohit Nambisan: At that point in time, they're about 54, 45 programs being run by Roivant. And so it was across a variety of therapeutic areas. And I guess the thing that hit us in the face primarily was I guess the best way I could say it is you can order a pizza, right? You can understand what is it, a $25 investment, $20 investment. Maybe it's gone up since then, since I ordered a pizza. But the point is that you can understand what time it was ordered, when it was when they said they were going to deliver it to you, and you can track it. And most of these apps now [show it] along its destination to a chain of custody to get to you. We were we could spend $3 to $50 million on any given trial and we were at struggling with our partners to actually identify what is the current state of enrollment in the last week? What is the current state of discontinuation? Where are we at with our with these particular sites in this region? Why are we seeing high screen failure rates, etc.? Right. That's egregious to me. That's just that should not be the case.

Rohit Nambisan: We are fairly frustrated with that. And then even when we when even at Roivant or even in my former experiences at Novartis or other pharma, when we brought in a source system to say, okay, well, we're going to have a representation of data ourselves, right? So that we can understand what's going on. Invariably what happened is you would have one source system here and then a duplicate version of that sort of system at the CRO or another vendor that's working with you. You spent your entire time trying to figure out which was the source of truth, because they're spending all your time doing data reconciliation, saying, is that really accurate? Is that really the signal? So that didn't work either. So we felt pretty frustrated about this. We initially tried not to build it ourselves. We worked with a few different collaborators outside of Roivant and tech vendors, etc., and we were fairly frustrated with what we came back with there. So at that point we started thinking, if we can't buy it, we need to take a lead user innovation approach to address this. So we started out with the data platform, as I mentioned to you, and we built that capability to connect, ingest and map from any source, deliver that within a canonical data model, one single canonical data model. And then initially we did a bunch of bespoke analysis on top of that for a few different biotech vants. 

Rohit Nambisan: That went really well. Some of the external collaborators looked to Roivant at that point we said we'd like to work with this technology outside of the Roivant family, and we realized we were on to something, and we externally launched the company in January of 2020, which was very interesting time and year to launch a company. That being said, we spent the first, I'd say, just under two years, really focused on externally subsidized R&D phase. We're pretty fortunate to have some partners that invested in us in that phase, and we focused on first one particular application in response and we talked a lot about risk. But then we also realized that the needs across different companies, different vendors, etc. for managing clinical trials are very varied. So we realized what we need to really build as generalized on that first application we built and create a highly configurable analytics platform on top of this data platform so that we could actually analyze many different things and configure it for use for any particular customer. And so now we built across, I'd say seven or six or seven different use cases now, and we've deployed most of them and we're continuing to aggregate information in a true product sense where the biggest pain points in the market and how do we build or configure a version of the platform and the platform to address that. And at the same time, we're delivering on global trials with a number of pharma studies as well as on the side of the vendors working through them to deploy on studies as well.

Harry Glorikian: So in a perfect world, right, if you had access to all the relevant data, if every drug developer in the world was taking advantage of your services, how would it change the business of clinical trials? What would the outcomes look like? Would it be like you get more drugs approved every year, at a lower cost, fewer disaster failures, I mean. What changes for the industry and for patients?

Rohit Nambisan: Yeah. I think the first piece is you would reduce—and this is a lofty question so I'm going to answer with a lofty response—the first thing to note is that, and we touched on this earlier, I think you'd see fewer bigger failures in the analytics phase. You'd be able to identify earlier on, both in terms of the lifecycle of a compound, right? So from phase one to phase three or even phase four, but especially within the study itself, you'd be able to identify that there would be an issue in the study earlier on and you could kill it early on. So that's one one aspect I think would be that's important to note. The other thing I think you would identify is less operational issues. So I think one in six trials across the globe failed just because of operational issues. And when I mean operational issues, I mean the protocol and the plans at the outset of a study say need to administer the study following these steps. And when those steps are not followed, there's compliance risk. And therefore, when there's enough compliance rates to throw out the data or you have to not submit the study.

Rohit Nambisan: And so one in six is, it's not that small. And so if we're tracking, if we're more rigorously tracking both what is happening and what could happen, right, based on the indication, leading indicators of risk across time, cost and quality, we should basically never see -- that's a that's one of our major goals -- never see a trial fail just because of an operational reason. Not to mention, how can you go to the patients with unmet clinical needs in a particular indication in particular disease and say, “Oh, I'm sorry, while the drug probably was effective, we just couldn't get it out into the market this time. And it's going to take us another trial, potentially.” A lot of times folks don't actually resurrect the failed study, a failed therapy. So even if they resurrected it and said it was because of an operational issue, “Oh, you've got to wait another six years.” That's just not acceptable. So I think those are the two components that come top of mind. And I think early in our in our tenure, our mission was no trial should fail due to operational error.

Harry Glorikian: What is the path to financial success for a company like Lokavant? Is it to just keep growing? To go public? To get acquired by a maybe by a big pharma. What's the path?

Rohit Nambisan: It's a good question. I think folks that that know exactly what their exit strategy are probably, for lack of a better term, often deluded. But I will say that we've seen a lot of growth. Not only during, there's been a lot of interest in Lokavant during the pandemic, I mentioned we were in this externally subsidized R&D phase, we were actively trying not to do too much externally. We wanted to figure out how to set up the platform for success. Coming out of that phase, in the last six months, we've seen an incredible amount of traction externally. And so I think we are still in the path of doing it on a growth trajectory ourselves. What does that mean in terms of opportunities to collaborate both commercially and partner and strategically? Well, it means that we can only do as much as we can, even if we continue to grow. There's data out in the market and partners that have access to that data that we would love to collaborate with. If that means that we need to be more strategic in our approach to what Lokavant can do or how to structure Lokavant, we'll do that just because we need to actually achieve our mission, which is to have no trials fail due to operate operational error. Right. And so I think that requires more data. That requires more data science. We have a lean, very, very proficient data science team. So I think there will be opportunities for strategic collaboration, but it's all related to the mission of bringing this clinical trial intelligence platform to address operational and other risks in a study as effectively as possible.

Harry Glorikian: You know, one of the things that crosses my mind is you could also use this from an investing perspective to analyze a trial that's going through its paces against historical information and determine, give it a weighting of probability of success versus failure from an investment perspective, that that seems attractive to me.

Rohit Nambisan: Yeah. So that's an interesting point to bring up. There are folks now asking us in the market about what we've been informed firmly in trial execution stage. Folks are asking us to move into feasibility and effectively feasibility. Is that the planning of the study? Tell me with this particular configuration of sites, countries and for this indication, knowing the standard of care in different countries, knowing the approach to clinical care, not just clinical research, how successful would this study be? Right. And obviously, the success of a study, when you think about biotech, the success of a study is the success of the company. When you think when you go up the market, depending on the study, it can still have incredible impacts, the success of the company. So there is definitely an afferent towards the investing world and financial. I think at first we probably take a progressive step towards that by moving into trial planning analytics in this manner and then validating our approach against progress in space and seeing how we can continue to grow in that sector.

Harry Glorikian: Well, Rohit, it was great having you on the show. I hope everybody enjoyed our discussion. You know, a lot of problems to solve in this industry. So there's there's no lack of opportunity from, you know, businesses that need to get started and the data that needs to be optimized to help move the process forward. But, you know, luckily, everybody I talk to on the show, that's the direction we're all moving. So hopefully we'll get there faster, because I'm not getting any younger. So, so good drugs are going to be needed at some point. So good to have you here. And I can't wish you and the team at Lokavant, you know, more success.

Rohit Nambisan: Thanks, Harry, for having me on the show. It was wonderful to be here.

Harry Glorikian: That’s it for this week’s episode. 

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

Two weeks ago, in our previous episode, I talked with Eddie Martucci, the CEO of a company called Akili Interactive that’s marketing the first FDA-approved prescription video game. 

It’s called EndeavorRx, and it’s designed to help kids with ADHD improve their attention skills.

It’s one of the first examples of what some people are calling “digital therapeutics.”

And this week we continue on that topic—but with a conversation about robots rather than video games. 

My guest Paolo Pirjanian is the founder and CEO of Embodied.

They’ve created an 8-pound, 16-inch-high robot called Moxie that’s intended as a kind of substitute therapist that can help kids with their social-emotional learning.

Moxie draws on some of the same voice-recognition and voice-synthesis technologies found in digital assistants like Siri, Alexa, and Google Home. 

But it also has an expressive body and face designed to make it more engaging for kids.

Moxie Video Clip: Hi, I’m Moxie. I’m a robot from the GRL. That’s the Global Robotics Laboratory. This is my first time in the human world. It’s nice to be here. Oh, where is here, exactly? It’s a pretty big world for a little robot.

Harry Glorikian: Moxie hit the market in 2020, and parents are already saying the robot helps kids learn how to talk themselves down when they’re feeling angry or frustrated, and how to be more confident in their conversations with adults or other kids.

But just like EndeavorRx, Moxie isn’t inexpensive. 

The robot has a purchase price comparable to a high-end cell phone, and on top of that there’s a required monthly subscription that costs as much as some cellular plans.

So, it feels like there are some interesting questions to work out about who’s going to pay for this new wave of digital therapeutics, and whether they’ll be accessible to everyone who needs them.

Paolo and I talked about that, as well as a bunch of other topics—from the product design choices that went into Moxie, to the company’s larger ambitions to build social robots for many other applications like entertainment or elder care.

So here’s my conversation with Paolo. 

Harry Glorikian: Paolo, welcome to the show.

Paolo Pirjanian: Thank you. Hey, for having me on the show.

Harry Glorikian: Paolo, you're the co-founder and CEO of a company called Embodied. And and you guys are in the field of, I'm going to call it educational robotics. But this is not your first robotics company, right? Can you can you start by filling in listeners about your history in the consumer robotics field?

Paolo Pirjanian: Absolutely. Yeah. So I actually got my education in Denmark. I got a PhD in A.I. and robotics and then moved to the US actually to work at NASA's JPL. Which was a childhood dream job. Shortly thereafter, I got approached by Bill Gross of Idealab, who started one of the earliest incubators, who wanted to start a robotics company. So I joined that company as the CTO originally and then eventually became the CEO. We developed Visual Slam Technology, which is a vision based, camera based ability for a robot to build a map of the environment and know how to navigate it autonomously. That company in 2012 was acquired by iRobot. And we integrated that technology across Roomba and the other iRobot portfolio products to allow them to be aware of the environment and know how to navigate around the home, primarily for cleaning the floors. I was a CTO there for a couple of years and then decided to move on to do something that's been a childhood dream, to really create AI friends that can help us through difficult times in our lives.

Harry Glorikian: But one of the projects you worked on, and correct me if I'm wrong, was the Sony's Aibo Robot Dog, right? It's not necessarily educational, but it was aimed at kids. So what sort of drew your focus on robotics for education and socialization, I want to say.

Paolo Pirjanian: Yes, correct. Sony Aibo, the robotic dog, my previous company, we developed a computer vision technology for it that enabled the robot to be able to see things and interact with things in the environment. And it was an amazing product, actually, the Sony Aibo. And I've always actually had interest in let's call it mental health. And of course, my craft is AI and robotics. And so after my last company was acquired, I decided the timing is now to go pursue that childhood dream of creating robots that can actually help us with mental health. So we don't categorize ourselves as education in the strict sense because we do not really focus on STEM education. We focus on for children. The first product is for children. It's called Moxie, and it's helping them with social emotional skills, learning, which in layman's term you could describe as EQ, emotional intelligence skills versus IQ, which are more related to STEM type education.

Harry Glorikian: Yeah. And it's it's supposed to complement traditional therapy if I was reading everything correctly.

Paolo Pirjanian: Exactly. Exactly. We don't believe in replacing humans in the loop. We want people to be treated by humans. But given the shortage and cost of mental health services, there's always room for complementing that with AI and other technologies. And that's what we are doing.

Harry Glorikian: So if I ask the question, is Moxie more like a toy that's supposed to be fun, or is it a tool that's supposed to be therapeutic or correct some help help a child that's using it or is it both?

Paolo Pirjanian: It's primarily a tool to help children with social emotional learning, things that you would go to a therapist for. The analogy that I use that may be helpful here is really Moxie is a tool to deliver therapy to children. But we we have to make it fun enough for the child to want to take that pill. So in a way, if you use pharmaceuticals as an analogy, a pill usually for children is sugar coated because you want them to take the pill to deliver the medicine to them. So the same way here, Moxie has a lot of fun activities and interesting things that attract a child to want to interact with Moxie. And then during those interactions, Moxie will find the opportunity to deliver techniques and therapies, for instance, to teach the child about mindfulness, teach them about emotion regulation, teach them social skills, to teach them about empathy and kindness, talking about your feelings and so on.

Harry Glorikian: I know many adults that may need Moxie for sure. With all those categories you mentioned. Right.

Paolo Pirjanian: I agree.

Harry Glorikian: But but let's talk about the range of challenges, problems or issues that you've designed Moxie to help with. So can it help with relatively mild issues like shyness, or is it designed to help kids with more severe issues like, Autism Spectrum Disorder or all of the above?

Paolo Pirjanian: Yeah, no, it's first of all, you're talking about the audience that it's appropriate for. Obviously, children that have been diagnosed with any neurodevelopmental challenges such as autism need to be trained on social emotional skills. But neurotypical children also can benefit from it. Actually in our customer base, we see a roughly 50-50 split between children that have mental, behavioral developmental disorders. And in the 50% are children that you would call neurotypical. But yet we know even within neurotypical children, they have to deal with things such as stress, anxiety, sometimes even depression. Covid obviously did not help it. It exacerbated a lot of mental health issues for every child, including adults, by the way, as you pointed out. And these techniques and tools that you use from therapy are really the same independent of the diagnosis. Now, some children may need more help with social skills. Let's say if there is a child on the autism spectrum, they may not be very comfortable making eye contact, which is an important social skill to have. When you're interacting with someone, you want to look them in their eyes and Moxie will help them, for instance, with that. And that's maybe something that a neurotypical child doesn't need. So Moxie will focus more on helping them with things such as coping skills, with coping with stress, coping with anxiety or managing anxiety, or even social skills. Like you can talk to Moxie about bullying and it will allow you to talk about it and understand how to navigate that and teach you skills about how to protect your own personal space. A lot of these foundational skills are are the type of skills that social emotional learning includes.

Harry Glorikian: So. Let's talk a little bit more about the actual product. And because this is a podcast, I'm sort of like need to talk through some of the features, right? Because they everybody can't see it. But so on the hardware side, you know, the arms, the waist, it bends, the rotating ears, the rotating base, the ears, the face, the speakers, the camera, you know, the program that animates the face and gives Moxie, a personality, the computer vision elements. Right. And then all the scripts of all the different interactions. Right, you know. Why was it important to give Moxie an LCD screen as a face rather than mechanical mouth or eyes.

Paolo Pirjanian: Yeah. Let me start maybe take a couple of steps back for the audience, as you said there are no visuals here. Think of Moxie as a AI character brought to real life. Right. So think of it as a, sorry, as a cartoon character brought to real life. So think of a cartoon character that has physical embodiment and it can talk to you. It can smile back at you. We can interact with you with body language and emotions and so on. To your question as to why it required a LCD display. We could potentially consider creating a mechanical face that can have enough expressivity, but that can add a lot of costs on one hand. On the other hand, if not done well enough, it can become uncanny and creepy. So we decided that the LCD display that, by the way, is very is curved because we did not want it to look like a monitor stuck in the head. But it was integral to the design. So it's curved and looks like a face. And what you see on the face is an animated character, Moxie's character, which is integrated very well with a hardware industrial design. So you can provide much more freedom of expression from facial expressions. And especially for children, you want to have a robot that has the ability to show facial expressions. By the way, the intonation of the voice will change as well, based on the type of conversation and the emotion we are trying to capture in the conversation.

Paolo Pirjanian: And then the other question, actually, a macro level question becomes embodiment, why did this need to be embodied? Why physical? Why not just a digital character on a screen? Well, so, evidence from neuroscience, from MRI, fMRI scans shows that when we interact with something that has physical embodiment and agency, it triggers our mirror neurons, our imitation neurons are triggered at a much higher level and much wider level than when you're interacting with something just on a screen. And the implication of that is that things you can learn through interaction with the embodied agency have a deeper impact in terms of retention of the information, something that we may be able to anecdotally relate to during COVID. All education went online and the post mortem on that was that te quality of education that was delivered online doesn't compare to what happens in the classrooms. And that's, again, the same thing when it's not embodied. You don't feel that emotional connection. You don't feel an obligation. Many children will just turn off the monitor and walk away, whereas with something that's physically embodied, you feel you can't do that. It has feelings, you feel it has a perspective. You can't just turn it off. By the way, on Moxie, if you look at it closely, there are no buttons on Moxie. There is no input device on moxie like a keyboard or a touch screen or anything else. The way you interact with moxie is the way we interact with each other, using conversation, body language, intonation of voice, emotion, facial expressions and so on. There is one switch actually on the bottom of the robot that you don't see. That's for emergency situations in case something goes wrong. For certification reasons, we have to put that physical switch to turn it off if something goes wrong.

Harry Glorikian: So not having played with it does, and only watching the video online, Moxie's voice synthesized like Siri or is it prerecorded? Like, how does it sound?

Harry Glorikian: It's synthetic. Yes. So, yeah. So we cast the character of Moxie, decided what this character stands for, what are its values, what is the background story? And then based on that, decided the voice of Moxie, what it should be. And then the way you develop the synthetic voices that you take in neural network and train it based on a lot of samples that we captured from a voice actress in a studio recording hundreds and hundreds of hours of speech from a script. So we have this script and we know how it sounds based on the character's voice recording, and that gets fed into a deep neural network that is trained over and over again until it models that voice. So that later I can just give a text and it will generate a synthetic voice that sounds exactly like that character.

Harry Glorikian: And then Moxie seems to emit a lot of sound effects and music. Does that element enhance the product somehow?

Paolo Pirjanian: Yeah. So we can underline mood and so on with sound effects or background music. For instance, one of the activities Moxie will suggest if the child is talking about things that are have to do with stress and so on, is a mindfulness journey. Where it will ask you to close your eyes. Imagine you are in a forest or other places as well. There's a library of them. Let's say you're in a forest, listen to the wind and then it will start playing some sound effects in the background and calming music to get the child to imagine they're in that space. For some children that have high sensitivity disorders to certain stimuli like sound, the parents can actually, through a parent app, provide that information which will adjust the settings. In that case, Moxie will actually not use sound effects or any jarring effects that may disturb that child.

Harry Glorikian: Interesting. So. Simple question, but is it battery operated? I mean, how long does it last on a single charge? Does it plug in?

Paolo Pirjanian: Yeah, it's battery operated because the child usually likes to move it around. You carry the round almost like a baby on your arm. If you remember the days where we had young babies, it was literally ergonomically, it sits exactly right on your arm very nicely. And it has a battery that can run for hours of active usage. And then at night, usually like your cell phone, you plug it in any charges overnight.

Harry Glorikian: So, you know, this begs the question of where did the idea of Moxie really come from? Because you don't decide on a whim to build a product this complex. You know, how did you persuade yourself and your investors that the technology is at a point where, you know, it could really make a difference with kids, you know, that have social emotional development issues?

Paolo Pirjanian: Yeah. I mean, the idea was sparked probably early in my early childhood, I would say. So, very briefly at a very early age due to a war, my world was turned upside down. And unfortunately, I had to flee my my homeland and seek refuge in another country where I looked different, sounded different and was different. Right? And and unfortunately, as such, you do get rejected by the society. You have a harder time in school. You get exposed to racism and rejection and all these things. So. I remember during that time I saw the first animated short by Pixar. Which was Luxo Jr., the two lamps, mama lamp and baby lamp playing with a ball. Which blew me away that a computer can generate millions of pixels on the screen that are moving to create, to induce or elicit such emotion in the audience. So that inspired me to actually seek education in computer science and robotics and A.I. because before that, as many immigrants you were taught that you were going to be a doctor, so that that's.

Harry Glorikian: Or a lawyer.

Paolo Pirjanian: Lawyer comes second, but obviously doctor first. So so that inspired me actually to buy a computer and start coding by myself. And I started learning coding and then I decided I'm going to do well in high school so I can get into university and pursue my education. And I did. And to be honest with you, this has been something I've been wanting to do for since I can remember. My previous company, as I mentioned, Evolution Robotics, that was a Idealab company and I was the CTO then became the CEO. I wanted it to do it then, but that's almost a decade ago, or maybe slightly more than a decade ago. We even tried. It was not possible. Absolutely not possible. I remember back then. Just to use an example that I think most people can relate to, voice recognition for even a single command was hard. All of us have had in-car navigation systems with a voice assistant that you would press a button, hold it down and say navigation, and would pull up navigation and say, Enter your address. It will enter the address. And you would have, to by the time you were done, enter the address because it would constantly misunderstand you and then give you options. Did you say A, B or C and no, no, no. I didn't say that. By the time you were done entering the address, you were at the destination. So that was state of the art only a decade ago. Just for voice recognition. Same thing with computer vision.

Paolo Pirjanian: My specialty actually was computer vision. Computer vision. Also, we couldn't recognize things very well. And the advancement that has happened in deep neural networks due to the increase in compute power, due to increase to labeled data sets that are available through many sources from YouTube and the Internet and so on. We have been able to solve age-old problems that for decades we were struggling with So it was not possible. The other piece that was probably not possible was that I was not ready as an entrepreneur probably to take on such a colossal challenge of building a product like this. So the stars aligned around 2015 when I decided to leave iRobot and said, You know what? The time is probably right now. And and fortunately, I was able to get some investors that believed in the vision of creating AI characters, AI friends that can help children with social emotional development. And obviously, this technology platform, we will in the future use it for also helping the elderly population with loneliness and Alzheimer's and dementia and so on. We have just scratched the surface with our first products, right? And there is a lot more work to do. But today it's possible. We have proven it. We have a product in the market. A five year old can will interact with it for months at a time without any human intervention. So yeah, so it was a series of events brewing over the last 30, 40 years for this to become possible today.

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

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It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: I mean, just looking at the system, there's probably a lot of innovations that were required to put Moxie together. And so. I don't know, maybe you can give us a few, you know, like "Oh, my God" moments that took place in this, right? I mean. I don't know if it's the physical movements. I don't know if it's the, you know, personality or the scripts. But, you know, give us the highlights of what you think was like the big breakthroughs that made this possible.

Paolo Pirjanian: Yeah. So there are many, many, many, many pieces of technology that we had to invent or partner for to make this happen. So   what I mentioned, deep neural networks, generally speaking, in the field of AI have advanced to the point where we can have very reliable speech recognition technology, for instance, right? Where you have an accent or not, you're speaking loud or soft and so on, you have background noise and so on, it will be able to transcribe what you're saying pretty accurately. There are still errors, but it's pretty accurate. It's accurate enough, let's put it that way. The next stage of the conversation pipeline is actually understanding. Now you have a transcript of what was said. Now I need to understand the semantics of what was meant, what was the intent behind this, this string of characters, and that's natural language understanding. In that area, Embodied has made huge advancements because we have to be able to understand what the child is saying. And the state of the art when we started is defined by Siri and Alexa and Google Home, where it's very command and response. "Alexa, play music for me. Alexa, how is the weather? Alexa, tell me a joke. Alexa, read a story or read the news for me." And so on. So short utterances and and direct mapping to a function that the device can do. Whereas in our case it's not about this transactional command and response, it's about relation and social interaction. So the child, Moxie will actually ask and encourage the child. It says, "So how was your day to day?" There is no way any human being can script all the possible answers that you could expect to hear because you could basically say anything possible to that question.

Paolo Pirjanian: So we had to develop natural language understanding that can understand what was said no matter what was said, and provide a relevant response. Because if you don't, if the robot says something that's absolutely not related to what the child wanted to talk about, then children get disappointed. They say, well, this thing is a dumb robot. It doesn't doesn't understand me. And they will dismiss it, right? The illusion of intelligence breaks away very quickly as soon as you you misunderstand or say something off script, let's say. So we had to develop a combination of systems to be able to address that. Another major challenge, and this was actually much bigger than I thought, we spent a lot of time on this challenge to solve. Again, it has to do with interaction using Alexa as an example also, and Siri as well as Google. They all have this notion of a wake word, Hey, Google, hey Siri or Alexa. When you say this keyword known as a wake word, the device is actually at the, when it's on standby, it's putting all of its attention to look for that keyword before it does anything else. So as soon as you say it, a couple of things happen. It's almost like turning on a switch to say, I'm going to speak, right? So number one, you're telling it, I'm going to say something now. Number two, as soon as you have said that phrase, these things have multiple microphones on them. And the mic array allows you to be able to be informed and focus your attention on the location from which you heard this phrase. With doing that, you can also filter out anything that's in the background. So you focus the attention of the device on that location of the user that said Alexa. And then you say a phrase and then it processes and executes the action. In our case, in social interaction, it will not be appropriate if you had to say Moxie in every volley of the conversation. Every time you want to say a sentence to me, you would start by saying Paolo and I and I would look at you, and then you would say something, and then I would stop listening. And then you say, Paolo, for every sentence, right. That would that would be a very awkward social interaction. So we had to solve that problem. It's a tough problem to solve. And we use a combination of cameras to know where the child is, the voice, where it's coming from, and what was being said to focus the attention of Moxie on the person that's engaged with it so that Moxie doesn't respond to the TV or mom and dad maybe having a conversation on the phone over there and it filters all of that automatically, without the need for having a wake word phrase. And I can go down the list. There is many, many more. But this is just examples of the type of things we have to solve.

Harry Glorikian: So, you know, I think some people might make the argument that kids should really be learning their social and emotional skills from other human beings. From a parent, from a teacher, from their peers, maybe their therapist if they have one. You know, how can a robot fit into that picture in a healthy, productive way? You know, how would you respond to the potential criticism, which I'm sure you've heard before. When a parent who buys Moxie for their kid, are they offloading their parental responsibilities?

Paolo Pirjanian: That's an absolutely valid concern and a good question to ask. And obviously, even before inception of the company, I personally myself was thinking about this because there is a there's a contradiction in saying that a child that is not very good at social interaction, let's put them in front of a robot, they'll get better at it. There's a contradictory element to that potentially. Right. So let's put it this way. In the extreme case, what if the child does not have the ability to have interaction with their peers? Right. So they do not get the opportunity to interact with other peers from which they're actually learning to hone in their social skills. Well, that happened during the pandemic. There's a huge mental health crisis happening in the US now that will take years for us to to address. That was because children were locked in their home without the ability to socialize with other children because of worries about being getting COVID, right. So now pandemics are rare events that hopefully don't happen that often. But now let's put ourselves in the shoes of children that are, for various reasons, are not successful in providing social interactions. An extreme case is a child on the autism spectrum. That does not have the right skills to have social interactions nor interpret social cues in a conversation. Let's say if you're annoyed at someone on the spectrum, it's likely that they may not even understand that you're annoyed at them and they may keep saying the same thing or doing the same thing. That's going to make you more and more agitated or the other end of the spectrum, which is not as severe.

Paolo Pirjanian: My example when I was a child. And I lived in a foreign country where I was different. I had an accent. I looked different. I came from a different cultural background and other kids didn't want to play with me. And there's everything in between. Right? So then. What do we do? Well, you can have therapies and that's what we do. There's a massive shortage of therapists. If you have a child, usually the way this works is that your school teacher will come and say, we think your your child may be on the spectrum or your child may have ADHD or your child have some other neurodevelopmental challenge. You should get your child diagnosed. Okay. Hopefully no one has to try this. The waiting list for getting diagnosed is minimum six months, minimum six months. And that's if you have connections and good providers and all these things. While imagine for six months your mind as a parent, you're like, dying. What the hell is going on with my child? I've got to figure this out quickly. Once your child is diagnosed and you spend 6000, 7000 hours on that, then you've got to find providers. There's a huge shortage of providers, and even when you get to the provider, there is a massive cost associated with it. So typically children on the spectrum, as an example, get diagnosed at the age of three or so. Ideally, actually, because the sooner you can intervene, the better the outcomes. And when they're diagnosed, they will be recommended to seek 20 to 40 hours of therapy per week. 20 to 40 hours of therapy per week. Yeah.

Harry Glorikian: They're not doing anything else.

Paolo Pirjanian: No. And many times, many times schools are supposed to provide it. But you have one or two special needs teachers that are to deal with the whole population of kids on the spectrum in their school as an example. So they're not going to get 20, 40 hours per week. The cost of therapy is super expensive. Insurance also has to pay for it. Nowadays, they're mandated to, but the cost still adds up. On average, a family will spend $27,000 out of pocket per year, even despite insurance coverage. So not everyone has access. And also if you live in rural areas and so on, you don't have access. So. Why not have an automated system that can do this, at least filling the gap? Right. We think of Moxie as a springboard to the real world. So we want to use Moxie as an opportunity to for the child to open up to Moxie, use that as an option, teach them a number of techniques for how they can be more successful in social interactions, and then Moxie will actually encourage them to go in the real world and experience these things and come and tell it about what what, how it went. So we use Moxie as a springboard to the real world. There is another phenomena that happens, and I don't know how to describe this. You may actually have more insights in neuroscience than I do. Children, especially children that have neurodevelopmental challenges, open up to a robot like Moxie better than they do to humans.

Paolo Pirjanian: Let's take autism as an example again. I remember the very first experiment we did with our first prototype. We took that prototype to a family's home. They had a ten year old son on the spectrum, and we put Moxie down. At the time we did not have the AI yet. It was the robot remotely controlled by one of our therapists. On an iPad they were typing what the robot should do and say. The child immediately opened up and start talking to Moxie. And if you look at that child, you say. And you know, as a matter of fact, I asked Mom: "I don't see anything wrong with your child. Why do you think he's on the spectrum?" And he says, well, you have to see him how he treats his peers. He doesn't open up to them. He doesn't want to talk to them. When he comes home from school it takes me, mom, a couple of hours to "find," quote unquote, my child. Tuning into the channel. So they shut down. And there's a few reasons for for sort of, I think, anecdotal or maybe rational reasons to why that is. One is that children that are on the spectrum, they completely understand feelings and emotions and so on. They are not very good at expressing themselves or or showing their feelings, but they understand if they are being rejected or teased out in a conversation and so on. So they shut down. A robot is non-judgmental, right? They understand that it's a safe, non-judgmental space.

Paolo Pirjanian: The other part is that when someone like me who comes with a warmer blood and too many gestures and intonation, voice and expressive, it's too much there's too many signals going on. And that's overwhelming to a lot of children on the spectrum. And they shut down. It's too much. I cannot deal with this. Right. And so hence, a robot is finding social doing social exercises and experiences on training wheels. And helping them develop those muscles and get better at how to handle different situations when they go in the real world to interact with their peers or other people in their circle, social circle, to be successful. And that success will hopefully breeds more success. So ideally we are successful when people actually stop using our product. And as a matter of fact, we have parents reaching out to us and say, my child could not stand up in front of their classroom to say a word. Now she stands up and gives a whole presentation and we have stopped using Moxie. Thank you so much for the help that that's what what it is. It's like it's stepping stone. It's training wheels for social emotional learning so that they can have a chance of being successful, because otherwise they do not have the chance to to have these exercises to learn. We learn a lot by interacting with each other.

Harry Glorikian: So the company describes Moxie as just the first iteration of a larger platform that I think you call SocialX. So what is SocialX and what other kinds of products do you envision coming out of it?

Paolo Pirjanian: Yes. SocialX is our technology platform, which which allows a machine to interact with us using real conversation, eye contact, body language, gestures, intonation of voice and and for the machine to do that as well as understand you on all those channels as well. That's what social platform is. The name SocialX is a juxtaposition to user experience, UX with an emphasis on the social experience. Right? We are creating a social experience. We are not just creating a user experience where you can push buttons or say a command, play music. Tell me the weather, what's the stock market like? But rather social interaction which involves social skills, emotion, skills, empathy and so on. And this is our first iteration. It's going to get exponentially more advanced. With every single user we add to our customer base, it allows us to improve SocialX because the data and the interactions that we can experience allows us to keep improving our algorithms to get better and better and better. So we decided to start with children because they are the most vulnerable in our society and we thought that's where we can have the most impact. The other end of the spectrum, where we become vulnerable again is when we are aging, right? And mental health is extremely important for aging people. And loneliness leads to a lot of mental health challenges that lead to a lot of physical challenges.

Paolo Pirjanian: We know this. The surgeon general of U.S. said a couple of years ago that loneliness for elderly is equivalent to smoking a pack of cigarettes in terms of the health implications it has. And it's true. Again, during COVID, a lot of elderly that were alone suffered massively because they were high risk for COVID. Even my mom, who lives 5 minutes away from me, I didn't visit her for a few months until we sort of figured out that we think we know how to handle COVID so it was safe to to meet meet each other. It's extremely difficult. So that's the other end of the spectrum that we intend to address. And then in between every age group, in between that, from your teens to your aging, every person in their lifetime deals with mental health challenges. As a matter of fact, the US population, 17 percent of the population at any given time deals with mental health challenges stress, depression, suicidal thoughts and so on. And having a life coach that can help you through these difficult times, we believe can have a huge impact. So eventually with those three pillars, we will be able to help the entire population. You can go beyond mental health, which is what we are focused on, because that's where we think we can have the biggest impact you could imagine.

Paolo Pirjanian: You go to Disney Park and you could have an interactive character coming up to you that's not a person inside a suit, but it's actually an animated character that's walking around and talking to you and entertaining you. You can imagine going to a hotel lobby where your intake to the lobby will be serviced by an interactive character, AI character. By the way, we are also working with hospitals and schools. Right now for hospitals we work with University of Rochester Medical Center. We are currently doing a pilot of using Moxie to help children, diabetic children, to educate them about how to treat themselves and how to adhere to their treatment plan. And then there is a number of other use cases that we are going to expand into, including intake to the hospital, dealing, sort of holding their hands and making sure they are not stressed out, coming to the hospital for the first time, pre-op and then post-op. Also a lot of complications you want to avoid by making sure there is someone to remind you about your care plan and so on. So to be honest with you, the sky is the limit. But the three areas we are focused on is children, elderly and then everyone in between that suffers from mental health or loneliness type of challenges.

Harry Glorikian: Yeah, there are so many other applications that I can think of that I would, you know that I could use my self. So hopefully, that will come into play because this would be something interesting for me even to interact with, depending on, you know - Don't forget to work out or, you know, there's something that you interact with regularly. Right. But so let's go to sort of the crux of the some of the issues. Right. It's it's not an inexpensive device. I mean, it does a lot. So you can't expect that it's going to be inexpensive. Right. It's it's $999 to purchase plus a separate monthly subscription of about, what is it, $39 per month for a minimum of 12 months. And so how how do you get this out to a larger group of people that really need it. Is it subsidized purchases? Is it insurance? What are you guys thinking of from a business model perspective?

Paolo Pirjanian: Yes. So we actually launched the product in the second half of last year for the first time and we sold out. But I agree with you that it would be much better if it was more affordable, because we don't want this to only be a product available for high income families, for rich kids to use a derogatory term maybe. We want it to be available to every every child. And for that to happen, there is a couple of different strategies we are pursuing. One is that once we get to a scale of efficacy studies that are convincing enough that we can get insurance, potentially insurance coverage to cover it or at least subsidize part of it to make it more affordable. The other approach is that we are working with bigger institutions such as hospitals and schools and libraries, by the way, which can buy it and make it available to their population. As an example, this library actually came to us, which is a very interesting business model that addresses the reach to the society that may not be high income. The library bought a fleet of Moxies from us, and they're lending them out to their society, to their members as a book. So a child gets to take Moxie home for a month and then bring it back, which is awesome because we have, by the way, we have done efficacy studies and it shows that even within a month you can see significant improvement on a lot of these social emotional skills.

Paolo Pirjanian: But ultimately, that's that's how it goes. And also, just to put it in perspective to two examples. One is that robots of this nature....By the way, there is nothing like Moxie because the technology has not existed today, but people have tried, actually, SoftBank has a subsidiary called SoftBank Robotics that have spent hundreds of millions of dollars developing this robot called Pepper that costs $14,000 to buy and $2,000 a month to subscribe to it. Yeah. So we are orders of magnitude better than that. And that was part of the design principle that we said we want to be on par with an iPhone ownership of a cell phone. Buy it for roughly about $1,000. And you pay roughly about $50 a month in subscription. So we met that goal, which was a major accomplishment, very hard to do, but we are not satisfied with that because as I said, this has to be available. The other part of the other example is that if you have a child that needs therapy and if this cuts your therapy by a handful of therapy sessions, it pays for itself. Right? Again, ideally, we will have insurance pay for it. And so that will take some time. As you know, sort of navigating the medical fields and insurance organizations and so on will take some time, but we will get there eventually.

Harry Glorikian: Yeah, I mean, I recently interviewed the CEO of Akili Interactive, Eddie Martucci, and they are the first group to get an FDA approved prescribed video game for children between eight and 12 years old with certain type of ADHD. And so, you know, they're using the prescription route as a way to have somebody pay for the clinical trials and everything else and the product itself. So I know that this business of robotics is not for the faint of heart. I mean, there's there's many different companies out there like Jibo, which was out here. Or I think there was a company in in San Francisco called Anki that, you know. You didn't pick an easy one, that's for sure, Paolo.

Paolo Pirjanian: Definitely not. Definitely not.

Harry Glorikian: But but, you know, I you know, I wish you incredible luck. I mean, this this thing sounds so exciting. I mean, it brings out, like, the Star Trekkie guy in me and wants to interact with it and have it do certain things or say certain things or or maybe even like interact with my wearable and be able to see something and then make a comment to me as I'm using it. So I can only wish you incredible luck and success.

Paolo Pirjanian: Thank you. I need it and I appreciate it.

Harry Glorikian: Excellent. We'll talk soon.

Paolo Pirjanian: Talk soon, thank you so much for having me.

Harry Glorikian: That’s it for this week’s episode. 

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

Can a video game help improve attention skills in kids with ADHD?

According to Akili Interactive in Boston, the answer is yes. 

They’ve created an action game called EndeavorRx that runs on a tablet and uses adaptive AI  to help improve focus, attentional control, and multitasking skills in kids aged 8 to 12.

And it’s not just Akili saying that.

The U.S. Food and Drug Administration agrees.

In 2020 the FDA cleared EndeavorRx as a prescription treatment for ADHD, based on positive data from a randomized, controlled study of more than 600 children with the disorder. 

It’s the first video game ever approved as a prescription treatment for any medical problem.

Kids are advised to play the game for 25 minutes a day, five days a week. After two months of play, two-thirds of parents of kids in the controlled study said they saw a meaningful change in their children’s day-to-day impairments. 

The FDA’s approval of EndeavorRx opens the way for a new wave of so-called digital therapeutics, designed to treat all kinds of problems with cognitive functioning, including depression, the cognitive side effects of multiple sclerosis, and even Covid-19 brain fog.

Akili is busy telling the world about EndeavorRx and working to get more doctors to prescribe the game for kids with ADHD and more insurance companies to pay for it. And here today to tell us about all of that is Akili’s co-founder and CEO Eddie Martucci.

Harry Glorikian: Eddie, welcome to the show.

Eddie Martucci: Thanks, Harry.

Harry Glorikian: So I'm dying to get into the company and all the things you guys are doing. But, like, before we jump into the company, I'd love our audience to get to know you a little bit. Right, because you're a long time health entrepreneur. You got your PhD at Yale in the departments of pharmacology and molecular biophysics and biochemistry, where you studied structure based drug design. But how did your personal path lead you from molecular biology, which is near and dear to my heart, to video games to treat cognitive impairment? I mean, that that's not exactly the Venn diagram I would see that somebody would just put together.

Eddie Martucci: No, it's not. And there is no there is no path for this. Right. Because this is so different and so new. I would say my personal passion is just new science findings. Like I just love brand new science. I was a researcher for a short stint while I did a PhD. I think I had some pretty cool research. But really, if I zoom back, it's new science and new discoveries that are moving the health world forward. And that can be whether it's insights about some part of our biology that we didn't know before, that leads us to understand the human body better. Or in the case of what I've really done from a professional perspective, it's scientific insights that can lead to new treatment modalities. And so that's really what got me most excited. I think the path that was most impactful for me, you know, I was a biochemist at Providence College and a biochemist and biophysicist at Yale, and I love proteins and structural biology and all that. I still do. But I came out of my PhD and and worked with a group called PureTech Health in Boston. And Puretech is really just this unique new health care company where they've done everything from, they have research and development and discovery, but they also have in many ways nailed down a process of starting new companies off of groundbreaking science. And so while I was in grad school, I was exposed to a couple entrepreneurs that really put a light bulb in my head that, wow, this is something I should look into. And then I got training at PureTech in Boston. And that's what kind of got me thinking about brand new medicine and brand new modalities that were never considered medicine before. And the rest is history. Once you get a framework where you can start thinking like that, then it's just work.

Harry Glorikian: Yeah. I mean, I knew Daphne, I think when she started PureTech and her advisory board was like, I mean, Nobel Prize winning, who's who sort of. Right. Just watched it evolve over time. But, you know, when you were at PureTech, I think one of their focuses was neurophysiological disorders. I mean, is that the real bridge that helped start Akili? Because I remember that came out of Adam Gazzaley's lab at UCSF, if I remember correctly.

Eddie Martucci: Yeah. Adam Gazzaley is where we found the core technology that which we call SSME, which has gone on to power our products, including our FDA approved product. But yes, what I was working on at PureTech, including directly with Daphne, who's really brilliant in helping and bringing new big ideas to life and board members, including people like Ben Shapiro, who used to be at Merck. And he was one of my longest term board members. It doesn't hurt to have folks like Bob Langer in the room once every quarter to bounce ideas off of as well. So like, a very privileged place to start a company. But yes, I was working on novel CNS technologies, in fact. In fact I was working on a few and one in particular that was new devices, new devices for various neurological conditions. And it was really from that effort in thinking about what are the newest modalities of medical devices that we leaped one big bridge further and said in 2010 or about or maybe 2009, could we go further from a user experience perspective now that the whole world is carrying cell phones and tablets every day? Could we go further? Could we could we think about digital? And that was right around the time when everyone and their mother was talking about digital helping medicine. And because we were in the headspace of novel therapeutic modalities, I think it was a natural leap to say, what about digital being the medicine? And then we had to find the science. And that's where I found Adam Gazzaley and, and, and we got off to the races with that technology from UCSF.

Harry Glorikian: Yeah. I mean this whole area of digital therapeutics, I've been talking about it for years now and trying to convince people and they look at me really weird when I say digital therapeutics and I try to explain it to them. But so but the game you have built is called EndeavorRx. If I got that correctly. And can you tell? Me more about the game itself. Like, what are the operative features or game mechanics that are thought to increase attention in kids who play the game?

Eddie Martucci: You kind of have to back up to the core technology. So the way we build the business is not building one product or one game. We're building a platform technology, meaning a technology that is not made for a single disorder. But instead the problem we're going after and that we started with all of those years ago, about a decade ago, is cognitive functioning. Cognitive dysfunction in medicine is not targeted well by molecular pharmacology. That is the problem statement. We don't target cognition very well in medicine, if at all. And so our whole theory and thesis for the business was, if we could bring in the best technologies in the world, that through software could actually target cognitive functioning directly, then we would be bringing a pillar of medicine that does something much, much different than what medicine does today. So the technology out of UCSF that we started the company around, that we have branded the Selective Stimulus Management Engine, the way this technology works, which will then help you understand how the products in for ADHD children works. The way this technology works is it is giving constant stimulus, both visual and motor. So it's creating conflicting and overwhelming stimulus to activate the part of the brain that controls attention, which is the midline prefrontal cortex. So the front part of the brain that really controls attention and speed of processing and integration, this technology is patented to be able to activate that part of the brain very strongly, but also enhance what's called long range coherence. So as you're using this technology, not only is the front part of the brain activating much more, so you can apply your attention downwards.

Eddie Martucci: And I'll get to exactly how this manifests, I promise. It is also more seamlessly based on the neurological data we have. It appears to more seamlessly be helping the brain communicate to the sensory processing regions. And so the way this manifests in ADHD children, when they're using our product EndeavorRx, which is meant for children 8 to 12 years old with ADHD who struggle with the attentional issues. This product is basically experienced like a racing video game where children are running a little alien figure down a course that is ever adapting. And they're getting information, meaning things that fly up to the screen that they have to make decisions on. And that's ever adapting because we have these deep, personalized algorithms so that everyone gets their own experience. So basically what people feel is they're using this technology that feels like a game, and it's just constantly challenging them in different ways. What's happening in the brain and this is how it's designed, is that the game is presenting very specific stimuli for each user that is pushing them at the edge of their processing ability. And that's part of the IP we have, is how to do that in a really seamless way so that by the end of using a game you haven't just been using a game, you have been essentially taxing the weak link in your attentional processing every single second for hours.

Harry Glorikian: I think every CEO of that we may know mutually needs to be prescribed this game.

Eddie Martucci: You know, CEOs and investors have been probably the most common people that in meetings will stop me and say, hey, I think I need this.

Harry Glorikian: Yes. So how did you ideate and test the game mechanics?

Eddie Martucci: Yeah. This is this is really a tenet of the business where we decided early on that to truly—we want to disrupt medicine and we want to create and integrate our type of medicine into mainstream medicine. Far too often, digital is kind of left to early adopters or on the sidelines of real medicine, excuse me. And our whole thesis was you have to run real validated and literally gold standard, rigorous clinical research. So when we had done this, no one had done a well designed trial before to study something that looks like a video game. And so that's really where we spent the first handful of years of our existence is after we built the kind of data infrastructure, which we can talk about, and the adaptive algorithms. We then invested years in how to run good clinical trials with this type of product that's an experiential product. So our goal all along was being able to run the same or better rigor of randomized controlled trials that you'd expect from a drug for this same disease area. Obviously, as an interactive product that you can see and you interact with, that means you have to take a little bit of a different approach.

Eddie Martucci: So we had to do a lot of work with some of our advisors and with places like Duke University on how to blind the protocol. Because it has to blind very differently. And how to how to have a control, an active controller sham that is actually controlled. And there's many nuances like that. But at the end of the day, the trials we run are meant to replicate or be analogous to drug trials, where you have really strong controls up front, that you're not biasing individuals and that the outcomes—and this is the differentiator in digital—that the outcomes are gold standard accepted outcomes for whatever you're studying. And so that was what we've done. What we did the first time we were in a trial, we were like, it took a lot of work and we were nervous about it. But we have a clinical ops team now and we've run a few dozen trials across, I think, nine or ten disease populations, so we've become pretty good at it.

Harry Glorikian: Yeah, I was going to say, I mean, coming up with the first one, everybody's probably scratching their head trying to figure out, are we doing the right thing? But, and I have this discussion with some of the people I work with all the time, what's the proprietary special sauce in the case of digital therapy? I mean, is there a defensible algorithm or insight at the heart of something like EndeavorRx that would be comparable to a patented small molecule in the you know, in the traditional drug industry?

Eddie Martucci: In our case, yes. And I'll tell you about that. I think what this really comes down to, though, that question about digital therapeutics, it's like a business question for the industry. To answer that question, it's important to recognize there's nuance in the industry. So the vast majority of digital approaches, I think, are tough to protect because they're taking well known human practices and putting them into an app. Right. So there's 90 percent of the digital therapy companies or products out there are using different forms of behavioral therapy or disease management techniques or strategies, and they're bringing them into an app that is not bad. It's hopefully very, very good for patients. There's a few validated products there that are, no question, good for patients. I think it does make those types of products harder to protect. We've taken a bit of a different tack. We're a little bit, I guess, iconoclastic within the industry in that what gets us excited is software that even though it's software, it's more drug like in that it's directly targeting and activating the dysfunctional physiology in the body. You can measure that and by virtue of that, you're having a really unique effect. 

Eddie Martucci: The second big difference is we are using algorithms that have not been ever reported on before. So we take much more of a drug lens where we actually do protect we patent our technology. So we call this whole class “physiologically activating digital therapeutics.” Some people have referred to them as mechanistic digital therapeutics or disease modifying. There's different phrases, but this idea of unique algorithms that you actually can protect with patents and copyrights, which we do. So we have about 50 issued patents for the technology that underlies EndeavorRx and another 100 that are filed on our various technologies. And you can demonstrate this has a real, unique physiological effect. I think what it enables, at least for these types of products is a feeling from the health care world that this is much more what I'm used to seeing in my traditional medicine where it's unique. I can't just go get this anywhere because I trust that this one product is the only one that has this unique technology. And by the way, it's been proven to work. And I trust that they're a stable company that's going to be around for a while. Those things are really important to our model.

Harry Glorikian: Well, it's a good thing that I've been explaining it to people the right way. So at least now that we've talked, you know, my explanation is aligning correctly. So I'm happy about what I'm reading is correct. Let's take a step back. So, there's a lot of kids with ADHD who have no problems concentrating on something for hours if they're really interested in it. But it strikes me the key feature of the of the product is not just keeping kids engaged. It's supposed to build or improve those skills. Is that the key thing that makes the game special or unusual or different from any other pastime, say, building LEGO spaceships?

Eddie Martucci: Yes, absolutely. So the engagement is critical, but the differentiator is the challenging and improving of that core cognitive functioning. And you don't get that just by engaging in something. And actually, the vast majority of entertainment products you engage with will allow you to either passively engage, meaning you could watch YouTube videos for hours and hours, but you're not actually challenging your brain or actively engage, but in a way that you don't really have to challenge what you don't do well. So in most video games, you can choose what most of us do in life, choose the path of least resistance, because we like certain ways of using a product. Our product is unique in that this this patented algorithm forces you—it's essentially measuring second by second where you're weak and processing the various streams of information and it is forcing you to work on those areas where you are weakest. But it's doing it naturalistically. It feels like you're using a treatment, but it's really that level of focus on, for lack of a better word, it’s really that level of focus of delivery of that algorithm that's actually going to stress you where it, for lack of a better word, hurts the most. That is the differentiator. The other big differentiator is, is the personalized algorithms that that we built in. And this is where, frankly, technology and data rich medicine has never gone before. But within seconds of using the experience, this product is tailoring to each individual user. And this is true whether we're talking about kids with ADHD or some of our trials and products and adults with depression or MS, these products can actually tailor to your functional level and then move you along from there. So those two those two bits of how the algorithm works are critically important. The engagement is really the delivery vehicle to make sure you're getting that level of medicine.

Harry Glorikian: Yeah, definitely, if this was available to people in a larger age range, there are people that I definitely need to recommend this to when well then that becomes available.

Eddie Martucci: But well, that's the interesting thing about cognitive dysfunction, right? The way I talk about it sometimes is cognitive functioning and or problems with cognitive functioning go across disease, right? They're in many ways disease agnostic. Almost anything that touches the brain results in some level of cognitive dysfunction or at least some proportion of patients that have longer term cognitive dysfunction. But it also goes above and below disease, meaning subclinical. So there are people that are not diagnosed with issues that, you know, that probability-wise there's 20 or 30 percent that are significantly below the mean they're struggling with these things. So this is a this is a basic human function that rears its head in a really nasty way in many diseases, but is actually relatable to all of us.

Harry Glorikian: Yeah. So. I mean, there's a lot of challenges when you're trying to design something like this. A ten year old will not spend much time playing a mobile game unless it's it's just as compelling as, you know, anything that they could download as a mobile app. So. How did you guys, what steps did you guys take? You know, it's almost like game design and, you know, therapeutic outcome, you know, together in one package. And so how did you guys, what steps did you guys take to make sure this thing was fun?

Eddie Martucci: Correct. Yeah. And it does depend on the population. Right. So we have products, obviously a marketed product in for children with ADHD, but we're developing products and have trials and data and adults of various ages. The I think you're right. If you focus on children, there's a there's an engagement bar that is not easy. Right. Kids are highly discerning. They know a good game and a bad game. And what we like to say is we have no delusions that we're going to come out with the next blockbuster entertainment game. That is not how we built the company. However, we do want to have a game that looks and feels like the type of games that you actually like to play. So it has to be worlds better than edutainment, as people call it, educational software, because kids know. And so the way we did that is this is one thing that makes Akili very unique. Instead of outsourcing or kind of outsourcing game development or adding game development at the end of our development cycle, we actually have built the company to have cognitive science, clinical science, and game development fully integrated from the earliest days. And data science, for what it's worth, is really a kind of foundational thread for all of those. And it's hard. It's really hard. I mean, developing a product that has both these things, the strong science and the engagement, is really hard, but it's also really hard for people from all these different industries to, you know, be speaking the same language and work together because the development processes are different, the language you use is different. Your mindset of how you think about developing is different. And so for us, what I always talk about is it's literally daily attention. I'm unwilling to sacrifice or give up on it. We have to do both. Well, I think where we are today with EndeavorRx as our first product out of the platform, it's a really good product. It was built to show clinical efficacy and engage people to a minimal degree. It does that. Some kids love it. They will play for months at a time, you know, five days a week for four months. But yeah, there's a lot of people that kind of get through it and then plenty of kids that say, I really don't want to use this. So we've built features around the edges, things like an app for parents to allow them to track and monitor and incentivize their children. And we try to educate our users on why you're doing this. And so it's got to be a mix of the engagement itself, but also a little bit of inherent motivation that, hey, your doctor's in the loop, this is your medicine. It's important to put the work in and accomplish it.

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. 

It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: What makes you optimistic? Because I've been, you know, enamored with this space for a while now and trying to watch like where it's going to grow and what's going to get in its way. And so what makes you optimistic about digital therapeutics, either as a venture scale business or a public company. Because I know you guys are thinking about that. Tell me what you're thinking.

Eddie Martucci: Yeah, a couple of things that make me very optimistic. I think the foundational groundwork is now done and we’ve shown it can be done. So we know that these products now can be developed, they can be protected, they can be brought through clinical trials and actually help patients. That's the most important thing. They can undeniably with strong clinical data, help patients and they can be brought through the FDA and now being prescribed by docs. So these prescription digital therapeutics, there's only a couple of them on the market. But literally at this point there's been now thousands of docs, not merely tens or hundreds, but we're talking now about thousands of docs who have prescribed prescription digital therapeutics to patients, where a couple of years ago that would have been essentially zero. So the foundations are there more. Every month that goes on, it becomes a a self-fulfilling cycle where doctors and patients hear about it, they're aware of it. They know someone who's tried it. And it's becoming a little bit common nature to think, wait, isn't there something digital that I've heard about for this? I think that will flip in the coming years to I expect to have a digital treatment or I expect to be told the digital option for my doc. So that makes me that makes me optimistic is that the groundwork is there. We know it can be done.

Eddie Martucci: The second thing is, frankly, society is demanding better medicine in many different ways. They're demanding, and mainly I'm talking about patients in many respects, they're demanding more accessible medicine. They're obviously…we all got the efficiency bug of telemedicine during COVID. And while I've seen the data that that has significantly receded, I don't think the concept of online or digital in medicine has receded from anyone's mind. I think we all know that it's far more efficient and we should expect to see more products that are digital in nature, whether that's scheduling with a doc or taking a treatment. And so I think there's this kind of wave in society that is that is pushing people to recognize that we should be open to these types of products. The other thing is, whereas docs and patients years ago when we did market research, there was a level of skepticism that was pretty healthy. I now see a level of openness where if there's good data and there's especially in our case, things like FDA approval and strong clinical data, there's a better chance than not that both patients and doctors are going to be not only acceptable or accepting, but they're going to want to at least try something like this. So all the groundwork is there. We've just got to keep keep plugging away because it's new.

Harry Glorikian: I talk about the whole digital therapeutic space in my book. And I always tell people look, if a product like this works for you, you're not going to have a side effect profile the way you have with some of the small molecule drugs that I've seen. It's trial and error with those things. And sometimes things don't go as well as you want them to and you end up with a very angry child if the drug doesn't do what it's supposed to do.

Eddie Martucci: It's egregious. It's egregious. I mean, medicines, pharmacological medicines for neurological conditions are critically important. Don't get me wrong, I think they're critically important. And EndeavorRx is not meant to be an alternative to medication, especially if it's working well for for a child. But the problem is, there are many components of these conditions that are just not well addressed. And so you're left as a clinician to try to use these blunt instruments, these molecules which weren't delivered for these problems or rather weren't designed for these problems. You're trying to use them, but you're fighting the side effect profile as much as you're fighting the efficacy the whole time. And so you're right. Trial and error is the right phrase. Like the fact that we're still doing trial and error in CNS conditions all these years later is crazy. And there's a better way because we now can have these more targeted products that are part of the patient's toolbox.

Harry Glorikian: Yeah, and we need more of them, so. Yeah, great. But let's talk about the business model, right? I mean, this is, you know, feels like fresh territory, right? And if I think about mobile games generally don't make money unless you sell millions of copies. Right. So you have you must have a different business model in mind from the beginning. I suspect this business model revolved around, you know, selling Akili games as a prescription based therapeutic at a cost that would be more typical for a drug than a mobile game.

Eddie Martucci: Right. Right. So the concept here is we want the products to get to the patients that really need them and we want to involve the doctor in the loop and we want to have products that are proven. And so all of that to me says a core medicine model, meaning prescription treatment, as you said, covered ideally by insurance largely, but with a little bit of out-of-pocket burden from the patient. You're right, the general cost is a little bit more in line with pharmacology, although the good news in mental health and behavioral health is that's that's relatively inexpensive. We're not talking about multi-thousand-dollar therapies here. We're talking about something that is in the low hundreds per month. And for the patient, really more like $30 to $50 a month. So these are the cost structures that we think are tenable and have been working well in behavioral medicine. And that's really where we're starting. But we're in the early days. I think one of the beauties of digital is we don't have to just stay there, meaning that is the core of the model, a prescription that scales and is paid for by both insurance and patient.

Eddie Martucci: But I think there's a lot of potential to evolve and iterate the model that has more consumer elements to it. For instance, like your best technology products, we can adapt the product itself to grow with you. Like your best technology products. We can serve, you know, services and help on the side beyond or in between your use of the actual treatment. So there's a level of connectivity with our end user and consumer that is that actually looks a lot more like best in class consumer software where you can have a long term relationship with a patient. Now, we have not pulled any of those levers yet, but I think what we're most excited about is the bringing both of those models to bear. A medical model, but that has some aspects to it that can actually grow and extend more like software. I actually think that's where the field will go. But it is early days. We'll have to see how this we'll have to see how this shakes out.

Harry Glorikian: Well, that's why I always I always tell people, like, you know, once you digitize something like you get to have a broader imagination about what is possible in that realm as opposed to, you know, sticking to exactly what we did before.

Eddie Martucci: Exactly.

Harry Glorikian: But taking a step back here, no one has ever marketed a prescription based video game or won marketing approval from the FDA for such a product. Right. So how did you frame yourself? You walk in there and you say, “Hey, here, play this. And you're going to like it.” What were the hurdles? What did you have to overcome to get regulatory approval for this? What was it like dealing with the FDA?

Harry Glorikian: No, it's a great question. Yeah, the FDA process is fascinating. We know it is rigorous, it's long, it's mostly collaborative. Right. The FDA wants to learn and help. But I think, number one, most importantly, there's unfortunately a myth out there today that digital therapeutics are actually medical devices generally don't have to go through efficacy analysis by the FDA. So I see this myth all the time. People say, well, you know, on the medical device side, they only look at safety. And so, unfortunately, with broad brushes, people have painted digital therapeutics as part of that. They've said, well, digital therapeutics may or may not have evidence, but the FDA looks at safety. I can unequivocally tell you that could not be farther from the truth. I would say 95 percent of our interactions with the FDA, which took the better part of two years because our product was so novel, you can imagine we were not only innovating the delivery mechanism, it's a video game. We were innovating the target, which is cognitive functioning, which there are no products labeled for cognitive functioning. And we were trying to look at what are the endpoints that, you know, that read on cognitive functioning. All of this is new, but not 95 percent of the questions we had, and that's—please don't quote me on the specifics, this is not a deposition—but in that range, were about efficacy. And we went through every little bit of our efficacy data so that the FDA could understand it, so that they could audit it.

Eddie Martucci: We even, midway through our regulatory process, brought on a fifth study. So we have five studies in our FDA label package. So we brought on the most recent study to show to address some questions FDA had around efficacy in the longer run or efficacy along with medication. So this was a very rigorous process. I always tell people the good news about this is you can trust it when it comes out because this is something that looks and feels a lot like the drug process, right? There's a lot of scrutiny put on the trials and the legitimacy of the trials. So so it looked a lot like that. It's highly iterative. From a business side, the one tough part with FDA is when you're when you have a new classification for a product, so a 510K de novo, so they're creating a classification, there is no hard timeline on the review. And so when you're a startup and you're building a business, you kind of just keep iterating until you get to a label or not, right? And luckily in our case, we did. But yeah, I mean, as a startup, you're going through a nearly two-year approval. It's stressful. It's stressful, but it's good for the industry, I believe, because it's really forcing a high bar of science.

Harry Glorikian: Well, no. And I mean, that's what you want. You don't want a low bar and then things go wrong, like you want it to be held to a higher standard. And usually when the FDA is taking on something new, they've also got to take the time to catch up to where you are. Right. They can't just walk in the room and be ready for this. So you're sort of paving the path for everybody that's coming behind you, which is a I guess there's a good part of that and a bad part of that.

Eddie Martucci: Yes. Yes.

Harry Glorikian: So there's a lot of stakeholders and gatekeepers in this space that we're talking about, right. Patients, parents, physicians, payers. I mean, each one of them needs to be persuaded that digital therapy or digital therapeutics are, you know, beneficial and worth prescribing or worth paying for. So, anything special you're doing to sort of win them all over?

Eddie Martucci: Well, we're doing the work to put time and attention towards it. So you're right. Just because it's digital does not mean people will use it or understand it. So you've got to sit with patients and educate. Just because docs have a new tool doesn't mean they'll trust it. So you've got to spend time to make sure they understand the data and more importantly, understand where we're trying to play in the treatment paradigm. Right. Because, again, we're not … the easy answer for a digital is, “Oh, this is supposed to be a digital equivalent of a drug.” No. It's more nuanced than that. This is supposed to help in a very specific way. And insurers are probably the biggest barrier because it's so new for them. Right. This is this is very new. They don't really, they're not really built to be able to adjudicate digital products. Right. And unfortunately, we've got some of these types of myths floating around, like the FDA medical device myth, which understandably makes insurers uncomfortable. Right. If they if that's what they've heard, they say, well, how in the world am I supposed to adjudicate efficacy if the FDA doesn't? I guess I'll look at this with all the other hundreds of wellness apps out there.

Eddie Martucci: So it's education time. Honestly, it's education time to unravel these myths, to really sit and make sure these stakeholders understand the data and the utility of the product. In terms of special things, one of the one of the nice things about growing a company, especially with digital company in this day and age, is you can test and iterate really quickly on all of these fronts. And so when we test you've got to have the meetings and you've got to fit into their review cycles. But for patients and docs, you know, we, we take a very clear test and learn approach. We are releasing certain types of educational content or certain types of marketing messages in pilot phase. Right now we see what works, we see what doesn't. We adapt. We do the same thing with the distribution infrastructure, frankly. Like how in the world do you get a video game therapeutic from your doctor? We built the infrastructure. We tested, we changed, we scrapped half of it and started again. So that is the beauty of living in a digital world. We can we can do that type of testing and learning.

Harry Glorikian: And good old AB testing on what works and what doesn't.

Eddie Martucci: Totally.

Harry Glorikian: All right, let's step out of ADHD for a minute. You've been talking about other neurophysiological sort of conditions. And I think the website, if I'm not mistaken, mentions depression, cognitive dysfunction, multiple sclerosis, autism spectrum disorder, and a few other future treatments is. Is there something about the EndeavorRx platform or the proprietary adaptive algorithm that gives you the ability to sort of generalize? And I think you mentioned that earlier, but sort of to dig into that a little bit.

Eddie Martucci: Yeah. So it really starts with what technology are looking for. And so we don't source technologies that are meant for any one condition. That is more common in the behavioral therapy space where there's behavioral therapy for disease X because it's a tried and true technique specific to the disease. The way we work is looking for technologies that actually activate specific brain regions and have data that they do that well. And so the interesting thing that we found about cognitive functioning, and we knew a little bit about this, but you know, I don't like to have revisionist history and say we we knew it all, with cognitive dysfunction and disease, independent of the etiology or the cause of why the brain is having issues, the downstream manifestation actually tends to bucket into very similar issues. And so our theory was, and so far it's proven true, is if you could bring technologies that are meant for the neurological processing issues, not the disease, not specifically the disease, then any condition that results in similar issues, you should be able to have a functional impact on. Because we're not we're not targeting, you know, dopamine reuptake and a dopamine driven disorder. We're not targeting myelination in am anti-myelinating disorder. We are targeting the end result, which is how well the brain is communicating. So we've because we start there, we, we theoretically have the ability to go across disease, and we've actually shown it now. So the same technology that has a treatment label for ADHD has been able to power two studies, including a larger randomized controlled study in multiple sclerosis adults, and showed clinically meaningful, large changes in speed of processing and related cognitive functions. That's the same technology under the ADHD product. ADHD and MS could not be farther from each other in terms of cause, but because the resulting functionally in the same in the same area, that's then you get that benefit. So that's our theory and that's how we're going to continue to develop products and take a functional and a neural network approach, if you will. And, and ideally, we have a much more efficient product pipeline because of it.

Harry Glorikian: So. In your mind, like what are the biggest unanswered questions, either for EndeavorRx or for the Akili business. Is it more product? Is it more market? I mean, for example, do you worry about whether it'll work, you know, in the real world, as well as it did in your initial studies, whether doctors will prescribe the game, whether payers will cover it. There's all these issues. And so I'm just wondering where you think the biggest hurdles lie?

Eddie Martucci: Sure. Yeah, I think I think my number one is not about the product. It's really systemic to the to the health care system and industry, which is it's important to me that the insurance industry, the doctors write prescriptions, but more importantly, the insurance industry and broader we could call it the payer industry. Right. Anyone that should be paying for medicine pays for digital therapeutics. Right. I don't think this should be the only class of medicine where patients bear the entire cost. That makes no sense. So we are not there yet for sure. Right. We're in such the early days that I think some payers are waiting, but I think we're starting to see a turn. We're beyond skepticism, beyond intrigue, probably into early acceptance. And I think the work needs to be done. And frankly, we need we need a couple folks in this industry and by folks, I mean both people, but also organizations to step up as the early pioneers for their patients. I think that's really important. Now, again, I have empathy for why that part of the industry moves slower. They're trying to protect patients. There's obviously cost arguments as well. And there are some of these myths or misconceptions out there about the industry. But I think when education is done right and when payers really engage, we're going to start to see a broader payer ecosystem adopting this like they would any other medicine. So I think that's kind of the biggest near-term barrier. And slightly longer term, I think the business model is a question. Which no one likes to hear, no investor likes to hear. And we're a company that's going to go public. I don't mean the business model is a question in that we don't know if we can make money or build a business. I just mean, what is…so, the foundations we know are there. Doctors will prescribe, patients will pay, payers are starting to pay. It has a benefit in people's lives. So the foundations are there. The business will grow. What the eventual business model is, is TBD, frankly. What is the top end business model that's going to allow a company to thrive at scale? I think we have to invest to learn that. And I'd say the same thing about the product. In terms of the product, it's not whether it will work, it's not whether it can help patients on the market. We've shown all of that. It's at this point, how well can you develop that product on the market so that it engenders long term compliance so that engenders loyalty and use in the future? And so I think in both those scenarios, I guess the health care system’s got to get there. Which is a secondary priority, more like an opportunism. We don't want to miss the opportunity to find the best business model or to iterate on the products because we have the ability to do so. I don't want to miss that opportunity to grow the best business model we possibly can.

Harry Glorikian: So you mentioned going public once or twice, and so I saw that there's paperwork with the SEC to go through a public filing with a special purpose entity backed by Chamath, whose I think it was Social Capital, through his venture fund. What's the thinking behind becoming public? Why now?

Eddie Martucci: Yeah. I think I always had a mantra and I didn't come up with it. This is from advisors to me and mentors: Stay private as long as you possibly can for the business to be able to adapt and iterate and a little bit more of a clean way. But I think that time has come, and the reason I say that is we have a product that is being prescribed by doctors now and we have a pipeline where I've already talked about it. We could help potentially up to dozens of different populations who are struggling today. On top of that, the need and urgency around mental health and behavioral health has had a step function change in the last year. Right. We know that President Biden talked about it at the State of the Union. The surgeon general has put out a national state of emergency on youth mental health. So the time is right for a real investment here and the time is right for the company to fill that need. We know all the all the foundations are right. So I've always wanted to wait till that moment why we chose this specific entry point and vehicle, which we hope is kind of middle of this year, that Akili becomes a publicly listed company is, I think, the opportunity to not only have capital and the type of flexible capital that the public markets gives you, but in the case of a special purpose acquisition company, the expertise of that acquiring entity, in this case, Chamath Palihapitiya, who's extremely well known and amazing at building disruptive technologies for different industries that scale to ubiquity using technology and data.

Eddie Martucci: But actually that the SPAC vehicle here is Social Capital Suvreta. Suvreta being a well known biotech hedge fund who specializes in early commercial biotech companies. So rarely do you get to become public with the right amount of capital, but also some new expertise around the table, strategic expertise in a disruptive business. And I think we get both of those with this deal. So we're still, it's too early to tell if the whole thing will go through. We're certainly crossing our fingers and hopefully if people listen to this in the longer future, Akili is already a public company and thriving.

Harry Glorikian: Well, I mean, it's a good thing I spoke to you now so that we could speak a little bit more freely than when you're under that public rubric.

Eddie Martucci: But oh, no, I'm already I'm already watching my words. It is important. It's a level of maturity as a business. Now, we have we've grown for about a decade. We grew methodically and slowly. We have over 100 employees now. And, you know, businesses change and mature. And I think it's the right time for us to do it.

Harry Glorikian: Oh, yeah. I mean, a lot of the companies that I interact with as an investor, I mean, when we're going to go public, it's like, “Oh, we got to do this, we've got to get that ready. We got to get accounting ready. We got it.” I mean, you've got to go through it methodically because being public is is not for the faint of heart for sure. So, well, I wish you the greatest success. I look forward to staying in touch and, you know, keeping up to date on how things are going with the company. And, you know, I hope a ton of people listen to this because it's easier for them to hear it from you than hear it from me.

Eddie Martucci: Thanks, Harry. This is a lot of fun. And thanks for your focus in innovation and these new areas that are really going to transform patients’ lives. So I'm hoping we're doing our part there.

Harry Glorikian: Thanks.

Harry Glorikian: That’s it for this week’s episode. 

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Those are the big questions being investigated right now by a four-year-old startup in California called Fauna Bio. And Harry's guests today are two of Fauna Bio’s three founding scientists: Ashley Zehnder and Linda Goodman. They explain how they got interested in hibernation as a possible model for how humans could protect themselves from disease, and how progress in comparative genomics over the last few years has made it possible to start to answer that question at the level of gene and protein interactions. The work is shedding light on a previously neglected area of animal behavior that could yield new insights for treating everything from neurodegenerative diseases to cancer.

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

It’s April and spring is well underway, even though it’s been a pretty cold one so far here in New England.

It’s the kind of weather that makes you want to pull the covers over your head in the morning and just sleep in. 

Or maybe just hibernate like a bear until summer is really here.

But when you think about it, what is hibernation? Why is it something that bears and squirrels do, but humans don’t?

Even more interesting, what’s going on inside a hibernating animal, physiologically, that allows them to survive all winter without freezing to death and without ingesting any food or water?

And what can we learn about that process that might inform human medicine?

Those are the big questions being investigated right now by a four-year-old startup in California called Fauna Bio

And my guests today are two of Fauna Bio’s three founding scientists: Ashley Zehnder and Linda Goodman. 

I asked them to explain how they got interested in hibernation as a possible model for how humans could protect themselves from disease.

…And how progress in comparative genomics over the last few years has made it possible to start to answer that question at the level of gene and protein interactions.

We’ve always looked to the natural world, especially the world of plants, for insights into biochemistry that could inspire new drugs. 

But what’s exciting to me about Fauna Bio is that they’re shining a light on a previously neglected area of animal behavior that could yield new insights for treating everything from neurodegenerative diseases to cancer.

So, here’s my conversation with Ashley Zehnder and Linda Goodman.

Harry Glorikian: Ashley. Linda, welcome to the show.

Ashley Zehnder: Thanks, Harry, we're excited to be here today. It's going to be fun.

Linda Goodman: Yeah, thanks for having us.

Harry Glorikian: Yeah, I mean, well, you guys are someplace sunny and warm, and I'm actually I shouldn't say that it's actually sunny right now on the East Coast. So I'm not I'm not.

Linda Goodman: Don't jinx yourself.

Harry Glorikian: But the temperature is going to drop. Like to I think they said 18. So everything will freeze tonight for sure. So it'll, you know, it's one of those days, but. I want to jump right into this because we've got a lot of ground to cover. Like there's so many questions that I have after sort of looking into the company and sort of digging in and, you know, but even before we jump into what you're working on. Right, I really want to talk about hibernation. Maybe because I'm jealous and I'd like to be able to hibernate. I have sleep apnea. So sleep is a problem. But humans don't hibernate. But there's a ton of other mammalian species that that do. And sometimes I do feel, though, that my teenager hibernates, but that's a different issue. So, but, what what is interesting to you about hibernation from a physiological point of view. What what goes on with metabolism or gene expression during hibernation, that's that's not found in humans, but that could be relevant to human health?

Ashley Zehnder: Yeah, I think this is a great question, Harry, because I think both Linda and I came to fauna from different backgrounds. I came from veterinary science, Linda from comparative genomics. We can go into our details later, but neither of us really appreciated the amazing physiology of these species. There are some of the most extreme mammals on the planet, and there are hibernating bears and literally every group of mammals. Right. This is something Linda specializes in. But there are primates in Madagascar that hibernate very similar to the 39 ground squirrels that we tend to work with. So it's this really deeply conserved trait in mammals, including primates. And, you know, it kind of highlights for us what our genes can do when they're adapted for extreme environments. And so that's kind of the lens that we take when we look at hibernation. It's how do these species protect their own tissues from being nearly frozen for six, seven months out of the year, having to protect their brains, their hearts, all their vital organs? They're not eating, they're not drinking. They're not moving for these really deep bodied hibernaters. When you think of 100 kilogram animal that's not eating for seven months, how do they survive that? Right. And it has to do with metabolic rates that change 200- to 300-fold over the course of a couple of hours. It has to do with oxygenation changes and protection from oxidative stress and ischemia reperfusion. And so if you look at a tissue by tissue level, you can start to see how these animals are finally adapted to protecting themselves from from damage. And then we can start to say, well, this is similar damage to what we see in human diseases. And that's why this is such an interesting system, because it's so dynamic and because it happens across so many groups of mammals, it really lends itself to this comparative genomics approach that we take to drug discovery.

Harry Glorikian: Yeah. Because I was wondering sort of like what ways of healing from different sort of traumas and conditions do hibernating animals have that that humans don't, that we sort of maybe wish we did? It's sort of like, you know, almost Marvel or one of those things where you like go to sleep, you wake up, you've totally healed again, which kind of be kind of be cool. Yeah. So, you know. But when did scientists first begin to think about whether having a better understanding of hibernation might help us solve? Some of these riddles that we have in human health. I mean, it surely it can't be like a new concept. It has to go further back. I mean, what has changed recently to make it more actionable? I mean, is it, you know, omics, costs coming down that are making it easier, computational capabilities that are, you know, making all these come together? I mean, those. What do you guys. What's. What's the answer? You guys know the answer better than I do.

Ashley Zehnder: I'll comment on a little bit on the physiology, and I will let Linda talk about the data revolution, because that's that's really what she knows very intimately. So from a physiology standpoint, these are species and not just hibernaters, but a lot of other species that we've been studying since the early 1900s, 1950s. I mean, these are some of our earliest biological experiments and our earliest understandings of biology. We're not necessarily done by studying humans. A lot of that was done by studying natural disease models, right? How did we figure out that genes cause cancer? So it's a little bit of a tangent, but bear with me, it was not by studying human cancer, it was by studying Rous Sarcoma Virus and how that virus picked up bird genes and then turn them on. Right and other in other individuals. So but then kind of this almost the same year in 1976 that we figured out that genes cause cancer by studying chickens. 1974 we figured out how to genetically modified mice. And we sort of figured out that like, okay, maybe we don't need to study natural biology anymore. And so I feel like we sort of lost a lot of those skills and figured out we had humans and we had model organisms and we were done. And I think now we're kind of in this renaissance where people are realizing that actually there's still a lot of natural biology that we can learn from. But it's being powered now by this data revolution and the decrease in cost and sequencing and availability of omics data like RNA. Seq and then I will pitch that over to Linda because that's really what she knows best.

Linda Goodman: Yeah, yes, absolutely. You know, Ashley's right. And I think just to add on to that, that there was this issue in which there were a lot of field biologists out there working with these really fascinating hibernating animals. They knew a lot about what these animals could do, the extreme environments they were exposed to, that they could overcome, they could protect all of their tissues. And there was so there was a group of field biologists who knew all that information. And then on the other side, you have all of these geneticists who are studying the genomes of probably humans and mouse and rat. And they weren't really talking to each other for a long time. And I've been in the genomics field for at least a decade, and not until very recently did I even hear about all these amazing adaptations that these hibernating mammals have. So I think some of it was just a big communication gap. And now that the genomics field is starting to become a little more aware that all these exciting adaptations are out there that we can learn from, I think that's going to be huge. And yes, of course, it certainly does not hurt that there's been a dramatic drop in sequencing costs. We can now sequence a reference genome for around $10,000. That was unheard of years ago. And so a lot of these species that people would previously consider untouchables because they were not model organisms with a pristine reference genome, we can now start to approach these and thoroughly study their biology and genomics in a way that was not possible several years ago.

Harry Glorikian: Yeah. I was thinking I was, you know, I was laughing when you said $10,000, because I remember when we did the genome at Applied Biosystems and it was not $10,000.

Ashley Zehnder: Yeah.

Harry Glorikian: Yeah. And it took I remember Celera, we had an entire floor of sequencers working 24/7 I mean, it was an amazing sight. And now we can do all that, you know, on a.

Ashley Zehnder: Benchtop. Benchtop. Exactly. On a benchtop.

Harry Glorikian: So. But, you know, it's interesting, like in a way, studying animals to learn more about disease mechanisms seems like a no brainer. I mean, we share a, what, about 99% of our DNA with chimpanzees. And for those listening. Yes, we do. You know, I'm sure there's people out there that, like, bristle when I say that. But what is it, 97.5% of our DNA with rats and mice. That's why we use all these things for sort of safety and effectiveness of drugs meant for humans. But. Still, I'm not used to drug hunters starting out by looking at animals, you know? Why do you think it's taken the drug industry, although I'm I say that very loosely, [so long] to wake up to that idea?

Ashley Zehnder: Yeah. I think it's I think it's again, this almost reversal of the paradigm that exists today, which is let's take a human disease that we want to make a new drug for. Let's take a mouse and let's try to genetically manipulate that mouse to mimic as closely as possible what we see in the human disease. And those are always imperfect. I mean, I did a cancer biology PhD at Stanford, and there's that trope of like, Oh, if I had a dollar for every time you occurred mouse in a human right, it would need to work anymore. That's replicated across many fields, right? They're not good models. And so we're saying like obviously that doesn't really work for discovery. It's fine for preclinical and safety and you have to use those models. But for pure discovery, that's not where you want to be, right? Instead, you want to take the approach of saying, where has nature created a path for you? Where is it already solved this problem? And I think there are companies like Varian Bio who are doing this in human populations. We're saying, let's look at humans that have unique physiologies and a unique disease adaptations. And of course then you have to find those niche pockets of human populations.

Ashley Zehnder: So that's not a not a simple problem either. But the approach is very analogous. What we're saying is we can use that rare disease discovery approach and just expand that scope of discovery. Look at highly conserved genes, look at how other species are using them to reverse how phosphorylation in the brain to repair their hearts after damage, to reverse insulin dependence. To heal, we'll heal their tissues or regenerate stem cells. Let's just see how nature did it right and just mimic that instead of trying to fix something that we artificially created. So it's literally reversing that paradigm of how we think about animals and drug discovery. But you have to know how to do that. You have to know which models are correct. You have to know how to analyze 415 genomes together in an alignment which is really complicated. Linda knows how to do that, so you have to know how to do it correctly, although you could screw it up very badly. So there's a lot of expertise that goes into these analyses and also again, the data availability, which wasn't there nearly a decade ago. So.

Harry Glorikian: So I asked this question out of pure naivete, because I'm not sure that I could sort of draw a straight line. But, you know, which drugs were have been discovered through research on genetic mechanisms of disease in animals. Is there, are there?

Ashley Zehnder: You know, I think directly it's a new field. Right. So I think, Linda, you and I have looked at some examples of looking at drugs for narcolepsy, looking at dog genetics and studies, looking at muscle disorders in certain species of cattle that have naturally beefed up muscles and translating those into therapies. I mean, there are examples of looking at animals for things like genotype, right, came from Gila monster venom, although that's not strictly a genetic program. Right? So I think this idea of looking at natural animal models is a source of innovation. It's just that, again, the data wasn't really available until fairly recently, but we know the strategy works by what's been done on things like PCSK9 inhibitors in humans, right? It's a very similar approach to that. It's just expanding that scope of discovery.

Harry Glorikian: So because you guys raised money and you guys are moving this forward, sort of and I don't want you to tell me anything that's confidential, but. So what was the pitch when you when you put that in front of everybody?

Ashley Zehnder: It was really that, look, drug discovery right now is really been hampered by a lack of innovation. And we're really stuck in looking at these very kind of currently limited data sources, which is humans and again, these handful of really imperfect animal models. But we can take what we've learned from working with human genomics and really greatly expand the opportunities for a number of diseases that still don't have good therapies. Right. We've had the human genome for really close to 20 years now. We spent a lot of money sequencing it. And still, if you go back and look at the FDA approvals in the last two years, which I did by hand a while ago, or more than three quarters of those are not new targets. They're new drugs for a new indication or new drugs, same drugs before a new indication or they're kind of meta pathway drugs or they're drugs for which we still don't know the mechanism. It's some small molecule. It's been around since fifties. And so like where is the innovation in the top ten diseases of people still have it changed? So like where I pulled these two headlines right not too long ago, one from 2003, which is like the era of the genomics revolution. Right? And then one from 2019, which was the genomics revolution question mark. Right. Like we're still sort of waiting for it. And so what is that missing piece of data that's really going to allow us to really leverage the power that's in the human genome? And to do that, we have to put our own genes in an evolutionary context to understand what's important. That's been that third dimension of genomics that's been missing. So it's really not necessarily about any particular species that we work on, all of which are amazing. It's really about using that data to shine a better light on what's important in our own genome. And so that's a lot of the pitches, like how are we going to use our own genome better and find better treatments?

Harry Glorikian: Yep. Understood. So. You have a third founder, Katie Grabek. Right. So. Tell me about yourselves. I mean, did the three of you get interested in comparative genomics and hibernation? How did you come together? How did you decide like, oh, hey, let's do a startup and get this thing going in this area? So tell tell me the origin story.

Ashley Zehnder: Linda, do you want to kick off?

Linda Goodman: Sure. I think it all really started, Ashley and I initially started batting a few ideas around. We both had this understanding that that drug discovery today did not look outside of human mouse rat very much. And we both understood there was this wealth of animal data that's just waiting to be used and no one was doing it and we couldn't really figure out why. And we were having trouble figuring out exactly which animal we wanted to study and which diseases we wanted to study. And it just so happened that we lucked out. There was another woman in our lab at Stanford, Grabek, who had the perfect study system for what we were thinking about. She had these amazing hibernates our animals that have exquisite abilities in terms of disease, resistance and repair. And once she started talking about all the amazing phenotypes these animals have, we thought, wow, that would make a great study system to make the next human therapeutic. Yeah. And I think it's interesting that both Katie and Linda have human genetics PhDs. Right. So I think both of them and Linda can expound on this. But from Katie perspective. Right, she she went in to do a human genetics Ph.D. trying to understand how genes can be used to improve human health and shouldn't be rotating the lab of somebody who studied the 39 ground squirrel and said this physiology is way more extreme than anything we see in humans, but they're doing it using the same genes.

Linda Goodman: What are those genes doing in these animals that we can adapt for human therapeutics? And so she brought that work with her to Stanford and was really one of the preeminent researchers studying the genetics and genomics of these species. My background is I'm of Marion, so my clinical training is in exotic species. So as a clinician, I treated birds, mammals, reptiles and saw that they all presented with different kinds of diseases or in some cases didn't present with diseases like cancer that were super interesting. And then coming to a place like Stanford to do a PhD, it was working with a bunch of human researchers, human focused researchers. They're all generally human researchers, but you know what I mean? It's a little bit tricky with the nomenclature. Generally, I have my doubts about, you know, maybe there's some chimpanzees doing research somewhere, people studying human diseases, right from a human lens who are completely ignorant of the fact that animals often also had these disease traits or in some cases were resistant to them. So there was this huge disconnect there of of biologists and veterinarians and physiologists who understood all these traits across different species and the people who knew the molecular mechanisms, even though a lot of those are shared.

Linda Goodman: And so one of the things that I found really interesting just from a cancer perspective was that a lot of our major oncogenes are highly conserved because these are core biological genes that if you screw them up, will give you cancer. But there's an evolutionary pressure to maintain these genes. And so there's a reason why they're conservative, because they're really important biologically, and that's true across many other diseases as well. So from that perspective, I was really interested in this intersection of human and animal health. I always wanted to do more genomics myself and just never had had the training. Linda had always been interested in veterinary science, and so we kind of immediately started collaborating and saying, Look, look, there's a huge opportunity in this, again, third space, third dimension of genomics that people are not looking at. What do we do trying to start a comparative genomics company? I'm using air quotes here for the podcast listeners is a little bit broad. Where do you start? And I think Katie really gave us that start in saying, here's a model. We have a biobank of samples that are proprietary to fauna. We have an expert in this field. We have a model that's good for so many different diseases. Let's prove that the process works here and then we can expand into multiple disease areas.

Harry Glorikian: You know, you got to love, people I think, underestimate that magic that happens when the right people get together and the spark happens, right? I mean, I'll take that. Any day. I mean, I love coming up with a plan and then, you know, working to the plan. But when it happens, when the right people in the room and they're all get excited, those are those are the most incredible start ups, in my opinion. Yeah. So you're starting off with targets in heart disease, stroke, Alzheimer's, diabetes, very different areas, right? Cardiovascular, neurodegenerative and metabolic. So. Why start with those areas in particular?

Linda Goodman: So I think for us it was really again showing showing what we can translate from this model. So some of the phenotypes that we see, the traits that we see in the ground squirrel, which is predominantly one of the species we use for our work, is that they're exquisitely resistant to ischemia, reperfusion injury. So the kind of injury that gets, if you have a heart attack and you go and get the heart attack on block, you get this rush of warm, oxygenated blood back into your heart that can actually be damaging. And that's a lot of what causes damage after a heart attack, what these animals happen, they do this 25 times over the course of a 6 to 7 month hibernation cycle. And if you look at their hearts in the peak of one of these periods, there is an upregulation of collagen, which is cause of fibrosis. There's an upregulation, there's histologically, there's a little bit of damage. It's less than you would I would have, but there's a little bit there. But if you get to the end of that whole cycle and look at their hearts, they look normal and they do it again next year. Right. So you and I could not survive 25 of these attacks over six or seven month period, right? Obviously not. So let's pick the strongest phenotypes we have in these animals and let's show that we can use information from that and come up with genes and compounds that are protective in our more standard models of these diseases.

Linda Goodman: And that's what we did really with the first round of data that we had is we generated four genetic targets and two compounds that came out of the heart data that we had from hibernating and that we tested them in human cardiomyocytes in a dish and said if we take oxygen and glucose away from these cells, they get really unhappy and die and we could double survival of human heart cells in a dish. And then we said, okay, great, let's actually move this into animals. And so we used AAV or some of these viral vectors to then knock down genes in vivo in hearts of rats. So we literally tied off a coronary artery and then let the blood come back in and saw that we could almost fully protect these hearts from damage by knocking down genes that we found in the hibernating data. So it was really closing that loop and saying, where are the strongest traits? Can we show that this works? And then it was really figuring out where are the really large areas of unmet need. And so in terms of metabolism, we end up connecting with Novo Nordisk, which is a publicly disclosed partnership. They are very focused on obesity. We have a model that increases this metabolism, 235 fold over an hour. Name another model that can do that, right?

Harry Glorikian: I need that. I need that. I need like, because...

Ashley Zehnder: We all need that!

Harry Glorikian: I could get rid of a few pounds right around here.

Linda Goodman: Exactly. So then it's really just figuring out where are the unmet needs, who is really interested in these areas we're looking at and do we have unique data that speaks to those models? And that's really we just try to be guided by the biology and saying, where do we have unique data sets that can answer high unmet needs?

Harry Glorikian: Okay. Well, all I mean, all sounds super exciting if we can make the translation, you know, in the right way and find those targets. But. You guys have built up a significant biobank, right? I understand you have a huge database of genomic readout from various hibernating animals. Can you tell us a little more about the extent of that biobank? How did you collect the data and how unique is that database in the industry?

Ashley Zehnder: Yeah. Linda, do you want to talk a little bit about the data sources that we're currently using at Fauna?

Linda Goodman: Yeah. So maybe, you might be the best person to talk about the Biobank and then I can talk about all the other data sources layering on top of that.

Ashley Zehnder: Yeah, I'll talk a little about the BiobanK. So we have yeah, we have a number of different data sources. The Biobank is one of them and probably one of the main ones that we use. So Katie, during her PhD, built a really unique biobank of very precisely time tissue samples from 39 ground squirrels across the whole hibernation cycle. And the reason why that timing is so important is because the cycle is so dynamic. If you don't have really precise sample timing, you end up with a big kind of smush of data that you can't tease apart by having really precisely timed data points, you can separate these genes into clusters and know exactly kind of where you are in time. And that timing relates to the physiological injuries that we study. So we know what time points their hearts are protected because those physiological studies have been done. We've looked at those time points very specifically. So we have that biobank of samples that we in licensed as founding IP at Fauna CANI literally drove it across the country in a U-Haul because we didn't trust anybody to move it. So that's that's now in our freezers and Emeryville with a cadre of backup batteries to protect it.

Ashley Zehnder: So that's the founding data that we have. And that's been really crucial because I look at other companies trying to use data for drug discovery, particularly in the early stage. A lot of it is kind of publicly available data or cell lines or kind of shared data sources. And part of what is unique about font, as we literally have truly novel data sources that we're starting with that are wholly owned that we control and we know the quality of those. So that's really the Biobank that we have is and it's 22 different tissues. I mean, it's brain, it's kidney, it's lung, it's hard. It's liver or skeletal muscle. Right? Pretty much every kind of tissue you would want in that founding biobank. But then on top of that, I think what we've done with the other data is super important. Yeah. And so we layer on top of that all sorts of publicly available data and also data we've been able to source, such as human data from the UK Biobank. But I really want to hit on the point of, of why the model species hibernate or data is so different. All of the other data that most people work with is trying to compare animals that are healthy to animals that are diseased, or people that are healthy to people who are have disease. What's really unique about the model species that we're working with is we're trying to figure out why they have these superpowers in terms of disease, resistance and repair.

Ashley Zehnder: So it's kind of the other end of the spectrum that we're making this comparison between a normal, normal hibernate or during, say, the summer months and then a hibernate or that has gene expression patterns that mean that it's resistant to many diseases and it can repair tissues when it gets damaged. So it's actually quite different from the normal types of comparisons that others would make. But yes, and then we integrate publicly available data from sources like Open Targets Reactance. And one of the other data sets that we work with that's that's valuable is that we go back through literature that is relevant to the disease, indications that we're going after. And we have a team of curators that mines these papers that where the biology is relevant and we integrate those transcriptomic studies generally into our database. And that that really helps with our comparisons. And I can kind of give you an example of the way that we would do this type of cross-species analysis compared to what other what others in the industry might do if they were just looking at humans or say, just looking at mouse and rat is that, you know, if you're if you're just looking at at a human study and you're trying to say, look, for what genes do we think are involved in heart failure? You would look at, say, transcriptomic, differences between healthy human hearts and failing human hearts.

Ashley Zehnder: And then you would have some type of gene list where you'd see the genes that have differential regulation between those two groups. And it fa not we we look at that type of data and then we also look at hibernate or data and then we can compare that. And that's really where the magic happens because we can look at hibernate hours when their hearts are protected during the winter months. So we have an example of these are genes that are involved in protection and then compare that to the summer months where they're not protected. And then we can integrate both of those to analyses so we can say what's really different about a human heart when it is failing to a hibernating heart when it is protected. And we do very fancy types of network analyses and then we layer on all of these data from external sources and the really exciting moments where we see these networks light up with the exact regulation patterns we are expecting that is relevant to our biology. Those are really fun. And I would say the other data source, Linda, that would be good to touch on is the genomic data, right? I think the comparative genomics data. So maybe give a little context on that. I think that really broadens the the views point of what we work with.

Linda Goodman: Yeah, absolutely. So that's another data source that we work with. We have a collaboration with the Broad Institute that is one of the leaders of the Zoonomia Project that has in the neighborhood of 250 mammals in a in a big alignment. So we can do comparative genomics across all of these animals. And what we like to look for are comparing the genomes of animals that have a specific phenotype to others that don't. So for example, what is different in the genomes of hibernaters compared to the mammals that cannot hibernate? And we typically do this with how fast or slow evolving genes are, right? So if a gene doesn't accumulate very many mutations in hibernate hours, then it's probably pretty important for hibernation because there's a lot of purifying selection on that versus say, in other mammals that are not hibernaters, like like a human or a rat. It got a lot of mutations in it because it didn't matter as much for those animals. So that's another way of pinpointing the genes that are really important to hibernation. And we know, of course, that some of those might relate to the overall hibernation trait, but many of them are going to be disease relevant because they've had to evolve these genes in a way to protect their hearts and their other organs from these extreme environments they're in during hibernation.

Harry Glorikian: So that, if I'm not mistaken, so did the Zoonomia Consortium, there was a big white paper about comparative genomics published in Nature.

Ashley Zehnder: Nature last year? Yep. Two years ago. Yeah. A little bit.

Harry Glorikian: Yes. Time seems to blur under COVID.

Ashley Zehnder: Yeah.

Harry Glorikian: How long have I been in this room? Wait. No.

Harry Glorikian: But. Can you guys I mean, because doing comparative genomics is not, you know. It's not new necessarily, but can you guys summarize sort of the. Arguments or the principles of that paper, you know, quickly. And then, you know, my next question is going to be like, do you feel that Fauna Bio is part of a larger movement in science and drug discovery that sort of gaining momentum? So I'll, I'll I'll let you guys riff on that launch.

Ashley Zehnder: Linda, you're you're the best one to do a perspective on that paper for sure.

Linda Goodman: Sure. Yeah. You know, I think this is really born out of the concept that in order to identify the most important genes in the human genome, we need to be looking at other animals and more precisely, other mammals to see their pattern of evolution. Because if you see a gene that looks nearly identical across all other mammals, that means that it's really important. It means that it has been evolving for somewhere in the neighborhood of 100 million years, not accumulating mutations, which really translates to if you got damaging mutations in that gene, you were a dead mammal. Those have been selected out. And that's really how you can tell these are the key genes that are important to to your physiology, the difference between life and death. And you can't understand those things as well by just looking within humans and human populations. We're all too similar to each other. But it's really when you get to these long time scales that the statistics work out where you can see, okay, this has been this mutation has not happened in 100 million years. We don't see it in anybody's genome. So that is obviously very important. And that's just this other way of looking at our own human genome that helps highlight the genes that are going to be important to diseases. And I think, you know, another side to this paper related to conservation and the fact that a lot of these animals with really exciting genomes, the ones that are exciting to people like us, are those that have these really long branch lengths where they're they're kind of an ancient lineage. And that's really where the gold is, because that helps us even more understand how quickly or slowly some of these genes are evolving, and it related to trying to conserve some of these species as well.

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

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It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: I should say congratulations because you guys did raise a $9 million seed round last fall from a group of venture funds, some in life sciences, some more general. Right. What does that funding do? What is it? What does that unlock next?

Ashley Zehnder: You. I will answer that question. I do want to jump back to your other question that was kind of is this part of a larger movement and comparative genomics? Right. I think that's an important question. I think you sort of hit the nail on the head there. We were invited to a symposium in August of 2019 called Perspective and Comparative Genomics that was held at NHGRI in Bethesda. And I think there's a recognition and actually some of our grant funding is also through NHGRI. And I think there's a recognition from the folks who sequenced the human genome, that they don't have all those answers. And so it's an interesting time where we realize that there is this kind of other data out there that can help us really understand that better. And it does feel a little bit like a rising tide. And so that's that's something that I think is important to recognize. But in terms of the seed round, really, that was meant to expand the platform and the pipeline that we built with our initial funding back from Laura Deming and Age One and True Ventures, who led around for us in early 2019. It's really saying like that initial $3 million or so is really to say like, does this work or is this crazy, right? Can we it's just a crazy idea.

Ashley Zehnder: And that's what we really started to generate those first few animal studies that said, yes, actually we can find genes and compounds from this data that meaningfully affect not only human cells, but animal models of human disease. And now we're really expanding into new disease areas. We're looking at areas like fibrosis. We're looking at areas like pulmonary disease. We've got some really interesting data coming out of animal models of pulmonary hypertension with a compound that we found on our platform. We've got the collaboration with Novo Nordisk, which of the five genes that they tested in animals? We have one that has a significant obesity phenotype. So I mean, 20% hit rate on a novel target discovery in vivo is not bad, right? So we've gotten to the point now where repeatedly over multiple disease areas, we've seen that between 20 and 30% of our either compounds or genes are hits, which shows us that this is not only kind of a we got lucky in cardiac disease, but actually this is a process for enriching for important drug targets. And now it's a matter of really expanding the pipeline. We brought on a really experienced head of Therapeutics Discovery, Brian Burke, who spent 20 years at NIBR running very early discovery programs and then seeing programs go into the clinic.

Ashley Zehnder: He worked on drugs like Entresto and then worked on a couple of startups after that. So he's kind of gotten both big pharma and startup experience, and his job at Fauna is to really look at the menu of things that we're presenting him from an early research and discovery phase and picking the winners and really figuring out how to take them forward and also killing the programs that are less exciting to him for a number of technical or practical reasons. So that's been really, really helpful to have someone come in truly from the outside and take a look at the science at Fauna and say this is as good or better as anything that I've worked on before. I'm really excited to work on this, and that's been kind of a nice external perspective on on the science and the pipeline at Fauna. So that's really what the $9 million is for. It's really expanding a lot of the computational expertise and and progress and Linda can talk a little bit about that, but also just expanding into new disease areas as well.

Harry Glorikian: Understood. So, you know, on this show, like, I talk a lot about, you know, technology, data, and how it's all affecting health care, which this all fits into. But one of the things we talk about a lot is how crappy, terrible, I should use, you know, terrible, right, electronic health records are in the lack of interoperability between them. And Ashley, you actually wrote a paper.

Ashley Zehnder: I did, yeah, veterinary medical records are just as bad, actually, veterinary medical records are probably a little bit worse, if it's possible.

Harry Glorikian: And to be quite honest, I'm sorry, I just hadn't thought about Fifi or Rover and their...

Ashley Zehnder: Their medical records.

Harry Glorikian: EHR. Is like is the problem bigger, even, when it comes to functional genomics? I'm trying to think of like obtaining and storing and analyzing 'omics of different species. I mean, who's working on this? Is that part of the Zoonomia consortium? Right. I'm just trying to think it through, like, how do you get all this information and then look at it across all these different species. And at some point, you know, look looking at it against humans also.

Ashley Zehnder: Yeah. I'll let Linda talk about the genomics side. I'll comment on sort of some of the validation, some of the externally curated data that Linda talked about. I think this is actually becoming a really important data set. It was a little bit of a slow burn to figure out how to get it and to curate it. But there are a lot of studies now coming out and not just your traditional model organisms, but naked mole rats and long lived rock fishes and primate studies and bats and all kinds of people looking at genomics and RNA seek metabolomics and proteomics across these species that have interesting phenotypes. The problem is, every one of those researchers really heads down on their own species of interest, right? Nobody's saying, oh, well, actually, we're seeing the same genetic signature in these bats that we're seeing in the naked mole rats that we're seeing in some of these long lived fish. Right. But that data is not in a very friendly format. So we were like originally we were like, okay, we're going to write some scripts, we're going to try to pull some of the stuff out of supplemental tables. It's going to be awesome. No, no, no. We have very highly trained curators who work on this data and bring it in. And we have a very standard pipeline and a process and a way to normalize the data across different studies and standard ontologies and ways to clean up this data in a way that it can be integrated with the genomics coming out of the platform. And that is a tedious and painful and ongoing effort to bring in all this data.

Ashley Zehnder: Now, we have data from well over 330 individual studies, over 30 species. I think Linda, you told me it was like more than 800,000 gene entries at this point that's curated and that's kind of growing month over month. So now that's becoming part of our defensible moat, is that we've taken the last two or three years, again, slow burn, pulling all this data together in a way that it can be reused. And now we can turn a paper around and put it on a platform in a week or two. So we're kind of always scanning for these studies. But yeah, it's, it's, it's out there, but it's not always in a usable format without a lot of pain and effort. And so we've kind of put that pain and effort into getting that data in a place that we can use it. And then, of course, the comparative genomics is like a whole 'nother level of complexity.

Linda Goodman: Yeah, so I can talk a little bit about how we do that within the comparative genomics community and how we've done that for Zoonomia. Because I referenced before that we like to do these sorts of studies to examine the genomes of hibernate ers and non hibernate and figure out what's different. And you'd think it would be a trivial question who is a hybrid nature amongst mammals? But it's actually not. And so along with our collaborators Alison Hindle and Cornelia Santer, as part of the Genome Project, Fauna tried to go through and categorize every every genome that was in Zoonomia. So we're talking about around 250 mammals for is it a hibernater, or is it not? And you'd be surprised how often it was digging through literature from the 1970s and someone would say, this animal is not often seen during the winter. So we think it hibernates and it's not always the most satisfying. And so it is an extremely tedious effort, but well worthwhile to go through and say this animal, I'm very sure, hibernates. This one, I'm very sure does not. And then there's this third category of animals that were unsure about we're going to remove those. And it's tedious, but you have to do that part, right? Because if you do the analysis with bad data, you're never going to find the genes that you want. And Linda, I remember you telling me when you were going through this very painful process, I think your threshold for being a perpetrator, quote unquote, was that you could drop your metabolism like 50%. Correct me if I'm wrong, and humans could go down to like 40 like in certain instances, like humans are almost there. You know, it's it's hard to know when there is only one paper about it, but certainly there are some really deep meditative states and humans and low oxygen environments where, you know, we're getting kind of close to the area where we might say that that's a hibernated, but certainly not the duration that you get out of hibernation. But it's it's it surprised me to see how close how much how much metabolic flexibility there really is when you start to look at it. Yeah.

Harry Glorikian: Yeah. We've got to go talk to the monks.

Linda Goodman: Absolutely. Absolutely. You know, we have that in mind. It sounds like an interesting travel experience. Yeah.

Harry Glorikian: So I want to jump back for a second because. You guys don't necessarily have from what I have pieced together, the normal sort of like startup story. Right. First of all, you're an all female founding team, right? Highly unusual, right? Not something I see every day. You guys started at an accelerator program in San Francisco called Age One.

Ashley Zehnder: Age One.

Harry Glorikian: And then you moved to QB3 and the East Bay Innovation Center.

Ashley Zehnder: Yep.

Harry Glorikian: And then I think they helped you with some paid interns.

Ashley Zehnder: Well, we got some from Berkeley. Yep, we did.

Harry Glorikian: Yeah. And then you went through a SBIR grant.

Ashley Zehnder: A couple of them.

Harry Glorikian: From the Small Business Administration. And then a small business technology transfer grant from the Human Genome Research Initiative at NIH. Right.

Ashley Zehnder: Yep.

Harry Glorikian: I'm hopeful, hopefully my notes are all correct. Talk a little bit about the on ramp or infrastructure today for sort of seed stage startups like you. I mean, what were the most important resources?

Ashley Zehnder: This is such an important conversation. I'm really glad you're asking this question. We had a call with a reporter from Business Insider yesterday who was talking to all three of us about this early founder ecosystems in biotech and sort of East Coast versus West Coast ways of starting biotechnology companies. Right. And that is a whole do a whole podcast on that, let me tell you. But I will say that there are a lot of resources for, let's call them founder led bio. Right. In the West Coast, which is kind of the buzzword these days, but people really supporting the scientists who originate the concepts and training them to be founders as opposed to assuming that you need to bring in an experienced CEO to run a company at this stage. Right. So I think we were very fortunate to meet Laura Deming at Stanford, who is one of the founding VCs. And longevity before that was a buzz word, right? She was one of the first longevity funds, literally Longevity Fund, and is really been a champion of founders, starting companies and really training founders to start companies who are deep science founders. So we started in age one. It was the first batch of age one. We're still very close to that cohort of companies doing interesting things from machine learning and image analysis through pure therapeutics development. And then Laura really helped us, her, her. We asked her later, like, why did you end up investing in us? She said, Well, the science was amazing.

Ashley Zehnder: This is totally a field with so much promise. I just needed to teach you guys how to pitch. The science was there, right? So she helped me just how to pitch and how to use less science words in our pitches, which we're still working on to some extent. But then it was this balanced approach of taking in some venture money to really support the growth of the company, but balance with some of this non-dilutive funding for specific projects where it made sense and some of that was some of that in the early stage is validation, right? Having having funding through groups like NHGRI, having an early partnership with a company like Novo Nordisk, which provided also some non-dilutive funding for the company, really validated all of the science that we were doing because we were first time founders, because we're a little bit outside of the normal profile. For me, I don't feel weird being a female founder only because 80% of veterinarians are female. Like, I'm used to being in a room with all women. You go to a bio conference, it's not the same thing, right? So for us, we're just we are who we are. Right. But it's helpful, I think, to get some of that external validation and then really be able to use that to to start to build on programs and show progress.

Ashley Zehnder: And then it becomes more about the data and the progress and what you can do with it. So that's a lot of how we started the company. There's I said there's a lot of support in the West Coast for this kind of thing. There's great programs like Berkeley Foreman Fund Talks, which I worked, which I was in as well, just about logistics around starting companies. There's a lot of good startup accelerators. I've got a really amazing all of us, how amazing a network of founders who we can reach out to on different. I got four or five different Slack channels of founders that I could reach out to for all kinds of advice. And usually it's always good to have a company that's one or two stages ahead of you, like talking to folks who IPO'd or something last year is is not as helpful as folks who recently raised a series B, right. And figuring out what those milestones look like and then particularly those that have taken mostly money from tech investors like we have all the lifeforce capital who led our last round is also has funded some very good therapeutics companies, Sonoma Therapeutics and Second Genome and other therapeutics companies as well. So I think it's it's helpful to see how people balance the needs of the companies at different stages in what you need.

Harry Glorikian: But so do you guys think that you could have started Fauna ten years ago? I mean, did the support systems exist for starting a company like this?

Ashley Zehnder: Well, no, for two reasons. We couldn't have started Fauna ten years ago. One is the data just simply wasn't in a place that the company was a tractable strategy. Everything was still too expensive and we had really shitty genomes for a few species at that point. And B, I think there really wasn't the kind of groundswell of support for deeply scientific technical founders to start their own companies and train them to be the kind of leaders they need to be to run those companies for a longer term. So I think it's a confluence of those things and being in an environment like Stanford that really encourages people to to try startups, it's not a crazy idea. Like people don't look at you like you're your heads backwards. If you start to start a company at Stanford, it's like, okay, cool. Like, when are you launching? You know.

Harry Glorikian: I think it's the opposite.

Ashley Zehnder: Yeah, exactly. Exactly. Like, why aren't you have a company yet? Whereas you know, a lot, many, many, many, many other places like that is seen as a very strange thing to do. So I think the environment plays a huge role. Yeah, for sure.

Harry Glorikian: Yeah. I think between East Coast and West Coast too, there's.

Ashley Zehnder: That's a whole, we should have a whole 'nother podcast on that.

Harry Glorikian: Yeah. Yeah, exactly. Well, I live here and I was I was born and raised on the West and I remember there and I came here and I was like, Oh, this is where you are not in Kansas anymore. Like, this place is different. So, I mean, I'm hoping that the East Coast is actually embracing risk a little bit more and sort of stepping out on the edge. But it's really slow. They don't call it New England for nothing. So. But, you know, it was great having you both on the show. I this was great. I we covered a lot of ground. I'm sure people's heads are spinning, thinking about, you know, you know, different animal species and how that's going to play into this. And I mean. It really does sound like I know we have to do the hard work, but there's a lot of computational effort that has to go on here to sort of. Make sense of this and bring it all together and align it so that you can be looking at it properly and make the right decisions going forward.

Ashley Zehnder: Yep. Millions of data points coming together to find drug targets for sure.

Harry Glorikian: So thanks for being on the show. And you know, I wish you guys incredible luck.

Ashley Zehnder: Thanks, Harry, so much. This was fun.

Linda Goodman: Thanks for having us.

Harry Glorikian: Thanks.

Harry Glorikian: That’s it for this week’s episode. 

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

These days, it feels like there’s a medicine for almost everything.

There are drugs to calm your restless legs. There are drugs to treat fungal infections under your toenails or fingernails. There are even drugs to calm down performers who suffer from stage fright.

So it feels odd that the drug industry has almost completely ignored one of our most important organs: our ears.

15 percent of people in the U.S. report at least some level of hearing loss, so you’d think drug makers would be doing more to figure out how they can help.

Well, now there’s at least one company that is. 

It’s a six-year-old company based in Cambridge, Massachusetts called Decibel Therapeutics.

Decibel went public in 2021 to help raise money to fund its research on ways to treat a specific form of deafness caused by a rare genetic mutation. 

It turns out that in about 10 percent of children who are born with auditory neuropathy, the problem is a mutation in the gene for a protein called otoferlin.

It’s involved in the formation of tiny bubbles or vesicles that carry neurotransmitters across the synapses between the inner hair cells that pick up sound and auditory neurons in the brain.

Decibel is testing a gene therapy that would be administered only to cells in the inner ear and would provide patients with a correct, working copy of the otoferlin gene.

Otoferlin wasn’t even discovered until 1999. So the fact that there’s a drug company working to correct mutations in the gene for the protein is a great example of how genomics is enabling big advances in medicine.

My guest today is Decibel’s CEO Laurence Reid.

And in our conversation he explained how the company’s work is coming along, and how Decibel hopes to make up for all those decades when the pharmaceutical business had basically zero help to offer for people with hearing loss.

Harry Glorikian: Laurence, welcome to the show. It's great to have you here.

Laurence Reid: Yeah. Hey, good morning, Harry. Great to see you again. Thank you. Thanks very much for the opportunity to join you. I'm looking forward to it.

Harry Glorikian: Yeah, I mean, we've known each other for, my God. I remember. Like, I want to go back in time to Warp or one of those companies way back when you were there.

Laurence Reid: Like ten or 15 years ago, I think I think we're both compressing our compressing our memories. I think it was a while before that. But, you know, you've been a student of personalized medicine, of course, a leader. Those ideas and I know a lot of those ideas for me started at least personally when I was at Millennium. And I think we were pretty you know, there was a lot of fantastic thinking that some of what was ahead of where we really were technologically. But I think that's when you and I first met. So, no, it's great to reconnect.

Harry Glorikian: Yeah. And now you're CEO of a company called Decibel, which is ironic because I remember when the company literally was coming out, they called me to help them think through diagnostics.

Laurence Reid: Oh, interesting. I wasn't aware of that. Yeah, the company got incubated at Third Rock and got launched in 2016. So we're about six years old now. And, you know, we believe that the time is is now for sort of molecular innovation coming to hearing loss. And I'd love to talk more about that. But the diagnosis remains, there's an interesting, there's almost a dichotomy because at least in the in the Western world, we put our babies religiously through a hearing test within 24, 48, 96 hours of being born. And then and then beyond that, like we sort of like almost, we don't quite ignore it, that would be unfair, but the caliber of follow up care, never mind when you're our kind of ages, is really poor. So we're like we're really good out of the gate. And then after that and part of that is diagnosis. I mean, we think a lot about it, which, you know, you would love, is trying to think about improved molecular diagnostics, particularly with respect to the genetic components of hearing loss. So love to talk more about that.

Harry Glorikian: Yeah. I mean, you know, you were talking to Kevin Davies on on another show. I mean, I think you mentioned you said something like "Hearing is a backwater of the pharmaceutical industry." And most of the focus is is what I would call a device, not necessarily a drug. So, you know, if we let's I mean, starting there, where do you see or how do you see that changing? And, you know, how have genomic tools and and these things made a difference in the direction that we're going. And I think that's what Decibel was sort of formed around, if I remember correctly.

Laurence Reid: Yeah, no, you're exactly right. But those are, those are the central questions. So where we are today is there are, so, so, both. And we think about both hearing loss and balance disorders, because they're both mediated by evolutionarily related organs that sit inside inside the inner ear. And, you know, the hearing loss afflicts literally hundreds of millions of people around the globe at all ages. It can come on, you can whether it's congenital or it's sort of later in life or noise induced. So it's a massive unmet need. And, you know, and there are no approved therapies. So it's it's a field of medicine today that is that is completely served, to the degree it is served, by assistive devices, namely hearing aids and then cochlear implants. And there are no approved therapies. And I think the pharmaceutical industry has been really, is just not invested in the field at all. Astellas works with our friends at Frequency and has been committed and a couple of other big companies have sort of dabbled and then and then exited. Translation has been has been a challenge. We should talk about that preclinical work not really replicating once you get to you know, human beings. And so it's been a quite a difficult field for for many years. And and so the pharmaceutical industry has really not dived in and, you know, in Third Rock was really incubating decibel which is how they how they start companies. It was one of their ones that was was a slow burn.

Laurence Reid: And they had they looked at assets out of one or two pharmaceutical companies and were really trying to get their heads around, is the time really now. And they they pulled the trigger in 2016 and went into it with a belief that that molecular innovation was coming and is coming and that that would that would give rise to therapies. So here we are six years later. And the playing field, as I like to say, is really, you know, dominated by small companies. We like to think about Decibel as a leader there, but there are other companies doing fine science, but they're small companies. And but that's going to change. It has to change. And it's going to be exciting from many aspects. When it changes, it affects how you build a company, when pharmaceutical companies are sort of watching, but they're not committed and they're not they're certainly not investing yet. But I think that's going to change. And I think we're going to see it change, I don't know, in the next couple of years. And I think 5 to 10 years from now, all the major pharmaceutical companies would have to be playing in this because, you know, there's the aging component, there's the cognitive health later in life. You could talk more about the specifics of why hearing is so important to our existence as human beings. And that's really not just a quality of life issue. And that's going to change. To have that happen.

Harry Glorikian: That's why I was going to I was going to say I mean, I think if I remember correctly and it was fascinating to me when I went into Decibel, like, you know, when it was first getting started and how it was having conversations, it was like the number of people that are losing, you know, certain parts of their hearing earlier in life because of all the headphones and how loud they listen to things and so forth, was staggering. And then the economic impact of that was even more staggering. And so you would think that it's not just the pharma industry that would be interested, but anybody that—-like I've got my AirPods in now. So I mean, Apple should be interested.

Laurence Reid: Those guys, those guys are working around the field. Bose, of course, a fine Massachusetts company with some of the best sound equipment. They've been investing in the hearing aid technology field for in recent years and have just launched a new generation of technology under that umbrella and come out with some pretty sophisticated marketing, trying to really get people to think about the quality of their hearing and why it's important. And so, as you say, so new people coming at it despite perhaps their contributions to it. And so, you know, so I think I think that's really very, very interesting. And but it is now devices, as you say. It's devices. So today, you know, a lot of it is treated nominally with hearing aids and then for very severe forms, particularly in in in young kids, but in adults as well. There's a technology which has been around for about 20 years now, known as a cochlear implant, where you have a surgical implantation of a very sophisticated device into your cochlea. And essentially it essentially hard wires, really a microphone directly to the onto the auditory nerve.

Laurence Reid: And so there's a device inside your head and then there's a detection device that is visible outside. But both of these we view as assistive devices. And I mean, with some of the things that we're thinking about for molecular therapies, you know, we really think we can be disease modifying. And the devices are, they're an attempt to sort of palliate, effectively, the manifestations of hearing loss. They don't work 24/7 because they can't and kids in particular hate wearing them. But, you know, our parents hate wearing them as well, particularly the hearing aids. And so the compliance is very poor. But I think more importantly, they can only be so effective, and particularly if you're very severely deaf, the difference between that status and, you know, what the kid next to you in the classroom is hearing and picking up and how that's affecting their development is really massive and to me is one of the big drivers certainly why I got excited about the field personally.

Harry Glorikian: Oh yeah. I mean, you know, if you're in a crowded restaurant and you can't hear the person across from you, there's all of a sudden it changes the entire dynamics of what's going on. I mean, that you know, that said, I think if my wife could implant a microphone that was directly wired into my brain, she would probably take advantage of that to make sure I hear everything.

Laurence Reid: And hard wired up straight into her larynx. And then then everything would be would be beautifully aligned. Yeah, I know. It's really interesting. So my beloved mother is 84 and you have a one on one conversation with her and it's fine. You know, it's absolutely it's completely normal, like you and I chatting or talking to a 20 year old. But you put her in a crowded restaurant and it's very hard for her to participate at all. And so it's a really interesting. So on one level that's trivial, right? It's a night out in a restaurant. But it's indicative of the challenge. So I always think most easily comes to me with thinking about congenital deafness and then deafness or loss of hearing in in older people. But that restaurant is sort of an analog for in the case of the older people losing, you know, we talk a lot about connection, losing connection with their loved ones or their coworkers or their family. And, you know, hearing loss is the number one risk factor in cognitive decline later in life. And nobody is suggesting it's necessarily causative. But that loss of connectivity clearly in some way is contributing to, you know, to a cognitive decline. And I think that's really the way to think about it. For me, I think about hearing loss as why, why does it matter? And it's not because I think it's, if you haven't dealt with it, you probably think about it in terms of a social discourse. But actually why it really matters is the impact on, I use the phrase cognitive health, which is probably not a phrase of professional would use. It's really how is your overall ability to interact with people, to process information and and to share it? And if you're disconnected, it's clearly contributing to that lack of of of interaction and ability to, you know, to have discourse with our with our with our families. And so you see that. Pivoting to loss of interactions later in life. And then for a kid.

Harry Glorikian: And how it affects the economy. I mean, if you're not going out to dinner or you're not or you don't hear everything at work or things like that, I think the impact is is dramatic. But you know how many I know you guys are working on different therapeutic approaches to solve this problem. So, you know. How many different forms of deafness right now, or maybe balance disorders, are monogenic or or caused by mutations of a single gene that, say, we can get in there and do something about it, because I think that's where you guys are starting.

Laurence Reid: That's where we're starting. And that's exactly the right way to think about it. So let me let me step back and then I'll answer your specific question. So the strategy that we've taken and other people have different views of this is really that the most robust understanding in 2022 of the molecular etiology of any form of hearing loss is, is that it's driven by overtly by monogenic conditions. So two mutated genes inherited from mom and dad that good old recessive genetics and that therefore we're able to understand precisely what's causing it and we're able to understand the impact of that of a child born with bi-allelic mutations in the otoferlin gene for example. And and the promise of gene therapy is the ultimate to put back a a functioning copy of the gene very early in life and put a child back to a physiological state of of hearing that mimics the kid down the street. And that's and that's the ambition. And what we think will that will enable is both these modifying treatments, maybe even cures for for those sections of the population. But it'll teach us about how to do gene therapy safely in the ear. We think the ear is a wonderful organ in which to do gene therapy. We should probably talk about that in a moment.

Harry Glorikian: Yeah, absolutely.

Laurence Reid: But that over time, the Holy Grail. So as you get into the bigger populations, it's a classic, you know, genetics and environment, viruses, noise, lots of chemicals or lots of things  that damage areas over the course of life. And we just naturally lose the sensory hair cells in areas over the course of life. Everybody approximately linearly is losing that, that sensitive and that sensitivity. So eventually you hit a threshold and we all suffer from some form of hearing loss or balance, you know, lack of equilibrium as we get to be a little bit older and. For for many different causes. So the Holy Grail is can we really have regenerative medicines that regenerate the sensory the sensory hair cells, as they're called, in the inner ear, potentially as a treatment for hearing loss or balance disorders. And so the way we think about this is our strategy is really to to start with the monogenic forms of hearing loss have a chance for very clear diagnosis, driving, very precise clinical trials, driving potentially therapies that are directly addressing mechanism and with very high potential molecular upside. And to build from there into a pipeline of gene therapies that will start to go into broader populations, populations of much older people, and that will be gene therapies that are regenerative medicine. So that's our sort of long term vision of how this will how this will evolve. But it's starting with the monogenic conditions which which are which are rare diseases, orphan diseases by all definitions. And I think for the reasons that rare diseases have been such an intellectual driver of our industry in the past 20 to 30 years, is because you can link mechanism and etiology and a potential molecular cure in a very linear fashion. But it teaches you so much about how to manipulate an organ and how to develop therapies that eventually will treat broader populations.

Harry Glorikian: Yeah. Laurence, you need to move faster, because I think I went to one too many rock concerts when I was younger. And, you know, I could tell you that.

Laurence Reid: I had friends, when I was in high school who were who were into certain, you know, I hated heavy metal when I was a kid, but I had friends and they would come back and they'd been to a concert and they'd they'd stuck their head inside the speaker and they they couldn't hear for like a day or two. And I, I think back to those I worry about where those guys are now because they're hearing I'm sure they're otherwise.

Harry Glorikian: Yeah. I mean, when you're when you're when your ear is ringing like a day afterwards, you probably recognized that was probably, it was a lot of fun at the time. But you pay for it later. But but stepping back, though, even if we were able to match every form of deafness to a specific genetic cause. Right. Very few infants or children get the kind of tests that would be needed. Like how widely available are these genetic tests for the hearing neuropathies today or.

Laurence Reid: Oh, it's. I'm sorry. Go ahead.

Harry Glorikian: No, no, no, go ahead. Because that would be my first question.

Laurence Reid: It's the minority. And so by definition and I appreciate you've worked and thought a lot about this over the last years. You know, good diagnosis is is gating to everything that can follow. And so part of our broader I mean, at some level actually even step back from molecular diagnosis, which I know is where you'd want to go, that just overall how we manage hearing how is is almost rudimentary compared to how we think about about our eyes for example. And just I had my annual physical a couple of days ago and and a new physician and and the doctor was like, oh, you know, you go and get your eyes tested on, on an annual basis and which I do. And we talked about all the the good things that are cutting edge, you know, ophthalmologists does these days to look at your optic health. And then I was like, you know, the real question you should be asking me is, when did I get my hearing tested? And but when did you last get. We just we just it just doesn't it's just not part of adult health care in a routine way unless you get really I mean, my wife and I joke about it occasionally. I'm like, oh, well, let's go together and get our hearing tested.

Laurence Reid: Not that, not that it's at all funny, it's not. It's a serious issue, but it's just not part of routine health care for helping adults think about about how how they manage their health. So. So we sort of we start with a, a broader set of educational issues. And then and then we dive down pretty quickly into how do we educate people about about the need and potential power of molecular diagnostics for children who, when we begin to figure out that they're hearing is developing, you know, in the early either days or early years of their life and as as in in the developed world, most children have a basic hearing test, you know, within hours of being born, literally, often while they're still in the hospital. And it's like, you know, in many, many places they catch them while mom is still, you know, literally in the hospital and and they do a basic hearing test so we can catch a lot of it like that. If it if you start if the hearing degenerates after that, it is still very challenging for that to get properly understood and picked up and diagnosed and managed even in, you know, developed cities and, you know, in the United States.

Laurence Reid: And the the ability to to reflex to molecular testing is is very variable. If you talk to our our audiology team, it starts to be very dependent on which city do you live in and what's the ability? I mean, we're sort of privileged in Boston, Mass Eye and Ear is obviously one of the world's leading hospitals. But but how do you get from a an early "Yeah there's an issue here" to any form of molecular. What that path looks like of your pediatrician driving you to real audiological analysis, driving you to a molecular diagnosis. It's a pretty fraught path. You think about it in in in cities like Boston. Fair enough. And aren't we privileged to live here? We're lucky to live here from that perspective, but it's very heterogeneous. And so part of our work is really we have a collaboration with our friends at Invitae, part of which is trying to just it's almost educational. It's offering a free genetic testing service for important genes related to your hearing health. But part of the purpose is, is educational, really.

Harry Glorikian: Yeah. Yeah, I was going to I was going to ask about that. I mean, in making it available, I mean, this is somewhat of a crusade, right, to educate people and get them on board, right. Because if you just don't know what's available, you may not think about it for your child. And if a parent knows they can help their child, I think most parents would go out of their way to do something positive. But just for everybody who's on the phone, you know, can you walk us through an example of, let's say, a single gene mutation can cause deafness? I mean, maybe you can concentrate on the example of, I think it's otoferlin, if I'm saying correctly, which you know, basically, if I've understood it correctly, it's the formation of the synaptic vesicles that carry neurotransmitters across the synapse, which is very, very tiny. And if the hair pulls away just enough, you start losing that ability to hear at that level because the chemical can't jump across to make that connection, which is, I think what's happening to me as I get older.

Laurence Reid: Yeah. Very good. Yeah. And I'd love to talk about otoferlin. So otoferlin is our first program where we and other people are thinking about this as well. Our friends are also are working hard on this problem as well. But it's the vanguard program for Decibel and the field in terms of gene therapy for modern forms of hearing loss. And so obviously, we we know the gene that causes this particular subset of severe hearing loss. The children are born profoundly deaf. They really have almost no no signaling capability whatsoever. Despite that, when you study their ears and when you look at animal animal genetic models of the condition, the ear, functionally, structurally appears to be normally constituted. So what you see start with a belief that we may be able to instate normal hearing in these people by in these children, by, by by providing a a wild type, a normal copy of the gene. And there are other forms of of genetic hearing loss where by the time the kids are born, the children are born, their ear has not developed properly, structurally and functionally. And I think that's a much harder problem and may be impossible to to solve postnatally. So so as we think about areas where we think we can have an impact with the first generations, we're looking for clear genetics. We're looking for an ear that appears to develop normally and in which we therefore have the chance to instate normal hearing. Otoferlin is a calcium sensor and it functions at the interface between the hair cells in the cochlea, the inner hair cells, as they're called, which are the cells that transduce... Sound is effectively a mechanical signal. It comes to us as a sound wave, and it disturbs structures and eventually molecular structures inside your inner ear and creates a molecular signal that is transmitted by the hair cell through the synapse. As you say, to the auditory nodes, there's a direct interface between these cells that are that are detecting the sound wave into the into the auditory nerve. And if you lack otoferlin your calcium sensing functionality and that synapse is not present and and there's essentially no signal. So we measure this with something called an auditory brainstem response, which is a test you could run in a human or an animal. And there essentially it's a flat line, which from a from a restoration of a normal signal, it's a really excellent clinical endpoint because we're going to, we hope, instate, a signal, a quantitative signal with quantitative richness as well, that we're going to be able to measure relatively early after we administer our therapy. But the children have this is what we call an auditory neuropathy. They have no ability to signal from the cell into the brain. And as I say, the structures appeared to be intact. And what we know is that in an animal, if you create an animal model of this genetic animal model, that we can go into that now with DB-OTO, as we call it, which is which is a adeno associated virus vector to to basically deliver a normal form of the gene. And we can do that within weeks of this mouse being born. But interestingly, we could also go to those animals as long as a year after they're born, which which for a small furry animal is is about half of their life.

Laurence Reid: So it's a big piece of their life. And and we can go in we can intervene at that at that one year point and still rescue the phenotype. So the is structurally intact. And when we provide the signaling molecule, we fairly quickly instate a normal signal. So that's that's exciting. Right. And A), it's a fantastic signal to measure in an animal. B) it gives us a lot of optimism that if we can get the gene to the right cells and get it turned on, then decent chance to to to solve to solve the signalling problem. So that's sort of our reason to believe. And actually maybe the last component, and then I'll breathe, is we think the ear is, is a fantastic place for gene therapy broadly because your inner ear is this tiny enclosed compartment. So we need a surgical route to get there, but we can then go directly to the site where one is trying to elicit a molecular effect and deposit a tiny amount of drug compared to what's required -- three or four orders of magnitude less drug than is required for systemic gene therapy -- directly at the site where we're looking to elicit the biological effect. And then almost none of it leaks out into the into the systemic circulation. So the ear, we think, is a fantastic order or organ for gene therapy, and we think we know some great genes to go after us, our first generation.

Harry Glorikian: Yeah. I mean, you know, whenever if if people have followed any type of gene therapy, like the eye has been in optimal place to sort of start with. And so, you know, I think you guys are learning from what has been done in ophthalmology to sort of transition this to the ear, which, you know, I always say to people like we always start on the outside because it's a lot easier and then we then we figure out how to go deeper in because it's a lot harder. But, you know, what kind of results are you seeing so far when you transfer genes into, maybe nonhuman primates.

Laurence Reid: Yeah. Yeah. No. So we've just in the last year or two, transitioned from rodent studies to non-human primates. You are correct that the characteristics of the ear that make us so excited about the possibility here, a lot of them are very much learning from why the eye has been really such a primary site of our efforts in gene therapy in the last ten years or so. And so as we move from small animals to larger animals to human beings, we start with, as I mentioned, genetic rodent models that we can knock genes out in the mouse that replicate the human genetics. The ear, it turns out, is it is evolutionarily highly conserved. So the the ear of a rodent is a lot smaller than than your ear in my ear. But structurally and molecularly and cellularly it's very analogous. And we can come back to your point about genomics and how it's opened up our understanding of these cells. But nonetheless, the basic structure and physiology is highly conserved from from lower mammals to to higher mammals. So so we start with genetic models that we can manipulate the genome and create what we believe is a pretty interesting analog rodent analog of the human condition. We don't have genetic models in non-human primates, so we end up doing studies in non-human primates where we we we mimic exactly the surgical procedure by which we will access the inner ear, and then we end up either using a surrogate marker, GFP, or we end up detecting the human otoferln, in the non-human primate, which is quite hard.

Laurence Reid: But we've sort of figured out how to do that now. And really what you're looking at is, is, is really is efficiency of the delivery and expression process. And then when you can't measure a fixing of the genetic burden and so at Decibel, we spend a lot of time using our genomics platform to really be able to define molecular control of our gene therapy. So we're really trying to express the transgene selectively in the cell types where nature intended it to function. So, you know, calcium sensor in the wrong in the wrong cell type one might fear, and we have data that suggests, that that may be a problem. So Decibel is really invested very significantly in sophisticated molecular control of our gene therapies. And so when we do the experimentation in the non-human primate we're looking at, are we getting good delivery throughout the cochlea? Are we getting good infectivity throughout the cochlea and then expression of basically a surrogate marker? Because we we can't change the physiology of a of a normal non-human primate. So it's really all about about surgery, delivery expression. And then obviously you then got a stable transgene expression, it turns out, rises over the over the weeks and months after after after the transduction. And so we're measuring that. And that's going to play ultimately into clinical trial design, both in terms of safety and an end points that will measure in human being. 

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. 

It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: I would assume that some level of spatial genomics, the new technologies that are out there, must be hugely helpful to see the different cell types, where they are and what type they are. And you know is actually lighting up and changing versus what you don't want to light up and change. So yeah. So I had a great interview with Resolve on their system, which I think is going to be the next frontier, because what you're saying is, what cell type, where it is, and did I make the change in the exact one that I wanted?

Laurence Reid: That's exactly right. So my my colleagues, long before I was here, invested in building a platform that we think is still, we have a database of over 3 million molecular profiles of the cells of the inner ear, which we think is a unique asset. And basically applying the tools of single cell genomics, which is the ability at the level of individual cells in the organ of an individual animal to analyze comprehensive gene expression. And so what we've been able to do, and I think this is part of just changing our attitude to how do we understand the cells of the inner ear and therefore how can we think about manipulating them pharmacologically to open up the field? And so we have a complete molecular characterization of, there are about 30 or so important cells in the inner ear and there's two or three subsets of those cells, starting with the cells that I talked about that are probably the critical therapeutic targets. And so we have a detailed molecular understanding of the composition of the level of gene expression of each of these different cell types. And we look at them a lot as they as they as they differentiate and form in a natural process, because we think that holds the answer ultimately to regenerating them as part of this next part of our strategy. But it's also taught us about how individual cells control gene expression. And I mean, otoferlin is expressed essentially in an adult animal only in the so-called inner hair cells. And that's what we then aim to replicate with our gene therapy. And so we've been able to take our genomics platform to define genetic regulatory elements that drive our trans genes in our gene therapies to express selectively in the most important cell type where you need it and not elsewhere. We know from our animal studies that that has a beneficial impact on on on the therapy and that the durability of the therapy. So that's our overall molecular goal, but it leverages this platform of single cell genomics.

Harry Glorikian: So I've seen company presentations. Like you guys are, you know, you intend to initiate a phase one, clinical trial of of DB-OTO. I mean, how is that going? I mean, what are the big technical or medical barriers, where you're thinking about testing gene therapy? Like, I mean, you know, where are you guys in all that?

Laurence Reid: Yeah. So so we what we've and I'm going to be precise as a public company, I need to be careful with my disclosures. So apologies in advance. But what we said is that we'll initiate will file an IND or a CTA in Europe this year and and move into our first in human study this year. And so we're in the you know, we're deep in all the almost classical, you know, pre-IND work of making material and, you know, and testing it in, in the final, you know, GMP tox studies and making material of a caliber that'll that'll go into human beings, which is very exciting. And that's, you know, that's that's what we're working on. Those are the two sort of basic barriers. I mean, we have published and talk publicly about a lot of our animal data, what I sort of recited a few minutes ago, small animals to large animals. I think we understand the basic pharmacology and now it's okay, scale up, make the material for human being, you know, GMP material for human beings, test the material, you know, in more prolonged formal toxicology studies, you know, and move it into human beings so that that work is ongoing. The other part that's really fascinating that you would appreciate is, you know, in a rare disease like this, a lot of very interesting discussions about about what's the exact patient cadre in which one starts a clinical trial.

Laurence Reid: And we spend a lot of time building relationships with with clinicians, particularly in Europe, but also in the US, who really invested in understanding the genetic basis of of children in their region with genetic forms of auditory neuropathy. And we have a fantastic collaboration with our colleagues in Madrid at the Roman y Cajal, who have a database that is essentially all of the all of the known diagnoses of otoferlin deficiency in Spain. And so they've done so we have been able to help them do a lot of natural history work. What is what is the progression of the condition and how do we find these kids? And so we ultimately not necessarily immediately, but the ultimate goal is to treat children very early in life. These kids are now once they're diagnosed, they would get a cochlear implant really probably around the end of their first year of life. It used to be more like two, but that age has come down from a medical perspective. Being born profoundly deaf is the phrase is is a neurodevelopmental emergency. And I talked a lot about about old people. But for a kid, the the or a baby, the issue is that hearing lack of hearing impacts that their initial social interactions that their generation of language skills and their ability there and that and that feeds into their cognitive development.

Laurence Reid: So there's a there's a whole set of emotional interactions that are happening very early in life. And of course, with so much cognitive development going on and the hearing is, is the absolute gate to a lot of that happening. And so it's widely, widely agreed that this phrase, a neurodevelopmental emergency, is what physicians use. So so ultimately, we need to be treating these kids in the first year or two of their life. And you know, how soon we'll get there remains to be seen. And it is an ongoing discussion. But that's that's where that's where ideally we would end up. While at the same time, as I said, we know we can intervene in animals later in their lives. So we're optimistic that we're going to be able to take adolescents and and children beyond the first year or two of life and still be able to have a positive impact on them. Well, that's the vision for sort of the broader applicability, not just in a newborn baby.

Harry Glorikian: Yeah. I mean, you know, I mean, a child's, you know, the neuroplasticity or how easily that their brain or their system adapts and changes. I could see, you know, the drug having a much more profound effect in that population. I mean, in older people, I like to believe that we still have neuroplasticity, because I'm constantly evolving and changing. But, you know, I also sometimes think we're sort of stuck and maybe maybe don't have. But, you know, the human body is an amazing machine. But, you know, it brings me like one of the biggest themes on this show is like data, data, data and how that intersects biology. And, you know, what you're talking about is identifying the right sets of data, the right patients to have this work done on so that you can achieve a level of success. We all know that if you pick the wrong patients. Like you're utterly almost doomed for failure, or you're going to have an effect that you really didn't want to have. So how much of of Decibel's work or approach is is rooted in "Here's the data, here's the patient." How much are you guys using that to drive every decision that you're making?

Laurence Reid: It's a it's a really great question, actually. And the answer is a lot. In fact, as I think about Decibel and where I think the team that my predecessor built, Steve Holtzman, who of course, you know, is really, really exceptional, is is effectively translation in its broadest sense. Right. I think what differentiates Decibel is an outstanding understanding of the biology of the inner ear and that we've invested in in turning that into a genomic molecular understanding of every cell type. But it's then, okay, who's my patient? What, their molecular profile. And how do I link that back, feed that back into my discovery process? What are my animal models look like and how am I looking forward, you know, into ultimately into a clinical trial? And with people suffering from from congenital hearing loss age, which we try and intervene, becomes a big variable, as you're suggesting. And so, you know, if you're in the pharmaceutical R&D, it's like, okay, that's translational medicine he's talking about. And and it is I just think we do it really well. And it's really the essence of the scientific core of Decibel is linking our molecular work in the cells of the inner ear to a fantastic understanding of the patients, their individual phenotypes and how we look to bridge that gap between preclinical research and the clinic. And the the the truth is, I mean, there are no approved therapies and there hasn't been a lot of work, as I said, up front.

Laurence Reid: But but it's not like we're we're complete, we're not we're not going to be the first people either to do a gene therapy in the ear, nor to try and develop a therapy. But the translation has been really poor. And I think that our ability to understand the mechanistic pharmacology, preclinical and clinically and then be confident that that was going to work in a human being has been really poor. And obviously genetics from a simplistic perspective is a fantastic way to bridge that gap. Right. We know which gene we're trying to fix. And therefore, is the ear able to be fixed in a child of one two years old? And can we get the gene there safely and effectively and turn it on in the right place? Right. But those are problems that you can break down and solve and you can analyze them in smaller animals and larger animals. Whereas I think historically, the preclinical data, how do you validate it in a human being or do we really know those mechanisms are going to work in a human being? Well, the outcomes have shown us that we didn't have all the understandings of that. And I think you look back on it and the ability to translate has been has been weak. And that's why the genetics is is so appealing as a formative place to to start and try and build a pipeline of therapeutics, at least in our opinion.

Harry Glorikian: Yeah. It's funny because we're always coming back to this genetic part of it. And I remember like somebody saying to me way back, No, it wasn't that long ago, relatively speaking, but why would you want to sequence anything? Right? And now it's like it's the cornerstone of everything we're doing. Yeah, but. But you guys have another drug, right?

Laurence Reid: We do.

Harry Glorikian: That prevents ototoxicity, right. Damage to the inner ear.

Laurence Reid: Yep.

Harry Glorikian: And it's that's one of the most common side effects of chemotherapeutic drugs like cisplatin. I mean, for those people that are listening, right, these little hairs, it's the same thing as like maybe the hair on your head.

Laurence Reid: Please don't go there. It confuses people.

Harry Glorikian: But essentially, you've got a drug that you're working on this in this space.

Laurence Reid: Yes, we do. So firstly, how are you just upset because of our relative quantity of hair here. The hair cells in your hair are very different than the hair cells on top of your head or other parts of your body. Their role is to transducer signals on the inside of your cochlea into the brain. So but the cisplatin based chemotherapy is still very, very commonly used around around the world and is quite efficacious in certain types of tumors. It's widely used, for example, in testicular cancer, just one example. And it comes but it comes with a couple of of fairly severe toxicities, one of which is it kills the hair cells in your ear. And it also damages their interactions with the nervous system. And earlier in Decibel's life when we were sort of using our biological thinking before we. That's what I would say when we started as a biology company and we explored different molecular molecular modalities as the right way to treat it. And now we are significantly focused on gene therapy. As we've been talking about, this program was home grown and we're pretty excited about it despite our core investment in gene therapy now. And what we have is a proprietary formulation of a molecule of sodium sulfate, which is a natural metabolite, and it chemically inactivates cisplatin. And so we actually administer this by an injection into the middle ear and then the active ingredient leaches into the inner ear. And we administer that about 3 hours or so in advance of the Cisplatin IV, so that by the time the cisplatin gets to the ear, the inner ear is already bathed in sodium sulfate. And so and then you have a chemical reaction in situ inactivates the cisplatin.

Laurence Reid: And you know, it's interesting because some people don't find that very sort of biotech sexy, but it's actually an incredibly elegant way to to to stop the side effects of a molecule that has multiple, multiple molecular forms of damage that are probably being imposed on different cell types. So solving that biologically or biochemically is a very hard, diverse problem, whereas solving it chemically in situ we think is is very powerful. The principle to give some credit was validated by a company called Fennec, but they have an IV administration and they are constantly fighting between achieving good things in. As you might imagine, preventing against that toxicity without inhibiting the efficacy of the drug. And it's correct. And that's that is a very and they hopefully eventually will get approval for a fairly narrow pediatric population because it's been very hard to sort of thread the needle of can I protect without inhibiting the efficacy? Now if you go directly to the organ where the damage is being done, local administration of a proprietary formulation, so it sits in the ear, it's there in advance. Essentially, none of it leaches out into the circulation. So we have, we believe, negligible risk of inhibiting in any way the cancer benefit of the circulating cisplatin. So we're achieving a local protection and we're looking where we will be reporting some human proof of concept data. We've said in the first half of this year. So pretty excited about that, actually.

Harry Glorikian: Yeah. I mean, you know, I don't need sexy. I just need something to, like, work, right? I mean, sexy is nice, but, you know, if it's working, it's working sometimes, you know.

Laurence Reid: Right. So, so not not to compare protection of hearing against protection from people who are going to die of cancer. But it's an interesting example of where hearing health or ear health gets neglected. So in the context, you know, cisplatin is used in many cases with what people refer to as an intent to cure and so people can get cured. Young men, I think the cure rate is something like 95%. So you're talking about a young man, maybe 20 years old. He's going to live for 100 years, right? Maybe more. Maybe more. And nowadays and so the the importance of protecting his hearing at that age. And there are female cancers as well. But his hearing at that age for his long term health is incredibly important. But it gets it gets, unsurprisingly, neglected because the focus is on is on the cancer, which is which is understandable. But but we think that there's a really important opportunity to, you know, to provide a better overall solution for for those people that's going to have an incredible impact later in their life as their hearing would be naturally degenerating anyway. And and I think because of the the understandable stress when you're going through chemotherapy, you know, worrying about the hearing decrement, is it's just not top of mind. And so we've got some awareness. We've got some work to do to increase awareness there and hoping that some of our animal data might replicate in human beings because we think this could be fairly effective and really hopefully get it into the minds of oncology physicians. Is the goal that you should be thinking about this. You're trying to cure this patient. You're trying to whether it's a woman with ovarian cancer in her fifties or a young man with testicular cancer, they're going to live for decades to come. And we think it's important that they're hearing health is protected and we can help you do that potentially in a very powerful, rather simple way, actually.

Harry Glorikian: So. I'm going to assume and you can correct me if I'm wrong, that if this gets through sooner than the gene therapy and can generate some revenue in the short term, you can then utilize that revenue to continue to fund the gene therapy programs.

Laurence Reid: We're all always looking for money to do this, right, Harry?

Harry Glorikian: So, unfortunately, that's the business we're in.

Laurence Reid: That's the nature of the beast. Certainly, after we have our data in hand on the proof of concept, we'll be looking for an FDA interaction to define the path to registration, which we think could be relatively efficient. We have, you know, the medicine that effectively becomes an oncology supportive care medicine. It needs to be administered probably in the chemotherapy suite right in advance of a patient receiving their chemotherapy. So it needs to be marketed to an oncologist with a lot of education in the audiology community so that they're leaning on their oncology colleagues to you need to do this and you need to think about this as you're putting your patient through through chemotherapy. Ultimately, I think that that marketing to the oncologists, I don't think that's what's going to do that in itself. We're going to eventually bring a partner partner in to do that who is a specialist in marketing to the oncology community. And we want to be involved in rethinking about making sure that the ideological education and understanding is transferred into the cancer into the cancer world. And so that's that's a commercial strategy and structure that will will put together, you know, potentially starting when the data is in hand, but certainly some time between now and approval of the drug.

Harry Glorikian: Well, Laurence, you know, I can only wish you the greatest success because and working in older people would be great, because I'm sure that I'm going to need this at some point, and some of my friends may also need it. But it was great to catch up with you. Great to talk. You know, I hope, you know, it's not as many years past again before we we get a chance to connect. So great to have you on the show.

Laurence Reid: Thanks, Harry. I really appreciate it. And hopefully I've been able to provide some of the color and why we're so excited and think we're opening up a new area of therapy here for people with hearing loss and balance disorders beyond that. So really appreciate the opportunity. Thanks very much and great to see you.

Harry Glorikian: Thank you.

Harry Glorikian: That’s it for this week’s episode. 

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.

If you’re a parent, you’ve probably had this experience many times: Your young child has a high fever, and maybe a sore throat, but you don’t know exactly what’s wrong. 

Is it a bacterial infection, in which case an antibiotic might help?

Or is it a viral infection, in which case, you just have to wait it out?

The symptoms of bacterial and viral infections are often the same, and most of the time, even a doctor can’t tell the difference.

Viral infections are more common, but sometimes, the doctor will prescribe an antibiotic anyway, if only to help the parents feel like they’re doing something to help.

But what if doctors didn’t have to guess anymore? 

What if there were a fast, easy blood test that a doctor could run in their own office to look for biomarkers that discriminate between bacterial and viral infections?

Well, that’s the seemingly simple problem that a company called MeMed has been working on solving for 13 years now. 

Recently MeMed’s first testing product got approval from the FDA, and now the company is finally beginning to roll out it out commercially in the US.

And here today to tell us more about how it got built, how artificial intelligence fits into this picture, and how rapid diagnosis could change the practice of medicine, is MeMed’s co-founder and CEO, Eran Eden.

MeMed has a growing office in Boston, but I reached him at the company’s first office in Haifa, Israel.

Harry Glorikian: Eran, welcome to the show.

Eran Eden: Thank you very much for having me.

Harry Glorikian: It's great to have you here, I know that there's a significant time difference, so I appreciate like but it still looks like it's really bright and shiny out there right now. So what time is it in in Israel right now?

Eran Eden: Five o'clock in the evening,

Harry Glorikian: It's five o'clock. All right. Well, you guys have a lot more sun than we do anyway because we're in the middle of winter, but absolutely.

Eran Eden: So this, here, is actually full of people as well. So yeah, you don't stop innovation as five o'clock in the evening.

Harry Glorikian: So, you know, I was looking at your background and I mean, it's really it's interesting. It's diverse. You have a degree in biology, computer science, systems biology. You were first job was in computer vision data and analysis. But then all of a sudden you wound up starting a company that builds sensors and software for infectious disease. Like, how did you end up down this path, and do you feel like everything that you were doing until you got here was preparing you for it?

Eran Eden: Well, I think... A great question. So I think, on the face of it, it obviously the background in data science, as you know, in molecular biology, obviously all of that relates to what we're doing is part of our day to day and it is a good starting point. But in reality, there's a very big gap between what I was trained to do and today, my every day, day to day activity. I would say that probably the most important training that I got during my days at the Weizmann Institute has got less to do with differential equations or molecular biology, and it was more about a story that my mentor, Professor Uri Alon, told me when I was three years into the PhD, about three years into the PhD, he asked me, Am I already in the cloud? He said what? And he said, are you in the cloud? I said, Well, what is the cloud? He said, Well, every PhD, every scientist, when you start your PhD, you know, you have you go you go and read the latest papers in Science and in Nature and you see how somebody starts at Point A, makes a hypothesis about point B and then take the straight line from A to B, and then you say, OK, I'm going to do the same thing and you start at Point A, the known. You shoot for the unknown and you start going and suddenly you hit a roadblock. And then you hit another one and another one. At a certain point, you'd really lose direction, which he called the cloud. You're in the cloud. And then if you have enough perseverance and luck, you find a point C which is not exactly where you thought you're going to end. You go there with, you know, your last energy. And if you're lucky enough, then you publish another paper about how you started at point A, went to point C and connected between the two dots with a straight line. And then you have another generation of PhDs that are asking themselves, Well, why am I the only one that's struggling? And that lesson about how to be in the cloud, how to deal with uncertainty, to deal with failure and still move on. That is probably more important in the training that I got to become an entrepreneur and CEO of a company than any specific scientific knowledge.

Harry Glorikian: Ok. Yeah, no, I mean, trial and error, dusting yourself off, getting up and moving forward is, you know, my wife calls me crazy when I keep doing it, but I think you have to be a little on the edge to constantly keep repeating and being willing to fail and then stand up and then move on. Maybe it's a, I think I was reading a paper recently that said forgetting quickly is evolutionary, you know, a positive trait so that you forget what happened, that wasn't good and you keep moving forward. So. But let's talk about your company, MeMed, like you started that in, I believe, it was 2009. And what was your founding vision? I mean, if you can talk about what you and your co-founder did when you came up with this idea, I think you were both studying at the Technion at the time?

Eran Eden: Yeah, so so he was studying at the Technion, M.D., Ph.D. I was studying at the Weizmann Institute and Data Science and Biology. And frankly, I would love to tell you a story about a vision, but it started with a game. I don't think we had the presumptions to have really something that would grow to what MeMed actually became today. It was playing. We both have had different reasons first of all for doing this. I can say that from my my end, it was probably a pretty big gap between the places, the caliber of where we were able to publish high impact journals. And when I was looking at myself in the mirror and I was asking myself, Is this actually going to have an impact on real patients? I couldn't really see the connection. There's another reason why I decided to found MeMed or co-found MeMed. That's probably off topic for today. We can take this on a beer some time when we meet face to face. But so it's first of all, it didn't start with a vision. It started with a scratch wanting to apply a some of the know how that we had had in converting between molecular immunology and data science, and to try to solve big, ugly problems that don't have a good solution in 21st century medicine and trying to find something pragmatic now rather than having it a eureka moment. You know, some pioneers describe a eureka moment where suddenly you have the best and coolest idea in vision. For us, it was darkness for almost a year rather than the eureka moment. It is was more like an evolutionary process. Trial and error. We tried a bunch of solutions to problems that didn't really exist until eventually we came up with what we want to work with, but again was no, no eureka, and the way that it actually started was again, Kfir was coming from from med school talking about this problem of of AMR, antimicrobial resistance and the problem of distinguishing between bacterial infections and given our different backgrounds, we said that's interesting. How can we apply immunology and then science to try to solve that, and then at that point, we formulated what was to become MeMed's vision. And MeMed is based on a very simple premise, a very simple idea. Our immune systems have evolved to tell us what's going on our bodies and all we do at MeMed is we listen to the immune response with biochemical sensors and machine learning and what have you. And we use that to translate or decode the immune system into insights that can potentially transform the way that we manage patients with acute infections and inflammatory disorders. The first problem we went after, because that's a very lofty goal, was potentially the most prevalent clinical indication on the face of this planet. A child with sniffles. Our elderly patients that coughs. Come to the doc, they have a fever. As a parent, you're many times hysterical, you're asking yourself, is it a bacterial infection or bowel infection. If it's a bacterium, antibiotics. It's a viral infection, chicken soup. And we said, Well, what if we can harness the immune system? What if we could measure or listen to the immune system in real time and use that to try to aid clinicians to tackle this seemingly simple problem? So the vision was listening to the immune response. In the first embodiment of the first problem we went after is this huge intractable problem, B versus V versus. Bacterial versus viral infection. To treat or not to treat.

Harry Glorikian: Yeah, I mean, you know, it's funny, you say simple, and I've worked in this area for a long time and now not simple, not simple, but I've been watching dozens of companies over time try and tackle this problem, and everybody always comes at it and says, Yep, we should be able to do it. And I'm like, OK, that's a big hill, you know, to go and try and die on so. But you got FDA approval for your device in the U.S., and I want to talk about that later. But it did take 13 years. Like to, you know which parts of the process turned out to be harder or slower than you thought it would be?

Eran Eden: Before I answer that, I just want a minor correction. I didn't say it's simple. I said it's a seemingly simple problem. In reality, it's an extremely difficult problem to go after. I think some of the most the biggest challenges that we have can be phrased in a very simple manner. But as you alluded to, yeah, it's an intractable problem. Bacterial and viral infections are often clinically indistinguishable. And it took us over a decade to take this from my idea on a napkin and grandmother's kitchen. That's where we found with no garage, it was Grandmother's Kitchen to what is considered a landmark FDA clearance that I think many folks did not believe we're going to be able to get this because it required so many innovations, not only on the technological side, but also on the regulatory side. And when you ask why only a decade? I think it's, we're very lucky that it took us only a decade and it sounds there, let's not call them challenge. Let's call it problems. Challenges is something I always envy the people that have challenges. We have problems with immune, and we work every day to solve those problems, right? So. So there's many problems or hurdles you have to go through. So there's first of all, you have to overcome some pretty big research issues, where do you find these hypothetical molecules of the immune response that go after bacteria and viruses. So research, then you learn the hard way.

Eran Eden: The research is very different from development, and development is very different than product, and product is very, very, very different than manufacturing, and manufacturing is very different than regular regulation, and regulation is very different than reimbursement in marketing, which is a very different than commercial, et cetera, et cetera. So it's not good, it's not enough to excel in one thing. You have to really reinvent the wheel on several things, and as a company and as a team, reinvent yourself, and that's probably one of the biggest challenge, probably your biggest impediment to progress is yourself and your team because you might be an excellent data scientist, but you have to talk with the clinician. You might be an excellent clinician, but you have to talk the language of the molecular immunology. You might be very versed in all these three, but it's still not product and it's still not the graphical user interface. And how is that connected to manufacturing and really creating a culture or a team that can combine these seemingly very diverse elements within a small company. That is a very, very daunting and big task, and again, we frankly failed on multiple avenues there. We had to go back, we were in the cloud and we had to reinvent point C again and again and again. So, you know, we were in a very far position that we are today that we thought we were going to be at this stage.

Harry Glorikian: So I'm going to ask at some point, you know, after this whole interview is I'm going to encourage you to write the next IVD book because everything you said is absolutely the way that I've seen it over time is, you know, having to bring all these pieces together is not trivial in our world. But let's step back here for a second for everybody that's listening, right? Talk a little bit about basic immune system biology and the, you know, technology behind your diagnostic system. So if someone presents with an inflammatory response, why is it so hard for doctors to destroying distinguish between the bacterial and viral infection?

Eran Eden: Because bacterial and viral infections are clinically indistinguishable and you don't have to be an M.D. to to understand this. Intuitively, we know our kids so well. But still, you know, when they have a fever or runny nose, you know, we know that it's 80 percent, 85 percent a viral infection. But what if? What if there's a lingering bacterial infection? And it just it turns out that because of the clinical manifestation is very similar. It's really hard to figure it out. Not only children, also adults with suspected LRTI or a fever without sores, and even when we apply modern, I would say diagnostics, there's still a big gap that remains. So for example, when as a scientific community or a clinical community, when we approach this problem, we have tools at our disposal. A rapid strep test. A rapid influenza tests. Multiplex PCR. In today's world, I think everybody, even my grandmother is talking to me about the difference between rapid antigen tests suddenly becomes a really interesting topic over, you know, weekend dinners, culture. So there's technologies out there. And going back to your question, why is it still, why is there still a gap? And we've identified several things. The first one is probably the most trouble is time to results. Many of the clinical encounters, you want to have the solution here and now where whereas that technology that we have often provide solutions in hours and even days, and that's not always good.

Eran Eden: That's one hurdle. Not the biggest one. The second one is that many times the infection site is inaccessible. Take, for example, otitis media, an ear infection or sinusitis or bronchitis or pneumonia. It's really hard to reach the infection side and therefore identify the pathogen. It's one in four patients in the most prevalent disease on Earth. That's really hard. Third, even if you use the best, most broad technology diagnostics to try to identify the bug, say a multiplex PCR. In more than 50 percent, five, zero percent of the cases, you're not be able to put your hands on any microorganism, but you still, as a clinician, have to make a decision. And lastly, there's the issue of colonization. So even if you're lucky, the infection that is readily accessible and you do get some sort of a virus, for example, that you detect, say, for example, an influenza or or let's take a rhinovirus, the rhinovirus is very prevalent in children. That's a problem. It's very prevalent in children. Even if you take seemingly healthy children in a very high percentage of those children, they're going to have a rhinovirus. So mere detection does not apply causality. All this complexity is sunk into this few minutes that the clinician basically needs to make a decision, and it's a really hard dilemma because it's hard to know to distinguish between the two and the ramifications could be quite significant.

Harry Glorikian: So I know the answer to the question, but I'm going to ask it so you can explain it is: So who cares? I mean, I know that it's ineffective to treat a viral infection with antibiotics and that only you know, that only work against a bacteria, but you know. We've been doing a trial and error, so what's the downside of doing that?

Eran Eden: So it's actually a pretty deep, it's a very deep question because there are several layers. You're right, this sometimes people actually say there's several layers to answering because the first one is, well, if you treat erroneously, with antibiotics, antibiotics, because of this uncertainty, there's a lot of antibiotics overuse that one of the consequences of this it drives anti microbial resistance, which basically means that the drugs don't work anymore. And if we continue on that path, we will potentially lose modern medicine because if you lose the potency of antibiotics, you cannot treat infants when they have an infection. Or an oncology patient that would succumb to a parasitic infection, or even yet have your wisdom tooth pulled out, because antibiotics won't be effective. And there's several quite influential studies that came out in the last few years. The last one actually in The Lancet came out two weeks ago portraying a world without antibiotics, which is, you know, we're seeing right now the consequences of COVID SARS-CoV2. Some might argue it's not a smaller problem. So that's and it has both clinical and health economic consequences. According to Jim O'Neill, over $100 billion by 2050 in lost GDP.

Eran Eden: And. And it's a big number, right? It's a really, really big number. And maybe, maybe it's overly inflated and maybe it's conservative, but it's a big problem. The issue is that nobody cares. Sometimes the individual doesn't care because the doctor, right now, when he has a patient in front of him, he doesn't think about the masses. He thinks about the patient. So you might ask, well, what the doctor care. Why does the patient care? And it turns out that there is an angle on the personal level as well, not only the societal level. If you give erroneous antibiotics, you can drive anaphylactic responses. You can drive allergies, which have a toll. But then there's another element that people are less aware of. In addition to overuse, there's also simultaneously underuse. One in five patients that have a bacterial infection are not receiving antibiotics in time. There's much less publication on that. But it is a reality. And that also has consequences, including prolonged disease, duration and sometimes even morbidity and mortality. So it's a really delicate equation, right? You don't treat. And you don't want to get ... some countries overtreat, some undertreat. And again, at the end of the day, the day to day, it does have ramifications both from the patient and on the doctor.

Harry Glorikian: You know, if we could accurately treat people right, I think there would be a whole host of issues that could get solved and a whole host of issues that wouldn't emerge because of overtreatment or treating the wrong people that you know, we could spend hours over a beer discussing the microbiome and allergies and all sorts of other consequences of doing this. 

[musical interlude]

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[musical interlude]

Harry Glorikian: So your system, which is, I love, is a basic blood test, right? So the MeMed BV looks at three immune system proteins in the blood: TRAIL, IP 10 and CRP. So how are these proteins related to infection and how can measuring their levels tell you about the nature of the infection?

Eran Eden: Ok, so. Each one of those proteins that you just mentioned plays a critical role in the immune response to different invaders, bacteria and viruses. What's special about this particular trio, is that they work really well as a team. Maybe if you take a step backward to identify them, we had to run for about four years what is arguably the largest prospective proteomic study ever to be conducted of the human response to acute infections. And start with a host of multiple tens of thousand proteins bioinfomatically and then down-select this eventually to three. And these three, while none of them is perfect in itself, they cover one another's blind spots. So let's go one level deeper. When we went on this, one of the things where we were surprised to find out that is a clinical community, we've been obsessed with the bacterial side of the equation. Every biomarker that you have in 21st century medicine, 20th and 21st century medicine, has been mostly predominantly upregulated in bacterial infection. Procalcitonin: bacterial infections, CRP: bacterial infections, white blood count: bacterial infections, absolute neutrophil count, which we use as part of routine day to day care: bacterial infections. What about the viral side of the equation? We couldn't find one that was used or cleared by FDA as part of 21st century medicine. The last. The reason the FDA cleared us, we actually just cleared the first viral protein ever to be cleared by FDA. And so we went on this fishing expedition and four years into the process, again, this was 2009-2013. We identified this trio. TRAIL Is a protein that shoots up in your bloodstream when you have a viral infection, whether it's a common influenza A, influenza B, parainfluenza or corona, and it has this very unique property that it goes down when you have a bacterial infection, why nobody really understands the reason. But it really creates a very dramatic full change because of this up and down type of a response. And the story there, there's a lot of interesting stories around TRAIL, but one of the ways mechanisms by which it does that it causes the cells that are infected by viruses to commit apoptosis. Cells suicide. And by that, protect the brethren cells. So that's one of the mechanisms that the body is using.

Eran Eden: The second one is called IP 10, which is an interferon. This protein basically shoots up in your bloodstream if you have a general infection, and more so if you have a viral infection. It recently got a lot of headlines in the context of SARS-CoV-2 and hyperactivity of the immune response. It's also associated with lung injury, but a really interesting biomarker that plays a critical role there in clearance of infections. The third one is called C-reactive protein, that's been around for about 40 years. Goes up in bacterial. And the nice thing about them? They work as a team. So as I said, they're not perfect. So take, for example, CRP. It's reasonably OK after 48 hours. But because it takes it to about 48 hours to reach maximum level, but in the early phases, you have a blind spot. Whereas TRAIL, at time of symptom onset, it's already differentiated, so they cover one another. By the way, we didn't identify this. The computer identified that. This is a lot of insights that we had in hindsight when we were looking.

Harry Glorikian: Yeah, that was going to be my next question, which is. You know, the the heart of the show is always like, you know, artificial intelligence and its whole basket of tools and biology. So how does machine learning come into this process? Is there some corpus of training data that shows that certain levels of these three proteins correlate? Or can you tell us, you know, how you developed that part of the system?

Eran Eden: Absolutely. And I think again, I was teaching a machine learning at the Weizmann Institute over a decade ago before it was a sexy topic. You know, people are using the term machine learning and data science so often so frequent. I think what's important to say is that machine learning is part of the component technology, but there's hardcore immunology and molecular biology. So it's not just one technology that we're, you know, it's a it's a very high entry barrier because of that and adds to the complexity. So that's one thing, just to put machine learning in context. Where machine learning plays an important role here is two places: in the development and in the final product. In the development, there's a process of how do you find the optimal team of biomarkers that would give you the the best performance? And over there, there's a lot of activities around using publicly available data sets and and proprietary data sets and data analysis and statistical analysis and feature selection and find the right models, et cetera, et cetera, coming up with what is the right model. Some of these are more conventional tests. Some of these are more cutting edge tests in the final product. It basically uses what's called a supervised learning approach, which basically means the following. Imagine every problem in here, I'm going to be a little bit technical here. Imagine you have, let's say one feature. Say a viral biomarker. TRAIL. High levels, viruses, low levels, bacteria. You find some sort of cutoff that separates between the two. It was the most informative biomarker that we found.

Eran Eden: Is it good? It's reasonably good, but there's no perfect biomarkers out there. We don't have them, nor do we believe they exist. Nor do we believe in unicorns, even though my daughter is trying to continually persuade me that there is one. Then you add another biomarker to that. Imagine that you have right now a two dimensional grid. And now suddenly, every patient is met this two dimensional coordinates and you have a cloud of bacterial and the cloud of viruses. And you find a line that separates the two. And then a third dimension and a fourth and so forth and so on. And eventually, the problem becomes how can I find this type of plane or hyper surface that separates between the cloud of bacteria and the cloud of viruses? This is the essence of the machine learning and the way you go about this. You train give it a lot of patients, a lot of data, and then you train the system. And the more data you have, the smarter it becomes. The same principle applies for doing diagnostics in oncology, span detection, computer vision and what have you. It's the same underlying, often similar underlying principles to try to solve many of these problems. So hopefully I was able to to simplify and somewhat exaggerate how this is actually working and where the AI plays here.

Harry Glorikian: So what's that accuracy rate of the diagnosis from your system? And is there are certain things, let's say it's good at in certain things, it's maybe not so good at?

Harry Glorikian: Yeah, absolutely. So so if you look at the overarching population, if you look at our pivotal FDA study, the AUC, the area under the operating curve, the entire population was 0.9 to 0.97 across different cohorts, which is considered very high. So that's the short answer. The more we see deeper level, it's there's obviously nuances across different populations. One of the things you have to show is what happens in children versus adults. Upper respiratory tract infections, lower respiratory tract infections, et cetera, et cetera. So we've shown a relatively consistent and robust response. That's how the system was developed. But there are, for example, certain viruses that we know that we perform less accurate. For example, adenoviruses. Adenoviruses are notoriously hard to to treat well. By the way, they're one of the most prevalent, for example, viruses in children, why? Because the immune response looks like a bacterial infection. For many, many reasons. So white blood count is going to be elevated. Procalcitonin is going to be elevated, CRP is going to be elevated and we're often going to overtreat with antibiotics. So if you look at our performance in that particular sub-cohort, our performance drops somewhat from, you know, a 0.90-something to maybe 0.89, but that's actually one of the viruses that we see the most added value because compared to standard of care, it's many times close to flipping a coin.

Eran Eden: So even though our performance is eroded in this particular virus. The standard of care in this particular situation is particularly challenged, and it's almost 0.5, 06. so that's one example. There's multiple examples. We can study the immune response to pathogens again for almost a decade now. This is just one interesting anecdote. And I think just connecting this to the who cares question that you had. So here's an interesting case that we had a few weeks ago. A child, young, three years old, coming to a major medical center, not really sure if it's a bacterial or viral. Ran a PCR, came positive for adenoviruses and it looked a little bit bacterial. But yeah, OK. Adenovirus explains everything. Released home. Got a 99 score. 99 probability of bacterial infection. So they start to do additional follow up and then it eventually turned out to be a bacterial axis in the spinal cord of that particular child. It had to be mechanically removed. This is a very dramatic case. This is one of those potentially underused cases that could be very dramatic. This is very rare. It doesn't happen often. But again, it's hard. It's really, really tricky to distinguish between infections and we added this right now, this is how everything maps together to the adenoviruses and and to why we think this could be helpful.

Harry Glorikian: So, you know, like I said earlier in the show, you know, you got FDA approval and granted 510K clearance back in September, which congratulations, that's a huge step. But you know, for everybody listening, what Gates does, does that open for you. What's the pathway to getting the device out into the market?

Eran Eden: So as you said, first of all, you have to get the clearance, which I think took us almost five years working with FDA. FDA, by the way, we've worked with them extremely collaboratively and they've been instrumental in helping us form and shape, what's the methodology to actually prove something. We didn't talk about this? But how do you prove that absence or presence of bacterial viral infection in the absence of a true gold standard? So let's put that thing aside. We were able to work with FDA and come up with a methodology to do that. So now, what is required to take it to the next step? There's several things. The first one you need, and we didn't talk about this, you need a way to measure those biomarkers. You need a platform. Right, one of the challenges that we had is that in the early days, none of the big strategic players, the Roches, the Abbotts, the DiaSorins of the world were willing to bet on this because the risk were so high, as you alluded to in the beginning, the graveyard. And nobody got FDA clearance, so they basically they wouldn't. They were not willing to bet on us today. Some of them become partners and we're working with them. So it's, you know, there's been great development. But at that time, it was really hard. The platform is also challenged because some of the proteins are picograms, some are nanogram and some are micro per mil, which poses again the challenge from a technological perspective. Again, not going too much into the technology side, but we've been able to come up with a technology or a platform that can actually measure multiple proteins across almost a six to nine order of magnitude range. And so you have to have a platform and can do that in about 15 minutes right now, serum working in whole blood.

Eran Eden: The second thing you need, you need obviously manufacturing capability, which is again, a different story, you have to manufacture the cartridge. The third thing you need is building the clinical evidence beyond, I mean, FDA's great, but you have to create what's called a clinical utility, real world evidence, what have you, working with peers. Work with partners or with clinicians working the societies. Publishing. Building a commercial team which we're right now established commercial team in the U.S. So there's multiple things. And probably last but not least, this is too big of a challenge to go at it by yourself. You need to have partners. Whether it's governments, the U.S. Department of Defense, the European Commission have funded this heavily and have been amazing partners, whether it's strategic partners, you can take it by yourself versus vs not one market. It's markets. You have patients in the wards and the EDs and the urgent care physicians' offices, retail clinics. No single player has enough of a presence in one platform that covers it all. So again, we've announced about a year ago, you know, the first partnership with DiasSorin, which has today almost a thousand installed installed across Europe and the US in these large automated immunoassay machines. And that's covering certain parts of the market that are overlapping or, sorry, that are complementary where we're going at. So that's a little bit of what needs to be done. But again, it's changing the boundaries of what what we've been doing so far, and that's always a it's always a challenge, but also an opportunity.

Harry Glorikian: So you guys raised I believe it was $93 million, if I remember the number correctly, in new funding, which sort of really adds to the firepower of being able to take that next step, but. You know, can you can you envision a future where we get a solid diagnosis and an appropriate treatment plan, you know, quickly while you're in the doctor's office?

Eran Eden: Oh, yeah.

Harry Glorikian: That was the shortest answer you've given yet.

Eran Eden: I think you can be much more radical. I think there's several things that are happening. There's two major discontinuities. There's a technological discontinuity. There's a regulatory discontinuity. And I'll actually add another one, which is there's a psychological discontinuity. The technology that we can do today that we have today, the tip of our fingers can do can provide so much valuable information that can help make better decisions. The regulatory framework has changed because of COVID, it's basically shattered a lot of things. And from a psychological perspective, I think there's a push to polarization, right? Both decentralization and centralization at the same time. And so I definitely see that happening. I think we can take this several step further. How can we take it from physician's office, also retail clinics and even further? And that will take time, obviously, because we're dealing here with some pretty, pretty deep questions. But I think the world across multiple fields and this is not different than anything else. I think it's definitely going in that direction.

Harry Glorikian: Yeah, no, I mean, I was going to, you know, looking at what you've created and, you know, obviously first getting everybody on board, but then seeing how it can be deployed at a CVS or something like that, it could drum, you know, you could have a dramatic impact on how we manage patients. The whole antibiotic dynamic and maybe even relieving stress on the system so that, you know, it doesn't get overwhelmed by what your system may be able to sort of help get to a much faster, much more accurate answer too.

Eran Eden: I wanted to say relieving stress from stressed mothers and fathers. But yeah, I agree with you also, relief. And by the way, as you start going from more centralized to decentralized, there's obviously additional workflow challenges. How do you make this simpler? There's regulatory bars that you have to meet. How do you go from a mod complex to a clear waived test that can actually go to those directions so that there's we still have we have work, there's work, work to be done. But I think we've been able to potentially break a glass ceiling in terms of getting the clearance. And now I think with that, there's going to be additional innovation that will come in both by us and others who are entering the space.

Harry Glorikian: So. Just slightly moving to one side is like, how has MeMed responded to say, COVID-19? I think you guys have developed a test that runs on your platform and predicts how severe the infection will be. How does that test work? Did your previous work, you know, and also did your previous work like on the platform prep you for this virus? Just curious how it works and how you got there?

Eran Eden: Absolutely so. So it always starts with the clinical question. I mean, many of us are technophiles, but at the end of the day it's about solving a real problem. And the problem here is the following. You have see SARS-CoV-2 positive patient presenting to the ED. And one of the questions that we have in mind is whether that patient is going to deteriorate or not. Do we escalate treatment or not? And it's a real question, right? And the more you know, the more stress the system is feeling because, you know, because of the the peak of a pandemic, the more important that is. So we said, Well, how can we harness the technology? Is the framework that we created host response in general, right? The biomarkers we've developed, the platform that we have, the Biobank and what have you. And so and how can we take a process that maybe took 10 years and now collapsed into something maybe that's 10 months? How do you get a 10 X? And and first of all, with amazing partners around the globe, you start running huge clinical studies to basically collect patient samples. We also use again information from the public domains, our own repositories, our own previous data because from many perspectives. Sars-cov-2 is very interesting, but guess what? Similar to SARS and to other types of severe viruses, there's differences, but also commonalities.

Eran Eden: So we use a lot of the bioinformatics, the previous data samples. Current data samples. The know how and the platform that's readily available right now. They can measure basically anything to collapse this and develop. This is probably just got clearance in Europe that basically allows to take a snapshot, the main response again in real time. Give you a risk stratification regarding the probability of a patient to experience severe outcome defined as ICU level of care and mortality within two weeks. Again, it's only clear right now in Europe, not cleared in the U.S. and we view this also as a stepping stone going beyond just COVID severity to a general severity signature. So what you do, both B versus V and severity, what if you could do it in the same cartridge or what have you? So I think what's what's really interesting, we build here this core technology. We went after one big problem, B versus V, but now that you have that, you're like a child in a candy store. There's many more things that you can do. And rather than taking you a decade, you can start to collapsing things because there's a lot of there's a lot of. Resources that you can now leverage or platform that you can leverage, so that's a story around MeMed and COVID severity.

Harry Glorikian: Yeah, platforms are wonderful in that way, right, that I like a platform more than I like, like a, you know, sharpshooter bullet, from an investment perspective. Thinking about it that way. But so. You recently got COVID. We were supposed to talk like over a week ago, and I, you know, we had to postpone it. Did you use the test on yourself? I mean, if you did, like did it work the way you thought it was going to?

Eran Eden: Yeah, so so yeah, I got my I got it from my daughter. We went on a trip and five out of five family members got infected. So yes, it was at least from our small experiment. It was very infectious. We got the Omicron. Actually we didn't have symptoms, apart from the fact that I think it just jacked up the energy level of my kids. So before we talk about running around the house and thank God, you know, my wife didn't didn't kill any one of them. So there's no casualties from this, from this infection. So because we didn't have symptoms, we didn't go to the ED. It was not relevant. You have to have SARS-CoV-2 symptoms. So in that case, no, no, no need. I mean, we were pretty much hunky dory. But what I can tell you is that on the B versus V. Again, it's potentially bacterial and viral infections are potentially the most prevalent indication in children. And my children, those little incubators of bacteria and viruses, are no exception. So I had a chance to use the technology on my kids many, many times, including last time was about a month and a half ago, and my eldest daughter, who is four, before going to a business trip. And my wife is asking, is it a bacterial infection? I said, I don't know. She spits on me. The shoemaker is going barefoot. So we ran it. It was a viral infection. No antibiotics. Went back to school. So and I got a lot of brownie points with my wife and my mother in law, which is obviously always very, very strategic.

Harry Glorikian: That's that's a good one. That's always helpful. Exactly.

Eran Eden: So we're actually using this quite often in our families as well. And it's very very gratifying.

Harry Glorikian: Interesting. Excellent. So now you guys are, you know, I believe you have an office in Haifa, which I remember as being beautiful and hilly and wonderful food, and then you have Boston. You know. What are the strengths of being in these two locations. What happens in Boston that can't happen in Haifa and vice versa?

Eran Eden: Well, again, we're going after a global problem and you have to have a global team to have a global perspective. Whatever you have in San Francisco today, you have tomorrow in Shanghai and the day after that in Tel Aviv. So you have to look at this from a global perspective, number one. Number two, since the US is the primary market, as I said, we have to build a very significant presence in the U.S.. Why specifically Boston? Very talented pool of, a pool of talent that's very wide in the domain. There's a big overlap in terms of hours between Boston and Tel Aviv, so you can grow one unified team. And that's basically, that's where we're basically building our U.S. headquarter. And the team is quite complementary. Again, we've we've recruited by now roughly 25 to 30 folks, folks with a very strong background, both IBD, Troy Battelle, formerly Thermo Fisher, who's buying commercial for microbiology in the Americas. Fred, who is running Corp Dev, from bioMérieux. Again, another large multinational, Jim Kathrein was former head of sales for BioFire. Again, not sure if your audience is familiar with but and so forth and so on.

Eran Eden: And Will Harris was running our marketing global marketing, is ex-Amazon and then before that, 15 years in IBD. So it's really bring here a blend of, we call this affectionately an anti disciplinary team. We don't care about disciplines, we care about solving big, ugly problems. So you have to bring the IBD experts with the clinicians, with the folks and the big data science side or in the molecular immunology and the manufacturing. And nobody knows, single location has all the know how, no single location has the recipe because frankly, we're doing here something new. There is no real tech like this. And so bringing those this pool of talent, I think has really helped us, propels us moving forward. And it is the bridge to be able to to launch in the U.S., a U.S. very focused, commercially marketing product where a lot of the I would say more of the molecular immunology data science team is more in Israel. I'm simplifying and exaggerating. That's some of the team.

Harry Glorikian: So the last funding round, was that the argument to the investors, like we need to hire these types of people to help blow this out? What was what was the rationale for that last round?

Eran Eden: So, so basically three things. Number one, commercialization. U.S. Europe, Israel. That's our initial focus and then the rest of the world. Second is product pipeline, so we talked about bacterial versus viral infection and a bit about COVID severity. But what would you do if you had a blank canvas and these platforms to go after the new response to measure the immune response? What additional big problems would you go after? So it turns out that there's some pretty interesting stuff in. We're working on additional activities. So that's the second thing product pipeline. And the third thing is a scaling manufacturing. So as I think people have a new appreciation for manufacturing and supply chain during COVID times, it's a really big topic and critical for success. So this these are the three major elements that we raise the funds for.

Harry Glorikian: No sounds I've I've been I've seen this rodeo a few times, so yes, I understand completely. So, well, you know, especially because I come from the diagnostic world and I can't wish you enough success because we need more products like this out on the market to help us manage patients and help give physicians better information so that they can make better decisions, right? More informed decisions than just, you know, looking at a patient and trying to figure out what's going on. So I wish you incredible success and I'm, you know, great. Great to have you on the show.

Eran Eden: Thank you so much for for the kind invitation. Enjoyed our discussion.

Harry Glorikian: Thanks.

Harry Glorikian: That’s it for this week’s episode. 

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare and life sciences. 

Almost two years ago, in the very first weeks of the coronavirus pandemic, we had a guest on the show named Jacob Glanville.

He had built a company called Distributed Bio around a new computational immunology platform that was designed search for new antibody therapies against a range of infectious diseases, potentially including coronaviruses.

We had a frank discussion about how quickly the biotech and pharma industries would be able to move to help stop the pandemic, and how antibody therapies might fit in.

And that conversation went on to become one of our most-listened-to episodes ever.

I wanted to have Jake back on the show, for a couple of reasons.

Obviously, we’ve been through a lot over the last two years, and I wanted to hear where Jake’s head is at today about whether and when we’ll get to the point where COVID-19 is under control and we can settle into some kind of new normal. 

But in the meantime, there were some big changes in Jake’s world. 

He sold Distributed Bio to a giant biopharma services company called Charles River Laboratories. 

As you’ll hear in our conversation, Charles River was mainly interested in the computational immunology platform, and they were happy to let Jake hold on to the therapeutics programs he was pursuing.

Those included a potential universal vaccine against influenza, or the flu, and coronavirus, as well as a vaccine for HIV. They’re even doing some fascinating work on antivenoms to treat snake bites.

All that science got repackaged into a biotech startup in South San Francisco called Centivax where Jake is once again the CEO. 

A couple of his former Distributed Bio colleagues came along as chief science officer and chief operating officer.

So I invited him back to hear about progress at Centivax, and also get his thoughts about where the pandemic is headed.

So here’s my full conversation with Jake Glanville.

Harry Glorikian: Hey, Jake, welcome back to the show, it's great to have you again. It's only been two years in the world has completely changed from what it was two years ago. Good to have you back.

Jacob Glanville: Hey, thanks for having me on again. It's great to see you.

Harry Glorikian: So. Before we talk about your companies, your research, you know, I think people would love to hear your high level thoughts of where we are now in this coronavirus pandemic. I think the last time we had you on the show was March of 2020. It was literally just the first wave was hitting. Now it's almost two years later. What has gone better than you expected in the science and political and public health? And what do you think is gone worse?

Jacob Glanville: Sure. Yeah. So, yeah, wow. What a wild ride the last two years have been. So there's some things that have gone definitely better than I expected. There's definitely been some things that have gone worse and. We're we're much better off than we were two years ago, but I think also it's important not to get unrealistic and thinking this is just going to go away. So the the areas where if I look back that we did really well, we got a bit lucky that these new vaccine technologies were very effective. That wasn't necessarily the case. There are some viruses and other pathogens that vaccines just don't work that well for. And it turns out they work pretty well for the coronavirus and that's helped. And they they developed them in record time and produced a lot. And that has reduced the number of deaths and significant illnesses and protects from long COVID. We're starting to see as well, and that's really good news. I think that's been impressive.

Jacob Glanville: The areas where I've been underwhelmed, I think there wasn't enough attention paid to to therapies and treatments. We are fortunate now that there's this very nice looking, there's some good antibodies that came out. Most of them got washed away by the Omicron variant. The Vir antibody still looks pretty effective so far, but larger than that, the Pfizer Paxlovid therapy. This is a pill that doesn't require binding to the outside of the virus mutates a lot. It interferes with the virus's ability to chop up and make copies of itself inside of a cell. And that's that's going to be a game changer. I think people aren't fully realizing how much of a game changer that is, and that's good. But I think we could have had more of these kinds of treatments if there was attention on the reality that we're going to need treatments and not just vaccines. The other areas where I wish there had been more effort done and we still need more effort are in the manufacture of enough vaccines. So right now, we don't have the ability as global ability to make enough vaccine before the virus changes a lot. Right now, the current vaccines are giving you, they're the original virus. This thing's already gone through multiple generations of new alpha, beta, gamma, delta, epsilon variants of concern. And so we're kind of living in that flu-like world of the vaccine, always being pretty outdated to the circulating variant.

Jacob Glanville: And like right now, there actually is no way to produce enough vaccine in time to vaccinate the world before the thing changes. So we're never going to have enough. The Third World had to wait in line and not get as enough vaccine. So Guatemala, where I grew up, they have only really 30 percent of the population have been vaccinated so far. Another 10 percent has had one shot. So they're not even going to have a reasonable vaccination level of like the vaccine, the virus from two years ago before new generations of vaccine come out. And those are going to go to the First World first again. So right, that's something that needs to get solved. I think there's also just in general, I was underwhelmed by how the world cooperated to address this, and I think this speaks to the need of a pandemic treaty.

Jacob Glanville: I think and I'm glad to hear that there's a discussions around this, but really, realistically, it's a major global collective goods problem. This is a virus which is not going to go away. It's going to get more manageable and there's going to be new pandemics coming in the future. We need a global pandemic treaty to make it so that we can better coordinate responses, surveillance and just a global reaction in a coordinated fashion. Part of the problem with this is that every country had different policies and the sort of like "deal with it your own way" policies caused a lot of problems with the realistic need that if you want to stop a big virus like this, you need every nation to act like well-marshalled troops to coordinate their response efforts. And that hasn't been fixed. And it really it should be fixed because that won't just help us with this virus and help us with all the other pathogens we're currently dealing with and the new ones that are going to be coming out of the woods as we march into the future.

Jacob Glanville: Part of that would also be like with Omicron. It came out of South Africa, or they detected it first. It could have come from a nation that wasn't doing surveillance, and then South Africa was like, "Hey, what's up, guys? Like, we are the ones who actually warned everyone about this, and then you guys just blocked all of our travel and isolated us." Like, that's going to actually encourage nations, I could imagine many nations being, like, "You know what, let's not test because we can't afford to have people block us." That's crazy. And that's esily addressed with, like, you know, in the U.S., we have a shared fund of federal funds to be able to enable disaster relief. So if any state gets particularly hit, the other forty eight lift them up and a similar system should be in place to provide disaster relief funds because a site which is heavily impacted, to get relief funds to say, "Look, we're going to quarantine you guys, you guys need to do all this extra stuff, but here's a bunch of money to do it because you're protecting all of us, so the problem doesn't reach us. So, handle it well and don't be afraid to report." 

Jacob Glanville: And so those are areas where I'm not so impressed, I guess, just to wrap up because there was a bunch of stuff we want to talk about. I think the testing has gotten really good. So they have these awesome little kits. Put it in your nose. My kid can go back to school the next day. And I think that's that's been a major advance. And I like that technology because it's useful not just against the coronavirus, but there's actually a lot of areas of infectious disease that have benefited from the last two years of dedicated research into these areas that'll make hopefully for our kids and our kids' kids an easier world to navigate with less pathogens.

Harry Glorikian: Yeah, I mean, I've been I've been talking about distributed diagnostics for, I feel like 15 years and it took the coronavirus to sort of help move the ball forward in an interesting way. So I hope it doesn't die. I hope it continues to be.... But do you see an identifiable end to the pandemic or do you just simply, you know, settle gradually from an emergency to something more of a normal, it's becoming endemic in the society, and public health just sort of manages it like we do the flu.

Jacob Glanville: Yeah. So here's the way to think about it. The bad news and the good news. The bad news is, yeah, it's definitely not going away. And like, really, we all knew this six months into it. The thing is so infectious, you saw how quickly it went from first off out of Wuhan to the world. Then you saw Delta, how quickly that got out of India to the world. And then with Omicron, you saw within a couple of weeks of South Africa reporting it, it was everywhere. It's so infectious that it's hard to reach a sufficient, even if you had a vaccine that would provide sterilizing immunity, you probably have to get 95 percent of people to take it to protect it. Above 80 is good, but you'd really have to be above 95 and you'd have to have better surveillance networks. It's not inconceivable to stop the virus. China has actually done pretty astonishingly good job, but they had to apply super draconian measures. And everywhere, everyone everywhere would have to apply those to potentially stamp the thing out. And that and realistically, that's not going to happen, and that means that the virus is here to stay.

Jacob Glanville: The good news is that it's not going to be like it was over the last two years. We have a lot of vaccines and good ones that are being administered. You also have natural immunity or natural infection induced immunity. People didn't really want to talk about it in the last year and a half because they were worried, if you heard a natural immunity can provide protection, that people would just throw up their hands and be like, "Well, just let me get infected." And that would cause a crisis of hospitals. But the reality is there are some countries like Guatemala just don't have enough vaccine, and there's people who aren't going to get vaccinated. And so as the thing infects everyone, that's going to provide an additional layer of repeat infection that gives rise to some population-level immunity that boosts people who are vaccinated, and that also helps people who weren't vaccinated. It gives them a level of immunity. Although again, this virus, you do not want to get it if you can avoid it.

Harry Glorikian: Yeah, I was going to say, I mean, I've seen people that have have even, you know, my friend's son got double vaccinated and ended up with pericarditis, right? So, you know, you don't know how you're going to react to it when if you if you happen to get it. So, you know, my advice to people is like, if you can, if you can avoid it, that's a good place to be.

Jacob Glanville: Yeah. And so that's where we're going to move into in the future. You're going to have a population which is increasingly got some established immunity to it. Kids are going to get exposed early. Hopefully, we'll have vaccines for kids, but otherwise they're going to be exposed early and then repeatedly being exposed to it will provide some level of immune protection. And that means that that plus the rapid testing technologies, means that we can move back to a semblance of normal. But with this being an endemic virus now. We've lived with other endemic viruses before, you know, HIV never went away. We still have it. We have tuberculosis, we have multidrug resistant bacteria, we have the flu and we find ways to make the medical crisis manageable through vaccination and treatments so that when you get sick, there's something.

Jacob Glanville: That it's not creating a crisis with the hospital centers. That's where we're going to be with the coronavirus. The the hope is that while people said, "Oh well, Omicron looks more mild, great. The virus is evolving to be more mild." You cannot count on that. Viruses do not naturally evolve to get milder. What happened was it shifts randomly. It can get lucky. They just get more transmissible. That's all the virus selects for. It happened to be this one's more mild. If we're lucky, that means the children of this one will tend to be more mild, but it could also go the other way. And so we just need to keep these.... That's what you're going to experience over the next five, 10 years. It's going to be more rapid testing and surveillance, which to your point about this, I think that's what's actually going to keep that alive and expand it is that that's going to be a part of life vaccinations and monitoring and better treatments. And that's the life we're going to live with and kids are going to grow up in this period, it's like, that's what they've always known like. We've grown used to flu like we've grown, used to. HIV is just part of life.

Harry Glorikian: Yeah, I'm hoping that this also, though, encourages sort of global sequencing capability so that we can keep an eye out not just for this, but for anything that's on the horizon so that we can react to it, you know, as quickly or even faster than we did for this one.

Jacob Glanville: They have this really cool tech that I've starting to see. I heard about it before the coronavirus pandemic, but I've started to see it used quite widely. And that is RNA sequencing or viral sequencing of sewage systems. It's a really cool technique where you can test. It's a way of testing like a citywide level, the prevalence, the the abundance of the coronavirus. But but you can use that technology for any pathogen. And so they're setting this up on a city by city basis. You learn early before people are even showing up at hospitals. You could start learning "Oh, this virus is starting to appear in the sewage. Therefore, it's in the city." And that technology, you could start testing for a whole panel of pathogens quickly, and it gives critical early guidance on disaster response or early response measures and contact tracing. So things like that, things like rapid, there are these cool new rapid sequencing technologies that are available. I do see see a very strong place for them early. I mean, that's it was really contact tracing and low tech testing that kept Ebola in Africa. And I think we have abundant tools like that available globally, especially around people in transit and people in local communities. That's that's useful for coronavirus suppression. And like the flu, we have barely had the flu in last two years, and that's partially because of all these measures. I'm actually hoping that with really good testing, really good sequencing and contact tracing measures, that gives us an advantage over all pathogens.

Harry Glorikian: Yeah, I just don't want to be the guy having to go get the sample from the sewage just so you know.

Jacob Glanville: Fair enough. Yeah.

Harry Glorikian: So, so the last time we, you know you were on the show like it was all about Distributed Bio, which used computational tools and discovery and development to for therapeutic antibodies, right? So you were working on a universal vaccine for humans and pigs, and you were hoping you might find a treatment for COVID-19. You sold that company to Charles River in 2021. Bring us up to date. Tell us the story here.

Jacob Glanville: Sure. So, yeah, the history of Distributed Bio was that we were taking advantage of advances in computational immunology and high throughput sequencing, various high throughput assays, which are super useful at analyzing the immune system, which is a very complicated system. So getting a lot of data out is good and in particular, using these to use the immune system as a system for generating drugs, so antibody therapeutics to discover them and engineer them. Using these computational methods that enable Distributed Bio to, we ended up servicing 78 different antibody discovery and optimization contracts for 60 companies. We built part of the generation of new drugs that are coming online over the next few years without taking on venture capital. Because we were profitable the whole time from that service and that I used some of those resources to pour into these internal research campaigns for things that I thought were really cool technologies that were would be too awesome to offer as a service contract, but also too risky. I needed to de-risk them. And so when we worked with Charles River, they were very interested in our service platform and our service business. And they I said, Well, look, I have these things that are near and dear to my heart I've been working on. I literally like like shovel pig manure in my own vivarium to go do some testing on this broad spectrum vaccine tech. And they said, Jake, that's great. We would just want the service business. In fact, our policy is we don't want to have any program internal and Charles River that would be a potential, a competitive therapeutic or vaccine program, to any one of our clients. We want to service the world. And so you can spin those out and have them independently.

Jacob Glanville: And so that's what happened at the end of, yeah, it was actually the last day of 2020, December 31st, 2020, and we did the announcement a few days later. In January, we separated the service business, which was the Distributed Bio that Charles River acquired. And that's that business is still super active, and they're generating antibodies for companies around the world. And we spun out the therapeutic portfolio of the broad spectrum or universal vaccines and a series of other typically broad spectrum focused medicines like a universal antivenom, broad spectrum anti-infective against a couple of other disease areas that are important, into Centivax. And so that's what Centivax is. We spent the last year, basically, you know, with a new entity, we were working on our broad spectrum vaccine technology, which you're going to be hearing a lot about from us over the next couple of years for obvious reasons. Our main program was flu that I was working on from 2012 on. And we have a broad spectrum, our universal flu shot. Now we're obviously applying our technology now to the coronavirus. And also we're working on HIV.

Jacob Glanville: Then we have these other programs, including, as you alluded to, the CoV-2. So we made an antibody that could act as an injectable treatment for SARS-CoV-2. That's gone through manufacturing. We went up to the in the IND process with the FDA. We're on hold right now with that one because of Omicron. So they basically sent out a an announcement saying, Look, everybody who if you're making therapies, if they don't hit Omicron specifically, we are going to pause the clinical development. So we were supposed to go into humans at the beginning of January. But right now we're looking for a partner. We have additional molecules internally or we'd look for a partner that we're going to create a cocktail with because we we do best in class on a whole bunch of other variants. But like most of the other antibodies, we got hit with Omicron, so that's where that one is sitting. It also just frankly reinvigorated our point where like, look, the broad spectrum vaccines are ultimately what needs to happen here because the best antibodies can be hit by a jump. Omicron was a big jump. We saw how rapid this thing was mutating and suddenly jumped like crazy and then it escaped our antibody. And so our vaccine technology is specifically designed to address problems like that. So we're kind of going all in on it right now as we move forward.

Harry Glorikian: Yeah, because I was, you know, looking at the website and it's interesting because, you know, the language for Centivax and the language for Distributed Bio, like computational antibody engineering, right? So they they sound very similar. But you know, if I got it correctly, it sounds like the services business is what got shunted off and then the therapeutic business where you're creating products you sort of kept. Did you keep some of the people or how did you, you know...

Jacob Glanville: Very few. So we had a couple of people that came over. But part of the acquisition or is it like, OK, you can go take this stuff, but we want you to build largely a new team because because we need an operating business, because they are acquiring it for the business. And if I went and like, did my little Pied Piper whistle and all my people came with me, then they wouldn't have an operating business, which totally makes sense. So and it actually worked out because therapeutics, going into clinic is a fundamentally different type of business than running lots of antibody discovery. So at Distributed Bio, I had a lot of bioengineers and a team of there was a lot of younger scientists because we just need lots of people to run lots of programs. And then there's a hierarchy of more senior leadership. Whereas Centivax is a therapeutics company, it's a vaccine company. And so we have less people. There are 15 employees right now and a portfolio of things that we're focusing on entirely and IP. And my team is much more senior because what I wanted to do is cherry pick my favorite people I worked with for the last 15 years to have a lot of experience of bringing medicines into clinic and driving them to successful conclusion. And so that's largely a different team. Sawsen Youssef, who is my chief science officer at D Bio, she came with so. And we negotiated hard for that. So she's with me at Centivax. Dave Tsao, he joined in the last year at D Bio, but he he joined with the idea he was going to move on to Centivax. So that was expected and otherwise the whole team was generated, we were building up Centivax so that we'd have an independent company. So we pulled them together over the last two years and right at the spin out, we had people ready to just jump in and continue work. And so that's the team we have now.

Harry Glorikian: Now, you know, one of the things we talked about, you know, in our last talk was the influenza, you know, vaccine against influenza. Where are you with that? Because I think I remember you saying 2025 was when you thought that you might have something.

Jacob Glanville: We might be off by a year, but we got the results back. So we have some pretty astonishing results. We got back on live challenge studies here in the United States. So I think at the time we were talking up up until then, all the studies we'd run on that technology, we're running the vivarium, the animal facility that we produced in Guatemala. So I run that in partnership with the University of San Carlos, I'm an affiliate professor down there, and it gave me the ability to do rapid iteration cycles on live challenges, or not live challenges, but immunizations in live organisms.

Harry Glorikian: And I think it was ferrets and pigs.

Jacob Glanville: That's right. So the studies up here, ferrets? Yeah, you've got a great memory. So yeah, so what happened was the Gates Foundation. We won this Grand Challenge in the Pandemic Threat Award, and the Gates Foundation gave us money to go

Speaker2: Run the studies. They're like, look, we want you to run live challenge studies, which is where you go, spray the pigs and the ferrets in the nose with virus to see if they get sick. It's the ultimate test, and I can never run that in Guatemala because I can never bring virus down there, and my facility wasn't bio contained enough to do it safely. Whereas up here we're working with the University of Georgia has this guy named Ted Ross, like everybody in the vaccine space, knows he's an expert at running these kinds of studies, and so we ran the ferrets with him. And then we have Konstantinos Caracas, who used to work with Ted Ross, and now he's a professor at Auburn University. He does a bunch of these pig challenge models. And so we ran those studies. Those were cooking for last year and then into all most of last year and they're long studies because you're giving vaccinations. And then after that, you're giving exposing the animals to virus and letting them recover and checking to see, you know, the ultimate question, like, did it protect them from getting sick? Did it protect them from producing the virus in the lungs and the trachea and in the nose and stuff? And the data looks absolutely outstanding. So we're preparing a paper on it right now. We're looking now into manufacturing it to go into clinical trials so that we can go develop a broad spectrum vaccine for flu for humans. We're also going into the pig market because it's a $180 million a year market in veterinary space and it's the the same sets of proteins we'd be using. So it's kind of a free additional line of revenue, which in general, I always like to work on a veterinary relevant application of the same therapeutic when you can, because it makes sure that your drug works because like nobody really cares about a mouse, the mouse is very different than a person, whereas like some farmer cares about that pig, so your drug better work. And you make money earlier, it's a great system. Yeah, so we're pushing on that. And then for the same reasons, you know, we've been slow cooking the same technology on HIV for the last year and a half, and it's looking very promising so far. Although it's early. 

Jacob Glanville: I had not activated it against the coronavirus for the first first year and a half. I didn't because I thought, Eh, this virus is it's got a proofreading mechanism, so it shouldn't mutate that fast. And B, there are these other technologies that were just blazing forward so fast with the RNA tech. I just said, you know, I want to see how they do. There's like 180 companies working on vaccines. Let's see how they do before I go dive in there. They may not need my technology, but then in the last few months, we've seen the rise of Delta and then Omicron and a whole bunch of other super-mutated versions. And that absolutely needs my technology because the current vaccines are having real problems keeping up with the new mutant versions. So we are running the accelerated studies right now using my same vaccine platform on the coronavirus as we move the flu program in first. The coronavirus is actually moves pretty fast because we can benefit from a lot of the legwork we did already in the pigs and ferrets for the flu program and then the the HIV program, the that one, I'm a little more hesitant on when it will be ready. It's a big, first off, it's a big lift. So I want to have really, really good data before I start trotting around being like, "Hey, you remember that HIV problem? Yeah, fixed it." You know, I can't say that yet, so I need to see the data. But also but like technically, as a bioengineer the HIV proteins are tricky. They tend to fall apart. And so we're doing a number of things to try to figure out how do you make a vaccine and actually make it practical to deliver it? And we haven't solved that yet, and I don't have a solved engineering solution. I always like double my timelines until because because you just don't know how annoying that little path is going to be. It could be easily solved or it could be worse than you think. And so that's sort of our our we're going to get it working. It's just going to take some time to make sure I have it nailed on how we deliver the HIV proteins.

Harry Glorikian: Yeah. And when you think about HIV, I actually, you know, I think it's becoming, you know, if you look at. The populations where coronavirus is mutating, it's immunocompromised HIV patients, right? So if we could solve one problem, you might. I don't want to say you could kill two birds with one stone, but you might actually solve another, you know, problem that that maybe hadn't been as big of a problem before coronavirus and now is becoming a problem. 

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

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It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Harry Glorikian: So, but, when you're working on these things, do you think there's enough government and private sector support for some of the other things or is coronavirus sort of, you know, sucking all the oxygen out of the room?

Jacob Glanville: Yeah, so that's a great question. Yeah, I spent a lot of time thinking about this. So the. You know, we have I have these other programs, right, so that I actually I inherited a whole portfolio, I have more stuff than I know what to do with it. Our decision was first off, because we had amazing data on the time, and this is the time where everyone says, Look, we need to, this is ridiculous. We need better, more broad spectrum vaccines and we have the IP that works. We're focusing on that. These other programs, like at least with the vaccines for flu and HIV, there's resources out there. Paradoxically, like we were told by some government agencies, you know, it's actually easier to apply for flu money than COV-2 money because it's so so like tied up and and policied out already or the bets have already been placed. With HIV, we actually have, basically with HIV I'm waiting for the first of these critical animal studies that will tell me when I can start waving the flag and saying, Hey, look, we have something exciting here and I can run those studies internally and they don't cost me that much. So I'm just waiting to make sure I have a pie to bake before I start yelling around and saying I need an oven. For the other programs it's a mix. I think it's definitely the case that, like one of the things we inherited is a cancer program, and four years ago, venture capital groups. I would show them my vaccine technology. They would listen to it. I'm like, Look, hi guys. You know, I want to be humble here, but like this will solve all of those mutating viruses that we've struggled with. How would you like it? We could contemplate a world without some of the pathogens that we've lived with since the beginning of time. And they're like, "Sweet. Can you apply it to cancer?" Because four years ago, all they all wanted cancer drugs that was really hot. Well, the thing flipped. That's the kind of amazing because it's good timing. I've worked on this thing for nine years. I show up and then suddenly I still thought I was going to have to fight this thing uphill. I have a cancer assets that I could use, talking with venture capital groups, and like much to my delight, they're like, "You know what, actually the vaccine thing is what we care about." So finally, it's like, become popular. I feel like a high school kid who like, went through puberty and suddenly I'm popular at the school ball or something. I'm like, "Oh, sweet, you like this now? This is great because this is what I'm really good at."

Harry Glorikian: Timing is everything, man. I mean, you know, if you're just off the mark, people don't see it. So, but you know, I think if I was reading correctly, you're also working on a bunch of antibodies against bacteria like Staphylococcus, Pseudomonas. You know, these are all bacteria that that seem to have evolved some resistance to the classic antibiotics. So is is, you know, why is your platform good at targeting these organisms and where are you at with that sort of thing?

Jacob Glanville: Sure. So the unifying concept of the company is like, we have this tagline "Smash the mutants." Yeah, the principle is we focus on areas of medicine where mutations are the thing that causes a problem of effective treatment. And so for our vaccines, we have this broad spectrum spectrum vaccine technology. For areas like cancer or bacteria or snake venom, there's also a problem of mutation, and we in many cases, we use broadly neutralizing antibodies to address them. So with the thing you brought up, which is the multidrug resistant bacteria, there, we're doing something that honestly I think other groups should have done eight years ago. But the there are there's this impending wave of doom of a bunch of new multidrug resistant bacteria. It's pretty easy for bacteria like staph and pseudomonas, the kind of nasty things that get in a wound to develop resistance to the antibiotics. Like within a year of creating a new antibiotic he bacteria has got, there's some new variant out there that's resistant to it, and within 10 years, that becomes the dominant variant if that antibiotic is used at hospitals at any frequency, and it's they're able to do this because there's a lot of genetic diversity in the population of the bacteria. They they have a half, every 30 minutes, they're making a new copy of a bacteria in some cases. And every time they do that, there's a chance of a mutant. So there's lots of mutant bacteria out there in the world. And so as soon as you start using an antibiotic, whatever one of those bacteria that's resistant to it is suddenly going to gain a lot of favor. And it's really easy for them to gain antibiotic resistant because all they need to do is have one mutation in one gene to block the one pathway for that one antibiotic. There were efforts to use antibodies as therapies against multidrug resistant bacteria 10 years ago, I was involved in some of them. But at the time, the feeling was that manufacturers would only want to produce one antibody. And the FDA would only want to prove one antibody. So you need to do the job with one antibody, which is super stupid, because, we knew this back then, bacteria are not, they're not one-trick ponies. Staph is an amazing engine of war. It's releasing multiple different toxins. Here's one to go knock out your B cells. Here's one to knock out your T-cells. This one's going to trip up your, your neutrophils. And so forth. And so we try to make one antibody block one of these things, the other, the other toxins would just do their job and mess you up. And so what we're doing is we're going back and saying, OK, first off, let's get some broadly neutralizing antibodies that can hit multiple toxins at the same family. And second, let's get a couple different antibodies to go after those different, those different toxin classes in parallel, and so we're creating a multivalent solution to go after multiple different toxins all at once. That's really how the body defends itself, and that's an effective drug solution. It uses broadly neutralizing antibodies. So we hit these shared sites that are found between the toxin flavors or variants, but it's otherwise it's a practical approach. I think our technology helps us go after those broadly neutralizing sites, and it also enables us to engineer the antibodies so that they don't require an IV bag, which right now with the coronavirus does. Antibody treatments usually required an IV bag, which means you had to go to an IV infusion center, which means they wouldn't give it to you because they didn't really want you going into the hospital with the virus because they had people that honestly were more sick than you already were. What you really want is an injection that could be done at it  Walgreens, or it could be done at home or an outpatient treatment facility. But to do that, you need to engineer your antibody so you can concentrate them like crazy so they can go in a syringe and they need to not be so thick that it's like toothpaste. They have to have low viscosity so you can inject them. And we are really good at engineering antibodies so they can do an ultra high concentration and low viscosity. And that's part of the reason why we have partnerships with the Navy and the Army because of our capacity to do this. And then it's also with the NIH. We're doing it for our antivenom program. Also, you want something that has no temperature control, can be delivered ultra high concentration and can be shipped around the world, which is really the way I think antibodies should be delivered everywhere. But but certainly in these applications is a great place to start.

Harry Glorikian: So, yeah, but you know, it's interesting, right? I mean, you're talking about all these incredible hurdles, right? I mean, you know, people say DARPA-hard, right, these these are not easy, right? So isn't there an intermediate step so that we can? Yeah, help a bunch of people along the way.

Jacob Glanville: Yeah. Let's see. Well, I mean, that stuff I just described is done. We like we have a cocktail, which is already a staff is our first, our first bug that we're going after that's done with the Naval Medical Research Center. We have a cocktail of antibodies there, concentration engineered and we already completed our concentration engineering technology. We used it actually on our SARS-CoV-2 program. It's the highest concentration antibody ever produced through GMP at 254  mgs per ml. So we've we've got that built. We just we just can apply this to antibodies when we want to now. And so we don't there's not a set of steps before we can get there. This is just how we produce our drugs.

Jacob Glanville: Now, the the thing that you know, the thing I worry about, which is fair, is it's like, OK, I actually have more things in the portfolio than than I have time to effectively focus on all of them. So our strategy has been all in on vaccines right now. This is the time for it and we have these programs. Some of them are supported by grants. And so the grant supports some continued development on them. Some of them, we've frozen down Some of them we continue because they're looking super promising and we have grant money like the the universal antivenom. That one is covered by an NIH grant. The Naval Medical Research Center is working with us on the Staph program and we're running live studies with them. Otherwise, our other programs are mostly being paused right now. This is the beginning of a portfolio later, but right now there's so much promise in the vaccine technology. I'm like, I'd rather just nail that like crazy. And then we have as we grow, as I contemplate an IPO, I'm going to have a nice, rich portfolio or I have a business development partnership portfolio I can I can engage other groups with. And that's kind of how I "Focus on focus" and prioritize these things.

Harry Glorikian: Yeah, because when you were talking about the the antivenom, I was like, OK, I don't know much about chemistry of snake venom. That one sort of slipped my purview over time, not been at the top of the list. Maybe if I lived in Arizona or something like that, I might take it a little bit more seriously. But Massachusetts, we don't have this problem. How does your how does your computational platform help identify like? Is it the same thing that we were just talking about when it, you know, when we come to the, you know, against the bacteria, is it the same approach?

Jacob Glanville: Yeah. So I mean, at heart, everything I'm talking about involves broadly neutralizing antibodies. These are these antibodies that recognize a shared site that either a virus can't mutate because if it mutates that site, the virus doesn't work anymore and we just get an antibody to go bind that site. It's the Achilles heel of the virus, or it's a shared site between different evolved versions of like a bacteria toxin that it can't change that site because that's the site that's necessary for the the toxin to do its job. If we get one antibody against it, it'll block all the toxins of that class. Or again, snake venom. We have a broadly neutralizing antibody that hits like the one shared site on the neurotoxins of all snakes. Then you have a single antibody that could act as a universal basis of getting rid of neurotoxin against all snakes, so therefore universal antivenom. So that's the power of broadly neutralizing antibodies. And then our technology platforms are either vaccines which are engineered to make you produce them. So we give you actually collections of proteins and you produce those broadly neutralizing antibodies or on the antibody side, we make them in the laboratory And then we deliver them to the patient. But that's the that's the central principle.

Harry Glorikian: It's interesting. I wouldn't have. Again, not knowing anything about snake venom, I'm like, is there is there a universal site across all venoms?

Jacob Glanville: There is? Yeah, it's actually pretty, this is a cool project, so it's a side project that I lov so much. Som there are five hundred and fifty venomous snakes that cause risk of death or or limb loss in humans, and it's more than you think. 100,000 people die every year, and another 300,000 or more get permanently injured. 550 different snakes and each snake is injecting like 10 to 70 different proteins in their venom. So it's a very complex mix, which seems insane. If you look at that, you're like, How are you ever going to make a universal antivenom? But if you look at it, the the snake venom, actually, if you take all those sequences of all the different toxins and you analyze them, they actually collapse down to 10 families of toxins. And of those, there's really only four that really matter. The other six, it's going to hurt. But you know, like, suck it up and walk it off, you'll be OK. If it's a neurotoxin that's going to stop your heart and stop your lungs and cause you to go rigid and die, that one's no bueno. You have a phospholipase that causes a whole bunch of other tissue problems. Serene protease metallo-proteins. These are these are the things that are going to tear you up. That's the reason you're you have to amputate. That's the reason you die.

Jacob Glanville: And so because all the animals use the same, all these snakes use those same four toxins. If you can make an antibody against each one of those four, really four antibodies would be a universal antivenom. Which gets to your question. Ok, but are they concerned enough that you could get a single antibody? And the answer is surprisingly is yes. And here's the reason. Ask yourself there's 550 snakes. These things are eating, they're eating birds, they're eating little mice, they're eating fish, you know, whatever. Like all these different animals, they're all very diverse. How is it that they're neurotoxin works on all these very diverse, diverse, different species? And the reason is this it's pretty cool. The neurotoxin binds to the nicotinic acetylcholine receptor. If you line that up the sequences, it is almost invariant all the way out to fish. And this is even a pretty conserved receptor, even more primitive creatures than fish. So this thing has not changed, particularly the sight of the nicotinic interaction since the dawn of time, right? Evolution hasn't changed the site, and that's because the and this is true for many neurotransmitter mediated brain genes, the ones that interact with a molecule that's providing signaling that those neuron networking, that's like the wiring in your house for the internet, it hasn't changed since the beginning. So they haven't been able to evolve it. And the neurotoxin in these snakes all binds that exact same site, which means that neurotoxin works on all species. That also means that the snake venom, even though the snakes have evolved elsewhere, they can't. They can't change that site that interacts with that gene that's never changed or their their their venom. Their drug won't work on all these species, and we've found an antibody that docks directly into that site and therefore it hits all the venoms.

Jacob Glanville: And where we got it from was a guy named Tim Friede, who spent 17 years and nine months self immunizing with escalating doses, 700 self injections of snake venom from Cobras, Taipans, Mambas, western diamondback, Mojave Crate. The list can go on, and he has meticulous records. He built up hyper immunity against the snakes to the point that he had received 202 bites from many of these snakes and survived snake bites that would kill a horse. And I reached out to him in 2017 because I geard what he did, and I built a technology that made it easy to go harvest the DNA sequences of the antibodies you're producing. And I wanted to test it on something. And so I found this. I was looking for someone who like I was looking for a clumsy snake researcher who'd maybe been bit three times. And instead, I found I found out about this guy and I was like, I got to test his blood. And so I said, Look, I think you may have done something amazing because you kept changing the snakes over and over again for almost 18 years. I think you selected for these broadly neutralizing antibodies, and I'd like to find them. And we started collaborating. And sure enough, we found the collection of these remarkable antibodies, and we know that he's got them in him because Tim isn't dead and he should be if he doesn't have broadly neutralizing antibodies. And so that he's the real cool. I mean, computational stuff helps us harvest them out and do some analysis. But the real the real magic for that program is Tim Friede.

Harry Glorikian: Yeah. "Honey, honey. Don't worry, I'm just it's just a bite. Nothing's going to happen. It's only a king cobra. Like, we'll survive this. It'll be fine." He's the guy you want walking in front of you through the jungle first. So well, that that's interesting. Well, I'm you know, this is why I do this show, right? I learn something every day. So but in the big picture, like, you know, COVID-19 seems to have like really changed the game in this whole space, right? I mean, it's all sorts of attention for therapies and it's bleeding into, like you said, you know, maybe infectious disease, including autoantibodies, I mean. You know, but here's the question is, is it is it getting easier to do the research in this area than it was Pre-Covid, is there more interest from partners and funders? You know, how is the technology itself improving? I mean. All of this, I think in a lot of areas that I've seen is caused forward thrust of activity, money technology, and so I'm asking you that question is what do you see, is it really helping?

Jacob Glanville: This is a mix, so I would say that. There are a number of technologies that benefited, so certainly rapid testing has gotten way better with respect to therapeutic development. There was a period where,

Jacob Glanville: And to their credit, the FDA did an amazing job during this period of triaging an insane number of incoming requests. And they did it super fast. Like everyone who I've ever worked with in this space was like, I cannot believe how responsive the FDA is being right now. And so we're all worried that that might change back to the old, you know, the more standard way. Pretty soon, we're hoping that they're actually this has given rise to an improvement in the the pace. It may not be crisis level pandemic fast, but there might be improvements in terms of the turnaround time with the FDA. And that would benefit drug development. There's been it spurred a lot of additional exploration into manufacturing platforms, discovery platforms. I think that stuff before and for me personally, it's I mean, really, it's the timing is brought on like a golden age of interest in better new vaccines, right? When my technology was right for prime time, which I am excited about. I think those things are good. I think there is also a lot of new research, like a remarkable new research. the whole world was so much of us were attending to these rapid inducible animal models for live challenge against new viruses and the like, these pseudo varion particles and all these cool new technologies that will serve us well as we go after additional pathogens. And there's like a level of like reluctantly induced immunology fluency and virology fluency among our politicians and our policymakers, which is helpful, I think. And you know, that'll wear off, but I think over the next five years, I think that's helpful because, you know, nobody used to know what I did. And now I go into rooms and people like, "Oh, what do you think about the immune imprinting theory of original antigenic sin after receiving a cross vaccine?" And I'm like, "Oh, hi, grandma, how are you doing?" It's crazy, people have a lot more knowledge of our space now. That's that's that's great. And because I think that really actually helps. The space is complicated. It's immunology, it's epidemiology. And so how do you even make people start to have good policy decisions if they don't have some level of exposure to the concepts? And I think that's just dramatically changed. The whole world got a master's degree in infectious disease in the last two years, and that's a good thing.

Harry Glorikian: Excellent. So you think the glass is half full as opposed to half empty on the future of. Drug development against infectious diseases.

Jacob Glanville: I do. I Actually do. I think we're actually in the kind of entering the golden age. It seems funny to say that in the middle of a pandemic we can't seem to squash. But I think this did serve to accelerate infectious disease therapy, vaccines, diagnostics, and I think that's going to serve us really well because there's lots of different pathogens out there that have made us. And I think we have encountered this this time in a golden age of biotechnology and I think a lot of tools and realization of interest. So venture capital is suddenly interested in these technologies. Big Pharmas are realizing, Hey, you can make big money from infectious disease, where before I think it had been relegated to the back seat or the big boy cancer and neuro and a couple of others, cardio, we're sitting up in the front seat and I think that's changing. And that's good because like infectious disease is unlike many other diseases that humans have in that you can eradicate. It's not outside of the question that you could solve the disease forever until the end of time. And that's exciting, to be able to deal with our in our hands and our in our lifetime, if you can contribute to that.

Jacob Glanville: Now I want to be blunt, it hasn't happened that often we've gotten really pushed down certain pathogens. We've only really succeeded in eradicating through vaccination one, close with a few others, but maybe we'll have the tools now to actually push farther. Maybe we'll create that network.

Harry Glorikian: Yeah, no. I mean, I totally. You know, I try to tell people when I'm giving talks like, some things that would have taken another five or 10 years at a minimum have been pulled forward, yeah, because of this dynamic, and, you know, it's not just in where you are, but like. telemedicine and digital products and all sorts of things that have sort of been thrust forward because of. You know, so I try to look at the silver lining of. I'm sure there are people going, "It still sucks." I know it sucks, but sometimes you need. A push to move this stuff forward or at least get people to take the right level of risk or chance to to to get something to jump forward.

Jacob Glanville: Yeah. You know. It's the presence of the crisis has given it a lot of attention and the fact that it's going to be annoying and frustrating and still dangerous for some years ahead means there's going to be sustained attention here I. I got to be blunt, I don't see how they're going to eradicate it in the near future. I think it's possible technically but impractical, and that means that there's going to be a period where this is still going to pose a significant economic burden and annoy people and that that serves to put more. That's going to drive innovation like we innovate in the squeaky wheel, gets the grease and the coronavirus, and pathogens are a squeaky wheel right now. And I think that is a silver lining here, I think. Beyond the wave of people going through yet another cycle of realizing, hey, this isn't just going to magically disappear, I think there is a silver lining on the other side of that where people are going to say other coronaviruses around, but it's manageable now. We don't have lockdowns anymore. We can live our lives. It's just something you have to watch out for it. But there are, there are vaccines and we have better ways of detecting and squashing the outbreak. So I think there is going to be a life resembling normal as we remember it, the new normal. I think that is going to emerge. And that's going to be good for people, but they're still going to be a pressure here, which we also, we it's not only good that we're doing this, we have to do this. Because we have more people on the planet than we've ever had before. And they travel much more than they ever had before. It's the astonishing level of speed that this thing got out of South Africa hit all the major hit all the major airline destinations like within a couple of weeks, the virus was everywhere, and that's the reality world we live in. So we need these tools as we become a rapidly moving and highly populous planet to be able to protect ourselves. Because otherwise we're just going to keep running into this and potentially at an accelerated pace compared to even previous generations, where at least they had slow moving boats that would stop the slowdown.

Harry Glorikian: Yeah. I mean, can you imagine if we had this level of travel in the 1918 flu? I mean, that would have been bad. Well, look, I'm I'm an optimist like you're an optimist, I'm really hoping that all this like makes the positive change and and I'm on the venture side anyway. So I like, you know, I invest in it and, you know, I'm hoping for, you know, a profitable outcome. But at the same time, you know, making a difference in people's lives and having a positive outcome for, you know, everybody on the planet. So God, it was great to catch up with you again. I mean, it's only been two years in the world is I don't think I've left this room for two years, but that's what it feels like.

Jacob Glanville: So we can't because of Omicron. So but we'll be getting out of these rooms soon. That's the point I want to make.

Harry Glorikian: Yeah, no, no. And believe me, nobody is looking forward to it more than I am. So I think the extrovert has become an introvert over time. So it was great to talk to you. And you know, I look forward to staying in touch and seeing the evolution of Centivax. 

Jacob Glanville: Right on. Well, thanks again for having me on, and it's always fun to talk.

Harry Glorikian: Thanks.

Harry Glorikian: That’s it for this week’s episode. 

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian. Welcome to The Harry Glorikian Show, the interview podcast that explores how technology is changing everything we know about healthcare.

Artificial intelligence. Big data. Predictive analytics. In fields like these, breakthroughs are happening way faster than most people realize. 

If you want to be proactive about your own health and the health of your loved ones, you’ll need to learn everything you can about how medicine is changing and how you can take advantage of all the new options.

Explaining this approaching world is the mission of my new book, The Future You. And it’s also our theme here on the show, where we bring you conversations with the innovators, caregivers, and patient advocates who are transforming the healthcare system and working to push it in positive directions.

People used to go through their lives not knowing very much about what they were eating or what was going on inside their bodies.

If you time-traveled back to the year 1900 and you stopped a person on the street to ask how much they weigh, they probably wouldn’t be able to tell you—because the bathroom scale didn’t become a common consumer item until the 1920s.

If you visited the 1960s and walked into a grocery store, you wouldn’t be able to figure out the calorie, protein, and carbohydrate content of anything—because nutrition labels weren’t a thing until the 1970s.

And until very recently, the only way to figure out your blood pressure was to visit a doctor’s office or find someone who’d been trained to use a blood pressure cuff. Now you can buy an automated home blood pressure monitor for under fifty dollars.

And of course, if you have a wearable device like an Apple Watch, a quick glance at your wrist can show your heart rate or even an EEG readout.

So, what’s the next health-related measurement that’s about to go from obscure to commonplace?

It might just be your glucose level. 

Until recently, getting a blood glucose measurement required a finger stick. The whole process was so painful and annoying that only diabetics taking insulin bothered to do it regularly, to avoid episodes of hyper- or hypoglycemia.

But there’s a new class of devices called continuous glucose monitors, or CGMs. They’re pain-free, and they’re rapidly coming down in price.

A CGM sticks to your arm and it has a tiny electrode that goes into your skin to measure glucose levels in the interstitial fluid. There’s also a radio that sends the measurement to an external device like your phone. 

I wear a CGM myself. Over time it’s teaching me which foods cause my glucose to spike the fastest, and which ones can help me keep it more even over time.

My guest today, Maz Brumand, works for a company called Levels that wants to use CGMs to help everyone understand how their choices about food and lifestyle affect their health.

Maz left a pretty high-level position at Apple last fall to join Levels.

And my first couple of questions for him were about what attracted him to the company, and why he would leave a company like Apple, with more than a billion users worldwide, for a health-tech startup that isn’t even out of beta.

So here’s my conversation with Maz Brumand.

Harry Glorikian: Maz, welcome to the show.

Maz Brumand: Thanks, Harry. Thanks for having me.

Harry Glorikian: So I want to start by maybe, [going over] the story behind Levels. I mean, you've got five great founders with, you know, stellar Silicon Valley credentials from companies like Google, SpaceX. And, you know, pretty much why they started the company, you know, and I'd love to understand sort of the special sauce and unique insight that you guys felt that you could bring to the market for mobile health monitoring.

Maz Brumand: Yeah, that was a good question. You know, we have found five founders, as you mentioned, and they're just fantastic group of people. They're they're very passionate about this area in health. And I think all of it started from Josh, one of the founders where he quickly understood that there is power in CGMs and that he has been in his account living a healthy life. But when he actually started measuring his glucose, he realized that a lot of the things common knowledge or advice around food was wrong. And there is great stories on that on our podcast. For example, drinking juice. And all of us think that drinking juice is the healthiest thing you could do. And so I think one of the investor meetings, he took a juice that was, you know, presumably very healthy, a green juice, and drank it and shot his saw his glucose spike sky high. And so that was kind of an indication that there is something here. But you know, the thesis behind the company is that we don't know what's going on in our bodies. And if we could create a dynamic where we have bio observability and by that, I mean, we can actually see what's going on inside our body based on our behavior and actions. For example, in the case of CGM, if you eat a hamburger, the CGM will tell you how your body's going to react to that in real time. Or if you eat a doughnut. It will tell you so. There is no two questions about it. It's very specific to you and it will show you in real time how your behavior is going to impact your health. And that's very powerful. And so the thesis of Levels is starting with CGM, can we create that feedback? Can we close it in real time? Can we show you how food and your lifestyle affects your health and create this path towards healthier lifestyle and healthier decisions?

Harry Glorikian: Yeah. You know, we're going to jump into all of that, but I want to step back for just a second. You spent nine years at Apple. You were head of business and strategic development for the Health Strategic Initiatives Division. So just did you? What? What products did you did you work on? Because that's super exciting.

Maz Brumand: Yeah, the stuff that's public. We worked on a lot of research efforts to really understand, for example, how human cognition works. One of the projects I led was quantifying cognition to understand how cognition changes based on lifestyle and then also based on decline due to disease. And that's just an example of one research. We had research around how screening affect early on will change the trajectory, so I spent a lot of time thinking about how does our behavior and how does technology allow us to improve human health.

Harry Glorikian: So I was reading about your background. I mean, you, you seemed like an outdoorsy guy like, former triathlete. If I if I read it correctly, were you always interested in health and wellness technology or did that something was that evolved over time?

Maz Brumand: Yeah, that's a tricky question. From the wellness perspective, I've always been interested. I've always been an athlete. I've always been active. I always try to manage my food. But if you asked me 10 years ago that I would end up in health, I would have told you, you're crazy. And the way I thought about health was always like being hospitals and IT systems, and it did not interest me at all. I thought it was slow and and not very interesting. But as Apple entered its health journey, obviously with releasing the Watch and then putting a heart sensor on the Watch, which was actually more elementary, yeah, I've got one, too. We quickly realized that there is so much power putting the consumer at the center of their data, and that kind of led to the whole platform that Apple created around HealthKit and ResearchKit and then built the products at the top. That being involved in that and I was part of the New Technologies Group within Apple on the commercial side. So I got introduced to that. And when I saw that, I fell in love with it because I saw that we can really change the discussion about health and put the consumer at the center. And nobody's better than the consumer to make decisions about their health. They're the one that probably cares most about their health. And so creating the dynamic where you allow consumers to take control of their health, by providing insights, by providing clarity, by providing services to help them manage that, seemed like a better way than having a disintermediation that we've obviously experienced in U.S. health care, and it's very well documented.

Harry Glorikian: Yeah, I mean, you know, I'm in the venture world, so I mean, I love the the way the technology is changing the entire, you know, center of power or center of gravity that that that's evolving over time. But I mean, Apple is like, I don't know, over a billion users in the world, so, you know, you left for a startup. Why?

Maz Brumand: Yeah, that's a good question. You know, working at Apple, I learned that to really make a difference, it has to be an ecosystem. And each of the players in the ecosystem have a different role. For example, Apple has really played a fantastic role in creating the platform and allowing people to take control of their health data on their phones. And it's built a platform where other people can now build on top of to help. But Apple plays a unique role in the sense that it is this platform and it is going into verticals and trying to help wherever they can. But there are many more opportunities for startups like Levels to come and build on top. And when I was doing a little bit of soul searching about what would I do, I want to do with my life for the next 10, 20, 30 years, I was thinking about what are the big problems that we need to solve in health, and two areas became pretty apparent to me. One was metabolic health, because it's the underlying of many of our chronic diseases, which has not only economic implications, but health implications around morbidity and mortality. And it's a big problem not just in the US, but around the world. And then the second was mental health. And looking at the space, what I thought made sense and looking at the companies, metabolic health is what I really wanted to go tackle. And I got introduced to Levels about a year ago and I've been watching them.

Maz Brumand: And the fact that they're building in public and being so transparent really helped me get to know them over the years. And I think the metabolic health space or in some of these things that are still in early innings, you need a startup to take the first step and accelerate and take risk to make this into something that consumers will accept. And there is a lot of things that needs to be done and put in place for this mission to be accomplished. But I felt that I could do that inside a startup faster. And then obviously, companies like Apple and others can help scale this and make it available to many, many, many more people, not just here in the U.S., but globally. But I think there's just a different role to be played by startups and Apple, and I felt like getting to know Levels, I felt like they've got the DNA that's not too different than Apple. High integrity, focus on customer trust. Just like Apple, focused on privacy and trust and the way they're building the company focusing on culture is also something that's quite differentiated. So even though there's different places in their evolution, I felt like it was similar DNA between Levels and Apple. And it's just that in metabolic health today, I think a startup like levels can move a lot faster and create that change.

Harry Glorikian: Yeah, I mean, I've looked at a lot of these different, you know, technologies people say, Well, you know, you're wearing an Apple Watch. What does that do? What does this do? And I always tell them, I'm like, I think of the Apple Watch as sort of a aggregator or data repository, and things that sit on top of it are the monitoring or applications that would then do something with the data that that then is useful to me. But I mean, I've I've worn a CGM, you know, I can tell you that Korean bibimbap like spikes the hell out of me and it stays up there for much longer. But but you know, I just I was talking to somebody the other day and they're like, OK, why would you wear a CGM? And and you know, how do I use it and so forth? And I was trying to walk them through the other things. But you get to now tell our listeners: So why do healthy people need this data? Why is this CGM data useful for people who are not diabetic or pre-diabetic, right?

Maz Brumand: Yeah, yeah, that's a really good question. Look, you can look at everything from a disease perspective and look at, so now I've got a disease, how do I treat it or treat the symptoms? Or you could think of a foundation. And so what is actually leading to these things that are now disease and or symptoms of disease? It's kind of like saying, I'm overweight, so I should go get a scale versus having a scale to measure yourself to make sure you don't become overweight, but then saying, I only sell you a scale if you're already overweight. So if I show up and I'm skinny, I cannot buy a scale. It's kind of a crazy thought experiment, right? And then the CGM is based, I think, you know, we should think about like, what are the underlying things that are leading to these diseases? And it is metabolic dysfunction, which is how your cell produces and uses energy. And this is a long journey. It doesn't happen overnight. So it may take 10 years for somebody to develop diabetes and you really want to measure their behavior today that's going to lead to that metabolic dysfunction and intervene today. So what CGMs do and other technologies like that, is they provide real time feedback at the molecular level, which is what called bio-observability, to help you change that. So if I don't know something is not working for me metabolically, how can I change that behavior? For example, I used to eat oats in the morning.

Maz Brumand: And I think many people do. I always thought that's the healthiest thing I could do, I sometimes would even skip the milk and just be literally oats, which is crazy, right? And I thought I was the healthiest person in the world until I put on a CGM or saw other people put on a CGM, that that oats are really bad for you, especially right smack in the middle of your morning, when you're actually trying to have sustained energy over the day. So CGM enable you to see that. Because first of all, I don't think science and knowledge around some of these things is is is well understood because it's so hard to do a clinical research to study food. There are just so many barriers. I think CMG for the first time at a personal level was telling me, like, what do I need to do today that would help me have a better outcome years from now? And also—that's the disease perspective, so you asked about disease—and then from a wellness perspective, there's a lot of benefits. Like the fact that I have higher energy. The fact that I am probably healthier, metabolically healthier. So I'm more resistant to disease. Obviously COVID being a big issue. So I think there's a lot of benefits in thinking, both from how can I address the underlying factors that lead to disease and then also on a day to day basis, how does that make me feel better?

Harry Glorikian: Yeah, so so for those you know, people listening, what's the benefit of keeping your glucose level flat and steady? I mean, I do my best to do that, but you know, I'm not sure that everybody fully appreciates what that does.

Maz Brumand: Yeah, I'll talk about it from the wellness perspective. When you have a glucose spike, your body produces insulin and it crashes that back down. And when you get back down, that's the afternoon lull where you feel low energy. That's our lethargic brain fog. So just from a wellness perspective, just from, you know, how do I want to live my life perspective, managing these spikes allows you to feel better during the day, and that's a pretty easily, like, you'll feel that. That's from the energy level, also from brain fog. You know how in the afternoon, you might feel like your brain is not working?

Harry Glorikian: Yes, I remember how it used to be.

Maz Brumand: Me too, I used to think that afternoon like dosing or feeling tired was normal. Until now, it's like now, why do people take naps in the afternoon? I don't even get it anymore. But you know, joking aside, I think there is a huge impact on energy levels and your mental fog. And then obviously long term leads to insulin insensitivity, which leads to all sorts of problems, chronic problems.

Harry Glorikian: Yeah. So. On the website, you know, you guys talk about hardware, software and then this very interesting word called insight. So I want to sort of focus on the insights part of it. What kinds of analysis or advice do you offer members about eating or exercise? And if you can describe the scoring system in the in the app, the I think it's called the zone score and the day score, right? So just if you could help me understand that, that would be good.

Maz Brumand: Yeah. Well, you know, one of the things one of the early decisions we made was really focus on creating content and education. So we publish hundreds of articles a year about metabolic health and how different things affect you, and some of them are really deep and well researched. And it's scientifically based. So we put a lot of energy into creating content that will help explain the science and explain the physiology. So there's a lot of content that is available on our blog that's available in our app. And so that's a primary focus for us. One of our objective is actually to make metabolic health into the zeitgeist. And if you go on Google, search that you'll find Levels is one of the top hits as explaining what all that is. So there's a huge philosophy within our company that we want to be science based. We want to help people understand what metabolic health is and how they can affect it. So that's the core philosophy. The second question you asked is around what is Insight. So you want to know, for example, if your glucose spiked right and you haven't logged anything, we ask the user, Hey, did something happen? And that's a teaching moment where they go on and put in, "I ate oats for breakfast," Or something like that's a teaching moment.

Harry Glorikian: And then having content that explains that is when you have that aha moment. Or let's say you ate something one day that affected you. Nothing. Fine. And then the next day, it's a crazy response. We give the ability for people to compare. So imagine one of the things is the order in which you eat your food actually matters, which is actually really mind blowing concept, meaning I can enjoy the same thing. I just have to change the order. For example, if you eat naked carbs at the beginning of your meal versus if you're having protein, fats and fiber and then eating the carbs later, glucose response will be different. So helping people compare different instances or behaviors is another insight. And for example, you could also do, easier, you could say, like I ate dinner, sat on the couch, watch TV, or I ate dinner and took my dog for a walk for 10 minutes. Not even something strenuous. And you'll see the response. So these are moments that it creates these aha moments or insights that will help you change your behavior.

Harry Glorikian: Does the app actually, you know, other than showing the spike, does it sort of make it digestible for someone? I've not played with it, so that's why I'm asking. Does it put it into, you know, human speak or some way to communicate with someone to let them know that these are things they they should be paying attention to?

Maz Brumand: Yeah, I think the short answer is yes and no. Yes, in the sense that we do it today and we're planning to make it better. No. Are we reached the end goal to make the perfect app? Not yet. We're in that journey and we're constantly innovating and creating new experiences and new ways to help people understand their behavior. But I'll give you an example. If you, for example, see a spike after a workout...What happens when you do strenuous workout, your body produces glucose to power you, and so you'll see a spike, but that spike is not the same as if you ate a donut. And so we will show content to people that say, Hey, did you know that this is a spike and we're not going to hold this against you? For example. We'll take it out of your score because it's generated based on good behavior, which is exercise, versus not so good behavior which is eating a donut.

Harry Glorikian: Right, right, right. And there's a difference between a spike that comes up and down, which is normal versus one that stays up for a long period of time.

Maz Brumand: Yeah. The area under the curve is important now. I think another angle we haven't talked about yet is research. I think we know a lot, but real time CGM in health and wellness, at least in the wellness side, is relatively young. So there is a lot of work to be done to actually understand at a deep level all these questions that we have and you have on the customer will have. So there's a lot to do there, which we could talk about separately.

Harry Glorikian: Yeah, I mean, I actually, I mean, I think about all the different companies in this space and I think like you guys are running probably one of the largest, sort of, I don't want to call it a clinical trial, but for a better word, right, on actually a healthy population looking at this space. So the data is going to be hugely valuable to   drive, you know, next level of how to communicate and what to communicate to each person.

Maz Brumand: Yeah. And also, you know, we take actually research pretty seriously and science pretty seriously. If you look at the list of our advisors, we have some of the most thoughtful people in the world being on this journey with us. People like Dr. Lustig wrote the book Metabolical. Or Dr. Ben Bikman that wrote Why We Get Sick. Or Dr. David Sinclair, that wrote Lifespan. So we have a lot of serious people that are involved with us trying to further science, and we also have a lot of research projects going on with some of these folks plus other folks to answer some of these questions.

[musical interlude]

Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. 

It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

[musical interlude]

Maz Brumand: How do you guys—there's a few different companies out there that are doing this. How do you guys differentiate yourselves from these different players that are out there?

Maz Brumand: Yeah, it's a good question. There is a couple of things. I think the consumer angle of this space metabolic health has, for the most part for a long time been ignored. A lot of people are creating products for payers, and kind of disease. And so we put on the hat and say, Look, who's the best person to manage or care about their health and take actions that improve their health? It's the consumer. So our whole approach is consumer-centric, including the consumer in the middle and creating value for them, building trust for them and helping them in their metabolic health journey. So I think that's differentiated in the sense that all of our decisions are ultimately driven by that mission. I think the second thing is that we are very much science based and research based. So if you look at how we think about these things and read our content, it's very much ground level up thinking about at the cell level what's happening. And we also haven't narrowed it to a specific disease, right? We don't call it diabetes management program, which we can't anyways because we're in the wellness space. But even if it could, we wouldn't. And so because we're looking at a much more broad metabolic health. How can we make sure that your cells are healthy and using energy and producing energy in a way that will prevent both the disease states, hopefully, one day, but also the wellness space. So really marrying this short term like I want to feel better, I want to look better. I want to have more energy. I will spend more time with my kids. I want to have high fertility. Whatever it is like that is just as important than trying to tackle disease through payers. So I think going from this broader angle is also something that's unique.

Harry Glorikian: Yeah, I mean, I'm a firm believer that everything is moving towards keeping people healthier as opposed to just treating them when they're sick, it's going to be much more profitable. But which brings me sort of: the website talks about customers as members, right, so I'm assuming the business model is around subscription. So can you explain sort of how that works, that subscription program and what features are included?

Maz Brumand: Yeah. So we think of it as a membership. To us membership means something different. We see as the health journey as a long term thing, like managing your health and improving your health is not a one time transaction. It's also a two way conversation between us and our members, meaning we want to engage with our members. We want to hear from them. We want to them to help us improve the product, but also create a community. So it's much more than just transactional. I'm selling you a single product and or a subscription. It's more about like, how can we create this long term relationship that's based on value creation for the member and building trust for the member for the long term so we can continuously drive value for them? And that continuous value creation trust and two way relationship is the basis of why we call it a membership because it will help inform our business vision and product decisions design decisions in a different way. When you think about this as a two way relationship over the long term.

Harry Glorikian: So just talking about business models, I mean, you know, people always ask me, you know, Harry, all these technologies are great, but they're usually pretty expensive, right? Depending on where they start. And then, you know, obviously, you know, these things come down over time is, you know, how do you see this? I know, you know, the group is starting with, which is usually the higher price. And how do you see this coming down for a much broader audience over time?

Maz Brumand: Yeah, it's a good question. I think the technology, obviously in the wellness space is relatively new, right? And so any new technology is going to be higher priced. So I think, as CGM and sort of all the technologies become more mainstream, the concept of not just CGMs but bio-observability, becomes more mainstream and it becomes a consumer thing, it will help drive down costs. And ultimately, I think there's two questions to be asked. One is, is the product and service providing more value than it's taking in in terms of cost and price? That's question number one that we have to answer regardless of what the price is. When Tesla came out for a subset of their customers, it was a $120,000 car but it created more value in their eyes than the price tag. So I think that has to be important and true. And so that's question number one. The second question is affordability, right? No matter how much value creating, if it costs $10,000 to get this membership per month, know nobody's going to be able to afford it, except a few. So you have to solve both problems, the value problem and the cost problem. And the cost problem is getting more efficient in terms of creating products and services, using technologies that become more mature and consumer friendly so their prices go down.

Maz Brumand: And one of the things in our membership, actually, I should have probably clarified, is, we will not mark up the hardware and services that we provide from third parties. And so we will try to do it at close as costs as we can. There may be a small difference just because prices go up and down and there may be volatility cost. But our problem is is that we will provide these products and services at cost to our members so that we have no incentive financial incentive to sell you more stuff up, sell you more stuff. Right. When I say you should buy another CGM, we don't make any money on that. And so therefore, when we say you should get another CGM, you want that to be truly aligned incentive with our members. Or when we say you should go get x y z down the line, that's all possible cost for us. And really, what we're focusing on is the membership fee, which is an annual number that's detached from your level of consumption.

Harry Glorikian: And yeah, and I think just for to so that people understand is, you know, you guys don't develop the CGM hardware, you know, the part that sticks into your arm, right? My understanding is that you ship, and correct me if I'm wrong, it's a Freestyle Libra CGM from Abbott, if I'm correct. Okay.

Maz Brumand: Yeah. So exactly. So we use third party products and services like the CGM, because that's the sensor that's been developed. Many, many years and a lot of work has gone into it. So we'll take that technology and then our experience and software and insights and scoring will leverage the hardware to help people make decisions about their behavior by closing them.

Harry Glorikian: Now, at the same time, I think, again correct me if I'm wrong, but I think you guys are still in beta, getting ready to launch. And when do you guys think, I mean, I know like, well, the last thing I got to see on your website was like, you've got 85,000 people signed up, right? And, you know, I don't know if that number has changed. So I don't know if you have a a newer number for me, but I'm assuming you're going to try and ship that, get this out sometime this year.

Maz Brumand: Yeah, I think the number is, I think upwards of 150,000. And the answer is yes, we want to ship it. But one of the decisions we made consciously is we want it to ship it in a way that that makes sense. And that needs a number of things. As you know, one of the strengths of start ups is to be able to iterate and learn fast, to be able to talk to their customers and learn from them. Under a beta, I think that enables you, without having huge volumes of people and problems to deal with, to innovate fast. So you can actually, in the end, get to the product that will really help people or create value for people faster. So that's kind of the thesis of why data. And when we plan to release beta is going to be sometime this year, hopefully sooner than later. Hopefully in Q2, but it all will be predicated on, Do we feel like we're ready to provide that experience?

Harry Glorikian: Well, yeah. I mean, if you've got 150,00o people and I think I read on, you've probably have changed this. But again, I want to say it was like, you know, two thousand kits a month. I mean, obviously, the company's got to ramp itself to be able to meet, you know, get the 150,000 out to people as quickly as it can.

Maz Brumand: Yeah, exactly.

Harry Glorikian: So is there a. I don't know, longer term play that you're thinking about, at Levels? I mean, beyond CGM, right? Beyond the, is that just the tip of the spear? Do you want to integrate more types of health data and apps in so that you can give more holistic advice?

Maz Brumand: Well, I think, you know, the North Star is bio observability, right? CGM is just one. But what's happening in my body based on my behavior? And can I show that to the user in a way that will help them change behavior that ultimately will lead to better outcomes for them and short term make them feel better on a day-to-day basis. So I think that's the North Star. Obviously, glucose CGMs are available, so we're using them. But that's the North Star. And if you think about, if you take that to its conclusion, like every action that we have affects a lot of things in our body, whether it's generating stress like the cortisol response or generating other reactions in the body. So I think the long term vision is, can we help close this loop based on our behaviors and what's happening at the molecular level in our body? So that's kind of like closing the feedback. So getting the assessment to the user and then also helping them now that they've got the insight and they see what needs to be done, help them with the products and services that will help them achieve that goal of of improved health.

Maz Brumand: So let me, I'm going to pick on your like, you've been at Apple, and now you're doing levels and you've been doing this for a while, like, your personal vision of of possibilities here. Like, can you imagine a time where everybody with a smartphone or a smartwatch is sort of getting daily feedback from their devices on how they can optimize nutrition, exercise, sleep for maximum health?

Harry Glorikian: Yeah, I think that's the vision, right? I mean, the consumerization of health. I think the stuff that Apple took to put the data and make their data available to the user and allow people to build on top is, I think, the revolution in personal health. And I think, you know, the market dynamics will drive innovation in many different ways. I mean, Levels is just an example. Levels wouldn't not have existed if this consumerization foundation wasn't set up by companies like Apple. At least that's what I believe. So I think the short answer is yes. I think by putting the tools in place and creating a business environment for people to innovate and provide services to consumers, I think the market will eventually figure out how to help people live healthier lives. Whether it's in this form or not, meaning whether it's a watch on your wrist or a CGM in your skin or whatever, it's hard to say, you know, 20 years from now, but I think the end conclusion is going to be that people are going to know based on their individual physiology, how to optimize their health. And I hope my hope personally is to not focus on just disease, but the wellness leading up to that because. There is a lot to do in that space to make sure people are living their fullest lives and happiest lives.

Harry Glorikian: Yeah, I mean, I find it fascinating, right, that Apple has basically created this ecosystem where they're not necessarily profiting off of the health and wellness space and the way that you would think, being charged for it, but that they've created an ecosystem that everybody says, I have to have these devices and interfaces that that makes them almost core to how this is all rolling out.

Maz Brumand: Now, because I think it's not a zero sum game, and if you change your mentality from how can I make the most amount of money to consumer-centric? Like, actually help consumers, like what does that look like? It no longer becomes a zero sum game.

Harry Glorikian: Yeah, I mean, but, you know, if you think about it, though, like, you know, I've been in the health world for....Everything we make is very purpose built, right? And there's a reimbursement or something that's attached to it. Apple is saying, "Listen, I'm going to create an ecosystem, I'm going to create a platform. You can, you know, use an API to get information in and out, right? And I'm going to make it easy for you to sort of do monitoring and apps and everything else. You just need to buy my devices, ad I'm fairly happy. I don't need to make money on the purpose built product like we do, like we have in health care historically." So it's a different way to make money, but in the same ecosystem, which is fascinating.

Maz Brumand: Yeah, yeah, completely. And people that build on top obviously can monetize that in a way. But yeah, I think just idea of being a platform is just a different model. Right. It's not about creating a purpose built product for revenue. It's a platform where other people can build on top and make revenue, but also strengthens your own business too. It's not completely for non profit. There is a business strategy there. But the business strategy is much more aligned with consumer interest and consumer value creation than it is this zero sum game, which unfortunately our health care system has devolved into, with the disintermediation that we've seen with the buyer being different than the end consumer. So when you're actually designing a product, natural incentives will make it so that you're designing it for the buyer, not the consumer. So you end up creating a product and optimizing features for the buyer that has certain interests. But then you expect the end user, which is a different person, to want to use it, and that's how you end up with kludgy products that maybe you don't want to use. Right? So nobody loves using a product that was created for an insurance company as a consumer. So I think this changes that dynamic completely.

Harry Glorikian: Oh yeah, I mean, I think, you know, had you looked pre iPhone in apps and so forth, I mean, this platform to lay all these other things on top of just, you know, again, they were either purpose built or they didn't exist. So this completely creates a brand new ecosystem for opportunities like Levels and other technologies like that.

Harry Glorikian: Yeah, definitely. And I think, you know, Apple's done a lot of great things, which I'm really proud to be part of and really have deep respect for for the company and leadership. You know, the work on research is quite groundbreaking, starting the virtual research, for example, at the scale that it did for the Apple Heart study and just just change the thinking about research. And you know, obviously you continue with the Research app and collaborating with researchers and then creating the platform ResearchKit for other people to research. It just completely changed the conversation. And I think, you know, it's I have tremendous respect for the impact that Apple has had in this space and will continue to.

Harry Glorikian: Yeah, I mean, you know, the conversation I always have with people is, you know, when we were working on a product we already knew like we were going to go for regulatory approval. Everything we were doing, like there was no time to sort of play like you had to have it sort of baked of where you were going to go from day one. Whereas a lot of these companies that are in the wellness space, let's say Apple, you get a chance to sort of get feedback, adjust, get feedback, adjust. And then when you if you want to step over the regulatory hurdle, you have a lot of information now to sort of make that play. Historically, the playing was not necessarily easy to do. I mean, getting this data, if you think about, you know, billions of users, that's a lot of data that you get to sort of look at and screen and decide what you're going to do next before you do it.

Maz Brumand: Yeah, you know, I think it's not that linear within Apple because very strong privacy stance. So it's not like you can just grab the data and do whatever you want with it. But I think your general concept is true, right? If you take the idea of being startups and think about like, OK, I'm going to iterate, I'm going to try a bunch of stuff, I'm going to iterate and then I'm going to come up with the product and I'm going to go build that right hypothesis test results building. You couldn't historically do that enough. Right? Because you just do what? It's locked, right? It's now locked. You cannot make a change. So even if you found outk, let's say you did that. You created a product and then things changed like, OK, I can't. Yeah, yeah, it's like, sorry guys, I know you really want that feature, but it's not going to happen. I do agree that it's just changing the conversation and then thinking has been fantastic. And, you know, it's also really important to say there is a reason why the regulatory space exists and the fact that we do need protections that the FDA and others put into place. So it doesn't take anything away from that. It's the question is like how do we create other ways to allow innovation to happen while keeping people safe? And in the right things?

Harry Glorikian: Oh, yeah, I mean, I believe me, I love the FDA. Don't don't misunderstand me, I think they they definitely like have to play their role, right? But on the other hand, I love the fact that you can actually interact with someone, get data, identify signals, be able to sort of iterate on that. And then when you, you know, when you find something really worth sort of moving on that may be beyond wellness, that that opportunity has now opened itself up assuming, you know, privacy and everything else is is kept, you know, under control. But I think the advances that have been made say in the last five years have been unbelievable. You know, some of these things that we're talking about five years ago were really not available. And now, you know, I can manage myself fairly remotely and get a longitudinal view that I can share with my physician that helps him understand my body better.

Maz Brumand: Yeah, yeah. I couldn't agree more. I think this idea that you would have these episodic visits with your doctor and they will not be informed from any of the past interactions or data, it's just we'll look back on this in 10 or 20 years and think, Wow, that was a huge influence on health, where every interaction is like a surprise to the doctor because there's nothing informing them other than a paper thing that you filled out, which nobody reads, and they've got to make decisions about your health.

Harry Glorikian: Yeah, I mean, I think about these things like, you know, I walk in, I give them a longitudinal view of my, you know, whatever I've been tracking. And the human brain is amazing at looking at a pattern and seeing something that's out of line. And if it looks normal, they just go, Hey, that looks great and move on.

Harry Glorikian: So we know, you know, obviously you're being in this space for a long time. You'll know a lot of the research is also done in, you know, in perfect situations. And it's done on a cohort that's probably not representative of the entire world. So, yeah, I think it's both things. I think one is if it's not out of the normal, which is probably a large standard deviation, it gets passed through. And then also, if we just don't know because we didn't have the tools to research the way that we're doing research today. And this is my point about Apple changing, also thinking about research not being, you know, 30 people in the northeast that we studied. And then we came up with the guideline for the entire world. It doesn't work that way. So I think, yeah, I think there is a lot. I think we're in the early innings of really changing health and health care, not just Levels, but everybody. I think the big players, us, the health care systems, the payers, and it's pretty exciting time. And you know, you asked me the question of why did I leave Apple to come do this? It is because there's just so much interesting stuff going on, and it is the time to actually make those leaps in collaboration with people like Apple and then hopefully one day also with the payers and the provider.

Harry Glorikian: Yeah. No, and I think their world is changing, too, just because now we're, you know, moving more towards paying for outcomes as opposed to, you know, I pay you for everything that you do. So. Anything else that I didn't ask you that is your burning to to tell us about Levels or do you think we covered it?

Maz Brumand: I think I think you covered most of it. I think there's just so many things to talk about in this space that we could probably go on forever if you want.

Harry Glorikian: Yeah, no, I've been I've been trying to convince people that that are interested in health, wellness, energy, optimal, you know, optimum performance that having a CGM and getting a good feel for. What's the right food, when to have it? What happens measuring it, et cetera? You know, and being able to give them the right feedback, being able to give them maybe an alternative food that so they don't have to give up something necessarily that they really like. Those are all important feedback loops to give them.

Maz Brumand: Yeah. And you know, you bring up a really good point because a lot of people think if they want to take control of their health, whether they lose weight or want to feel better, they have to make these massive changes. They've got to stop eating all the foods that they like. They've got to go to the gym, you know, two hours a day. And my personal CGM experience showed me the opposite. There was just a few tweaks I needed to make to change the outcomes completely. And, you know, and the reason I was doing the things I was doing wasn't because I was like, Hey, that's my cheat, and I really want to enjoy that. All this stuff was I didn't even care about it. Like, I really didn't think that oats is so much better than eating eggs in the morning. Like that was not but science. I mean, the best available science of the time was that eggs are bad due to cholesterol and oats are heart healthy. And so so a lot of it is also not just figuring out based on real data that's personalized to you, like one of those small changes that I can make that will completely change my life. I mean, that's what's magical about this technology. It's not somebody writing a hypothesis piece about the general population that's know makes no sense with your lifestyle, but also but instead figuring out okay, based on you, your physiology and your lifestyle. How can I? I can help, you know?

Harry Glorikian: Yeah, I mean, my new book just came out on, you know, how to incorporate technology into your life. And I always tell people, I'm like, Pick one. Like your scale. If you see the if you see the line going in the wrong direction, maybe it's time to course correct, right? Or, you know, a wireless blood pressure cuff, right? I mean, blood pressure is one of those things that sneaks up on most people. They don't see it until it's too much of a problem. Well, if you notice that it's moving in the wrong direction, right? Maybe you'll lose some weight. Maybe you won't add as much salt. It's these aren't huge changes because you're trying to do it early enough that you affect where the line is going. And so a CGM is the same thing in a sense. And if you have enough of these in your arsenal over time, I think you can do a pretty good job of managing, at least extending. That you know how healthy you'll be for how long.

Maz Brumand: Yeah. You know, we think about this in a... I'll explain how we think about this. So we kind of look at certain metrics or bio metrics or information from your body. You can think about it. There's a law. There are high frequency, and give you feedback. Let's just call them feedback metrics for a second. Right, these are things that, for example, my glucose, when I see that move in real time high frequency, I can change my behavior. And these are all high frequency, completely correlated to your behavior on short-term outcomes. And then there are other metrics that are much lower frequency, meaning you don't take them all the time but are really representative of your of your health, right? Which is, for example, is my A1C below or above a certain amount, is my blood pressure below or a certain amount, is my waist circumference below a certain amount. That really shows you the outcome. And then the question is how can I influence behavior by measuring these feedback metrics today and based on the science and correlations that we know leads to better target metrics or health metrics in the future? And so that's kind of the framework where help affect behavior today with high frequency metrics to drive better outcomes with lower frequency, more outcome driven metrics in the future.

Maz Brumand: Yeah, no. And I totally agree, and it really is going to come down to the data that you're putting in the way the software does its analytics and then communicates back with the individual because some of this has to be put into normal speak as opposed to sometimes when you talk to a physician. They're using acronyms and a language that most people can't necessarily easily understand.

Maz Brumand: Yeah, yeah, definitely. And I think there are three problems probably to solve to really get to mass market. I think one is the hardware-software, making it the software more intuitive and more insightful. The hardware cheaper, less intrusive, so on and so forth. I think the second problem is the research problem, right? How can we actually find understand these real time metrics better and its correlation to long term metrics? And what are the best ways to influence behavior? So there's a big research component there, given that a lot of these things are new. And then the third one is the social aspect of it, to make sure that people understand it, providers understand it, payers understand it. So how can the ecosystem adopt this new way of thinking and new way of affecting health and wellness? So I think you have to have all those three to really make a big impact at the much larger scale than earlier.

Harry Glorikian: Yep. No, couldn't agree more. It was great having you on the show. I wish you and the rest of the Levels team good luck in this upcoming launch. And you know, I should probably go get another CGM and tack it on and and see what's changing over time.

Maz Brumand: Sounds great. Thanks, Harry. It was a pleasure.

Harry Glorikian: Thanks.

Harry Glorikian: That’s it for this week’s episode. 

You can find a full transcript of this episode as well as the full archive of episodes of The Harry Glorikian Show and MoneyBall Medicine at our website. Just go to glorikian.com and click on the tab Podcasts.

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Thanks for listening, stay healthy, and be sure to tune in two weeks from now for our next interview.

That’s why we like to meet companies that are working to make EHR data more useful. And in this episode we welcome a pair of guests from a company called Verana Health that’s trying to do just that. The company recently brought in $150 million in new venture capital funding to help scale up its data services, which currently focus on the subspecialties of ophthalmology, neurology, and urology. Verana takes data on patients in these fields, cleans it up, analyzes it, and pulls out insights that could be useful—both for clinicians who want to increase the quality of the care they’re providing, and for pharmaceutical companies who need new ways to measure the effectiveness of their drugs and better ways  to find patients for clinical trials. Here to explain more about all of that are Verana’s CEO, Sujay Jadhav, as well as its senior vice president of clinical and scientific solutions, Shrujal Baxi. (If you’re a longtime listener you might remember that we had Shrujal on the show once before, back in 2018, when she talked about her previous company Flatiron Health.) 

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Transcript

Harry Glorikian: Hello. I’m Harry Glorikian. Welcome to The Harry Glorikian Show, the interview podcast that explores how technology is changing everything we know about healthcare.

Artificial intelligence. Big data. Predictive analytics. In fields like these, breakthroughs are happening way faster than most people realize. 

If you want to be proactive about your own health and the health of your loved ones, you’ll need to learn everything you can about how medicine is changing and how you can take advantage of all the new options.

Explaining this approaching world is the mission of my new book, The Future You. And it’s also our theme here on the show, where we bring you conversations with the innovators, caregivers, and patient advocates who are transforming the healthcare system and working to push it in positive directions.

Healthcare is one of those areas where more data is almost always better. And I talk a lot on the show about how data is helping doctors and patients make smarter decisions.

But a lot of the data we’d still like to have is stuck in those arcane Electronic Health Record systems or EHRs that medical practices or hospital systems use to track their patients.

These systems tend to be closed, proprietary, user-unfriendly, and incompatible with one another.

And I haven’t been shy here on the show about my opinion that the chaotic state of EHR technology is holding back innovation across the healthcare market.

That’s why I’m always interested in talking  with companies that are working to make EHR data more useful.

And today I have a pair of guests from a company called Verana Health that’s trying to do just that.

The company recently brought in $150 million in new venture capital funding to help scale up its data services, which currently focus on the subspecialties of ophthalmology, neurology, and urology. 

Verana takes data on patients in these fields, cleans it up, analyzes it, and pulls out insights that could be useful—

both for clinicians who want to increase the quality of the care they’re providing, and for pharmaceutical companies who need new ways to measure the effectiveness of their drugs and better ways  to find patients for clinical trials.

Here to explain more about all of that are Verana’s CEO, Sujay Jadhav, as well as its senior vice president of clinical and scientific solutions, Shrujal Baxi.

If you’re a longtime listener you might remember that we had Shrujal on the show once before, back in 2018, when she talked about her previous company Flatiron Health. 

We’re glad to welcome her back.

Now, on to the show.

Harry Glorikian: Sujay, welcome to the show, and Shrujal, welcome back to the show, now that you're at a different place. It's great to have you both here.

Sujay Jadhav: Thanks, Harry.

Shrujal Baxi: Happy to be here.

Sujay Jadhav: Happy to be here as well. Thanks.

Harry Glorikian: So. I want, you know, I want to ask you guys like if one or both of you can describe Verana's reason for existing, at least at a high level, and what is the unmet need in in the world of patient care or drug development that you are meeting?

Sujay Jadhav: Yeah, yeah. Happy to jump in and Shrujal, you can sort of add in sort of the health care sort of goals that we have as well, but you know, in essence, what Verana is all about, we have an exclusive real-world data network focused on three therapeutic areas: ophthalmology and neurology and urology. And in essence, what we are doing is we're helping provide insights to providers in helping improve quality of care, helping improve their participation in clinical trials and also provide insights to life sciences companies across the drug lifecycle all the way from study design helping out in trial recruitment to helping them out in launching drugs, commercializing drugs so they can overall improve the quality of care in a more holistic fashion. You know, the crux of how we're going about doing it, in essence, is accessing HER data and eventually identifying it to provide these particular insights and high level there's data which is very, very structured and there's data which is unstructured and there's a sort of an increased focus on the unstructured data because I would say that's probably where there is the largest opportunity out there to provide insights across that overall value chain.

Harry Glorikian: Yeah, I know I know the area well, but I want to sort of spend a moment on the origin story of of Verana Health, and I'm assuming it has something to do with the relationship between Verana and the American Academy of Ophthalmology, since I think the Academy's CEO David Parke is also a co-founder and executive chairman on Verana. You also have partnerships with the American Academy of Neurology and the American Urological Society. So it seems like these. And it's funny because I think of these associations as publishing journals or, you know, organizing conferences or maybe, you know, having representation in Washington. But it seems like you guys were a spinoff or a piece that came out of at least the American Academy of Ophthalmology. Is that correct?

Sujay Jadhav: Yeah, you're absolutely correct. I mean, really, Verana was founded on, you know, sort of the ophthalmology registry, in essence. And, you know, the ophthalmology registry, is probably one of the leading registries in terms of the way that well, first of all, participation, you know, from the specialists, I think it's close to 70 percent of ophthalmologists are part of the registry, but they're one of the leaders in terms of taking the actual data from the ophthalmologists. And they were actually processing that particular data via third party out there to help provide insights, you know, to predominantly the ophthalmologists out there, but eventually to provide insights to help further research. And so Verana was really founded on sort of the ophthalmology registry. They decided to spin out that capability as an independent company, then bring in some external investors, sort of investors, which are very committed to digital health. Brooke Byers from Kleiner Perkins. Google Ventures. And they funded the separate entity. And then ultimately, the goal was to take that data capability that they have and then help normalize it and provide more insights around it to further the overall drug lifecycle. And then, you know, along the way, you know, other societies saw the progress that were making and decided to also partner with Verana, starting with the neurology society and then urology as well.

Harry Glorikian: Now, you know, just so like for the listeners, if and you guys can correct me if I'm wrong, but I think like and because I like to give credit where credit is due, right is a lot of these, you know, medical associations began to gather a lot more data and build some giant databases. But I think that was driven by the, you know, CMS or, you know, Centers for Medicare and Medicaid Services sort of setting up this merit based incentive payment system and sort of driving this. So it's sort of like I always like to give government credit when they actually do something right, but they actually put some money behind this to encourage this sort of activity, which has resulted in this sort of dataset that's now available for us to really glean some insights for patients.

Shrujal Baxi: I mean, I think I think when we when we look back sort of the development of the electronic health record is what set this off. And that was also a government initiative right to really move us all from paper charts and to electronic health records. And then sort of the potential that comes from that. If we think about the brilliance of these registries, they were smart enough to start collecting the data early on and sort of answering your first two questions from a medical perspective, like what is Verana here to do? We're here to help transform that data that's available in the electronic health record, sort of generated as part of regular care, and get all of the insights we can in health care, the way that data is generating insights and every other industry out there. But there is a particular sensitivity in health care to de-identification, making sure we're taking care and responsibility of that process, which makes sort of what Verana is doing a little bit different than what might be happening out there. But when you when you think about why there's so much excitement around what we're doing, it's that we're actually going to do it from a technological standpoint. So the scale at which we're hoping to do it should drive insights like we haven't been able to do with sort of the first pass at getting value from the EHR, if that makes sense.

Harry Glorikian: Oh yeah. I mean, if you've listened to many of my shows, I have a big pet peeve with the normal EHR system. I mean, I've gone so far to say, you know, if anything breaks health care because of its inability to change, it's this arcane accounting system that got morphed into, you know, quote, we're going to manage patients. But you know, you mentioned from a physician's perspective, what kind of data do these databases that you have access to contain that's important or valuable for, you know, assessing quality of care, let's say?

Shrujal Baxi: It really, it spans the gamut, right? Because data is just that. It's what we do is we provide, we transform it into a structured format that can be analyzed. But what you use it for is really it's limitless, right? Do we want to look at how to optimize how patients are seen? Who sees those patients? How do we get them into a clinical trial? How do we get a trial set up at a practicing site that happens to be seeing a lot of patients of a particular disease subtype? Are we starting to pick up patterns in how new medications are released into routine care that have been tried in clinical trials? Can we pick up safety signals in the real world that you can never capture when you only have a small clinical trial of 200 patients? But when you launch that drug at a larger cohort, what is actually happening in the real world? All of that is possible once you figure out how to transform all the information that's entered into the EHR into analyzing all formats. And so, you know, it's interesting, because Sujay gave all the real use cases, but in my mind, what we're doing is the technology, which is how are we going to do this in a sort of scalable way? So as the data is coming in, we can take it and output structured data that can then be used for analysis. And the better we get at that transformation step, the faster and the more reliable that is that really sort of unlocks what we can do with the data.

Harry Glorikian: Yeah, it's funny. Yeah, it's funny. You're covering, like, I don't know, half a dozen podcasts I think I've done with various companies that are doing different parts of this. But I mean, I've looked at the company's literature and you put a lot of emphasis on what's called real-world data. And this is a topic, you know, I've covered on the show many times. You know, last year, late last year, I did an interview with Jeff Elton from Concert AI, where they collect post-approval data and help improve decision-making inside drug companies. So I want to ask you first, what do the folks at Verana have in mind when they talk about real world data? Does it basically mean any data collected outside of the context of a clinical trial?

Sujay Jadhav: I know the real world data terminology has different types of descriptions, but fundamentally we look at it, generally any observational data, you know, is sort of what we categorize as real world data, and what we are focused on from a high level perspective. And you know what we see within the EHR, you know, there's a lot of that data available there. And in essence what we are doing is accessing it, extracting it, normalizing it, and then providing different levels of insights depending on the different types of use cases, which are important to improve the quality of care at the provider level there, and also help further research and within the life sciences arena as well. So, you know, that's high level the way we look at it. You know, one of the things, you know, in order to finish up sort of or complete the overall patient journey and have a holistic perspective, we need to also match that up with other types of data there. And so, you know, claims data, for example, at times there are longitudinal elements to it as well. So we spent a lot of effort and work doing matching there, you know, as well. You know, we're bringing in other types of data forms of imaging data and as well.

Sujay Jadhav: So while we are very focused on the data there, we are actively complementing it with other types of data sources to get a more holistic picture there. But you know, I would say that a lot of companies out there have been doing a really good job of accessing this data from a more structured format perspective, right? And one of the things that I've seen, and this is more of a high level comment, is when you look at some of the structured data that can be an element of sort of extra latency in terms of getting that information to make certain decisions or decisions such as hey, for a particular clinical trial, what are the right patients that you should target, et cetera. And so what we are really focused on is the unstructured data, you know, the physician notes, and then leveraging sort of AI techniques there to provide those signals. So that allows us to, on a close to real-time basis, target particularly particular patients, which could be a better fit for a particular trial versus historical means, which have been a little bit more sort of delayed in terms of getting those data inputs.

Harry Glorikian: So, you know, this begs the question before we jump into the product itself is. Do you guys have an opinion on why the medical establishment has not been so great at tracking or analyzing real world data up till now? I doubt it's lack of interest. It's probably more like technical limitations is my guess or maybe lack of interoperability, or all of the above.

Shrujal Baxi: Harry, since the last time I was here, a lot has happened in the real-world data space. To start with, and I think we talked about this last time, which is real-world data has been here forever, right? Clinicians have been doing chart reviews and publishing case series. That's all real-world data. It's taking a look at what happens in the routine care of patients, pulling it out and analyzing it in order to deliver insights. I think what the electronic health record did or what we believed it would do is allow us to do that type of work at a scale that we couldn't do it before. The second thing, I think, that real-world data is now considered potentially useful for that it wasn't previously, is causation and the analytic ability to actually make linkage between input and output in a way that isn't just hypothesis generating. And the regulators are really sort of driving the space with the guidances that are coming out and really framing for companies like ours, how we should be thinking about data and the data quality and sort of where this data could be used.

Shrujal Baxi: So there's sort of two parts, right? One is how do we generate it in real time at scale so that we can understand important questions? And then the other is the part that I think really sort of leaned in on, which is the entirety of a patient's journey. And this is a very patient-centric problem. It isn't captured in a single EHR. So how do we bring together all the different components of a patient's journey such that we get the complete picture, the genetics, the imaging, the multiple different providers, the claims for what was paid for, right? And so it's kind of an exciting time in the sense that we've sort of gotten to second base. Maybe we figured out how to get all the data. Now we're figuring out how to transform the data. Now we're going to figure out how to link the data. In the meantime, in parallel, we're figuring out how to analyze observational data. And a company like Verana is really well poised to do those things because of all the different components and the partners that we have to do that, I think.

Sujay Jadhav: Yeah. And I'll just add to what Shrujal said there. And I think it's sort of inherent in your question around the technology was, has the technology been there before, et cetera? I think to some extent it has, but it has been evolving and obviously in the last decade with sort of AI techniques, natural language processing techniques, they've started to mature and kind of scale. But one of the key things around our industry is patient privacy, right? And so we have the technology and it's been leveraging a lot of other different industries. But the stakes are very, very high here because of protecting patient data overall. And so, you know, working through how can you access this data at scale and ensure and make sure that you're adhering to the patient’s privacy? There has taken a little bit longer to do, but now we have it. Currently, right now, we have a lot of techniques on the de-identified data identified realm where we can now leverage that to address that particular point. And I think, you know, it's an exciting time right now. You know, which is we now have the tools to do this at scale, but ensure that we're keeping patient privacy intact as well.

Shrujal Baxi: But we also have a responsibility on the end of that spectrum, which is we have to have high quality data. So we need to protect the patient's privacy. We need to be very responsible with the data, but we also have to be very responsible for how the data is generated, such that we don't end up with conclusions that are harmful. The integrity of the data throughout that process needs to be maintained because people are going to act on the output of our analysis using the data that we're generating. And so that's an incredible responsibility that I think we take on and sort of critical to how we think about what we do. It's not just data, it's data that's generated to make decisions in health care that impacts patients very directly.

Harry Glorikian: Yeah, absolutely. So, you know, I think this is a maybe a good segue or opportunity. What have you guys actually built? Because we've been talking around it. What is, I think it's called the VeraQ health data engine, if I got it right? Tell me a little bit about the product. And you mentioned natural language processing and machine learning, and so how does that fit, at what point and where does it fit? And I'm sure there's a few people who are going to like listen closely at this part because they're interested in this stuff. Some others may not listen as closely. But if you could tell me a little bit about the product, that would be great for everybody listening.

Sujay Jadhav: Sure. Sure, absolutely. So, you know, in essence, what we have is we do have a real-world data network where we access 20,000 providers. We have 90 million de-identified patients currently, and it's growing at a good clip right now. And what we do is we take that data, we ingest the data, we normalize it and curate it to provide insights to providers to improve quality and participate in trials and then also to the life sciences community as well to help further research. Broadly speaking, there, you know, sort of our technology platform is called VeraQ. We released it last year, in essence, it's sort of the secret sauce around ingesting it and normalizing and curating the data. And then once we do that, then what we do is we deliver it in what we call de-identified data modules called Qdata modules, which are aligned according to the therapeutic areas in certain disease states. And so what we do is within each of the three therapeutic areas we release on a quarterly basis different disease modules, Qdata modules there. And then that helps serve out, you know, a lot of insights that life sciences companies can use across different areas. So anything from a helping out in trial design work with a lot of large pharma companies around helping improve sort of how they target particular patients out there by leveraging these de-identified data modules to helping out on recruitment as well. In terms of working through what are the right providers that you're targeting to have that patient population trial to eventually see when you actually launch the drug, you know how the use is occurring out there in the marketplace, how they can better target it to improve the value of the particular drug they have there as well. And, you know, we eventually set that up instead of application modules across that overall drug lifecycle. So, you know, to summarize, our platform is VeraQ. We then serve it up in these Qdata modules and then we deliver it in these solution sets, which are provider facing and also life sciences space.

Shrujal Baxi: I was going to say something that's really unique about Verana and shouldn't be glossed over is the fact that so many different EHRs are out there and they're they're created differently and they are so specialized to the provider with bells and whistles that each different practice pays for. And to take all of that disparate information and ingest it and harmonize it such that the output or variables that can be generated at a scalable fashion across millions of charts and then use that for analysis. I mean, GE made it sound really good and clean, but that's actually a that's a lot of work for anyone who's ever touched an EHR and try to get value out of the data that's entered. It's a feat. And I think that that that engine now that it's built, is sort of poised to take in and give out, right? That was the infrastructure build that was 2021. And 2022 is the data that's going to come out as he was describing. But I just want to, I'm particularly passionate about this because I've worked now at different companies that think about this and that particular part of harmonization from the starting point that are so many different places is really, I think, a technological advancement.

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Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.

All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. 

It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.

And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.

It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.

The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.

And now, back to the show.

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