The Harry Glorikian Show

Harry's guest this week, Jen Nwankwo, is the founder and CEO of a drug discovery company in Boston called 1910 Genetics. Her PhD is in pharmacology, which shows through in her practical focus on fixing the drug discovery process to get more and better therapies into the hands of doctors. To hear Jen tell it, 1910 Genetics is focused on finding the most promising new drug candidates for stubborn health problems—and it takes a refreshingly agnostic approach to everything else. 

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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Tears are a signal of more than just our emotions. The liquid in tears comes from blood plasma, and contains a lot of the same proteins and other biomolecules that circulate in the bloodstream. But what this liquid doesn’t have are a lot of the extra components like antibodies that would get in the way if you were looking for specific biomarkers—such as the low-molecular-weight proteins released as a byproduct of the inflammation around tumors. 

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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It may feel like generative AI technology suddenly burst onto the scene over the last year or two, with the appearance of text-to-image models like Dall-E and Stable Diffusion, or chatbots like ChatGPT that can churn out astonishingly convincing text thanks to the power of large language models. But in fact, the real work on generative AI has been happening in the background, in small increments, for many years. One demonstration of that comes from Insilico Medicine, where Harry's guest this week, Alex Zhavoronkov, is the co-CEO. 

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:

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

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3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

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There have been a lot of stories in the news over the last few months about AI chatbots like ChatGPT that can respond to your questions with convincing and well-written answers. These so-called large language models can tell you how to build a treehouse, how to bake a cake, or how to sleep better. But notice that word large. Behind the scenes, these models have learned which word tend to cluster together by sifting through hundreds of billions of pieces of data—basically the entire Internet, in the cast of ChatGPT, including all of Wikipedia and thousands of published books. Now imagine that another chatbot came along that could learn how to generate convincing text response by studying only, say, 18 sentences. Something like that is what this week’s guest Raphael Townshend, the founder and CEO of Atomic AI, has accomplished when it comes to predicting the structure of RNA molecules.

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:

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

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3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

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The medical news publication STAT calls Will Flanary “the Internet’s funniest doctor.” The guests we bring on the show usually talk about how technology is changing healthcare, but Will and his wife Kristin are changing healthcare in a very different way—through comedy. A former standup comic who trained as an ophthalmologist and runs a successful ophthalmology practice in Oregon City, Oregon, Will is better known by his alter ego “Dr. Glaucomflecken.” His short videos have millions of views on YouTube and TikTok, and feature a cast of quirky characters, all played by Will himself, who lightly satirize medical culture and the idiosyncracies of the US healthcare system. And now Will and Kristin have a hybrid comedy and interview podcast called “Knock, Knock, Hi” where they bring on guests who share their own weird and hilarious medical stories.

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:

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

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3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

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There's a quiet revolution happening in the field of genetic screening of newborns. Within the last couple of years it’s become possible to sequence the entire genome of a newborn baby, all six billion base pairs of DNA, and diagnose potential genetic disorders in about 7 hours. That’s already happening in a handful of hospitals, with a focus on babies who are showing symptoms of rare genetic disorders. But within five years, says Harry's guest, Dr. Stephen Kingsmore, it should be possible to extend this rapid whole-genome sequencing to every baby in every hospital, whether they’re showing symptoms or not.

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:

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

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.

3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

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Last October, medical imaging company Arterys announced that it had been acquired by healthcare AI giant Tempus. That caught our attention here at The Harry Glorikian Show, because back in the fall of 2018—exactly 100 episodes ago, as it turns out—we welcomed Arterys co-founder and CEO Fabien Beckers as our guest. 

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:

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

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.

3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

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6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

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You can wear an Oura ring or a WHOOP armband to tell you how your body is adapting to exercise. A continuous glucose monitor can send your phone information about your blood sugar levels are changing. And during the pandemic, a lot of people bought home pulse oximeters to monitor their blood oxygenation levels. But there’s one part of the body where home health sensors haven’t reached yet, and that’s our brains. They're protected inside our thick skulls, which means it’s pretty hard to measure what’s going on in there. Until recently, the only real instruments available to doctors and neuroscientists were big hospital-based machines like X-Rays, CT-scans, EEGs, and MRIs.

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 

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. 

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.

3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

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Out of all the dozens of types of cancer that occur in humans, we habitually screen for only five: breast, cervical, colon, prostate, and lung. But what if there were a single test that could detect 50 types of cancer, based on a simple blood draw? That's exactly what's possible today, thanks to the Galleri test, introduced by Illumina spinoff Grail in 2021. The $949 test, which won breakthrough designation from the FDA in 2019, uses machine learning to assess the patterns of methyl groups—molecules that attach to chromosomes and control gene activity—in free-floating DNA shed by tumors. 

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 

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. 

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.

3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

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These days, there's an explosion of digital imaging technology for almost every part of the body. There are the familiar types of imaging everyone knows, like CT scans, MRIs, ultrasound, and of course, X-rays. But now doctors and medical researchers are also exploring newer types of digital imaging technology, such as Optical Coherence Tomography, or OCT.

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 

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. 

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.

3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

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That's it! Thanks so much.