The Harry Glorikian Show – Details, episodes & analysis

🎙️ In this episode, we discuss:

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

🎙️ In this episode, we discuss:

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

If you look back at all the health-tech and drug development companies Harry has hosted on the show, an interesting pattern starts to emerge: a very large number of those companies have gone on to enormous growth and success in their markets. It could be that being on the podcast is like a catapult to success—or it could be that we're pretty good at finding companies that are already on a promising trajectory. Either way, there's no better example than Concert AI. 

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 

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This week Harry interviews Gregory Bowman, an associate professor in the department of biochemistry and molecular biophysics in the School of Medicine at Washington University in St. Louis. Bowman is the current director of Folding@home, a distributed computing project currently focused on analyzing the structures of coronavirus proteins to find targets for new drug therapies that could help end the pandemic.

Understanding and modeling the 3D structures of tiny, ever-shifting protein molecules is a notoriously complex problem. Folding@home cuts through it by sending crystallography data and other information to thousands of home computers and using it to model possible protein configurations—effectively creating a large, networked supercomputer. The project has been underway in various forms since 2000, but has recently concentrated fully on the SARS-CoV-2 virus that causes covid-19. The hope is that the work will reveal locations on viral proteins where small-molecule drugs could bind, disrupting the virus's ability to enter human cells and replicate itself. 

By patching together so many distributed machines, "We are the first computer to reach the exascale," Bowman says. "Our peak performance is about 10-fold that of the world's fastest traditional supercomputer. Even before the 100-fold growth we have experienced since starting our work on covid-19, we were running calculations that would have cost millions of dollars to run on the cloud." Now that number is in the hundreds of millions of dollars. 

Anyone can contribute to the effort by going to foldingathome.org and downloading the Folding@home software to their Windows, Mac, or Linux machine.

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Harry's guest this week is the founder and CEO of a New Zealand firm, SaferMe, that had developed proximity-based smartphone apps for worker safety. When the coronavirus came along, their apps turned out to be a great way to help companies build their own "contact tables" to identify, test, and isolate SARS-CoV-2 carriers.

In epidemiology, contact tracing is the art of determining who has crossed paths with an infected individual, so that those exposed can be alerted and can take appropriate action, such as self-isolating. Health agencies around the world are building public smartphone apps to assist with contact tracing, but they're being deployed at a national scale, whereas many businesses need more detailed information to protect their workers. 

Van Marrewijk says SaferMe had already built technology that creates a "virtual safety bubble" around each worker—issuing an alert, for example, if lightning is approaching or if they come too close to a hazard such as a mine shaft. "We already had this technology going and we had already done GDPR [data privacy] compliance," he says. When the company noticed early in the pandemic that some of its clients were using the app as the foundation for in-house COVID-19 contact tracing efforts, it quickly built a dedicated app.  

"Someone reports sick, your contact tracer can hit a button and quickly see 'These are the eight people out of a group of 40 that perhaps should stay home or be tested until we sure,'" Van Marrewijk explains. "That gives some assurance there's a proper process in place."

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Though the p-value "determines everything we do in  drug development or medical research," says Dr. Ulo Palm , it may be one of the most misunderstood and misused quantities in experimental science—drug discovery included. At its core, the p-value shows the probability that an observed effect was due to random chance. In other words, if a drug seems to outperforms a placebo with an associated p-value of 0.05, there's only a 5 percent chance that the study was wrong and that the drug is, in fact, no better than the placebo. A p-value of 0.05 is the accepted threshold for validity in most scientific research, even though it's an arbitrary standard set nearly a century ago by statistician Sir Ronald Fisher. "People don't often realize that this p-value of 5 percent was pulled out of thin air," Dr. Palm says. "If Sir Ronald Fisher had had six fingers, we would all be using a p-value of 6 percent."

The issue, Palm says, is that an arbitrary dividing line of 0.05 leads journal publishers (and paper authors themselves) to reject or ignore real effects that don't happen to meet the threshold. If a drug trial yields a p-value of more than 0.05, "You should never ever say it is not working," he tells Harry. "You can only say we were not able to make a determination. That's it." By examining the spread of a data set, confidence intervals, data from individuals, and other measures, Palm says, today's researchers can get a more realistic picture of the promise of a new compounds as medicines.

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Building on his March 2020 interview with Jake Glanville, the founding partner and CEO of South San Francisco-based computational antibody engineering startup Distributed Bio, Harry speaks with three company scientists in the trenches: JP Buerckert, director of computational immunology, and Shahrad Daraekia and Jack Wang, both senior scientists. Together they're working on projects such as engineering existing human antibodies to the SARS virus so that they'll also work against the novel coronavirus, SARS-CoV2.

The company's special sauce lies in its computational algorithms for analyzing antibody gene sequences and generating billions of new candidate antibodies against different pathogens. "We have a very strong wet lab team that is generating data for us and then we have a very strong data team that is sorting through these data" to help scientists decide which antibody leads to move forward with, Buerckert explains.

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If you've seen the recent Netflix docu-series "Pandemic," about efforts to check previous viral outbreaks, you've seen former Pfizer scientist Jacob Glanville in action. The inventor, entrepreneur, and Ph.D. immunologist capitalized on the advent of cloud computing to provide vaccine and drug developers with high-throughput genomic sequencing of antibodies in humans and other species. He calls it "using the ability to look deep into these maelstroms of antibodies to try to understand why vaccines fail to hit conserved epitopes [where antibodies attach to antigens] on influenza or HIV, or how to better produce an antibody medicine." Revenue from the service allowed the startup to grow without outside capital. Today the company is developing a universal flu vaccine for pigs and humans. 

Glanville says we'll know by April whether existing anti-malarial, anti-HIV or anti-Ebola antivirals work against the COVID-19 coronavirus. A vaccine will take far longer to develop, he says. Meanwhile, Distributed Bio is using its search platform to find new antibodies—derived from antibodies that neutralize the SARS virus—that could recognize the new coronavirus and provide instant (but relatively short-lived) protection. Glanville compares the search to "taking five billion spaghetti noodles and throwing them against the wall and seeing what sticks."

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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Schrödinger makes software that models the physics of atomic-scale interactions to predict the chemical properties of candidate drug molecules, helping its customers speed up drug discovery. A decade ago, Farid tells Harry, the company faced the chicken-and-egg challenge of convincing customers that its computational platform works, so that they would scale up their commitment, so that they could gather evidence it was working. Close collaborations with customers like Nimbus Therapeutics helped it improve the software and surmount that challenge. 

"In order to really take it to the next level and make a difference, it was necessary to use the software as customers ourselves," Farid says. "You get real-time feedback, honest feedback. You can imagine how much we learned from that."

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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Rapid sequencing of viral genomes is giving physicians and epidemiologists new ways to identify, track, and potentially slow outbreaks of viral infections such as the novel Wuhan coronavirus. That means high-throughput genome sequencing—which had predominantly been a research tool—is taking its place as a front-line weapon in the fight to prevent pandemics, says Febbo, a medical oncologist. "Last year, 40 percent of our consumables in sequencing were for clinical testing, and we see the clinical testing increasing at a pace that's faster than research testing," he says.

Whole-genome viral sequencing, as a supplement to more traditional PCR-based testing for RNA sequences, can not only reveal exactly which virus is afflicting a given patient, but can reveal where a virus originated and how it is evolving to evade vaccines or other interventions.  

"The fact that the WHO heard of the first cases [of the Wuhan coronavirus] at the end of December, and the New England Journal published the full genome on January 24, within a month, because of the availability of sequencing, already, places like the CDC are using that information to design the probes for the RT-PCR to develop front line tests—never before has anything like that happened," Febbo notes. 

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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