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."

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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."

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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. 

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Doctors helping couples conceive through in-vitro fertilization typically must screen multiple fertilized embryos to select one embryo for implantation—but the process is fraught with risk and subjectivity. from In 2018 Gilboa and her colleagues Daniel Seidman and Eyal Schiff co-founded AIVF, an Israel-based startup developing decision support tools that use deep learning and computer vision to lower the risk by identifying the most promising embryos for intrauterine implantation.

The company's technology takes the place of old-fashioned visual evaluation of embryos by humans, instead of capturing time-lapse video of embryos from the moment of conception to the fifth day after conception, at multiple focal planes. "It's an obscene amount of data," Gilboa says. "Instead of looking at the embryo once a day under the microscope, we have tons of images to annotate and look for the biological features that we know are correlated with success."

Proprietary machine learning algorithms use the video data, together with patients' health history and genomic data, to predict which embryos have the highest chance of developing into a healthy newborn. In theory, the technology will lower failure rates, decreasing the number of fertility cycles required for conception and therefore lowering the overall cost of IVF treatment.

"Many people don't get to fulfill their dream of having a child, and this is really heartbreaking for me," Gilboa tells Harry. "This is what really drives me as an embryologist to be able to provide a new, next-generation IVF treatment that would be accessible, that wouldn't be so expensive."

Check out the full show notes for this episode and other MoneyBall Medicine episodes on our website. For more on how data is transforming reproductive medicine, listen to Harry's interview with Alan Copperman.

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Tom Davenport knows analytics, big data, and AI—he teaches executive courses on the subject at Babson College, Harvard Business School, the Harvard School of Public Health, and the MIT Sloan School of Management, and is widely known for his books on analytics and AI in business, Competing on Analytics (2007), Only Humans Need Apply (2016), and The AI Advantage (2018).

Davenport notes that a number of life science startups are attempting to use machine learning, big data, and AI to reinvent drug discovery (a subject thoroughly covered in previous episodes of MoneyBall Medicine). But in other areas, progress has barely begun. A few startups are trying to bring machine learning into the world of providers and payers, to offer insight-based recommendations about care gaps and treatment. And a few researchers are studying the use of deep learning for pattern recognition in radiology and pathology imaging. But substantive advances are years away.

On the clinical side, Davenport says, "The biggest changes are in the institutions that have more data—combined provider/payer organizations like Geisinger and Kaiser—who absorb the risk of care and need to make informed decisions about it, and are more focused on treating the entire patient and keeping the patient as well as possible. But even there it's still early days."

Healthcare organizations that haven't already started to implement analytics may never catch up, Davenport warns. "This is not an area where it's going to be successful to take a fast-follower strategy, because it requires so much data, so much learning, and so much trial and error over time."

This episode is part of a special series of interviews with speakers at the AI Applications Summit produced by Corey Lane Partners. Check out the full show notes and other MoneyBall Medicine episodes at our website.

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Milind Kamkolkar joined Cellarity in January 2019 to help the company to prove that it is now possible to "encode a cell" digitally—to use big data, deep learning, and other methods to model many different interconnected networks of molecular interactions.  "The whole idea...is really only feasible now," he says. "What changed over the last number of years is the ability to compute at scale."

The promise of Cellarity's computational models, Kamkolkar says, is that they look broadly at cell behavior, rather than taking a reductionist approach. "If you could see the forest and the trees, what does that look like?" he says. "Really taking into account all of these networks that exist not only at the molecular level, not only at the cellular level, but also at the tissue level, and being able to look at all of it at once. You could argue it sound quite preposterous, but I love the ambition."

Kamkolkar joined Cellarity from Sanofi, where he was the industry's first enterprise chief data officer, driving the transformation of Sanofi into a data-driven organization. Previously he was the global head of data science and AI and digital medicine at Novartis.

This episode is part of a special series of interviews with speakers at the AI Applications Summit produced by Corey Lane Partners. Check out the full show notes and other MoneyBall Medicine episodes at our website.

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