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The Legacy of Stanford's Biomedical Informatics Program

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Manage episode 300113035 series 2526494
Вміст надано Harry Glorikian and Harry Glorikian. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Harry Glorikian and Harry Glorikian або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

Harry traveled to the San Francisco Bay Area this summer, and while there he interviewed the co-founders of three local data-driven diagnostics and drug discovery startups, all of whom participated in the same graduate program: the Biomedical Informatics Program at Stanford's School of Medicine. Joining Harry were Aria Pharmaceuticals co-founder and CEO Andrew Radin, BigHat Biosciences co-founder and chief scientific officer Peyton Greenside, and Inflammatix co-founder and CEO Tim Sweeney. The conversation covered how each company's work to advance healthcare and therapeutics rests on data and computation, and how the ideas, skills, connections each entrepreneur picked up at Stanford have played into their startups and their careers.

Radin's company, formerly known as twoXar, models pathogenesis computationally to identify potential drug molecules, shaving years off the drug development process. Radin developed Aria’s core technology, a collection of proprietary algorithms for discovering novel small molecule therapies. The algorithms incorporate system biology data, disease-specific data, chemistry libraries, and more than 60 separate AI methods to sift through molecules with known chemistry to find those with novel mechanisms of action and favorable safety profiles absorption properties. Whereas traditional drug discovery methods have a 1-2% success rate after 4 years, Aria claims its approach has a 30% success rate after just 6 months. It has a pipeline of at 18 drug candidates in areas including kidney, lung, and liver diseases, lupus, cancers of the liver and lung, glioblastoma, and glaucoma. Radin holds MS and BS degrees in computer science from Rochester Institute of Technology, studied computational biology and medicine through the Stanford Center for Professional Development, and was formerly an advisor to several venture capital firms and startup accelerators.

Greenside started BigHat to combine wet-lab science and machine learning with the goal of speeding up the design of antibody therapies. BigHat’s lab consists of numerous “workcells,” each of which cycles through multiple tests of a given set of antibodies synthesized from in silico designs. Assays characterize each antibody variant for traits such as yield, stability, solubility, specificity, affinity, and function. Machine learning algorithms determine how mutations affected each of these properties and feed this learning back into a new set of designs for the next round. The company says this approach allows it to identify therapeutic-grade antibodies faster than traditional bulk screening techniques (in days rather than weeks or months). Greenside is a computational biologist with a PhD from Stanford, an MPhil from Cambridge University, and a BA from Harvard. Silicon Valley Business Journal named her to its 2021 list of “Women of Influence in Silicon Valley.”

Sweeney co-founded Inflammatix to develop a new class of diagnostic tests that—rather than searching for a specific bug—“read” the host response of a patient’s immune system for clues about the cause and severity of an infection. The problem, as Sweeney originally saw it, is that traditional tests can only detect infections once a pathogen has spread to the bloodstream, meaning that doctors often guess incorrectly about whether a patient needs an antibiotic, or which one they need. Inflammatix is built around the idea that the human immune system has evolved targeted responses to different kinds of infections and other diseases. These responses are complex and vary according to age and setting, but by analyzing mRNA samples from multiple, diverse cohorts, the company believes it can identify a “reproducible signal in the ‘noise’ of multiple datasets.” Inflammatix is developing a cartridge-based system called Myrna for use in emergency rooms, urgent care clinics, and outpatient clinics that can screen for acute bacterial infections, viral infections, and sepsis in 30 minute

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119 епізодів

Artwork
iconПоширити
 

Архівні серії ("Канал неактуальний" status)

When? This feed was archived on September 17, 2023 20:15 (6M ago). Last successful fetch was on August 09, 2023 19:04 (8M ago)

Why? Канал неактуальний status. Нашим серверам не вдалося отримати доступ до каналу подкасту протягом тривалого періоду часу.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 300113035 series 2526494
Вміст надано Harry Glorikian and Harry Glorikian. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Harry Glorikian and Harry Glorikian або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

Harry traveled to the San Francisco Bay Area this summer, and while there he interviewed the co-founders of three local data-driven diagnostics and drug discovery startups, all of whom participated in the same graduate program: the Biomedical Informatics Program at Stanford's School of Medicine. Joining Harry were Aria Pharmaceuticals co-founder and CEO Andrew Radin, BigHat Biosciences co-founder and chief scientific officer Peyton Greenside, and Inflammatix co-founder and CEO Tim Sweeney. The conversation covered how each company's work to advance healthcare and therapeutics rests on data and computation, and how the ideas, skills, connections each entrepreneur picked up at Stanford have played into their startups and their careers.

Radin's company, formerly known as twoXar, models pathogenesis computationally to identify potential drug molecules, shaving years off the drug development process. Radin developed Aria’s core technology, a collection of proprietary algorithms for discovering novel small molecule therapies. The algorithms incorporate system biology data, disease-specific data, chemistry libraries, and more than 60 separate AI methods to sift through molecules with known chemistry to find those with novel mechanisms of action and favorable safety profiles absorption properties. Whereas traditional drug discovery methods have a 1-2% success rate after 4 years, Aria claims its approach has a 30% success rate after just 6 months. It has a pipeline of at 18 drug candidates in areas including kidney, lung, and liver diseases, lupus, cancers of the liver and lung, glioblastoma, and glaucoma. Radin holds MS and BS degrees in computer science from Rochester Institute of Technology, studied computational biology and medicine through the Stanford Center for Professional Development, and was formerly an advisor to several venture capital firms and startup accelerators.

Greenside started BigHat to combine wet-lab science and machine learning with the goal of speeding up the design of antibody therapies. BigHat’s lab consists of numerous “workcells,” each of which cycles through multiple tests of a given set of antibodies synthesized from in silico designs. Assays characterize each antibody variant for traits such as yield, stability, solubility, specificity, affinity, and function. Machine learning algorithms determine how mutations affected each of these properties and feed this learning back into a new set of designs for the next round. The company says this approach allows it to identify therapeutic-grade antibodies faster than traditional bulk screening techniques (in days rather than weeks or months). Greenside is a computational biologist with a PhD from Stanford, an MPhil from Cambridge University, and a BA from Harvard. Silicon Valley Business Journal named her to its 2021 list of “Women of Influence in Silicon Valley.”

Sweeney co-founded Inflammatix to develop a new class of diagnostic tests that—rather than searching for a specific bug—“read” the host response of a patient’s immune system for clues about the cause and severity of an infection. The problem, as Sweeney originally saw it, is that traditional tests can only detect infections once a pathogen has spread to the bloodstream, meaning that doctors often guess incorrectly about whether a patient needs an antibiotic, or which one they need. Inflammatix is built around the idea that the human immune system has evolved targeted responses to different kinds of infections and other diseases. These responses are complex and vary according to age and setting, but by analyzing mRNA samples from multiple, diverse cohorts, the company believes it can identify a “reproducible signal in the ‘noise’ of multiple datasets.” Inflammatix is developing a cartridge-based system called Myrna for use in emergency rooms, urgent care clinics, and outpatient clinics that can screen for acute bacterial infections, viral infections, and sepsis in 30 minute

  continue reading

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