• De-Risking Drug Translation with Jo Varshney from VeriSIM Life

  • 2024/11/11
  • 再生時間: 29 分
  • ポッドキャスト

De-Risking Drug Translation with Jo Varshney from VeriSIM Life

  • サマリー

  • As machine learning becomes increasingly widespread, AI holds the potential to revolutionize drug development, making it faster, safer, and more affordable than ever. In this episode, I'm joined by Jo Varshney, Founder and CEO of VeriSIM Life, to explore how her company is transforming drug translation through hybrid AI.

    With her unique blend of expertise as a veterinarian and computer scientist, Jo leverages biology, chemistry, and machine learning knowledge to tackle the translational gap between animal models and human patients. You’ll learn about VeriSIM Life’s innovative approach to overcoming data limitations, synthesizing new data, and applying ML models tailored to various diseases, from rare conditions to neurological disorders. Jo also reveals VeriSIM’s unique translational index score, a tool that predicts clinical trial success rates and helps pharma companies identify promising drugs early and avoid costly failures.

    For anyone curious about the future of AI in healthcare, this episode offers a fascinating glimpse into the world of biotech innovation. To discover how VeriSIM Life’s technology is poised to bring life-saving treatments to patients faster and more safely than ever before, be sure to tune in today!


    Key Points:

    • How Jo's background and interest in translational challenges led her to found VeriSIM Life.
    • Addressing translational gaps between animal models and human trials with hybrid AI.
    • Combining biology-based models with ML to enhance drug testing accuracy.
    • Small molecules, peptides, large molecules, clinical trial outcomes, and other data inputs.
    • Ways that VeriSIM’s models are tailored per data type, ensuring maximum accuracy.
    • Insight into the challenge of overcoming data gaps and how VeriSIM solves it.
    • How hybrid AI reduces overfitting, boosting model accuracy in data-limited scenarios.
    • What goes into validating VeriSIM’s models through partnerships and external testing.
    • Measuring the impact of this technology with VeriSIM’s translational index score.
    • Jo’s advice for AI-powered startups: be specific, validate technology, and be adaptable.
    • Her predictions for the impact VeriSIM will have in the next few years.


    Quotes:

    “[Hybrid AI] helps us not only unravel newer methods and mechanisms of actions or novel targets but also helps us identify better drug candidates that could eventually be safer and more effective in human patients.” — Jo Varshney


    “Biology is complex. We need to understand it enough to create a codified version of that biology.” — Jo Varshney


    “If you're just using machine learning-based methods, you may not get the right features to see the accuracy that you would see with the hybrid AI approach that we take.” — Jo Varshney


    “Focus on validation and showing some real-world outcomes [rather than] just building the marketing outcome because, ultimately, we want it to get to the patients. We want to know if the technology really works. If it doesn't work, you can still pivot.” — Jo Varshney


    Links:

    VeriSIM Life

    Jo Varshney on LinkedIn

    Jo Varshney on X


    Resources for Computer Vision Teams:

    LinkedIn – Connect with Heather.

    Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

    Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

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あらすじ・解説

As machine learning becomes increasingly widespread, AI holds the potential to revolutionize drug development, making it faster, safer, and more affordable than ever. In this episode, I'm joined by Jo Varshney, Founder and CEO of VeriSIM Life, to explore how her company is transforming drug translation through hybrid AI.

With her unique blend of expertise as a veterinarian and computer scientist, Jo leverages biology, chemistry, and machine learning knowledge to tackle the translational gap between animal models and human patients. You’ll learn about VeriSIM Life’s innovative approach to overcoming data limitations, synthesizing new data, and applying ML models tailored to various diseases, from rare conditions to neurological disorders. Jo also reveals VeriSIM’s unique translational index score, a tool that predicts clinical trial success rates and helps pharma companies identify promising drugs early and avoid costly failures.

For anyone curious about the future of AI in healthcare, this episode offers a fascinating glimpse into the world of biotech innovation. To discover how VeriSIM Life’s technology is poised to bring life-saving treatments to patients faster and more safely than ever before, be sure to tune in today!


Key Points:

  • How Jo's background and interest in translational challenges led her to found VeriSIM Life.
  • Addressing translational gaps between animal models and human trials with hybrid AI.
  • Combining biology-based models with ML to enhance drug testing accuracy.
  • Small molecules, peptides, large molecules, clinical trial outcomes, and other data inputs.
  • Ways that VeriSIM’s models are tailored per data type, ensuring maximum accuracy.
  • Insight into the challenge of overcoming data gaps and how VeriSIM solves it.
  • How hybrid AI reduces overfitting, boosting model accuracy in data-limited scenarios.
  • What goes into validating VeriSIM’s models through partnerships and external testing.
  • Measuring the impact of this technology with VeriSIM’s translational index score.
  • Jo’s advice for AI-powered startups: be specific, validate technology, and be adaptable.
  • Her predictions for the impact VeriSIM will have in the next few years.


Quotes:

“[Hybrid AI] helps us not only unravel newer methods and mechanisms of actions or novel targets but also helps us identify better drug candidates that could eventually be safer and more effective in human patients.” — Jo Varshney


“Biology is complex. We need to understand it enough to create a codified version of that biology.” — Jo Varshney


“If you're just using machine learning-based methods, you may not get the right features to see the accuracy that you would see with the hybrid AI approach that we take.” — Jo Varshney


“Focus on validation and showing some real-world outcomes [rather than] just building the marketing outcome because, ultimately, we want it to get to the patients. We want to know if the technology really works. If it doesn't work, you can still pivot.” — Jo Varshney


Links:

VeriSIM Life

Jo Varshney on LinkedIn

Jo Varshney on X


Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

De-Risking Drug Translation with Jo Varshney from VeriSIM Lifeに寄せられたリスナーの声

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