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How Can AI Help Predict Patient Drug Response? (Genialis)

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Manage episode 373752301 series 2359900
Content provided by Tjasa Zajc. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Tjasa Zajc or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Drug development is undeniably expensive. For years, the pharmaceutical industry cited an estimate of 3 billion US dollars. However, a recent study published in 2020 discovered that the median cost actually falls between 985 million and 1.3 billion US dollars. Even within this range, it remains a substantial amount. The high cost primarily stems from the significant failure rate of new potential medications that never progress beyond clinical trials. Computational biology and AI have already assumed significant roles in drug development. The aspiration is for them to expedite the creation of new, more precise, and tailored medications. Today, we will delve into biotech and explore how technology aids in predicting a specific patient's response to a particular drug. In a conversation with Rafael Rosengarten, the CEO of Genialis - a company using machine learning and high-throughput omics data to capture underlying disease biology and predict how patients will likely respond to targeted therapies, we explored the impact of computational biology on drug development and pricing, the application of generative AI in discovering novel molecules, and the challenges companies encounter in acquiring patient data to advance their work.

Sponsor: Magic Mind

Learn more at: magicmind.com/digitalhealth

Use the code: digitalhealth20

Find more at:

www.facesofdigitalhealth.com

Newsletter: https://fodh.substack.com/

  continue reading

309 episodes

Artwork
iconShare
 
Manage episode 373752301 series 2359900
Content provided by Tjasa Zajc. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Tjasa Zajc or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Drug development is undeniably expensive. For years, the pharmaceutical industry cited an estimate of 3 billion US dollars. However, a recent study published in 2020 discovered that the median cost actually falls between 985 million and 1.3 billion US dollars. Even within this range, it remains a substantial amount. The high cost primarily stems from the significant failure rate of new potential medications that never progress beyond clinical trials. Computational biology and AI have already assumed significant roles in drug development. The aspiration is for them to expedite the creation of new, more precise, and tailored medications. Today, we will delve into biotech and explore how technology aids in predicting a specific patient's response to a particular drug. In a conversation with Rafael Rosengarten, the CEO of Genialis - a company using machine learning and high-throughput omics data to capture underlying disease biology and predict how patients will likely respond to targeted therapies, we explored the impact of computational biology on drug development and pricing, the application of generative AI in discovering novel molecules, and the challenges companies encounter in acquiring patient data to advance their work.

Sponsor: Magic Mind

Learn more at: magicmind.com/digitalhealth

Use the code: digitalhealth20

Find more at:

www.facesofdigitalhealth.com

Newsletter: https://fodh.substack.com/

  continue reading

309 episodes

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