Using AI to Predict How Any Patient Will Respond to Any Therapy – Life Sciences Today Podcast Episode 2

We’re excited to be back for another episode of the Life Sciences Today Podcast by Healthcare IT Today.  I had the privilege of hosting Orr Inbar from Quanthealth.  Quanthealth is helping us find a better way to select drugs that go into the clinic and optimize study designs to achieve positive outcomes.

Their ability to construct simulations using a large database of provider-sourced and anonymized American patient health records is impressive.  They’ve done over 120 simulations to date and helped pharma improve their protocols in Phase 2 and Phase 3 clinical trials, reducing costs and times required to run studies.  They’re moving upstream into Phase 1 where there is less historical data.

We’re excited to keep an eye on Quanthealth and see what they’re able to accomplish next.

Check out the main topics of discussion for this episode of the Life Sciences Today podcast:

• Tell us about yourself and how you got into this adventure?
• Quanthealth’s site mentions over 120 trial simulations—how did you choose those, and why not 1,000 or 10,000?
• You reference 350 million patient records. What are the sources and how do you account for differences in standards of care across the globe?
• Can you walk me through a concrete example—preferably one that wasn’t an ‘easy win’—where your platform predicted a trial outcome, and how that prediction compared to the actual results? Preferably one that wasn’t already well validated by existing PD-1/PD-L1 data
• Also, is this validation retrospective or prospective?
• Do customers pay you per simulation, is it a subscription model, or a hybrid? What kind of ROI or value do they see—for instance, have any trial designs actually changed based on your platform’s insights? And how do you capture value for your company?
• Your August 2024 press release mentions using data from 350M patients to reduce a respiratory trial by 251 subjects and save $200M. That figure might seem high to some—can you clarify how you got that number? Did your customer actually save $200M in clinical operations?
• Does the platform predict Phase 1? If you can move upstream to Phase 1 – then well, now we’re talking!
• Is there anything else you’d like to share about QuantHealth or the future of trial optimization?

Now, without further ado, we’re excited to share with you the next episode in the Life Sciences Today podcast.

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If you work in Life Sciences IT, we’d love to hear where you agree and/or disagree with our takes on health IT innovation in life sciences. Feel free to share your thoughts and perspectives in the comments of this post, in the YouTube comments, or privately on our Contact Us page. Let us know what you think of the podcast and if you have any ideas for future episodes.

Thanks so much for listening!