Impact AI Better Therapeutics Using Lab-Grown Tissue with Andrei Georgescu from Vivodyne
One of the biggest hurdles in medical research is the gap between animal studies and human trials, a disconnect that often leads to failed drug tests and wasted resources. But what if there was a way to bridge that gap and create treatments that are more effective for humans from the start?
Today, I am joined by Dr. Andrei Georgescu, Founder and CEO of Vivodyne, a groundbreaking biotechnology company that is transforming how scientists study human biology and develop new therapeutics. In this episode, he reveals how Vivodyne harnesses lab-grown tissue and advanced multimodal AI to create more effective therapeutics. We explore the challenges of gathering human tissue data, the collaboration between biologists, robotics engineers, and machine learning developers to build powerful machine learning models, and the profound impact that Vivodyne is poised to make in the fight against diseases. To discover how Vivodyne’s innovations can lead to more successful treatments and faster drug development, tune in today!
Key Points:
- Insight into Andrei’s background and how it led him to create Vivodyne.
- What Vivodyne does and why it’s so important for drug discovery.
- The role that AI and machine learning play in analyzing vast amounts of data.
- Different data inputs and outputs for Vivodyne’s advanced multimodal AI.
- The value of biased and unbiased AI outputs depending on the context.
- Why interpretability and explainability are crucial in fields like biotechnology.
- Challenges associated with collecting human tissue data to train Vivodyne’s models.
- What goes into validating Vivodyne’s machine learning models.
- Difficulties in integrating biology knowledge with robotics and machine learning.
- Andrei’s business-focused advice for technical founders.
- The profound impact that Vivodyne will have on drug discovery in the future.
Quotes:
“Vivodyne grows human tissues at a very large scale so that we can understand human physiology and we can test directly on it in order to discover and develop better drugs that are both safer and more efficacious.” — Andrei Georgescu
“We use machine learning and AI as a mechanism to understand the complexity of very deep data and to very efficiently apply that complexity and infer from what we've learned across the very large breadth of data that we collect.” — Andrei Georgescu
“To address [the problem of a] glaring lack of trainable data, we create that data by growing it at scale.” — Andrei Georgescu
“If you're a technical founder, do something that is incredibly hard because the ability for you to do that thing will grant you much more leverage than creating what is otherwise a much more simple and generic business.” — Andrei Georgescu
“[With Vivodyne], we will enter a world of plenty where the development of new drugs against diseases becomes a far more successful, reliable, and predictive process, and we're able to make much safer and much more effective drugs just by virtue of being able to optimize that therapeutic on human tissues before giving it to people for the first time in-clinic.” — Andrei Georgescu
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