Empowered Patient Podcast Computational AI Bringing Predictability Precision to Drug Discovery and Clinical Trials with Shai Shen-Orr CytoReason
Shai Shen-Orr, the Co-Founder and Chief Scientist of CytoReason, is developing computational disease models powered by AI to improve the probability of success in phase 2 clinical trials. Their goal is to more precisely develop drugs using AI to analyze large amounts of biological and clinical data and overcome the challenges of the complexity and uncertainty in medical information. Working with large pharmaceutical companies, CytoReason is supporting companies in their drug development efforts and helping them choose the right drug target and patient populations to study.
Shai explains, "There are many challenges. I would say that CytoReason right now isn't tackling images. And there are certainly companies in the field that have been doing this. We've been focused more on the molecular side of the data, the genes, the proteins, the cells, the genetics. But the challenge is that when you work in this field and try to do AI in biology, it's very difficult for us to know the ground truth. We don't know when we're wrong or when we're right."
"We strive to support as many diseases as we can. Where we've placed the focus now has been immunology and oncology and, particularly, immuno-oncology. We kind of grew up as a company that really can specialize in the immune system. While the immune system plays a role in almost every disease, there are particular diseases, autoimmune diseases, and oncology, where it plays a very large role."
"So now when we develop drugs, drug developers are thinking very much across the board, they're thinking about the mechanisms of the disease. They keep asking themselves where are there similar mechanisms that are triggering diseases that may be ultimately, from a physiological perspective, from a disease perspective, the patients may look very different, they may even have a different disease. Yet, from the drug perspective, what the drug is hitting, the mechanisms are similar. And so we need to support that. We need to support the ability of drug developers to think across the board. That also plays into the role of which models we prioritize."
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