insitro AI meets Lilly data: a new ML model for in vivo PK prediction

Summary

Share Last year, insitro and Eli Lilly and Company (Lilly) teamed up to build AI models that predict in vivo properties of small molecules, powered by the corpus of proprietary data that Lilly has accumulated over decades of R&D. Together, we built the in vivo PK model available in Lilly TuneLab as of October 6, to predict in vivo pharmacokinetics (PK) across four preclinical species: mouse, rat, dog, and monkey. Designing a clinic-ready small molecule drug requires optimizing the pharmacokinetic profile of that molecule—ensuring the drug reaches the right tissues at appropriate concentrations for the required length of time to have a therapeutic effect. Existing ML models that predict the PK profile of small molecules have focused on predicting in vitro ADME values due to limited in vivo PK data availability [1]. Lilly’s corpus of historical in vitro ADME and in vivo PK data lets us go one step further: training models to directly predict the in vivo PK behavior of a molecule. Our models are designed to outperform today’s industry standard method based on extrapolation from in vitro assays, potentially enabling prioritization of compounds entering expensive and lengthy animal studies. The gap between a dish and an animal Medicinal chemists designing a safe and effective small molecule have to cross a critical translation gap between what happens in a dish (in vitro) and what happens in the body (in vivo). The first critical checkpoint on that path is a preclinical PK study. Animal models have translation challenges, but they are where a candidate molecule has to survive the complexity of an intact physiological system capable of performing all of the aspects of a drug’s fate in the body. A single IV dose study in animals produces three PK data points that are important signals about whether the candidate molecule has the potential to be a drug: - Plasma clearance (CL): How efficiently the body breaks the molecule down and/or excretes it and its metabolites. - Volume of distribution at steady state (Vdss): How widely the molecule spreads into tissues from the bloodstream. - Mean residence time (MRT): The average time a molecule remains intact in the body before it’s eliminated. Getting this data is slow, expensive, and requires animal testing. Anything that predicts these outcomes reliably ahead of time means running fewer studies on molecules that fail, re…