Assay Development and Screening
Currently, drug discovery is a slow and sequential process with a high rate of failure. The Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium is an academia, industry, and government partnership with the goal of rapidly accelerating drug discovery by integrating modeling, machine learning, and human-relevant complex in vitro models. One of our goals in ATOM is to optimize preclinical safety predictions, so we can incorporate predictive toxicology early in the drug discovery process. Hepatocyte toxicity, or drug induced liver injury (DILI), is a leading cause for attrition during drug development as well as one of the main reasons drugs are withdrawn from the market. We will describe our efforts to profile multiple 2D and 3D High Content assay formats to measure and predict hepatocyte toxicity. These multi-parametric data, coupled with Quantitative Systems Toxicology (QST) tools and deep machine learning, are enabling us to predict DILI from the structure of a proposed drug lead. In summary, by integrating high-performance computing and human-relevant in vitro models, we plan to transform drug discovery into a rapid, integrated, and patient-centric model.