Accelerating cancer drug discovery through accurate safety predictions: one goal of The ATOM consortium

SLAS 2019 Innovation and Application | Washington, DC

Date: February 6, 2019

Speaker: Sarine Markossian, PhD

Authors: Sarine Markossian [1, 2], Kenny Ang [1, 2], Thomas D. Sweitzer [2, 3], Andrew Weber [2, 3], Claire G. Jeong [2, 3], Michelle R. Arkin [1, 2]
1 ATOM, San Francisco, CA 94158, USA
2 Small Molecule Discovery Center and Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
3 GlaxoSmithKline, King of Prussia, PA 19406, USA

Title: Accelerating Cancer Drug Discovery through Accurate Safety Predictions: One Goal of the ATOM Consortium

Abstract: The Accelerating Therapeutics for Opportunities in Medicine (ATOM) consortium is an academic, industry, and government partnership with the goal of rapidly accelerating drug discovery by integrating modeling, deep 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injury (DILI), is a leading cause for attrition during drug development as well as one of the main reasons drugs are withdrawn from the market.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, will allow us to predict DILI from the structure of a proposed drug lead. Currently, drug discovery is a slow and sequential process with a high rate of failure. 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.