Computational Pipelining of in silico Cardio- and Hepatotoxicity Models

GSK Data Science Symposium 2018 | Upper Providence, PA

Date: October 3, 2018

Presenter: Margaret Tse, PhD

Title: Computational Pipelining of in silico Cardio- and Hepatotoxicity Models

Abstract: Accelerating Therapeutics for Opportunities in Medicine (ATOM) is a public-private consortium with the goal of accelerating the drug delivery process to deliver an oncology medicine from target to patient in less than one year. We are combining high performance computing, mechanistic modelling, diverse biological data, and new biological technologies and tools to reduce the time, financial cost, and need for animal testing during the drug discovery process.

Cardio- and hepatotoxicity are the focus of our initial safety work in ATOM, since they are often key areas limiting the success of new molecular entities (NMEs). Typically, potential candidate compounds are assessed for possible safety liability using a cascading screen of high throughput cross-reactivity screening (eXP), mechanistic in vitro assays, and ultimately, animal model testing. However, traditional toxicity screening can be a slow and costly process that can be under-predictive of human safety liabilities or can potentially eliminate safe and effective candidates.

To enhance the efficiency and accuracy of preclinical screening, we are developing an in silico pipeline for assessing safety liabilities. We propose the use of machine learning (ML) models to predict the structure-activity relationship between NMEs and binding activity against key off-target molecules. To generate in vivo toxicity prediction, the outputs of the ML are used in Quantitative Systems Toxicology models, such as DILI-sim for hepatoxicity and the Comprehensive in Vitro Proarrhythmia Assay Initiative (CiPA) in silico ventricular myocyte model for cardiac liabilities. Using the combined results of our in silico safety and physiologically based pharmacokinetic models, we are developing clinically relevant, dose-specific metrics of liability to inform drug candidate selection. Our goal is to generate a new paradigm that reduces preclinical attrition by generating more comprehensive, accurate, and efficient toxicity prediction paradigms and ultimately provides a framework to reduce the need for animal trials once expanded to be sufficiently comprehensive and accurate to generate regulatory buy-in.