ABSTRACT

Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to predict drug-induced toxicity. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell–derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those predicted to have cardiotoxic liabilities using a single-parameter score based on deep learning. Compounds with major predicted cardiotoxicity included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks with predicted cardiotoxic liabilities. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that protect against diseased phenotypes and deleterious mutations.

GRAPHICAL ABSTRACT

CONTRIBUTION TO THE FIELD

In this article, Grafton and colleagues use induced pluripotent stem cell technology and deep learning to train a neural network capable of detecting patterns of cardiotoxicity. To identify bioactive and chemical classes that lead to cardiotoxicity, they combine the neural network with high-content screening of 2560 compounds. The methods described in this study can be used to de-risk early-stage drug development, triage hits, and identify drugs that protect against disease. This screening paradigm will serve as a useful resource for drug discovery and phenotypic interrogation of stem cells and stem cell–derived cell types.