Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes

eLife

Sara Ranjbarvaziri¹  Kevin E Loewke²  Mahnaz Maddah²  Kristina Green³  Mohammad Ali Mandegar³  Snahel Patel³  Tim Hoey³  Farshad Farshidfar³  Stephanie Steltzer³  Jaclyn Ho³  Francis Grafton³  Anastasiia Budan³ 

[1] Cardiovascular Institute and Department of Medicine, Stanford University, Stanford, United States;Dana Solutions, Palo Alto, United States;Tenaya Therapeutics, South San Francisco, United States;

关键词: iPSC;    iPSC-CMs;    cardiomyocyte;    deep learning;    high-content screen;    cardiotoxicity;   

DOI  :  10.7554/eLife.68714

来源: DOAJ

【 摘 要 】

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 detect drug-induced toxicity in vitro. 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 with potential cardiotoxic liabilities in iPSC-CMs using a single-parameter score based on deep learning. Compounds demonstrating cardiotoxicity in iPSC-CMs 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 that show cardiotoxic signal in iPSC-CMs. 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 may protect against diseased phenotypes and deleterious mutations.

【 授权许可】

Unknown