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

Autor:	Grafton F; Tenaya Therapeutics, South San Francisco, United States., Ho J; Tenaya Therapeutics, South San Francisco, United States., Ranjbarvaziri S; Cardiovascular Institute and Department of Medicine, Stanford University, Stanford, United States., Farshidfar F; Tenaya Therapeutics, South San Francisco, United States., Budan A; Tenaya Therapeutics, South San Francisco, United States., Steltzer S; Tenaya Therapeutics, South San Francisco, United States., Maddah M; Dana Solutions, Palo Alto, United States., Loewke KE; Dana Solutions, Palo Alto, United States., Green K; Tenaya Therapeutics, South San Francisco, United States., Patel S; Tenaya Therapeutics, South San Francisco, United States., Hoey T; Tenaya Therapeutics, South San Francisco, United States., Mandegar MA; Tenaya Therapeutics, South San Francisco, United States.
Jazyk:	angličtina
Zdroj:	ELife [Elife] 2021 Aug 02; Vol. 10. Date of Electronic Publication: 2021 Aug 02.
DOI:	10.7554/eLife.68714
Abstrakt:	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. Competing Interests: FG, JH, FF, AB, SS, KG, SP, TH, MM is an employee of Tenaya Therapeutics and has stock holdings in the company. SR No competing interests declared, MM, KL is affiliated with Dana Solutions. The author has no other competing interests to declare. (© 2021, Grafton et al.)
Databáze:	MEDLINE
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