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Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes
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,...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367386/ https://www.ncbi.nlm.nih.gov/pubmed/34338636 http://dx.doi.org/10.7554/eLife.68714 |
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author | Grafton, Francis Ho, Jaclyn Ranjbarvaziri, Sara Farshidfar, Farshad Budan, Anastasiia Steltzer, Stephanie Maddah, Mahnaz Loewke, Kevin E Green, Kristina Patel, Snahel Hoey, Tim Mandegar, Mohammad Ali |
author_facet | Grafton, Francis Ho, Jaclyn Ranjbarvaziri, Sara Farshidfar, Farshad Budan, Anastasiia Steltzer, Stephanie Maddah, Mahnaz Loewke, Kevin E Green, Kristina Patel, Snahel Hoey, Tim Mandegar, Mohammad Ali |
author_sort | Grafton, Francis |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8367386 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-83673862021-08-18 Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes Grafton, Francis Ho, Jaclyn Ranjbarvaziri, Sara Farshidfar, Farshad Budan, Anastasiia Steltzer, Stephanie Maddah, Mahnaz Loewke, Kevin E Green, Kristina Patel, Snahel Hoey, Tim Mandegar, Mohammad Ali eLife Stem Cells and Regenerative Medicine 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. eLife Sciences Publications, Ltd 2021-08-02 /pmc/articles/PMC8367386/ /pubmed/34338636 http://dx.doi.org/10.7554/eLife.68714 Text en © 2021, Grafton et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Stem Cells and Regenerative Medicine Grafton, Francis Ho, Jaclyn Ranjbarvaziri, Sara Farshidfar, Farshad Budan, Anastasiia Steltzer, Stephanie Maddah, Mahnaz Loewke, Kevin E Green, Kristina Patel, Snahel Hoey, Tim Mandegar, Mohammad Ali Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes |
title | Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes |
title_full | Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes |
title_fullStr | Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes |
title_full_unstemmed | Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes |
title_short | Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes |
title_sort | deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes |
topic | Stem Cells and Regenerative Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367386/ https://www.ncbi.nlm.nih.gov/pubmed/34338636 http://dx.doi.org/10.7554/eLife.68714 |
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