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 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.
Highlights
Drug development is a lengthy and expensive endeavor, often requiring an estimated 10 years and $0.8–2.6 billion
Following standard differentiation of induced pluripotent stem cells (iPSCs)-CMs (Figure 1A), blasticidin selection reproducibly enriched a heterogenous population of iPSC-CMs to greater than 95% pure iPSC-CMs as measured by ACTN2, TNNT2, and MYBPC3 immunostaining (Figure 1B, C)
Compared to previously published protocols, recovery in culture media leads to improved maturity metrics based on quantitative polymerase chain reaction markers, contractility, and immunostaining (Figure 1—figure supplement 1C–F)
Summary
Drug development is a lengthy and expensive endeavor, often requiring an estimated 10 years and $0.8–2.6 billion. To reduce costs and risk, pharmaceutical companies need effective screening methods to prevent drug attrition at late stages of the development process. To decrease the potential for toxicity, and for late-stage drug attrition, pharmaceutical and biotechnology industries seek in vitro systems that can identify drug-induced toxicity with phenotypic screening at early stages of development (Moffat et al, 2014). This screening enables interrogation of a large number of perturbagens (e.g., small molecules, siRNAs, CRISPR gRNAs) in a target-agnostic assay that measures phenotypic changes (Eder et al, 2014)
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