1. Enhanced drug classification using machine learning with multiplexed cardiac contractility assays
- Author
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Reza Aghavali, Erin G. Roberts, Yosuke K. Kurokawa, Erica Mak, Martin Y.C. Chan, Andy O.T. Wong, Ronald A. Li, and Kevin D. Costa
- Subjects
Cardiac tissue engineering ,Pharmaceutical compound testing ,Drug discovery ,Cardiac contractility ,Electrophysiology ,In vitro screening ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Cardiac screening of newly discovered drugs remains a longstanding challenge for the pharmaceutical industry. While therapeutic efficacy and cardiotoxicity are evaluated through preclinical biochemical and animal testing, 90 % of lead compounds fail to meet safety and efficacy benchmarks during human clinical trials. A preclinical model more representative of the human cardiac response is needed; heart tissue engineered from human pluripotent stem cell derived cardiomyocytes offers such a platform. In this study, three functionally distinct and independently validated engineered cardiac tissue assays are exposed to increasing concentrations of known compounds representing 5 classes of mechanistic action, creating a robust electrophysiology and contractility dataset. Combining results from six individual models, the resulting ensemble algorithm can classify the mechanistic action of unknown compounds with 86.2 % predictive accuracy. This outperforms single-assay models and offers a strategy to enhance future clinical trial success aligned with the recent FDA Modernization Act 2.0.
- Published
- 2024
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