Key result
An ensemble machine learning algorithm combining multiplexed engineered cardiac tissue assays classified the mechanistic action of unknown compounds with 86.2% predictive accuracy.
Why the study?
Because 90 % of lead compounds fail safety and efficacy benchmarks in human trials despite preclinical testing, a preclinical cardiac screening model more representative of the human response is needed.
Does an ensemble machine learning algorithm using multiplexed cardiac contractility assays improve the classification of drug mechanistic action in engineered human cardiac tissue?
Does an ensemble machine learning algorithm using multiplexed cardiac contractility assays improve the classification of drug mechanistic action in engineered human cardiac tissue?
An ensemble machine learning algorithm combining multiple engineered cardiac tissue assays can accurately classify the mechanistic action of drugs, offering a promising preclinical screening platform.
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.
No takes yet. Share an insight, caveat, or question.
Aghavali et al. (2024) studied Cardiac screening of newly discovered drugs. Ensemble machine learning algorithm with multiplexed cardiac contractility assays vs. Single-assay models was evaluated on Predictive accuracy for classifying the mechanistic action of unknown compounds. An ensemble machine learning algorithm combining multiplexed engineered cardiac tissue assays classified the mechanistic action of unknown compounds with 86.2% predictive accuracy.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: