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The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial indrug safety assessment. In this study, we discuss machine learning approachesin the prediction of drug-induced TdP risks using preclinical data.Specifically, the random forest model was trained on the dataset generated bythe rabbit ventricular wedge assay. The model prediction performance wasmeasured on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assayinitiative. Leave-one-drug-out cross-validation provided an unbiased estimationof model performance. Stratified bootstrap revealed the uncertainty in theasymptotic model prediction. Our study validated the utility of machinelearning approaches in predicting drug-induced TdP risks from preclinical data.Our methods can be extended to other preclinical protocols and serve as asupplementary evaluation in drug safety assessment.

Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay