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