Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay
This work addresses drug safety assessment for pharmaceutical development by providing a supplementary evaluation tool, though it is incremental as it applies an existing method (random forest) to a specific new dataset.
The study tackled predicting drug-induced Torsades de pointes (TdP) risks using machine learning on preclinical data from rabbit ventricular wedge assays, achieving validation through leave-one-drug-out cross-validation on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assay initiative.
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.