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

TL;DR

This study demonstrates that machine learning models, trained on preclinical rabbit ventricular wedge assay data, can effectively predict drug-induced Torsades de pointes risks, aiding in drug safety evaluation.

Contribution

It introduces a machine learning approach, specifically a random forest model, for predicting TdP risks from preclinical data, validated on a set of 28 drugs.

Findings

Machine learning accurately predicts TdP risks.

Model validation shows reliable performance.

Method can extend to other preclinical protocols.

Abstract

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.