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

• URL: http://arxiv.org/abs/2201.05669v1
• Date: Fri, 14 Jan 2022 21:03:20 GMT
• Title: Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay
• Authors: Nan Miles Xi and Dalong Patrick Huang
• Abstract summary: We discuss machine learning approaches in the prediction of drug-induced TdP risks using preclinical data. The model prediction performance was measured on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assay initiative. Our study validated the utility of machine learning approaches in predicting drug-induced TdP risks from preclinical data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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