论文标题
使用机器学习和兔子心室楔形测定法预测药物诱导的TDP风险
Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay
论文作者
论文摘要
药物诱导的扭转点（TDP）风险的评估对于药物安全评估至关重要。在这项研究中，我们讨论了使用临床前数据预测药物诱导的TDP风险的机器学习方法。具体而言，随机森林模型是在兔心室楔形测定法产生的数据集中训练的。该模型预测性能是根据全面的体外促性促动脉症分析计划中的28种药物进行测量的。一对一的杰出交叉验证提供了模型性能的公正估计。分层的自举揭示了渐近模型预测的不确定性。我们的研究验证了机器学习方法在预测临床前数据中预测药物诱导的TDP风险方面的实用性。我们的方法可以扩展到其他临床前方案，并作为药物安全评估中的补充评估。
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.
