Ordinal logistic regression and ordinal random forest models accurately predicted low-, intermediate-, and high-risk drugs for Torsades de pointes based on preclinical data.
Can ordinal logistic regression and ordinal random forest models accurately predict drug-induced Torsades de pointes risk from preclinical data?
Statistical learning models using ordinal logistic regression and random forests can accurately predict drug-induced Torsades de pointes risk in preclinical safety assessments.
Torsades de pointes (TdP) is an irregular heart rhythm characterized by faster beat rates and potentially could lead to sudden cardiac death. Much effort has been invested in understanding the drug-induced TdP in preclinical studies. However, a comprehensive statistical learning framework that can accurately predict the drug-induced TdP risk from preclinical data is still lacking. We proposed ordinal logistic regression and ordinal random forest models to predict low-, intermediate-, and high-risk drugs based on datasets generated from two experimental protocols. Leave-one-drug-out cross-validation, stratified bootstrap, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The potential outlier drugs identified by our models are consistent with their descriptions in the literature. Our method is accurate, interpretable, and thus useable as supplemental evidence in the drug safety assessment.
Xi et al. (Wed,) conducted a other in Drug-induced Torsades de pointes (TdP). Ordinal logistic regression and ordinal random forest models was evaluated on Prediction of low-, intermediate-, and high-risk drugs for TdP. Ordinal logistic regression and ordinal random forest models accurately predicted low-, intermediate-, and high-risk drugs for Torsades de pointes based on preclinical data.
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