  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.
