Key result
An iPSC-cardiomyocyte voting classifier predicts drug-induced TdP risk with 100% accuracy.
Why the study?
A suitable model using human induced pluripotent stem cell-derived cardiomyocytes to predict the risk of drug-induced torsade de pointes has not been fully established.
A soft voting prediction model combining logistic regression and AdaBoost based on hiPSC-CMs accurately predicts drug-induced torsade de pointes risk, offering a potential preclinical screening tool.
May enhance preclinical TdP screening with hiPSC-CM models; leaves open clinical translation pending prospective validation.
Drug‑induced cardiotoxicity is one of the main causes of drug failure, which leads to subsequent withdrawal from pharmaceutical development. Therefore, identifying the potential toxic candidate in the early stages of drug development is important. Human induced pluripotent stem cell‑derived cardiomyocytes (hiPSC‑CMs) are a useful tool for assessing candidate compounds for arrhythmias. However, a suitable model using hiPSC‑CMs to predict the risk of torsade de pointes (TdP) has not been fully established. The present study aimed to establish a predictive TdP model based on hiPSC‑CMs. In the current study, 28 compounds recommended by the Comprehensive in vitro Proarrhythmia Assay (CiPA) were used as training set and models were established in different risk groups, high‑ and intermediate‑risk versus low‑risk groups. Subsequently, six endpoints of electrophysiological responses were used as potential model predictors. Accuracy, sensitivity and area under the curve (AUC) were used as evaluation indices of the models and seven compounds with known TdP risk were used to verify model differentiation and calibration. The results showed that among the seven models, the AUC of logistic regression and AdaBoost model was higher and had little difference in both training and test sets, which indicated that the discriminative ability and model stability was good and excellent, respectively. Therefore, these two models were taken as submodels, similar weight was configured and a new TdP risk prediction model was constructed using a soft voting strategy. The classification accuracy, sensitivity and AUC of the new model were 0.93, 0.95 and 0.92 on the training set, respectively and all 1.00 on the test set, which indicated good discrimination ability on both training and test sets. The risk threshold was defined as 0.50 and the consistency between the predicted and observed results were 92.8 and 100% on the training and test sets, respectively. Overall, the present study established a risk prediction model for TdP based on hiPSC‑CMs which could be an effective predictive tool for compound‑induced arrhythmias.
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Pan et al. (2022) studied Drug-induced torsade de pointes (n=35). Voting Classifier (VC) prediction model was evaluated on Area under the curve (AUC) on the test set. A soft voting classifier model based on human iPSC-derived cardiomyocytes predicted drug-induced torsade de pointes risk with an accuracy, sensitivity, and AUC of 1.00 on the test set.
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