Establishment and validation of a torsade de pointes prediction model based on human iPSC‑derived cardiomyocytes

• Published in: Experimental and therapeutic medicine Vol. 25; no. 1; p. 61
• Main Authors: Pan, Dongsheng, Li, Bo, Wang, Sanlong
• Format: Journal Article
• Language: English
• Published: Greece Spandidos Publications 01.01.2023; Spandidos Publications UK Ltd; D.A. Spandidos
• Subjects: Accuracy; Adverse and side effects; Algorithms; Arrhythmia; Cardiac arrhythmia; Cardiomyocytes; Data collection; Drug development; Drugs; Experiments; Health aspects; Heart cells; Machine learning; Potassium; Risk factors; Sensors; Statistical models; Stem cells; China; Beijing China; drug-induced arrhythmias; torsade de pointes; prediction model; human induced pluripotent stem cell derived-cardiomyocytes; field potential
• ISSN: 1792-0981; 1792-1015
• DOI: 10.3892/etm.2022.11760

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 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.