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

Abstract

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.

Application of k-Nearest Neighbors Method for Drug Concentration and Cardiotoxicity Classification Using Extracellular Field Potentials and Reconstructed Action Potentials of Cardiac Cells

“…In some papers, researchers use manually determined parameters like FP dura�on (FPD), peak FP amplitude (FPA) or short-term variability (STV) to classify the cardiotoxicity risk in recorded signals. This approach requires signal processing to determine the parameters and an expert to separate them into classes, but it also allows to employ more simple and interpretable ML algorithms like k-Nearest Neighbours (k-NN), Support Vector Machines or Random Forest [7]. The downside of the parameter-based classifica�on is that different data is sensi�ve to different parameters.…”

Healthy human induced pluripotent stem cell-derived cardiomyocytes exhibit sex dimorphism even without the addition of hormones

“…Numerous models of CVD have been generated using hiPSC-CM since their first derivation more than a decade ago 18,19 . These include disease models of IHD and ischemia reperfusion injury [20][21][22] and even more commonly studies of cardiac arrythmias are done using hiPSC-CM; these include hiPSC-CM models of long-QT, Torsades de Pointes and atrial fibrillation which have all been seen to be sex dimorphic in the clinic [23][24][25][26][27] . Interestingly, there has been very little consideration of sex dimorphism in these models.…”