Prediction of Drug-Induced Arrhythmogenic Risk Using In Silico Populations of Models

In silico tools hold potential to improve drug cardiotoxicity predictions. However, computational models do not usually consider inter-individual variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In this study we analyze the effect of incorporating inter-individual variability in the prediction of drug-induced TdP-risk. Specifically, the effects of the 12 training CiPA drugs were simulated on a single baseline model and on an electrophysiologically calibrated population of 848 models. Ternary classifiers based on support vector machines and logistic regression, were built using biomarkers obtained from simulation results. Classifiers were validated using the 16 validation CiPA drugs as an external data set. The classification accuracy increased to 80.1% when using the population of models, with respect to an accuracy of 62.4% obtained using the baseline model. Simulations with population of models allowed to identify individuals more prone to develop TdP. The methodology presented provides new opportunities to assess drug-induced TdP, taking into account inter-individual variability and may be helpful to improve current cardiac safety screening methods.