Assessment of Drug Proarrhythmicity Using Artificial Neural Network with in Silico Deterministic Model Outputs

Abstract

Methodologies for predicting the occurrence of torsade de pointes by drugs via computer simulations have been developed and verified recently, as part of the Comprehensive in vitro Proarrhythmia Assay initiative. However, the predictive performance still requires improvement. Herein, we propose a deep learning algorithm based on artificial neural networks that receives nine multiple features and ​considers the action potential morphology, calcium concentration morphology, and charge characteristics to further improve drug toxicity evaluation performance. The voltage clamp experimental data for 28 drugs were augmented to 2,000 data entries using an uncertainty quantification technique. By applying these data to the modified Ohara Rudy in silico model, nine features (dVm/dtmax, APresting, APD90, APD50, Caresting, CaD90, CaD50, qNet, and qInward) were predicted. These nine features were used as inputs to an artificial neural network (ANN) model to classify drug toxicity into high-risk, intermediate, and low-risk groups. The model was trained with data of 12 drugs and tested with the data of the remaining 16 drugs. The proposed ANN model demonstrated an AUC of 0.94 in the high-risk group, 0.73 in the intermediate group, and 0.91 in the low-risk group. This is higher than the classification performance of the method proposed in previous studies.