QInward Variability-based Proarrhythmic Risk Assessment of Drugs Using Deep Learning Model

Many researchers have suggested evaluation methods and Torsades de Pointes (TdP) metrics to assess the proarrhythmic risk of a drug based on the simulation, as part of the Comprehensive Proarrhythmia Assay (CiPA) project. In the previous study, we validated the robustness of 12 features using the ordinal logistic regression (OLR) model by comparing the classification performances of metrics according to the experimental datasets used; however, the OLR model using 12 features did not provide desirable results. This study proposed a convolutional neural network (CNN) model using the variability of promising TdP metrics hypothesizing that the variability of features based on beats has more information than the single value of features. We performed the action potential (AP) simulation using a human ventricular myocyte model to calculate seven features representing the electrophysiological cell states of drug effects over 1,000 beats: qNet, qInward, intracellular calcium duration at returning to 50% baseline (CaD50) and 90% baseline (CaD90), AP duration at 50% repolarization (APD50) and 90% repolarization (APD90), and dVm/dtMax_repol. The proposed CNN classifier was trained using 12 train drugs and tested using 16 test drugs among CiPA drugs. The torsadogenic risk of drugs was classified as high, intermediate, and low risks. We determined the CNN classifier by comparing the classification performance according to the variabilities of seven biomarkers computed from the drug simulation using the Chantest dataset. The proposed CNN classifier performed the best when using qInward variability to classify the TdP-risk drugs with 0.94 AUC for high risk and 0.93 AUC for low risk. In addition, the final CNN classifier was validated using the qInward variability obtained after merging three datasets, but the model performance decreased to a moderate level of 0.75 and 0.78 AUC. These results suggest the need for the proposed CNN model to be trained and tested using various types of drugs.