Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR-ORd in silico model output

• Published in: Frontiers in physiology Vol. 15; p. 1374355
• Main Authors: Mahardika T, Nurul Qashri, Qauli, Ali Ikhsanul, Marcellinus, Aroli, Lim, Ki Moo
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 04.04.2024
• Subjects: artificial neural networks; explainable artificial intelligence; grid search; Physiology; Tomek–O’Hara Rudy ventricular in silico cell model; torsade de pointes; explainable artificial intelligence; Tomek–O’Hara Rudy ventricular in silico cell model; artificial neural networks; torsade de pointes; grid search
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2024.1374355

Torsades de pointes (TdP) is a type of ventricular arrhythmia that can lead to sudden cardiac death. Drug-induced TdP has been an important concern for researchers and international regulatory boards. The Comprehensive Proarrhythmia Assay (CiPA) initiative was proposed that integrates testing and computational models of cardiac ion channels and human cardiomyocyte cells to evaluate the proarrhythmic risk of drugs. The TdP risk classification performance using only a single TdP metric may require some improvements because of information limitations and the instability of generalizing results. This study evaluates the performance of TdP metrics from the simulations of the Tomek-O'Hara Rudy (ToR-ORd) ventricular cell model for classifying the TdP risk of drugs. We utilized these metrics as an input to an artificial neural network (ANN)-based classifier. The ANN model was optimized through hyperparameter tuning using the grid search (GS) method to find the optimal model. The study outcomes show an area under the curve (AUC) value of 0.979 for the high-risk category, 0.791 for the intermediate-risk category, and 0.937 for the low-risk category. Therefore, this study successfully demonstrates the capability of the ToR-ORd ventricular cell model in classifying the TdP risk into three risk categories, providing new insights into TdP risk prediction methods.