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Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR-ORd in silico model output
Nurul Qashri Mahardika T1, Ali Ikhsanul Qauli1,2, Aroli Marcellinus1
1Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea.
Abstract:
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 in vitro Proarrhythmia Assay (CiPA) initiative was proposed that integrates in vitro 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 in silico 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.
Insights
This study enhances drug-induced Torsades de pointes (TdP) risk prediction using an artificial neural network with computational models. The approach accurately classifies TdP risk, improving cardiac safety assessments.
Area of Science:
- Cardiovascular pharmacology
- Computational biology
- Pharmacogenomics
Background:
- Drug-induced Torsades de pointes (TdP) is a serious arrhythmia linked to sudden cardiac death.
- Current risk assessment methods have limitations in accuracy and generalizability.
- The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative aims to improve drug proarrhythmic risk evaluation.
Purpose of the Study:
- To evaluate the TdP risk classification performance of metrics from the in silico Tomek-O'Hara Rudy (ToR-ORd) ventricular cell model.
- To develop and optimize an artificial neural network (ANN)-based classifier using these metrics.
- To assess the ANN model's ability to categorize drug TdP risk into high, intermediate, and low categories.
Main Methods:
- Utilized in silico simulations from the ToR-ORd ventricular cell model to generate TdP metrics.
- Employed an artificial neural network (ANN) classifier, with TdP metrics as input.
- Optimized the ANN model using grid search (GS) for hyperparameter tuning.
Main Results:
- The ANN classifier achieved high performance in TdP risk categorization.
- Area Under the Curve (AUC) values were 0.979 (high-risk), 0.791 (intermediate-risk), and 0.937 (low-risk).
- Demonstrated the ToR-ORd model's capability in predicting TdP risk.
Conclusions:
- The ToR-ORd ventricular cell model effectively classifies drug TdP risk into three categories.
- The ANN-based approach offers improved TdP risk prediction and cardiac safety assessment.
- This study provides valuable insights for developing more accurate drug proarrhythmic risk evaluation methods.
