Key result
An artificial neural network model using nine electrophysiological features achieved an AUC of 0.92 for predicting high-risk proarrhythmic drugs, outperforming a logistic regression model.
Why the study?
Existing computer simulation methodologies for predicting drug-induced torsade de pointes still require improvement in predictive performance.
Does an artificial neural network model using 9 electrophysiological features improve the prediction of drug-induced Torsades de Pointes risk compared to logistic regression in an in silico model?
Does an artificial neural network model using 9 electrophysiological features improve the prediction of drug-induced Torsades de Pointes risk compared to logistic regression in an in silico model?
An artificial neural network model using multiple electrophysiological features from in silico simulations improved the prediction of drug-induced Torsades de Pointes risk compared to traditional logistic regression.
As part of the Comprehensive in vitro Proarrhythmia Assay initiative, methodologies for predicting the occurrence of drug-induced torsade de pointes via computer simulations have been developed and verified recently. However, their predictive performance still requires improvement. Herein, we propose an artificial neural networks (ANN) model that uses nine multiple input features, considering the action potential morphology, calcium transient morphology, and charge features to further improve the performance of drug toxicity evaluation. 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 O’Hara Rudy in silico model, nine features (dVm/dt max , AP resting , APD90, APD50, Ca resting , CaD90, CaD50, qNet, and qInward) were calculated. These nine features were used as inputs to an ANN model to classify drug toxicity into high-risk, intermediate-risk, and low-risk groups. The model was trained with data from 12 drugs and tested using the data of the remaining 16 drugs. The proposed ANN model demonstrated an AUC of 0.92 in the high-risk group, 0.83 in the intermediate-risk group, and 0.98 in the low-risk group. This was higher than the classification performance of the method proposed in previous studies.
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Yoo et al. (2021) studied Drug-induced torsade de pointes (proarrhythmicity) (n=28). Artificial Neural Network (ANN) model with 9 input features vs. Logistic regression model was evaluated on Area under the curve (AUC) for classifying high-risk drug toxicity (AUC 0.92, 95% CI 0.85-0.96). An artificial neural network model using nine electrophysiological features achieved an AUC of 0.92 for predicting high-risk proarrhythmic drugs, outperforming a logistic regression model.
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