Key result
An artificial neural network model using nine in silico features achieved an AUC of 0.94 for classifying high-risk proarrhythmic drugs, improving upon previous logistic regression methods.
Why the study?
Methodologies for predicting drug-induced torsade de pointes using computer simulations have been developed, but their predictive performance still requires improvement.
An artificial neural network using nine in silico electrophysiological features improves the prediction of drug-induced proarrhythmia risk compared to traditional logistic regression models.
May aid early in silico drug screening; leaves open prospective clinical validation before practice adoption.
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.
No takes yet. Share an insight, caveat, or question.
Yoo et al. (2021) studied Drug-induced proarrhythmicity (Torsade de Pointes) (n=28). Artificial Neural Network (ANN) model vs. Logistic regression model was evaluated on Area Under the Curve (AUC) for high-risk proarrhythmia classification (95% CI 0.75-1.00). An artificial neural network model using nine in silico features achieved an AUC of 0.94 for classifying high-risk proarrhythmic drugs, improving upon previous logistic regression methods.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: