Key result
Incorporating electrophysiological variability in computational models provides new opportunities to assess drug-induced TdP-risk and may improve current cardiac safety screening methods.
Why the study?
Computational models for drug cardiotoxicity assessment usually omit electrophysiological variability, use binary classification, and lack external validation.
Does incorporating electrophysiological variability improve the in silico prediction of drug-induced Torsade de Pointes risk?
Does incorporating electrophysiological variability improve the in silico prediction of drug-induced Torsade de Pointes risk?
Answers are grounded in this paper and cite their sources.
Incorporating electrophysiological variability into computational models may improve the accuracy of in silico screening for drug-induced Torsade de Pointes risk.
BACKGROUND AND OBJECTIVE: In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets. METHODS: The effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database. CONCLUSIONS: The methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.
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Llopis‐Lorente et al. (2022) studied Drug-induced Torsade de Pointes (TdP). Incorporating electrophysiological variability in computational models vs. Single baseline model was evaluated on Prediction of drug-induced arrhythmogenic-risk. Incorporating electrophysiological variability in computational models provides new opportunities to assess drug-induced TdP-risk and may improve current cardiac safety screening methods.
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