Key result
Action potential duration at 50% repolarization (APD50) was the best individual in silico feature for determining Torsade de Pointes risk, while repolarization velocity was best when merging datasets.
Why the study?
Research to validate the robustness of in silico features for drug proarrhythmic risk assessment across different in vitro experimental datasets had not been conducted.
In silico action potential features, particularly APD50 and repolarization velocity, are reliable computational biomarkers for assessing the proarrhythmic risk of drugs across different in vitro datasets.
In silico AP features reliably stratify TdP risk across datasets; leaves open clinical translation pending prospective validation.
Since the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiation, many studies have suggested various in silico features based on ionic charges, action potentials (AP), or intracellular calcium (Ca) to assess proarrhythmic risk. These in silico features are computed through electrophysiological simulations using in vitro experimental datasets as input, therefore changing with the quality of in vitro experimental data; however, research to validate the robustness of in silico features for proarrhythmic risk assessment of drugs depending on in vitro datasets has not been conducted. This study aims to verify the availability of in silico features commonly used in assessing the cardiac toxicity of drugs through an ordinal logistic regression model and three in vitro datasets measured under different experimental environments and with different purposes. We performed in silico drug simulations using the Tomek-Ohara Rudy (ToR-ORD) ventricular myocyte model and computed 12 in silico features comprising six AP features, four Ca features, and two ion charge features, which reflected the effect and characteristics of each in vitro data for CiPA 28 drugs. We then compared the classific performances of ordinal logistic regressions according to these 12 in silico features and used in vitro datasets to validate which in silico feature is the best for assessing the proarrhythmic risk of drugs at high, intermediate, and low levels. All 12 in silico features helped determine high-risky torsadogenic drugs, regardless of the in vitro datasets used in the in silico simulation as input. In the three types of in silico features, AP features were the most reliable for determining the three Torsade de Pointes (TdP) risk standards. Among AP features, AP duration at 50% repolarization (APD 50 ) was the best when individually using in silico features per in vitro dataset. In contrast, the AP repolarization velocity (dVm/dt Max_repol ) was the best when merging all in silico features computed through three in vitro datasets.
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Jeong et al. (2022) studied Drug-induced proarrhythmia (Torsade de Pointes) (n=28). In silico biomarkers (AP, Ca, and ion charge features) was evaluated on Classification performance (AUC) for Torsade de Pointes risk. Action potential duration at 50% repolarization (APD50) was the best individual in silico feature for determining Torsade de Pointes risk, while repolarization velocity was best when merging datasets.
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