Key result
Genetic variants in hERG and Nav1.5 alter APD90 and qNet responses to amiodarone in silico.
Why the study?
Current in-silico models often overlook genetic variability, limiting their ability to capture patient-specific responses to drug-induced cardiotoxicity.
Does incorporating genetic variants of hERG and Nav1.5 into in silico cardiac cell models improve the prediction of amiodarone-induced proarrhythmia risk compared to wild-type models?
Does incorporating genetic variants of hERG and Nav1.5 into in silico cardiac cell models improve the prediction of amiodarone-induced proarrhythmia risk compared to wild-type models?
Integrating genetic variants into in silico cardiac models reveals patient-specific and cell-type-specific vulnerabilities to drug-induced proarrhythmia that are missed by standard wild-type models.
May support genotype-informed amiodarone dosing; leaves open prospective clinical validation.
Introduction Drug-induced cardiotoxicity remains a leading cause of drug development failures and market withdrawals. Despite advances in preclinical testing, current in-silico models often overlook genetic variability, limiting their ability to capture patient-specific responses. We present a computational modeling framework that integrates known genetic variants in cardiac ion channels to predict individual differences in drug-induced arrhythmogenic risk. Methods We simulated concentration-dependent effects of amiodarone across combinations of hERG and Nav1.5 alleles in five human cardiac cell types: endocardial, epicardial, midwall, Purkinje, and atrial cells. APD90 values were evaluated across all cell types, whereas qNet was calculated in ventricular cells only to assess genotype and cell type dependent differences in electrophysiological response and torsadogenic risk. Results Our results highlight how genetic variations and cell type context influence electrophysiological response to amiodarone, uncovering high-risk profiles otherwise masked in population-averaged models. These simulations reveal key insights with translational and regulatory relevance: genetic background meaningfully alters drug response; midmyocardial cells are disproportionately vulnerable; the same mutation can produce different effects across cell types; Purkinje cells may serve as silent proarrhythmic substrates; and celltype- specific differences in APD90 and qNet may provide additional insight beyond APD prolongation alone, and consequently improve prediction of torsades de pointes risk. Conclusion Our work provides a foundation for the creation of digital twin models that incorporate patient-specific electrophysiology, offering a scalable platform for in-silico cardiac safety. By linking genetic polymorphisms to context-dependent functional outcomes, this approach supports early-stage candidate prioritization, offers a scalable platform for genotype-specific risk stratification, and advances the implementation of precision cardiotoxicity screening in drug development and clinical safety assessments.
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Henedi et al. (2026) studied Drug-induced proarrhythmia risk. Amiodarone vs. Drug-free baseline and wild-type alleles was evaluated on Action potential duration at 90% repolarization (APD90) and qNet. In silico simulations demonstrated that genetic variations in hERG and Nav1.5 channels significantly influence cell-type-specific electrophysiological responses to amiodarone, altering APD90 and qNet.
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