Abstract Body: 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.

Hypothesis: We hypothesized that integrating hERG and Nav1.5 variants into an in silico framework would improve the prediction of amiodarone-induced arrhythmogenic risk by capturing tissue-specific changes in APD90 and qNet.

Methodology: We present a computational modeling framework that integrates known genetic variants in cardiac ion channels to predict drug-induced arrhythmogenic risk. By simulating concentration-dependent effects of amiodarone across several combinations of hERG and Nav1.5 channel alleles in multiple human cardiac cell types, we evaluated changes in action potential duration at 90% repolarization (APD90) and qNet (a CiPA-endorsed digital biomarker) as predictors of torsadogenic potential.

Results: Our results highlight that genetic variations and cell-type context influence electrophysiological response, uncovering high-risk profiles otherwise masked in population-averaged models. Across several hERG and Nav1.5 allele combinations evaluated in five cardiac cell types at six amiodarone concentrations, ANOVA showed significant differences between groups, whereas paired t-test identified significant differences among selected allele combinations for APD90 and qNet (p < 0.05). These simulations reveal key insights with translational relevance: genetic background alters drug response; midwall cells are disproportionately vulnerable; the same mutation can produce different effects across tissues; Purkinje cells may serve as silent proarrhythmic substrates; and inter-tissue dispersion, more than APD prolongation alone, may more effectively predict torsadogenic risk.

Conclusion: Genetic background influences amiodarone response in a tissue-specific manner. We aimed to provide a foundation for digital twin models that incorporate patient-specific electrophysiology and supports genotype-specific risk stratification, early-stage candidate prioritization, and personalized cardiotoxicity screening in drug development and clinical safety assessment.