Can in silico models predict drug-induced cardiac risk in vulnerable populations?
Abstract
This study evaluates virtual cardiac populations for preclinical assessment of drug-induced QT interval prolongation and arrhythmic risk. Traditional predictions often rely on small, healthy cohorts, excluding vulnerable populations. Using computational models of realistic heart anatomies and electrophysiology, we generated a virtual cohort of 512 subjects across healthy and diseased hearts (heart failure, dilated and hypertrophic cardiomyopathy, ischaemia, and myocardial infarction). We assessed QT prolongation and arrhythmic events following administration of moxifloxacin (benchmark antibiotic) and contraindicated drugs including quinidine, bepridil, and flecainide. Patients with heart failure, hypertrophic and dilated cardiomyopathy showed greater QT prolongation to moxifloxacin, unlike ischaemia and myocardial infarction, which resembled healthy subjects. Females exhibited consistently higher QT prolongation than males. Contraindicated drugs markedly increased arrhythmia risk in populations with heart failure, dilated and hypertrophic cardiomyopathy, and ischaemia, frequently leading to lethal arrhythmias such as Torsades des Pointes or ventricular fibrillation, particularly in females. These findings demonstrate that computational models capture variability in drug response across pathologies and sexes, offering a predictive framework for preclinical safety evaluations and supporting safer, more personalized drug development strategies.
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 512 participants or records.
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