Sex-specific virtual population for the prediction and assessment of arrhythmia risk

Women are at higher risk of serious ventricular arrhythmias, including torsades de pointes, when repolarization reserve is reduced, but sex-stratified mechanisms and quantitative risk assessment remain challenging. We aimed to develop scalable, tissue-scale, sex-aware ventricular virtual populations to quantify and explain sex differences in inherited and acquired proarrhythmic susceptibility.

We constructed male and female virtual cohorts using a one-dimensional ventricular cable model with pseudo-electrocardiogram (pseudo-ECG), integrating sex-specific ionic conductance backgrounds and acute sex-hormone modulation. Virtual populations were filtered under multi-condition stress tests and calibrated to clinical corrected QT interval (QTc) distributions. We generated long QT syndrome types 1-3 (LQT1-3) cohorts, simulated sympathetic stress, and performed virtual drug trials for 109 compounds using multichannel block profiles at 1× effective free therapeutic plasma concentration. Proarrhythmic risk was defined by tissue-scale instability events (premature ventricular complexes, T-wave alternans, or repolarization failure). Drivers were analysed using regression and channel-sensitivity analysis, and clinical 24 h concentration-ECG data after dosing were used for external validation. The female cohorts exhibited higher simulated event risk across long QT syndrome subtypes and across multichannel drug block profiles, including drugs with <10 ms mean QTc prolongation. Female-to-male risk ratios tracked clinical risk categories. Simulated QTc time-courses and concentration-QTc trends agreed with 24 h clinical ECG data for key reference drugs.

Sex-aware tissue-scale virtual populations enable in silico trials that quantify proarrhythmic risk beyond mean ΔQTc, provide mechanistic drivers, and support sex-informed cardiac safety evaluation and monitoring strategies.