AI-enhanced cardiac digital twins extend drug proarrhythmic risk assessment through experimental data uncertainty propagation and overdose exploration: a loperamide case study

Abstract

Drug-induced QT interval prolongation is a key biomarker of proarrhythmic risk and central to drug cardiac safety evaluation alongside in vitro assays and animal studies. Current preclinical frameworks, however, provide limited insight into how experimental uncertainty and extreme exposures translate into real-world arrhythmic risk, despite both factors critically modulating outcomes. To address this, we used sex-specific machine learning surrogate models trained on 3D cardiac digital twins--mechanistic electrophysiology models of anatomically detailed ventricles that integrate multichannel ion-channel block data. These emulators combine the realism of 3D simulations with high-throughput capability, enabling rapid, ethically unconstrained assessment of proarrhythmic risk. We illustrate the approach using loperamide, safe at therapeutic doses but linked to fatal arrhythmias at extreme exposures. Two analyses were performed: propagating experimental IC50 and Hill coefficient variability to quantify its effect on predicted QT prolongation and arrhythmic probability, and simulating extreme exposures to identify sex-specific arrhythmogenic thresholds. Experimental variability substantially broadened predicted QT prolongation and arrhythmic risk near decision thresholds. Extreme exposure simulations identified arrhythmogenic thresholds of approximately 107-109 x Cmax in female models and 213-286 x Cmax in male models. This framework offers a scalable, physics-based tool for early-stage drug cardiac safety evaluation.