Sex-specific cardiac emulators predicted drug-induced QT interval prolongation with an average relative error of less than 4% compared to 3D simulations, accelerating computational time by five orders of magnitude.
Do sex-specific cardiac emulators accurately predict drug-induced QT interval prolongation compared to high-fidelity 3D simulations?
Sex-specific cardiac emulators can rapidly and accurately predict drug-induced QT prolongation with only a 4% error rate compared to computationally expensive 3D simulations, enabling efficient in silico drug safety testing.
In silico trials for drug safety assessment require many high-fidelity 3D cardiac simulations to predict drug-induced QT interval prolongation, which is often computationally prohibitive. To streamline this process, we developed sex-specific emulators for a fast prediction of QT interval, trained on a dataset of 900 simulations. Our results show significant differences between 3D and 0D single-cell models as risk levels increase, underscoring the ability of 3D modeling to capture more complex cardiac responses. The emulators demonstrated an average error of 4% compared to simulations, allowing for efficient global sensitivity analysis and fast replication of in silico clinical trials. This approach enables rapid, multi-dose drug testing on standard hardware, addressing critical industry challenges around trial design, assay variability, and cost-effective safety evaluations. By integrating these emulators into drug development, we can improve preclinical reliability and advance the practical application of digital twins in biomedicine.
Dominguez-Gomez et al. (Thu,) conducted a other in Drug-induced proarrhythmic risk (n=900). Sex-specific cardiac emulators vs. 3D high-fidelity electrophysiological simulations was evaluated on Average relative error of ΔQT prediction compared to simulations. Sex-specific cardiac emulators predicted drug-induced QT interval prolongation with an average relative error of less than 4% compared to 3D simulations, accelerating computational time by five orders of magnitude.