Biology subjects

Aguado-Sierra, J.

Publications and source records attributed to Aguado-Sierra, J..

3 recordsLinked to original sources

bioRxiv2026

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

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.

bioengineering↗

bioRxiv2025

Can In Silico Models Predict Drug-Induced Cardiac Risk in Vulnerable Populations?

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.

bioengineering↗

bioRxiv2024

Real-time prediction of drug-induced proarrhythmic risk with sex-specific cardiac emulators

In silico trials for drug safety assessment require a large number of high-fidelity 3D cardiac electrophysiological simulations to predict drug-induced QT interval prolongation, making the process computationally expensive and time-consuming. These simulations, while necessary to accurately model the complex physiological conditions of the human heart, are often cost-prohibitive when scaled to large populations or diverse conditions. To overcome this challenge, we develop sex-specific emulators for the real-time prediction of QT interval prolongation, with separate models for each sex. Building an extensive dataset from 900 simulations allows us to show the superior sensitivity of 3D models over 0D single-cell models in detecting abnormal electrical propagation in response to drug effects as the risk level increases. The resulting emulators trained on this dataset showed high accuracy level, with an average relative error of 4% compared to simulation results. This enables global sensitivity analysis and the replication of in silico cardiac safety clinical trials with accuracy comparable to that of simulations when validated against in vivo data. With our emulators, we carry out in silico clinical trials in seconds on a standard laptop, drastically reducing computational time compared to traditional high-performance computing methods. This efficiency enables the rapid testing of drugs across multiple concentration ranges without additional computational cost. This approach directly addresses several key challenges faced by the biopharmaceutical industry: optimizing trial designs, accounting for variability in biological assays, and enabling rapid, cost-effective drug safety evaluations. By integrating these emulators into the drug development process, we can enhance the reliability of preclinical assessments, streamline regulatory submissions, and advance the practical application of digital twins in biomedicine. This work represents a significant step toward more efficient and personalized drug development, ultimately benefiting patient safety and accelerating the path to market.

pharmacology and toxicology↗