AI-Driven Heart Digital Twin for In Silico Pre-Clinical Pharmacology Trials

Cardiovascular drug attrition during pre-clinical and early clinical phases remains a critical bottleneck in pharmaceutical development, driven largely by the poor translational fidelity of animal-based cardiotoxicity models and the high false-positive rate of hERG-only screening. This paper presents an AI-driven Heart Digital Twin (HDT) framework for high-fidelity, patient-specific in silico pharmacology trials that addresses these limitations of conventional pre-clinical screening. The system integrates a three-layer multi-scale cardiac architecture spanning cellular electrophysiology, tissue mechanics, and haemodynamics with an AI model stack comprising Physics-Informed Neural Networks (PINNs), U-Net segmentation, Transformer attention mechanisms, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs) for virtual cohort synthesis. A virtual cohort of 12,000 synthetic patients, stratified by genotype variants (LQT1, LQT2, SCN5A) and phenotypes (HFpEF, HFrEF, Brugada syndrome), was evaluated using physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) modelling integrated with real-time electrocardiographic and biomarker data streams. The framework achieved 92.7% prediction accuracy, a clinical correlation of r = 0.89 (p < 0.001, n = 47 Phase II/III compounds), a 79% reduction in cardiotoxicity false positives, and a 30% shorter pre-clinical timeline, saving an estimated US$2–5M per drug candidate. These findings establish the HDT framework as a regulatory-grade in silico evidence platform for pre-clinical cardiac safety evaluation, aligned with FDA AI/ML SaMD and CiPA guidelines.