Computational modeling of drug-eluting balloons in peripheral artery disease: Mechanisms, optimization, and translational insights.

Abstract

Drug-eluting balloons (DEBs) represent a promising alternative to stent-based interventions for peripheral artery disease (PAD), and their therapeutic efficacy is dependent on optimizing drug transfer, mechanical deployment, and vessel-wall interactions. This review synthesizes current advancements in computational modeling; systematically analyzes studies identified through comprehensive ScienceDirect, Scopus, and PubMed (2015-2025) searches; and selects them according to PRISMA guidelines. Key strategies, including computational fluid dynamics (CFD), finite element analysis (FEA), fluid-structure interaction (FSI), and machine learning (ML), are critically examined to elucidate how drug kinetics, coating stability, and mechanical stress govern therapeutic outcomes. CFD-based mass transfer models capture flow-driven drug dispersion and washout dynamics, whereas FEA links balloon mechanics, plaque morphology, and drug penetration efficiency. FSI frameworks provide insight into the coupled effects of wall deformation and hemodynamics, identifying high-risk regions of drug underdelivery. ML-driven surrogates and physics-informed neural networks (PINNs) enable real-time, patient-specific predictions with computational accelerations exceeding 600 × while maintaining less than 2 % deviation from high-fidelity solvers. Persistent challenges include anatomical simplifications, limited <i>in-vivo</i> validation, and insufficient integration of biological remodeling. Future directions emphasize hybrid <i>in-silico</i> pipelines integrating imaging-derived 3D geometries, multiscale simulations, and AI-driven pharmacokinetic modeling to establish clinically translatable digital twins for precision-guided DEB therapies in PAD.