IN-SILICO SIMULATION OF CHOLESTEROL DEPOSITION AND ATHEROSCLEROTIC PLAQUE GROWTH IN HUMAN ARTERIES: A COMPREHENSIVE REVIEW

Abstract

Atherosclerosis is a progressive disease driven by the accumulation of cholesterol-rich low-density lipoprotein (LDL) particles within the arterial wall, followed by chronic inflammation, smooth muscle cell (SMC) migration, and extracellular matrix remodeling. These processes lead to plaque formation, luminal narrowing, and ultimately acute cardiovascular events. In recent years, in silico modeling has emerged as a powerful approach for investigating the complex, multiscale mechanisms linking systemic lipid metabolism, local hemodynamics, vascular wall biology, and plaque progression.This review provides a comprehensive overview of computational models of LDL transport, cholesterol deposition, and atherosclerotic plaque development in human arteries. We first summarize the biological basis of cholesterol metabolism and the response-to-retention paradigm underlying early atherogenesis. We then examine key biophysical determinants of LDL deposition, including wall shear stress (WSS), endothelial permeability, and transmural transport. Subsequently, we categorize existing modeling approaches into continuum mass-transport models, multilayer fluid–structure interaction (FSI) frameworks, multiscale reaction–diffusion models, hybrid CFD–agent-based models, and data-driven or machine-learning-based surrogates. We further discuss major applications of these models, including mechanistic insights into plaque initiation, patient-specific risk stratification, and the integration of systemic lipid dynamics with local arterial processes. Finally, we highlight current challenges, such as parameter uncertainty, limited validation data, and high computational cost, and outline future directions toward patient-specific “digital artery” models that combine multiscale physics with data-driven methods. With continued advances in computational techniques, data integration, and model validation, in silico approaches have the potential to become integral tools for understanding atherosclerosis and supporting personalized cardiovascular risk assessment and therapeutic decision-making.