Abstract
Introduction: Atherosclerosis is a complex disease influenced by biological and mechanical factors, leading to plaque formation within arterial walls. Understanding the interplay between hemodynamics, cellular interactions, and biochemical transport is crucial for predicting disease progression and evaluating therapeutic strategies. Methods: We developed a hybrid in-silico model integrating computational fluid dynamics (CFD), mass transport, and agent-based modeling to simulate plaque progression in coronary arteries. The model incorporates key factors such as wall shear stress (WSS), low-density lipoprotein (LDL) filtration, and the interaction between smooth muscle cells (SMCs), cytokines, and extracellular matrix (ECM). Results: Our simulations demonstrate that the integration of CFD, transport phenomena, and agent-based modeling provides a comprehensive framework for predicting atherosclerotic plaque growth. The model successfully captures the mechanobiological interactions driving plaque development and suggests potential mechanisms underlying lesion progression. Discussion: The proposed methodology establishes a foundation for developing computational platforms to test therapeutic interventions, such as anti-inflammatory drugs and lipid-lowering agents, under patient-specific conditions. These findings highlight the potential of hybrid multi-scale in-silico models to advance the understanding of atherosclerosis and support the development of personalized treatment strategies.