Fully Coupled Hybrid In-Silico Modeling of Atherosclerosis: A Multi-Scale Approach Integrating CFD, Transport Phenomena, and Agent-Based Modeling

Ricardo Caballero ^1* Miguel Angel Martinez ^1,2 Estefania Peña ^1,2*

• ¹ Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
• ² Center for Biomedical Research in the Network in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Madrid, Spain

Atherosclerosis is a complex disease driven by various biological and mechanical factors, leading to plaque formation and progression in arterial walls. We present a hybrid model combining computational fluid dynamics (CFD), mass transport, and agent-based modeling (ABM) 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) to predict plaque growth and its effects on vascular morphology. Specifically, the model accounts for the temporal evolution of LDL concentration and its diffusion and convection across the artery wall, alongside the migration, proliferation, and phenotypic changes of SMCs driven by cytokine signals. Our results demonstrate that integrating CFD, transport phenomena, and ABM offers a holistic understanding of atherosclerotic plaque development, accurately predicting inflammation and potential rupture sites. The proposed methodology sets the basis for developing a platform to test therapeutic interventions, such as anti-inflammatory drugs and lipid-lowering agents, in various patient scenarios. This study highlights the potential of hybrid multi-scale in-silico models to advance the understanding of atherosclerosis and to develop personalized treatment strategies.