May 22, 14:00, S3 055

TITLE:

In silico modelling of Cardiac Allograft Vasculopathy: a multiscale approach to unravel disease mechanisms

ABSTRACT:

Cardiac Allograft Vasculopathy (CAV) is a leading cause of late graft failure in heart transplant (HTx) recipients. It is characterized by progressive intimal thickening and luminal narrowing of coronary arteries, driven by both immunological factors (i.e., lymphocyte (LYM) and macrophage (MP) infiltration) and biomechanical stimuli (i.e., low wall shear stress (WSS)). Current clinical tools lack the sensitivity for early detection and offer limited insight into disease progression, contributing to a CAV incidence of nearly 50% within 10 years post-HTx. Therapeutic options remain limited, with re-transplantation as the only definitive treatment, yet rarely a feasible one. Traditionally, CAV has been studied using animal models, especially murine models. However, advances in computational modelling have introduced in silico approaches as powerful tools to complement and enhance traditional research pipelines. Our team has previously developed two complementary models as proof of concept for accurately simulating CAV progression in murine coronary arteries. The first is an Agent-Based Model (ABM) of the arterial wall, incorporating stochastic rules to simulate vascular cell behavior in response to analytically derived WSS and immune cells (ICs) stimuli. Key processes modeled include ICs activation and infiltration, as well as smooth muscle cell (SMC) media-to-intima migration. The second is a Computational Fluid Dynamics (CFD) model based on 3D reconstructions of murine coronary geometries (n=6), used to simulate unsteady blood flow and extract hemodynamic indices. This model aimed to identify correlations between vascular morphology and flow patterns, highlighting the role of geometry in coronary disease development. These two components are now integrated into a multi-modular framework, in which CFD-derived parameters serve as inputs to drive the ABM on realistic geometries. Additionally, a LYM transport module is under development to simulate LYM behavior in the bloodstream and identify regions of preferential accumulation, potential early indicators of vasculopathy. The integrated model successfully replicates hallmark features of CAV, including spatially heterogeneous LYM accumulation, MP activation, intimal thickening, and luminal narrowing. Simulations highlight correlations between low WSS regions and elevated LYM concentrations, underscoring the interplay between hemodynamics and immune responses. In conclusion, once calibrated against in vivo data, the in silico platform will provide a reliable and accurate framework for studying CAV dynamics. It will offer valuable insights into the interplay between immune responses and local hemodynamics, demonstrating the potential of computational modeling to complement and enhance traditional experimental approaches in addressing HTx complications.