Predictive vascular growth and remodeling in pulmonary hypertension: simulating intervention effects from captured evolution

Abstract

Pulmonary hypertension (PH) is characterized by progressive structural and mechanical remodeling of the pulmonary vasculature, yet few computational frameworks directly link disease mechanisms to longitudinal progression and therapeutic response. In this study, we utilized a multiscale pulmonary arterial growth and remodeling (G&R) framework to capture evolving functional metrics from rat models of PH. This framework couples morphometric tree hemodynamics, constrained mixture theory-based wall mechanics, and maladaptive cellular remodeling. Disease progression was driven by three mechanistically interpretable parameters governing excess smooth muscle production, remodeling activation, and passive stiffening. These parameters were calibrated to longitudinal monocrotaline (MCT) measurements of pressure, wall thickness, and stiffness from prior work using a multiobjective optimization. To show the predictive value of this model, we simulated therapeutic intervention within the same disease-specific framework by using functional cell-level responses to therapy to inform changes in parameter values. Calibration to the study-specific MCT dataset reproduced the temporal increases in pressure, wall thickness, and stiffness, demonstrating that the model could capture multiple features of vascular remodeling simultaneously, with R2 values of 0.81, 0.83, and 0.95, respectively. Simulated treatment reduced pressure, wall thickness, and stiffness. Predicted pressure and wall-thickness responses agreed closely with the corresponding experimental treatment effects, whereas stiffness recovery was overpredicted, suggesting that additional mechanisms may contribute to persistent vascular stiffening after intervention. The framework also captured the overall progression of pulmonary pressure increases across both aggregated MCT and Sugen-hypoxia datasets, suggesting utility across studies and animal models. This work outlines a physics-based, multiscale framework that simulated quantities of direct clinical interest in a mechanistically interpretable platform for linking pulmonary vascular remodeling and treatment response. It supports comparisons across experimental phenotypes and interventions while identifying where constitutive refinements are needed to improve predictive capability across phenotypes.