Pulmonary arterial hypertension (PAH) is a progressive and multifactorial disease characterized by pathological vascular remodeling, metabolic shifts, and dysregulation of key pathophysiological pathways. Predicting patient-specific responses to treatment requires a detailed understanding of pulmonary arterial mechanics, particularly the complex interactions between vascular geometry, hemodynamics, and pharmacological effects. However, most existing computational models are centered on healthy vasculature and fail to incorporate the influence of pharmacological treatment pathways in diseased states. To bridge this gap, we have developed a novel computational framework: a bio-chemomechanical model that integrates the essential biomechanical features of PAH-affected arteries and predicts arterial responses to various therapeutic interventions.Our research group has previously established a healthy pulmonary arterial vasculature model using a homeostatic optimization process, an extension of Murray’s law. This optimization minimizes the total energy required to maintain blood flow, accounting for viscous dissipation, metabolic costs, and mechanical equilibrium constraints. By doing so, it generates a geometrically and energetically optimized arterial tree representative of a healthy physiological state. However, in contrast to the healthy vasculature model, which is the result of the optimization of metabolic energy consumption, there have been a growing body of evidence that the homeostatic stress status and metabolic energy consumption of residential cells are altered during the progression of PAH. For instance, studies have shown that pulmonary artery smooth muscle cells (PASMCs) in PAH shift towards glycolysis, even in the presence of oxygen, a phenomenon known as the Warburg effect. Mitochondrial dysfunction reduced oxidative phosphorylation, and decreased ATP production further disrupt energy dynamics in PAH-affected cell. Additionally, the upregulation of hypoxia-inducible factor (HIF) in PAH patients triggers cellular responses that promote vascular remodeling and metabolic shifts. Therefore, rather than utilizing metabolic optimization, we create an in-silico PAH model using a data-driven approach, i.e., the work relies on the experimental data to inform its structural and functional changes, reflecting the complexity of the disease. Specifically, starting from the healthy model, we incorporate changes in geometry, hemodynamics, and pathological factors derived from experimental studies on PAH. Given the limited availability of metabolic cost data specific to PAH, we propose a set of testing hypotheses that computes metabolic energy consumption in the diseased vasculature, which enhance our understanding the role of metabolic process alteration by using existing literature. Once the biomechanical structure of the PAH vasculature is established, we conduct an in-depth study of the chemical pathways involved in PAH treatment. This includes the development of mathematical models for key signaling pathways such as the nitric oxide-cGMP-PKG pathway, which plays a pivotal role in smooth muscle cell relaxation and vasodilation. Additionally, we perform pharmacokinetic analyses on various drugs, including PDE5 inhibitors, and Sotatercept, to evaluate their effects on the vasculature. The resulting bio-chemomechanical model integrates these biomechanical and chemical processes, offering a comprehensive framework capable of predicting arterial responses to different PAH treatments. The model captures the dynamic interactions between hemodynamics, vascular geometry, and the pharmacological mechanisms underlying various therapies. By simulating these interactions, the model provides valuable insights into how different treatments impact arterial mechanics and can be used to guide personalized therapeutic strategies. In conclusion, this integrated framework presents a promising tool for advancing personalized medicine in PAH management. By simulating both the mechanical and chemical responses of the pulmonary vasculature to various treatments, the model enhances our ability to predict patient-specific treatment outcomes. Moreover, it can be extended to explore other therapeutic pathways and vascular diseases, providing a versatile platform for future research into vascular remodeling and pharmacological interventions.
Read
