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Pulmonary hypertension (PH) is a severe medical condition with a number of treatment options, the majority of which are introduced without consideration of the underlying mechanisms driving it within an individual and thus a lack of tailored approach to treatment. The one exception is a patient presenting with apparent pulmonary arterial hypertension and shown to have vaso‐responsive disease, whose clinical course and prognosis is significantly improved by high dose calcium channel blockers. PH is however characterized by a relative abundance of available data from patient cohorts, ranging from molecular data characterizing gene and protein expression in different tissues to physiological data at the organ level and clinical information. Integrating available data with mechanistic information at the different scales into computational models suggests an approach to a more personalized treatment of the disease using model‐based optimization of interventions for individual patients. That is, constructing digital twins of the disease, customized to a patient, promises to be a key technology for personalized medicine, with the aim of optimizing use of existing treatments and developing novel interventions, such as new drugs. This article presents a perspective on this approach in the context of a review of existing computational models for different aspects of the disease, and it lays out a roadmap for a path to realizing it.
Pulmonary hypertension (PH) is a severe medical condition with a number of treatment options, the majority of which are introduced without consideration of the underlying mechanisms driving it within an individual and thus a lack of tailored approach to treatment. The one exception is a patient presenting with apparent pulmonary arterial hypertension and shown to have vaso‐responsive disease, whose clinical course and prognosis is significantly improved by high dose calcium channel blockers. PH is however characterized by a relative abundance of available data from patient cohorts, ranging from molecular data characterizing gene and protein expression in different tissues to physiological data at the organ level and clinical information. Integrating available data with mechanistic information at the different scales into computational models suggests an approach to a more personalized treatment of the disease using model‐based optimization of interventions for individual patients. That is, constructing digital twins of the disease, customized to a patient, promises to be a key technology for personalized medicine, with the aim of optimizing use of existing treatments and developing novel interventions, such as new drugs. This article presents a perspective on this approach in the context of a review of existing computational models for different aspects of the disease, and it lays out a roadmap for a path to realizing it.
Pulmonary arterial hypertension (PAH) is a disease resulting in increased right ventricular (RV) afterload and RV remodeling. PAH results in altered RV structure and function at different scales from organ-level hemodynamics to tissue-level biomechanical properties, fiber-level architecture, and cardiomyocyte-level contractility. Biomechanical analysis of RV pathophysiology has drawn significant attention over the past years and recent work has found a close link between RV biomechanics and physiological function. Building upon previously developed techniques, biomechanical studies have employed multi-scale analysis frameworks to investigate the underlying mechanisms of RV remodeling in PAH and effects of potential therapeutic interventions on these mechanisms. In this review, we discuss the current understanding of RV structure and function in PAH, highlighting the findings from recent studies on the biomechanics of RV remodeling at organ, tissue, fiber, and cellular levels. Recent progress in understanding the underlying mechanisms of RV remodeling in PAH, and effects of potential therapeutics, will be highlighted from a biomechanical perspective. The clinical relevance of RV biomechanics in PAH will be discussed, followed by addressing the current knowledge gaps and providing suggested directions for future research.
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