Asthma is a chronic airway disorder that impacts 22.9 million people in the United States and causes a substantial economic burden. Asthma attacks (exacerbation) are triggered by stimulation of chronically inflamed airways and hyper responsive airway smooth muscle, leading to airway constriction and obstructed airflow. Reducing the frequency of exacerbations is the primary goal of asthma disease management. Severe asthma is especially challenging to treat, as this cohort of patients (~15%) does not respond well to inhaled therapeutics, resulting in significantly higher heath care costs compared to patients with milder disease severity. One of the keys to asthma management is to identify early which set of patients may benefit from alternative treatment strategies. However assessing dosimetry experimentally is not currently feasible especially on a patient-to-patient basis. Computational models, on the other hand, provide a unique opportunity to uncover anatomical and physiological features that lead to inadequate dosing. Thus, the main goal of this R21 proposal is to determine which set of patients may benefit from alternative treatment strategies. To do this, existing datasets collected from as part of the NIH funded Severe Asthma Research Program (SARP) will be incorporated to study inhaled medications. A subset of this study includes hyperpolarized 3He ventilation and high- resolution CT images, thereby providing the opportunity to couple patient-specific anatomy and ventilation distribution with advanced modeling techniques. Incorporation of segmental-level ventilation defects percent (VDP) will enable accurate ventilation distributions to be incorporated into the respiratory in silico models and for peripheral delivery to be correlated with the heterogeneous ventilation. With this existing dataset, we will test our hypothesis that abnormal drug delivery concentrations are correlated to airway morphometric features which may be identified directly from CT images. Within the scope of this proposal, we will make key advances in determining which set of patients would benefit from alternative treatment strategies (e.g. systemic medications) because of inadequate peripheral airway delivery.
A central goal of asthma treatment is to maintain asthma control by reducing the number of asthma attacks, however while patients with mild forms of asthma are readily treated, those with moderate to severe persistent asthma do not respond well to inhaled medications. Patient-centered experimental studies and computer simulations independently provide valuable characterizations of asthma pathology and disease progression, however to make key advances in severe asthma treatment and management, it is imperative to combine sophisticated experimental and computational methodologies. Within the scope of this study, we aim to couple hyperpolarized 3He gas magnetic resonance imaging (MRI) with respiratory in silico models to gain insight into phenotypes that lead to ineffective aerosol medication treatment in severe asthmatics.
