The ultimate goal of this research is to predict and optimize deposition of orally inhaled particles in the human lungs for targeted drug delivery by employing an integrative, statistics-guided, multi-scale physiologically- realistic imaging-based and subject-specific computational fluid dynamics (CFD) lung model. To assess inter- subject variability in delivery of oral inhalation drug products to small airways in asthmatic lungs, we adopt a novel statistics-guided computational framework that applies cluster analysis to computed tomography (CT) imaging-based structural and functional variables over a population of asthmatic lungs to identify asthmatic sub-populations. We then apply our subject-specific lung model suite to selected representative subjects in each sub-population to study inter-cluster variability. This strategy allows bridging individual and population scales. Our airway model and CFD boundary conditions are based on CT volumetric image data of the human lungs scanned at multiple volumes, representing normal and disease phenotypes in a physiologically-realistic and subject-specific manner. Our model is multi-scale because the airway model spans from the mouth cavity to the trachea as a three-dimensional (3D) geometry, then to the terminal bronchioles using a novel 3D-1D (three-dimensional large airways coupled with one-dimensional small airways) and 3D-3D (3D large airways coupled with 3D small airways) coupled approach, bridging from the central airways to the lung parenchyma as well as describing regional ventilation. Our mechanics model is further integrated with thermodynamics models for heat and water vapor transfer. Our CFD employs large eddy simulation, allowing accurate representation of laminar, transitional, and turbulent flows under various breathing conditions. To address inter-subject variability in small airway delivery, we propose the following specific aims. (1) Obtain representative regional ventilation and geometry. (2) Construct physiologically-consistent 3D geometry of small airways. (3) Reproduce heterogeneous airway constriction for asthmatic subjects. (4) Develop oropharyngeal air flow, heat, water vapor, and particle models. (5) Develop acinar air flow and particle models. (6) Simulate air flow, heat, water vapor, and particle in small airways. (7) Validate computer models. We propose to analyze existing non- imaging and imaging databases from the Severe Asthma Research Program (SARP). As a step towards a population-based analysis, individual airway models in healthy and severe asthmatic sub-populations will be used for CFD analyses under different breathing conditions to examine regional deposition in small airways.
TO PUBLIC HEALTH: This study aims to apply a statistics-guided multi-scale imaging-based lung model to study inter-subject variability in delivery of orally inhaled drug products to small airways in asthmatic subjects to improve the efficacy of drug delivery.
