Modeling Airflow and Particle Deposition in a Human Acinar Region

The alveolar region, encompassing millions of alveoli, is the most vital part of the lung. However, airflow behavior and particle deposition in that region are not fully understood because of the complex geometrical structure and intricate wall movement. Although recent investigations using 3D computer simulations have provided some valuable information, a realistic analysis of the air-particle dynamics in the acinar region is still lacking. So, to gain better physical insight, a physiologically inspired whole acinar model has been developed. Specifically, air sacs (i.e., alveoli) were attached as partial spheroids to the bifurcating airway ducts, while breathing-related wall deformation was included to simulate actual alveolar expansion and contraction. Current model predictions confirm previous notions that the location of the alveoli greatly influences the alveolar flow pattern, with recirculating flow dominant in the proximal lung region. In the midalveolar lung generations, the intensity of the recirculating flow inside alveoli decreases while radial flow increases. In the distal alveolar region, the flow pattern is completely radial. The micron/submicron particle simulation results, employing the Euler–Lagrange modeling approach, indicate that deposition depends on the inhalation conditions and particle size. Specifically, the particle deposition rate in the alveolar region increases with higher inhalation tidal volume and particle diameter. Compared to previous acinar models, the present system takes into account the entire acinar region, including both partially alveolated respiratory bronchioles as well the fully alveolated distal airways and alveolar sacs. In addition, the alveolar expansion and contraction have been calculated based on physiological breathing conditions which make it easy to compare and validate model results with in vivo lung deposition measurements. Thus, the current work can be readily incorporated into human whole-lung airway models to simulate/predict the flow dynamics of toxic or therapeutic aerosols

Importance of Spray–Wall Interaction and Post-Deposition Liquid Motion in the Transport and Delivery of Pharmaceutical Nasal Sprays

Nasal sprays, which produce relatively large pharmaceutical droplets and have high momentum, are primarily used to deliver locally acting drugs to the nasal mucosa. Depending on spray pump administration conditions and insertion angles, nasal sprays may interact with the nasal surface in ways that creates complex droplet–wall interactions followed by significant liquid motion after initial wall contact. Additionally, liquid motion can occur after deposition as the spray liquid moves in bulk along the nasal surface. It is difficult or impossible to capture these conditions with commonly used computational fluid dynamics (CFD) models of spray droplet transport that typically employ a deposit-on-touch boundary condition. Hence, an updated CFD framework with a new spray–wall interaction (SWI) model in tandem with a post-deposition liquid motion (PDLM) model was developed and applied to evaluate nasal spray delivery for Flonase and Flonase Sensimist products. For both nasal spray products, CFD revealed significant effects of the spray momentum on surface liquid motion, as well as motion of the surface film due to airflow generated shear stress and gravity. With Flonase, these factors substantially influenced the final resting place of the liquid. For Flonase Sensimist, anterior and posterior liquid movements were approximately balanced over time. As a result, comparisons with concurrent in vitro experimental results were substantially improved for Flonase compared with the traditional deposit-on-touch boundary condition. The new SWI-PDLM model highlights the dynamicenvironment that occurs when a nasal spray interacts with a nasal wall surface and can be used to better understand the delivery of current nasal spray products as well as to develop new nasal drug delivery strategies with improved regional targeting