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  • Title: One (sub-)acinus for all: Fate of inhaled aerosols in heterogeneous pulmonary acinar structures.
    Author: Hofemeier P, Koshiyama K, Wada S, Sznitman J.
    Journal: Eur J Pharm Sci; 2018 Feb 15; 113():53-63. PubMed ID: 28954217.
    Abstract:
    Computational Fluid Dynamics (CFD) have offered an attractive gateway to investigate in silico respiratory flows and aerosol transport in the depths of the lungs. Yet, not only do existing models lack sufficient anatomical realism in capturing the heterogeneity and morphometry of the acinar environment, numerical simulations have been widely restricted to domains capturing a mere few percent of a single acinus. Here, we present to the best of our knowledge the most detailed and comprehensive in silico simulations to date on the fate of aerosols in the acinar depths. Our heterogeneous acinar domains represent complete sub-acinar models (i.e. 1/8th of a full acinus) based on the recent algorithm of Koshiyama & Wada (2015), capturing statistics of human acinar morphometry (Ochs et al. 2004). Our simulations deliver high-resolution, 3D spatial-temporal data on aerosol transport and deposition, emphasizing how variances in acinar heterogeneity only play a minor role in determining general deposition outcomes. With such tools at hand, we revisit whole-lung deposition predictions (i.e. ICRP) based on past 1D lung models. While our findings under quiet breathing substantiate general deposition trends obtained with past predictions in the alveolar regions, we underscore how deposition fractions are anticipated to increase, in particular during deep inhalation. For such inhalation maneuver, our simulations support the notion of significantly augmented deposition for all aerosol sizes (0.005-5.0μm). Overall, our efforts not only help consolidate our mechanistic understanding of inhaled aerosol transport in the acinar depths but also continue to bridge the gap between "bottom-up" in silico models and regional deposition predictions from whole-lung models. Such quantifications provide what is deemed more accurate deposition predictions in morphometrically-faithful models and are particularly useful in assessing inhalation strategies for deep airway deposition (e.g. systemic delivery).
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