Previous studies have demonstrated that factors such as airway wall motion, inhalation waveform, and geometric complexity influence the deposition of aerosols in the alveolar airways. However, deposition fraction correlations are not available that account for these factors in determining alveolar deposition. The objective of this study was to generate a new space-filling model of the pulmonary acinus region and implement this model to develop correlations of aerosol deposition that can be used to predict the alveolar dose of inhaled pharmaceutical products. A series of acinar models was constructed containing different numbers of alveolar duct generations based on space-filling 14-hedron elements. Selected ventilation waveforms were quick-and-deep and slow-and-deep inhalation consistent with the use of most pharmaceutical aerosol inhalers. Computational fluid dynamics simulations were used to predict aerosol transport and deposition in the series of acinar models across various orientations with gravity where ventilation was driven by wall motion. Primary findings indicated that increasing the number of alveolar duct generations beyond 3 had a negligible impact on total acinar deposition, and total acinar deposition was not affected by gravity orientation angle. A characteristic model containing three alveolar duct generations (D3) was then used to develop correlations of aerosol deposition in the alveolar airways as a function of particle size and particle residence time in the geometry. An alveolar deposition parameter was determined in which deposition correlated with d2t over the first half of inhalation followed by correlation with dt2, where d is the aerodynamic diameter of the particles and t is the potential particle residence time in the alveolar model. Optimal breath-hold times to allow 95% deposition of inhaled 1, 2, and 3 μm particles once inside the alveolar region were approximately >10, 2.7, and 1.2 s, respectively. Coupling of the deposition correlations with previous stochastic individual path (SIP) model predictions of tracheobronchial deposition was demonstrated to predict alveolar dose of commercial pharmaceutical products. In conclusion, this study completes an initiative to determine the fate of inhaled pharmaceutical aerosols throughout the respiratory airways using CFD simulations.

中文翻译：

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颗粒在肺泡气道中的沉积：用药物气雾剂吸入和屏气

先前的研究表明，气道壁运动、吸入波形和几何复杂性等因素会影响气溶胶在肺泡气道中的沉积。然而，在确定肺泡沉积时，无法使用沉积分数相关性来解释这些因素。本研究的目的是生成肺腺泡区域的新空间填充模型，并实施该模型以开发可用于预测吸入药品的肺泡剂量的气溶胶沉积的相关性。基于空间填充的 14 面体元素，构建了一系列包含不同数量的肺泡管代数的腺泡模型。选定的通气波形为快进深吸入和慢进深吸入，与大多数药用气雾剂吸入器的使用一致。计算流体动力学模拟用于预测一系列腺泡模型中的气溶胶传输和沉积，其中具有重力，其中通风由壁运动驱动。主要研究结果表明，增加肺泡管代数超过 3 对总腺泡沉积的影响可以忽略不计，并且总腺泡沉积不受重力方向角的影响。然后使用包含三个肺泡管代 (D3) 的特征模型来开发肺泡气道中气溶胶沉积的相关性，作为几何形状中颗粒大小和颗粒停留时间的函数。确定了肺泡沉积参数，其中在吸入的前半段沉积与 d2t 相关，然后与 dt2 相关，其中 d 是颗粒的空气动力学直径，t 是颗粒在肺泡模型中的潜在停留时间。允许吸入的 1、2 和 3 μm 颗粒在肺泡区域内沉积 95% 的最佳屏气时间分别约为 >10、2.7 和 1.2 秒。沉积相关性与先前对气管支气管沉积的随机个体路径 (SIP) 模型预测的耦合被证明可以预测商业药品的肺泡剂量。总之，这项研究完成了一项计划，即使用 CFD 模拟确定整个呼吸道中吸入的药物气溶胶的归宿。和 3 μm 颗粒一旦进入肺泡区域，分别约为 >10、2.7 和 1.2 秒。沉积相关性与先前对气管支气管沉积的随机个体路径 (SIP) 模型预测的耦合被证明可以预测商业药品的肺泡剂量。总之，这项研究完成了一项计划，即使用 CFD 模拟确定整个呼吸道中吸入的药物气溶胶的归宿。和 3 μm 颗粒一旦进入肺泡区域，分别约为 >10、2.7 和 1.2 秒。沉积相关性与先前对气管支气管沉积的随机个体路径 (SIP) 模型预测的耦合被证明可以预测商业药品的肺泡剂量。总之，这项研究完成了一项计划，即使用 CFD 模拟确定整个呼吸道中吸入的药物气溶胶的归宿。