Asgharian B, Price O, Borojeni A A T, Kuprat A P, Colby S, Singh R K, Gu W, Corley R A, Darquenne C
Applied Research Associates, Arlington Division, Raleigh, NC, USA.
Department of Medicine, University of California, San Diego, CA, USA.
J Aerosol Sci. 2022 Nov;166. doi: 10.1016/j.jaerosci.2022.106050. Epub 2022 Jul 19.
Predictive dosimetry models play an important role in assessing health effect of inhaled particulate matter and in optimizing delivery of inhaled pharmaceutical aerosols. In this study, the commonly used 1D Multiple-Path Particle Dosimetry model (MPPD) was improved by including a mechanistically based model component for alveolar mixing of particles and by extending the model capabilities to account for multiple breaths of aerosol intake. These modifications increased the retained fraction of particles and consequently particle deposition predictions in the deep lung during tidal breathing. Comparison with an existing dataset (J. Aerosol Sci., 99:27-39, 2016) obtained under two breathing conditions referred to as slow and fast breathing showed significant differences in 1 μm particle deposition between predictions based on subject-specific breathing patterns and lung volume (slow: 30 ± 1%, fast: 21 ± 1%, (average ± standard deviation), N = 7) and measurements (slow: 43 ± 9%, fast: 30 ± 5%) when the prior version of MPPD (single breath and no mixing, J. Aerosol Sci., 151:105647, 2021) was used. Adding a mixing model and multiple breaths moved the predictions (slow: 34 ± 2%, fast:25 ± 2%) closer to the range of deposition measurements. For 2.9 μm particles, predictions from both the original (slow: 70 ± 2%, fast: 57 ± 2%) and the revised MPPD model (slow: 71 ± 2%, fast: 59 ± 3%) compared well with experiments (slow: 67 ± 8%, fast: 58 ± 10%). This was expected as suspended fraction of 2.9 μm particles was small and thus the addition of alveolar mixing and multi breath capability only slightly increased the retained fraction for particles of this size and greater. The revised 1D model improves dose predictions in the deep lung and support human risk assessment from exposure to airborne particles.
预测剂量学模型在评估吸入颗粒物的健康影响以及优化吸入药物气雾剂的递送方面发挥着重要作用。在本研究中，常用的一维多路径颗粒剂量学模型（MPPD）得到了改进，包括纳入了一个基于机制的颗粒肺泡混合模型组件，并扩展了模型功能以考虑多次呼吸的气溶胶吸入情况。这些修改增加了颗粒的滞留分数，从而提高了潮气呼吸期间深部肺脏中颗粒沉积的预测值。与在两种呼吸条件（称为慢呼吸和快呼吸）下获得的现有数据集（《气溶胶科学杂志》，99:27 - 39，2016）进行比较，结果表明，当使用MPPD的先前版本（单次呼吸且无混合，《气溶胶科学杂志》，151:105647，2021）时，基于个体特定呼吸模式和肺容积的预测（慢呼吸：30 ± 1%，快呼吸：21 ± 1%，（平均值 ± 标准差），N = 7）与测量值（慢呼吸：43 ± 9%，快呼吸：30 ± 5%）在1μm颗粒沉积方面存在显著差异。添加混合模型和多次呼吸后，预测值（慢呼吸：34 ± 2%，快呼吸：25 ± 2%）更接近沉积测量值范围。对于2.9μm颗粒，原始MPPD模型（慢呼吸：70 ± 2%，快呼吸：57 ± 2%）和修订后的MPPD模型（慢呼吸：71 ± 2%，快呼吸：59 ± 3%）的预测结果与实验结果（慢呼吸：67 ± 8%，快呼吸：58 ± 10%）相比吻合良好。这是预期的，因为2.9μm颗粒的悬浮分数较小，因此添加肺泡混合和多次呼吸能力只会略微增加该尺寸及更大尺寸颗粒的滞留分数。修订后的一维模型改进了深部肺脏中的剂量预测，并有助于对暴露于空气中颗粒的人体风险进行评估。
