Nasal delivery of lung targeted pharmaceutical aerosols is ideal for drugs that need to be administered during high flow nasal cannula (HFNC) gas delivery, but based on previous studies losses and variability through both the delivery system and nasal cavity are expected to be high. The objective of this study was to assess the variability in aerosol delivery through the nose to the lungs with a nasal cannula interface for conventional and excipient enhanced growth (EEG) delivery techniques. A database of nasal cavity computed tomography (CT) scans was collected and analyzed, from which four models were selected to represent a wide range of adult anatomies, quantified based on the nasal surface area-to-volume ratio (SA/V). Computational fluid dynamics (CFD) methods were validated with existing in vitro data and used to predict aerosol delivery through a streamlined nasal cannula and the four nasal models at a steady state flow rate of 30 L/min. Aerosols considered were solid particles for EEG delivery (initial 0.9 μm and 1.5 μm aerodynamic diameters) and conventional droplets (5 μm) for a control case. Use of the EEG approach was found to reduce depositional losses in the nasal cavity by an order of magnitude and substantially reduce variability. Specifically, for aerosol deposition efficiency in the four geometries, the 95% confidence intervals (CI) for 0.9 and 5 μm aerosols were 2.3-3.1 and 15.5-66.3%, respectively. Simulations showed that the use of EEG as opposed to conventional methods improved delivered dose of aerosols through the nasopharynx, expressed as penetration fraction (PF), by approximately a factor of four. Variability of PF, expressed by the coefficient of variation (CV), was reduced by a factor of four with EEG delivery compared with the control case. Penetration fraction correlated well with SA/V for larger aerosols, but smaller aerosols showed some dependence on nasopharyngeal exit hydraulic diameter. In conclusion, results indicated that the EEG technique not only improved lung aerosol delivery, but largely eliminated variability in both nasal depositional loss and lung PF in a newly developed set of nasal airway models.

中文翻译：

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鼻到肺气雾剂输送的变异性


肺部靶向药物气雾剂的鼻腔输送对于需要在高流量鼻插管 (HFNC) 气体输送期间给药的药物来说是理想的选择，但根据之前的研究，预计输送系统和鼻腔的损失和变异性会很高。本研究的目的是评估传统和赋形剂增强生长 (EEG) 输送技术中使用鼻插管接口通过鼻子向肺部输送气雾剂的变异性。收集并分析了鼻腔计算机断层扫描 (CT) 扫描数据库，从中选择了四个模型来代表各种成人解剖结构，并根据鼻表面积与体积比 (SA/V) 进行量化。计算流体动力学 (CFD) 方法通过现有的体外数据进行了验证，并用于预测在 30 L/min 的稳态流速下通过流线型鼻插管和四个鼻模型的气雾输送。考虑的气溶胶是用于 EEG 传输的固体颗粒（初始空气动力学直径为 0.9 μm 和 1.5 μm），以及用于对照情况的传统液滴（5 μm）。研究发现，使用脑电图方法可以将鼻腔内的沉积损失减少一个数量级，并大大减少变异性。具体而言，对于四种几何形状的气溶胶沉积效率，0.9 和 5 μm 气溶胶的 95% 置信区间 (CI) 分别为 2.3-3.1 和 15.5-66.3%。模拟表明，与传统方法相比，使用脑电图将通过鼻咽的气溶胶输送剂量（以渗透分数（PF）表示）提高了大约四倍。与对照病例相比，通过 EEG 传输，PF 的变异性以变异系数 (CV) 表示，降低了四倍。 对于较大的气溶胶，穿透分数与 SA/V 相关性很好，但较小的气溶胶显示出对鼻咽出口水力直径的一定依赖性。总之，结果表明，EEG 技术不仅改善了肺气雾输送，而且在新开发的一组鼻气道模型中很大程度上消除了鼻沉积损失和肺 PF 的变异性。