School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan 48859.
School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan 48859.
J Pharm Sci. 2017 Nov;106(11):3303-3315. doi: 10.1016/j.xphs.2017.06.011. Epub 2017 Jun 20.
In spite of widespread use of modeling tools in inhalation dosimetry, it remains difficult to quantify the output uncertainties when subjected to various sources of input variability. This study aimed to develop a computational model that can quantify the input sensitivity and output uncertainty in pulmonary drug delivery by coupling probabilistic analysis package NESSUS with ANSYS Fluent. An image-based mouth-lung model was used to simulate the transport and deposition of drug particles and variability in particle size, density, and inhalation speed were considered. Results show that input variables have different importance levels on the delivered doses to lungs. For a given level of variability, the delivered dose is more sensitive to the variance of particle diameter than that of the inhalation speed and particle density. The range of input scatters has a profound impact on the outcome probability of delivered efficiencies, while the input distribution type (normal vs. log-normal) appears to have an insignificant effect. Despite normal distributions for all input variables, the output exhibits a non-normal distribution. The proposed model in this study allows easy specification of input distributions to conduct multivariable probabilistic analysis of inhalation drug deliveries, which can facilitate more reliable treatment planning and outcome assessment.
尽管在吸入剂量学中广泛使用建模工具，但当受到各种输入变量变化的影响时，仍然难以量化输出不确定性。本研究旨在开发一种计算模型，通过将概率分析包 NESSUS 与 ANSYS Fluent 耦合，量化肺内药物输送中的输入敏感性和输出不确定性。使用基于图像的口-肺模型来模拟药物颗粒的输送和沉积，并考虑了粒径、密度和吸入速度的变化。结果表明，输入变量对肺内输送剂量具有不同的重要性水平。对于给定的变化水平，输送剂量对粒径的变化比对吸入速度和颗粒密度的变化更为敏感。输入散射范围对输送效率的结果概率有深远影响，而输入分布类型（正态与对数正态）似乎影响不大。尽管所有输入变量都呈正态分布，但输出呈非正态分布。本研究提出的模型允许轻松指定输入分布，从而对吸入药物输送进行多变量概率分析，这有助于更可靠的治疗计划和结果评估。
