Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.
Int J Pharm. 2021 Sep 5;606:120821. doi: 10.1016/j.ijpharm.2021.120821. Epub 2021 Jun 24.
Drug delivery via dry powder inhaler (DPI) is a complex process affected by multiple factors involving gas and particles. The performance of a carrier-based formulation depends on the release of active pharmaceutical ingredient (API) particles, typically characterized by fine particle fraction (FPF) and dispersion fraction (DF). Computational Fluid Dynamics coupled with Discrete Element Method (CFD-DEM) can capture relevant gas and particle interactions but is computationally expensive, especially when tracking all carrier and API particles. This study assessed the efficacy of two coarse-grained CFD-DEM approaches, the Discrete Parcel Method and the representative particle approach, through highly-resolved CFD-DEM simulations. The representative particle approach simulates all carrier particles and a subset of API particles, whereas the Discrete Parcel Method tracks parcels representing a specified number of carrier or API particles. Both approaches are viable for a small carrier-API size ratio which requires modest degrees of coarse-graining, but the Discrete Parcel Method showed limitations for a large carrier-API size ratio. The representative particle approach can approximate CFD-DEM results with reasonable accuracies when simulations include at least 10 representative API particles per carrier. Using the representative particle approach, we probed powder characteristics that could affect FPF and DF in a model problem and correlated these fractions with the maximum carrier-API cohesive force per unit mass of API particles.
干粉吸入器(DPI)给药是一个复杂的过程,受到涉及气体和颗粒的多个因素的影响。基于载体的制剂的性能取决于活性药物成分(API)颗粒的释放,通常用细颗粒分数(FPF)和分散分数(DF)来表征。计算流体动力学与离散元法(CFD-DEM)相结合可以捕捉相关的气体和颗粒相互作用,但计算成本很高,特别是在跟踪所有载体和 API 颗粒时。本研究通过高分辨率 CFD-DEM 模拟评估了两种粗粒 CFD-DEM 方法(离散包方法和代表性颗粒方法)的功效。代表性颗粒方法模拟所有载体颗粒和一部分 API 颗粒,而离散包方法则跟踪代表指定数量载体或 API 颗粒的包。这两种方法对于需要适度粗化的小载体-API 粒径比都是可行的,但离散包方法对于大载体-API 粒径比显示出局限性。当模拟中至少包含每载体 10 个代表性 API 颗粒时,代表性颗粒方法可以用合理的精度来近似 CFD-DEM 结果。使用代表性颗粒方法,我们研究了可能影响模型问题中 FPF 和 DF 的粉末特性,并将这些分数与单位质量 API 颗粒的最大载体-API 内聚力相关联。