Role of CFD based in silico modelling in establishing an in vitro-in vivo correlation of aerosol deposition in the respiratory tract

被引:55
|
作者
Huang, Fen [1 ,2 ]
Zhu, Qixuan [3 ]
Zhou, Xudong [2 ]
Gou, Dazhao [3 ]
Yu, Jiaqi [4 ]
Li, Renjie [4 ]
Tong, Zhenbo [1 ]
Yang, Runyu [3 ]
机构
[1] Southeast Univ, Sch Energy & Environm, Nanjing 210096, Peoples R China
[2] Monash Univ, Dept Chem Engn, Clayton, Vic 3800, Australia
[3] Univ New South Wales, Sch Mat Sci & Engn, Sydney, NSW 2052, Australia
[4] JITRI, Inst Proc Modelling & Optimizat, Suzhou 215000, Peoples R China
关键词
Respiratory airway; Aerosol deposition; In silico modelling; in vitro-in vivo correlation; Computational fluid dynamics;
D O I
10.1016/j.addr.2020.09.007
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Effective evaluation and prediction of aerosol transport deposition in the human respiratory tracts are critical to aerosol drug delivery and evaluation of inhalation products. Establishment of an in vitro -in vivo correlation (IVIVC) requires the understanding of flow and aerosol behaviour and underlying mechanisms at the microscopic scale. The achievement of the aim can be facilitated via computational fluid dynamics (CFD) based in silico modelling which treats the aerosol delivery as a two-phase flow. CFD modelling research, in particular coupling with discrete phase model (DPM) and discrete element method (DEM) approaches, has been rapidly developed in the past two decades. This paper reviews the recent development in this area. The paper covers the following aspects: geometric models of the respiratory tract, CFD turbulence models for gas phase and its coupling with DPM/ DEM for aerosols, and CFD investigation of the effects of key factors associated with geometric variations, flow and powder characteristics. The review showed that in silico study based on CFD models can effectively evaluate and predict aerosol deposition pattern in human respiratory tracts. The review concludes with recommendations on future research to improve in silico prediction to achieve better IVIVC. (c) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页码:369 / 385
页数:17
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