CFD provides a powerful approach to evaluate the deposition of pharmaceutical aerosols; however, previous studies have not compared CFD results of deposition throughout the lungs with in vivo data.
The in vivo datasets selected for comparison with CFD predictions included fast and slow clearance of monodisperse aerosols as well as 2D gamma scintigraphy measurements for a dry powder inhaler (DPI) and softmist inhaler (SMI). The CFD model included the inhaler, a characteristic model of the mouth-throat (MT) and upper tracheobronchial (TB) airways, stochastic individual pathways (SIPs) representing the remaining TB region, and recent CFD-based correlations to predict pharmaceutical aerosol deposition in the alveolar airways.
For the monodisperse aerosol, CFD predictions of total lung deposition agreed with in vivo data providing a percent relative error of 6% averaged across aerosol sizes of 1–7 μm. With the DPI and SMI, deposition was evaluated in the MT, central airways (bifurcations B1-B7), and intermediate plus peripheral airways (B8 through alveoli). Across these regions, CFD predictions produced an average relative error <10% for each inhaler.
CFD simulations with the SIP modeling approach were shown to accurately predict regional deposition throughout the lungs for multiple aerosol types and different in vivo assessment methods.
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Airway bifurcation number
Computational fluid dynamics
Chronic obstructive pulmonary disease
Dry powder inhaler
Functional residual capacity
High performance liquid chromatography
Left lower (lung lobe)
Liters per minute
Low Reynolds Number
Metered dose inhaler
Mass median aerodynamic diameter
Next Generation Impactor
Peak inspiratory flow rate
Particle size distribution
Slow-and-deep or standard deviation
Stochastic individual pathway
Single-photon emission computed tomography
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ACKNOWLEDGMENTS AND DISCLOSURES
Katharina Bormann and Xiangyin Wei are acknowledged for their assistance in measuring the initial size of the Novolizer DPI. Navvab Dalasm is credited with creating the alveolar model shown in Fig. 3 while at VCU. This study was supported by Award U01 FD004570 from the US FDA and Award R01 HL107333 from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US FDA, the National Heart, Lung, and Blood Institute or the National Institutes of Health.
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Tian, G., Hindle, M., Lee, S. et al. Validating CFD Predictions of Pharmaceutical Aerosol Deposition with In Vivo Data. Pharm Res 32, 3170–3187 (2015). https://doi.org/10.1007/s11095-015-1695-1
- airway dosimetry predictions
- computational fluid dynamics (CFD)
- pharmaceutical aerosols
- predictions of aerosol deposition
- respiratory drug delivery