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Validating CFD Predictions of Pharmaceutical Aerosol Deposition with In Vivo Data

Pharmaceutical Research volume 32, pages 3170–3187 (2015)Cite this article

ABSTRACT

Purpose

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.

Methods

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.

Results

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.

Conclusions

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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Abbreviations

1D:

One dimensional

2D:

Two dimensional

3D:

Three dimensional

B#:

Airway bifurcation number

C:

Central

CFD:

Computational fluid dynamics

COPD:

Chronic obstructive pulmonary disease

CT:

Computed tomography

DF:

Deposition fraction

DPI:

Dry powder inhaler

EXP:

Experimental

FRC:

Functional residual capacity

HPLC:

High performance liquid chromatography

I:

Intermediate

LL:

Left lower (lung lobe)

LPM:

Liters per minute

LRN:

Low Reynolds Number

MDI:

Metered dose inhaler

MMAD:

Mass median aerodynamic diameter

MP:

Mouthpiece

MT:

Mouth-throat

NGI:

Next Generation Impactor

P:

Peripheral

PIFR:

Peak inspiratory flow rate

PSD:

Particle size distribution

QD:

Quick-and-deep

SD:

Slow-and-deep or standard deviation

SIP:

Stochastic individual pathway

SMI:

Softmist inhaler

SPECT:

Single-photon emission computed tomography

TB:

Tracheobronchial

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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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Affiliations

  1. Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, 401 West Main Street, P.O. Box 843015, Richmond, Virginia, 23284-3015, USA

    Geng Tian & P. Worth Longest

  2. Department of Pharmaceutics, Virginia Commonwealth University, Richmond, Virginia, USA

    Michael Hindle & P. Worth Longest

  3. Office of Pharmaceutical Quality, Center for Drug Evaluation Research, United States Food and Drug Administration, Silver Spring, Maryland, USA

    Sau Lee

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  1. Geng Tian
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  4. P. Worth Longest
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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

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KEY WORDS

  • airway dosimetry predictions
  • computational fluid dynamics (CFD)
  • pharmaceutical aerosols
  • predictions of aerosol deposition
  • respiratory drug delivery