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Pharmaceutical Research

, Volume 32, Issue 10, pp 3170–3187 | Cite as

Validating CFD Predictions of Pharmaceutical Aerosol Deposition with In Vivo Data

  • Geng Tian
  • Michael Hindle
  • Sau Lee
  • P. Worth Longest
Research Paper

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.

KEY WORDS

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

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

Notes

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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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Geng Tian
    • 1
  • Michael Hindle
    • 2
  • Sau Lee
    • 3
  • P. Worth Longest
    • 1
    • 2
  1. 1.Department of Mechanical and Nuclear EngineeringVirginia Commonwealth UniversityRichmondUSA
  2. 2.Department of PharmaceuticsVirginia Commonwealth UniversityRichmondUSA
  3. 3.Office of Pharmaceutical Quality, Center for Drug Evaluation ResearchUnited States Food and Drug AdministrationSilver SpringUSA

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