title: Validating CFD Predictions of Pharmaceutical Aerosol Deposition with In Vivo Data.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, 401 West Main Street, P.O. Box 843015, Richmond, Virginia, 23284-3015, USA.
authors.forename: Geng
authors.initials: G
authors.lastname: Tian
authors.affiliation: Department of Pharmaceutics, Virginia Commonwealth University, Richmond, Virginia, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Hindle
authors.affiliation: Office of Pharmaceutical Quality, Center for Drug Evaluation Research, United States Food and Drug Administration, Silver Spring, Maryland, USA.
authors.forename: Sau
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, 401 West Main Street, P.O. Box 843015, Richmond, Virginia, 23284-3015, USA. pwlongest@vcu.edu.|Department of Pharmaceutics, Virginia Commonwealth University, Richmond, Virginia, USA. pwlongest@vcu.edu.
authors.forename: P Worth
authors.initials: PW
authors.lastname: Longest
mesh_terms: D000280:Administration, Inhalation; D000336:Aerosols / Q000378:metabolism* / Q000494:pharmacology*; D003198:Computer Simulation; D016503:Drug Delivery Systems / Q000379:methods; D058995:Dry Powder Inhalers / Q000379:methods; D006801:Humans; D008168:Lung / Q000187:drug effects* / Q000378:metabolism*; D008953:Models, Anatomic; D008962:Models, Theoretical; D010316:Particle Size; D014018:Tissue Distribution / Q000502:physiology
chemical_list: D000336:Aerosols
keywords: airway dosimetry predictions; computational fluid dynamics (CFD); pharmaceutical aerosols; predictions of aerosol deposition; respiratory drug delivery