title: Modeling breath-enhanced jet nebulizers to estimate pulmonary drug deposition.
abstract: BACKGROUND
  Predictable delivery of aerosol medication for a given patient and drug-device combination is crucial, both for therapeutic effect and to avoid toxicity. The gold standard for measuring pulmonary drug deposition (PDD) is gamma scintigraphy. However, these techniques expose patients to radiation, are complicated, and are relevant for only one patient and drug-device combination, making them less available. Alternatively, in vitro experiments have been used as a surrogate to estimate in vivo performance, but this is time-consuming and has few "in vitro to in vivo" correlations for therapeutics delivered by inhalation. An alternative method for determining inhaled mass and PDD is proposed by deriving and validating a mathematical model, for the individual breathing patterns of normal subjects and drug-device operating parameters. This model was evaluated for patients with cystic fibrosis (CF).
  
  
  METHODS
  This study is comprised of three stages: mathematical model derivation, in vitro testing, and in vivo validation. The model was derived from an idealized patient's respiration cycle and the steady-state operating characteristics of a drug-device combination. The model was tested under in vitro dynamic conditions that varied tidal volume, inspiration-to-expiration time, and breaths per minute. This approach was then extended to incorporate additional physiological parameters (dead space, aerodynamic particle size distribution) and validated against in vivo nuclear medicine data in predicting PDD in both normal subjects and those with CF.
  
  
  RESULTS
  The model shows strong agreement with in vitro testing. In vivo testing with normal subjects yielded good agreement, but less agreement for patients with chronic obstructive lung disease and bronchiectasis from CF.
  
  
  CONCLUSIONS
  The mathematical model was successful in accommodating a wide range of breathing patterns and drug-device combinations. Furthermore, the model has demonstrated its effectiveness in predicting the amount of aerosol delivered to "normal" subjects. However, challenges remain in predicting deposition in obstructive lung disease.
authors.affiliation: 1 Division of Undergraduate Medicine, University of Toronto , Toronto, Ontario, Canada .
authors.forename: Wallace B
authors.initials: WB
authors.lastname: Wee
authors.forename: Kitty
authors.initials: K
authors.lastname: Leung
authors.forename: Allan L
authors.initials: AL
authors.lastname: Coates
mesh_terms: D000280:Administration, Inhalation; D000336:Aerosols; D016022:Case-Control Studies; D003550:Cystic Fibrosis / Q000188:drug therapy / Q000378:metabolism* / Q000503:physiopathology; D004867:Equipment Design; D006801:Humans; D008168:Lung / Q000187:drug effects / Q000378:metabolism* / Q000503:physiopathology; D008954:Models, Biological; D009330:Nebulizers and Vaporizers; D010316:Particle Size; D029424:Pulmonary Disease, Chronic Obstructive / Q000188:drug therapy / Q000378:metabolism* / Q000503:physiopathology; D015203:Reproducibility of Results; D012119:Respiration / Q000187:drug effects; D015656:Respiratory Mechanics; D019141:Respiratory System Agents / Q000008:administration & dosage* / Q000378:metabolism; D013997:Time Factors
chemical_list: D000336:Aerosols; D019141:Respiratory System Agents