Use of computational fluid dynamics models for dosimetry of inhaled gases in the nasal passages

Citation
Js. Kimbell et Rp. Subramaniam, Use of computational fluid dynamics models for dosimetry of inhaled gases in the nasal passages, INHAL TOXIC, 13(5), 2001, pp. 325-334
Citations number
27
Categorie Soggetti
Pharmacology & Toxicology
Journal title
INHALATION TOXICOLOGY
ISSN journal
08958378 → ACNP
Volume
13
Issue
5
Year of publication
2001
Pages
325 - 334
Database
ISI
SICI code
0895-8378(200105)13:5<325:UOCFDM>2.0.ZU;2-B
Abstract
Computational fluid dynamics (CFD) models of the nasal passages of a rat, m onkey, and human are being used (1) to determine important factors affectin g nasal uptake, (2) to make interspecies dosimetric comparisons, (3) to pro vide detailed anatomical information for the rat, monkey, and human nasal p assages, and (4) to provide estimates of regional air-phase mass transport coefficients (a measure of the resistance to gas transport from inhaled air to airway walls) in the nasal passages of all three species. For many inha led materials, lesion location in the nose follows patterns that are both s ite and species specific. For reactive, water-soluble (Category 1) gases, r egional uptake can be a major factor in determining lesion location. Since direct measurement of airflow and uptake is experimentally difficult, CFD m odels are used here to predict uptake patterns quantitatively in three-dime nsional reconstructions of the F344 rat, rhesus monkey, and human nasal pas sages. In formaldehyde uptake simulations, absorption processes were assume d to be as rapid as possible, and regional flux (transport rate) of inhaled formaldehyde to airway walls was calculated for rats, primates, and humans . For uptake of gases like vinyl acetate and acrylic acid vapors, physiolog ically based pharmacokinetic uptake models incorporating anatomical and phy sical information from the CFD models were developed to estimate nasal tiss ue dose in animals and humans. The use of biologically based models in risk assessment makes sources of uncertainty explicit and, in doing so, allows quantification of uncertainty through sensitivity analyses. Limited resourc es can then be focused on reduction of important sources of uncertainty to make risk estimates more accurate.