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Toxicol Sci. 2001 Nov;64(1):111-21.

Dosimetry modeling of inhaled formaldehyde: binning nasal flux predictions for quantitative risk assessment.

Author information

  • 1CIIT Centers for Health Research, P.O. Box 12137, 6 Davis Drive, Research Triangle Park, North Carolina 27709, USA. kimbell@ciit.org

Abstract

Interspecies extrapolations of tissue dose and tumor response have been a significant source of uncertainty in formaldehyde cancer risk assessment. The ability to account for species-specific variation of dose within the nasal passages would reduce this uncertainty. Three-dimensional, anatomically realistic, computational fluid dynamics (CFD) models of nasal airflow and formaldehyde gas transport in the F344 rat, rhesus monkey, and human were used to predict local patterns of wall mass flux (pmol/[mm(2)-h-ppm]). The nasal surface of each species was partitioned by flux into smaller regions (flux bins), each characterized by surface area and an average flux value. Rat and monkey flux bins were predicted for steady-state inspiratory airflow rates corresponding to the estimated minute volume for each species. Human flux bins were predicted for steady-state inspiratory airflow at 7.4, 15, 18, 25.8, 31.8, and 37 l/min and were extrapolated to 46 and 50 l/min. Flux values higher than half the maximum flux value (flux median) were predicted for nearly 20% of human nasal surfaces at 15 l/min, whereas only 5% of rat and less than 1% of monkey nasal surfaces were associated with fluxes higher than flux medians at 0.576 l/min and 4.8 l/min, respectively. Human nasal flux patterns shifted distally and uptake percentage decreased as inspiratory flow rate increased. Flux binning captures anatomical effects on flux and is thereby a basis for describing the effects of anatomy and airflow on local tissue disposition and distributions of tissue response. Formaldehyde risk models that incorporate flux binning derived from anatomically realistic CFD models will have significantly reduced uncertainty compared with risk estimates based on default methods.

PMID:
11606807
[PubMed - indexed for MEDLINE]
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