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HERO ID
157704
Reference Type
Journal Article
Title
An approach to risk assessment for TiO2
Author(s)
Dankovic, D; Kuempel, E; Wheeler, M
Year
2007
Is Peer Reviewed?
Yes
Journal
Inhalation Toxicology
ISSN:
0895-8378
EISSN:
1091-7691
Volume
19 Suppl 1
Page Numbers
205-212
Language
English
PMID
17886069
DOI
10.1080/08958370701497754
Web of Science Id
WOS:000249434300028
Abstract
Titanium dioxide (TiO2) is a poorly soluble, low-toxicity (PSLT) particle. Fine TiO2 (<2.5 microm) has been shown to produce lung tumors in rats exposed to 250 mg/m3, and ultrafine TiO2 (< 0.1 microm diameter) has been shown to produce lung tumors in rats at 10 mg/m3. We have evaluated the rat dose-response data and conducted a quantitative risk assessment for TiO2. Preliminary conclusions are: (1) Fine and ultrafine TiO2 and other PSLT particles show a consistent dose-response relationship when dose is expressed as particle surface area; (2) the mechanism of TiO2 tumor induction in rats appears to be a secondary genotoxic mechanism associated with persistent inflammation; and (3) the inflammatory response shows evidence of a nonzero threshold. Risk estimates for TiO2 depend on both the dosimetric approach and the statistical model that is used. Using 7 different dose-response models in the U.S. Environmental Protection Agency (EPA) benchmark dose software, the maximum likelihood estimate (MLE) rat lung dose associated with a 1 per 1000 excess risk ranges from 0.0076 to 0.28 m2/g-lung of particle surface area, with 95% lower confidence limits (LCL) of 0.0059 and 0.042, respectively. Using the ICRP particle deposition and clearance model, estimated human occupational exposures yielding equivalent lung burdens range from approximately 1 to 40 mg/m3 (MLE) for fine TiO2, with 95% LCL approximately 0.7-6 mg/m3. Estimates using an interstitial sequestration lung model are about one-half as large. Bayesian model averaging techniques are now being explored as a method for combining the various estimates into a single estimate, with a confidence interval expressing model uncertainty.
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