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An approach to risk assessment for TiO2.
Dankovic-D; Kuempel-E; Wheeler-M
Inhal Toxicol 2007 Aug; 19(Suppl 1):205-212
Titanium dioxide (TiO2) is a poorly soluble, low-toxicity (PSLT) particle. Fine TiO2 (< 2.5 mu m) has been shown to produce lung tumors in rats exposed to 250 mg/m(3), and ultrafine TiO2 (< 0.1 mu m diameter) has been shown to produce lung tumors in rats at 10 mg/m(3). 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 m(2)/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/m(3) (MLE) for fine TiO2, with 95% LCL approximately 0.7-6 mg/m(3). 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.
Statistical-analysis; Demographic-characteristics; Animal-studies; Lung-burden; Lung-irritants; Breathing-atmospheres; Particulate-dust; Particulate-sampling-methods; Models; Humans; Dose-response; Risk-factors; Risk-analysis; Quantitative-analysis; Nanotechnology
David Dankovic, NIOSH, EID, 4676 Columbia Parkway, Cincinnati, OH 45226
Page last reviewed: March 11, 2019
Content source: National Institute for Occupational Safety and Health Education and Information Division