Lung Dosimetry Models in Rats and Humans: Use and Evaluation for Risk Assessment of Nanoparticles
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2006/06/01
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Description:Lung dosimetry models are useful in the risk assessment of inhaled particles of all sizes by providing a biologically-based, mechanistic approach to predicting the internal dose to the lungs. Human data are limited for the risk assessment of nanoparticles, but dose-response data are available in rats exposed to ultrafine or fine particles. These studies have shown a consistent relationship between the lung dose as particle surface area and pulmonary inflammation and lung cancer in rats. To use these data in risk assessment, a scientifically reasonable approach for extrapolating the rodent data to humans is required, including adjustment for species differences in the relationship between airborne exposures and internal dose. We illustrate the key steps in a quantitative risk assessment, using dose-response data in rats with chronic inhalation exposure to poorly-soluble ultrafine and fine particles. The excess risk estimates are extrapolated from rats observed vs. model-predicted lung burdens in rats exposed to fine or ultrafine poorly-soluble to humans using either allometric adjustments or lung dosimetry models. We also compare particles. Finally, we describe ongoing research to evaluate and extend current rat and human lung dosimetry models to improve dose estimation of inhaled nanoparticles. [Description provided by NIOSH]
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NIOSHTIC Number:nn:20030839
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Citation:Frontiers in Aerosol Dosimetry Research: Proceedings of a Conference, October 24-25, 2005, Irvine, California. Phalen RF, Oldham MJ, Akhavan SW, Hoover MD, Asotra K, eds., Irvine, CA: Air Pollution Health Effects Laboratory, APHL Report No. 06-01, University of California, 2006 Jun; :5-18-5-26
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Email:ekuempel@cdc.gov
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Federal Fiscal Year:2006
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Peer Reviewed:False
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Source Full Name:Frontiers in Aerosol Dosimetry Research: Proceedings of a Conference, October 24-25, 2005, Irvine, California
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Main Document Checksum:urn:sha-512:fccdb4a41880cd95661f5be912e4a163b8a2fcc3a340c742ff309dc7c779d8a1638ec82e1a19b6411e869fa61c7ac8e619b1e4366c981d94b65fe45b18a8f3b7
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