Predicting Skin Permeability: Incorporation of Chemical Mixture Effects into Simple Quantitative Structure Permeation Relationships (QSPER)
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2010/03/01
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Description:Predicting the degree of dermal absorption of topically exposed chemicals is an important issue in both environmental and occupational risk assessment. Most risk assessment approaches and QSPeR models are based on data obtained from dosing chemicals neat or in simple aqueous vehicles, yet most exposures are to complex mixtures. We have previously demonstrated that a simple QSPeR model based on 12 penetrants in 24 mixtures could be constructed if the physical chemical properties of the mixture components and vehicle were also incorporated. The present study significantly expands on this analysis by increasing the number of penetrants studied using in vitro porcine skin diffusion cells to 16 for a total of 384 treatment combinations. In addition, we applied the model to 31 penetrants dosed in a total of 189 treatment combinations using an isolated perfused porcine skin model previously shown to be predictive of in vivo human absorption. These studies demonstrated that mixture chemical descriptors including topical polar surface area/or ovality significantly improve prediction of dermal absorption using different base QSPeR models (e.g. Abraham 5-term or Potts and Guy 2-term models). These studies suggest that such information could be incorporated into dermal risk assessment protocols to improve prediction of chemical absorption based on more realistic exposure scenarios. [Description provided by NIOSH]
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ISSN:1096-6080
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Volume:114
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Issue:1
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NIOSHTIC Number:nn:20036607
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Citation:Toxicologist 2010 Mar; 114(1):107
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Federal Fiscal Year:2010
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Performing Organization:North Carolina State University, Raleigh, North Carolina
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Peer Reviewed:False
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Start Date:20010601
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Source Full Name:The Toxicologist. Society of Toxicology 49th Annual Meeting and ToxExpo, March 7-11, 2010, Salt Lake City, Utah
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End Date:20100331
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Main Document Checksum:urn:sha-512:dfb7c245b544a021c88cf7da06c764973f0312815fe9033b93410ca0ab06a955e9a60d4fbc069cca6b134d45d37f978c2579da1d03ee4fc986425435177d69ea
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