Literature DB >> 12473413

Impact of physiologically based pharmacokinetic modeling on benchmark dose calculations for TCDD-induced biochemical responses.

Amy H Kim1, Michael C Kohn, Christopher J Portier, Nigel J Walker.   

Abstract

In risk assessment, noncancer risk is currently estimated using a no observed adverse effect level (NOAEL) from an experimental dose-response study, divided by uncertainty factors, to estimate a presumably safe level of human exposure. A benchmark dose approach, in which an effective dose (ED) resulting in a specified percentage increase over background for effects is estimated by empirical modeling, has been proposed as a replacement for the NOAEL methodology. The aim of this analysis is to compare methods for estimation of body burden resulting in a 1 or 10% maximum increase over background (BB(01) or BB(10)) for biochemical responses following exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in female Sprague-Dawley rats. In one method, an ED resulting in a prespecified increase in response over background was estimated using average daily doses and an empirical Hill model. The ED was then converted to an equivalent body burden by a simple kinetic model assuming steady-state conditions, half-life of TCDD in the rat, and 100% absorption of TCDD. Alternatively, a mechanistic physiologically based pharmacokinetic (PBPK) model of TCDD in the rat was used to predict body burdens for administered doses. These PBPK-modeled body burdens were then used directly by the Hill model to calculate a BB(01) or BB(10). In general, the body burden values derived from EDs were within five-fold of BB(01) or BB(10) calculated from the PBPK model. BB(01) and BB(10) values from both methods were within two orders of magnitude of current human general population exposure to all dioxin-like compounds.

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Year:  2002        PMID: 12473413     DOI: 10.1006/rtph.2002.1590

Source DB:  PubMed          Journal:  Regul Toxicol Pharmacol        ISSN: 0273-2300            Impact factor:   3.271


  1 in total

1.  Analysis of nonlinear regression models: a cautionary note.

Authors:  Shyamal D Peddada; Joseph K Haseman
Journal:  Dose Response       Date:  2006-05-01       Impact factor: 2.658

  1 in total

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