Literature DB >> 12052003

Genetic polymorphisms in assessing interindividual variability in delivered dose.

L T Haber1, A Maier, P R Gentry, H J Clewell, M L Dourson.   

Abstract

Increasing sophistication in methods used to account for human variability in susceptibility to toxicants has been one of the success stories in the continuing evolution of risk assessment science. Genetic polymorphisms have been suggested as an important contributor to overall human variability. Recently, data on polymorphisms in metabolic enzymes have been integrated with physiologically based pharmacokinetic (PBPK) modeling as an approach to determining the resulting overall variability. We present an analysis of the potential contribution of polymorphisms in enzymes modulating the disposition of four diverse compounds: methylene chloride, warfarin, parathion, and dichloroacetic acid. Through these case studies, we identify key uncertainties likely to be encountered in the use of polymorphism data and highlight potential simplifying assumptions that might be required to test the hypothesis that genetic factors are a substantive source of human variability in susceptibility to environmental toxicants. These uncertainties include (1) the relative contribution of multiple enzyme systems, (2) the extent of induction/inhibition through coexposure, (3) allelic frequencies of major ethnic groups, (4) the absence of chemical-specific data on the kinetic parameters for the different allelic forms of key enzymes, (5) large numbers of low-frequency alleles, and (6) uncertainty regarding differences between in vitro and in vivo kinetic data. Our effort sets the stage for the acquisition of critical data and further integration of polymorphism data with PBPK modeling as a means to quantitate population variability. (c) 2002 Elsevier Science (USA).

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Year:  2002        PMID: 12052003     DOI: 10.1006/rtph.2001.1517

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


  11 in total

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8.  Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment.

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