A Bayesian analysis of the influence of GSTT1 polymorphism on the cancer risk estimate for dichloromethane.
The carcinogenicity of dichloromethane (DCM) is related to metabolic activation mediated by glutathione transferase theta 1 (GSTT1), whereas oxidation serves as a detoxification pathway. The aim of this study was to calculate the excess cancer risk from DCM, using Bayesian statistics. In a first step, a previously developed population physiologically based pharmacokinetic (PBPK) model for DCM was simultaneously fitted to extensive human toxicokinetic data from 27 male volunteers exposed to 250-1000 ppm DCM (Astrand et al. Scand. J. Work Environ. Health 1, 78-94, 1975; Engström and Bjurström, Scand. J. Work Environ. Health 7, 215-224, 1977) using Markov chain Monte Carlo simulation. Improved population estimates were obtained for the PBPK model parameters. In a second step, excess cancer risk was calculated for lifelong exposure to 1-1000 ppm DCM by Monte Carlo simulation. Data on GSTT1 gene frequencies in the Swedish population were used, including all three genotypes. Estimated mean and median excess risks were in general agreement with those previously derived (El-Masri et al. Toxicol. Appl. Pharmacol. 158, 221-230, 1999). However, we estimate higher excess risks at the upper confidence limits. Furthermore, our simulations suggest that 1% of the Swedish population is not covered by a factor 4.2-7.1 away from the mean target dose. The majority of the fraction of the population not covered was classified as GSTT1 homozygote. This indicates that a higher uncertainty factor than the commonly used 3.16 should be considered in noncancer risk assessment for substances with polymorphic bioactivation.[1]References
- A Bayesian analysis of the influence of GSTT1 polymorphism on the cancer risk estimate for dichloromethane. Jonsson, F., Johanson, G. Toxicol. Appl. Pharmacol. (2001) [Pubmed]
Annotations and hyperlinks in this abstract are from individual authors of WikiGenes or automatically generated by the WikiGenes Data Mining Engine. The abstract is from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.About WikiGenesOpen Access LicencePrivacy PolicyTerms of Useapsburg
