The assessment of the variability of human responses to foreign chemicals is an important step in characterizing the public health risks posed by nontherapeutic hazardous chemicals and the risk of encountering adverse reactions with drugs. Of the many sources of interindividual variability in chemical response identified to date, hereditary factors are some of the least understood. Physiologically based pharmacokinetic modeling linked with Monte Carlo sampling has been shown to be a useful tool for the quantification of interindividual variability in chemical disposition and/or response when applied to biological processes that displayed single genetic polymorphisms. The present study has extended this approach by modeling the complex hereditary control of alcohol dehydrogenase, which includes polygenic control and polymorphisms at two allelic sites, and by assessing the functional significance of this hereditary control on ethanol disposition. The physiologically based pharmacokinetic model for ethanol indicated that peak blood ethanol levels and time-to-peak blood ethanol levels were marginally affected by alcohol dehydrogenase genotypes, with simulated subjects possessing the B2 subunit having slightly lower peak blood ethanol levels and shorter times-to-peak blood levels compared to subjects without the B2 subunit. In contrast, the area under the curve (AUC) of the ethanol blood decay curve was very sensitive to alcohol dehydrogenase genotype, with AUCs from any genotype including the ADH1B2 allele considerably smaller than AUCs from any genotype without the ADH1B2 allele. Furthermore, the AUCs in the ADH1C1/C1 genotype were moderately lower than the AUCs from the corresponding ADH1C2/C2 genotype. Moreover, these simulations demonstrated that interindividual variability of ethanol disposition is affected by alcohol dehydrogenase and that the degree of this variability was a function of the ethanol dose.
All Science Journal Classification (ASJC) codes
- Alcohol dehydrogenase
- Physiologically based pharmacokinetic modeling
- Risk assessment