The influence of genetic polymorphisms on population variability in six xenobiotic-metabolizing enzymes.

This review provides variability statistics for polymorphic enzymes that are involved in the metabolism of xenobiotics. Six enzymes were evaluated: cytochrome P-450 (CYP) 2D6, CYP2E1, aldehyde dehydrogenase-2 (ALDH2), paraoxonase (PON1), glutathione transferases (GSTM1, GSTT1, and GSTP1), and N-acetyltransferases (NAT1 and NAT2). The polymorphisms were characterized with respect to (1) number and type of variants, (2) effects of polymorphisms on enzyme function, and (3) frequency of genotypes within specified human populations. This information was incorporated into Monte Carlo simulations to predict the population distribution and describe interindividual variability in enzyme activity. The results were assessed in terms of (1) role of these enzymes in toxicant activation and clearance, (2) molecular epidemiology evidence of health risk, and (3) comparing enzyme variability to that commonly assumed for pharmacokinetics. Overall, the Monte Carlo simulations indicated a large degree of interindividual variability in enzyme function, in some cases characterized by multimodal distributions. This study illustrates that polymorphic metabolizing systems are potentially important sources of pharmacokinetic variability, but there are a number of other factors including blood flow to liver and compensating pathways for clearance that affect how a specific polymorphism will alter internal dose and toxicity. This is best evaluated with the aid of physiologically based pharmacokinetic (PBPK) modeling. The population distribution of enzyme activity presented in this series of articles serves as inputs to such PBPK modeling analyses.

Genetic Polymorphism in Glutathione Transferases (GST): Population distribution of GSTM1, T1, and P1 conjugating activity.

Glutathione transferases (GST) catalyze the conjugation of glutathione (GSH) with electrophiles, many of which may otherwise interact with protein or DNA. In select cases such as halogenated solvents, GST-mediated conjugation may lead to a more toxic or mutagenic metabolite. Polymorphisms that exert substantial effects on GST function were noted in human populations for several isozymes. This analysis focuses on three well-characterized isozymes, GSTM1, T1, and P1, in which polymorphisms were extensively studied with respect to DNA adducts and cancer in molecular epidemiologic studies. The current review and analysis focused upon how polymorphisms in these GST contributed to population variability in GST function. The first step in developing this review was to characterize the influence of genotype on phenotype (enzyme function) and the frequency of the polymorphisms across major population groups for all three GST. This information was then incorporated into Monte Carlo simulations to develop population distributions of enzyme function. These simulations were run separately for GSTM1, T1, and P1, and also for the combination of these isozymes, to assess the possibility of overlapping substrate specificity. Monte Carlo simulations indicated large interindividual variability for GSTM1 and T1 due to the presence of the null (zero activity) genotype, which is common in all populations studied. Even for GSTM1 or T1 non-null individuals, there was considerable interindividual variability with a bimodal distribution of enzyme activity evident. GSTP1 polymorphisms are associated with somewhat less variability due to the absence of null genotypes. However, in all cases simulated, the estimated variability is sufficiently large to warrant consideration of GST function distributions in assessments involving GST-mediated activation or detoxification of xenobiotics. Ideally, such assessments would involve physiologically based toxicokinetic (PBTK) modeling to assess population variability in internal dose.

Differences in pharmacokinetics between children and adults--II. Children's variability in drug elimination half-lives and in some parameters needed for physiologically-based pharmacokinetic modeling.

In earlier work we assembled a database of classical pharmacokinetic parameters (e.g., elimination half-lives; volumes of distribution) in children and adults. These data were then analyzed to define mean differences between adults and children of various age groups. In this article, we first analyze the variability in half-life observations where individual data exist. The major findings are as follows. The age groups defined in the earlier analysis of arithmetic mean data (0-1 week premature; 0-1 week full term; 1 week to 2 months; 2-6 months; 6 months to 2 years; 2-12 years; and 12-18 years) are reasonable for depicting child/adult pharmacokinetic differences, but data for some of the earliest age groups are highly variable. The fraction of individual children's half-lives observed to exceed the adult mean half-life by more than the 3.2-fold uncertainty factor commonly attributed to interindividual pharmacokinetic variability is 27% (16/59) for the 0-1 week age group, and 19% (5/26) in the 1 week to 2 month age group, compared to 0/87 for all the other age groups combined between 2 months and 18 years. Children within specific age groups appear to differ from adults with respect to the amount of variability and the form of the distribution of half-lives across the population. The data indicate departure from simple unimodal distributions, particularly in the 1 week to 2 month age group, suggesting that key developmental steps affecting drug removal tend to occur in that period. Finally, in preparation for age-dependent physiologically-based pharmacokinetic modeling, nationally representative NHANES III data are analyzed for distributions of body size and fat content. The data from about age 3 to age 10 reveal important departures from simple unimodal distributional forms-in the direction suggesting a subpopulation of children that are markedly heavier than those in the major mode. For risk assessment modeling, this means that analysts will need to consider "mixed" distributions (e.g., two or more normal or log-normal modes) in which the proportions of children falling within the major versus highweight/fat modes in the mixture changes as a function of age. Biologically, the most natural interpretation of this is that these subpopulations represent children who have or have not yet received particular signals for change in growth pattern. These apparently distinct subpopulations would be expected to exhibit different disposition of xenobiotics, particularly those that are highly lipophilic and poorly metabolized.

Population distribution of aldehyde dehydrogenase-2 genetic polymorphism: implications for risk assessment.

The role of genetic polymorphisms in modulating xenobiotic metabolism and susceptibility to cancer and other health effects has been suggested in numerous studies. However, risk assessments have generally not used this information to characterize population variability or adjust risks for susceptible subgroups. This paper focuses upon the aldehyde dehydrogenase-2 (ALDH2) system because it exemplifies the pivotal role genetic polymorphisms can play in determining enzyme function and susceptibility. Allelic variants in ALDH2 cause decreased ability to clear acetaldehyde and other aldehyde substrates, with homozygous variants (ALDH2*2/2) having no activity and heterozygotes (ALDH2*1/2) having intermediate activity relative to the predominant wild type (ALDH2*1/1). These polymorphisms are associated with increased buildup of acetaldehyde following ethanol ingestion and increased immediate symptoms (flushing syndrome) and long-term cancer risks. We have used Monte Carlo simulation to characterize the population distribution of ALDH2 allelic variants and inter-individual variability in aldehyde internal dose. The nonfunctional allele is rare in most populations, but is common in Asians such that 40% are heterozygotes and 5% are homozygote variants. The ratio of the 95th or 99th percentiles of the Asian population compared to the median of the U.S. population is 14- to 26-fold, a variability factor that is larger than the default pharmacokinetic uncertainty factor (3.2-fold) commonly used in risk assessment. Approaches are described for using ALDH2 population distributions in physiologically based pharmacokinetic-Monte Carlo refinements of risk assessments for xenobiotics which are metabolized to aldehyde intermediates (e.g., ethanol, toluene, ethylene glycol monomethyl ether).

Evaluation of child/adult pharmacokinetic differences from a database derived from the therapeutic drug literature.

Pharmacokinetics (PK) of xenobiotics can differ widely between children and adults due to physiological differences and the immaturity of enzyme systems and clearance mechanisms. This makes extrapolation of adult dosimetry estimates to children uncertain, especially at early postnatal ages. While there is very little PK data for environmental toxicants in children, there is a wealth of such data for therapeutic drugs. Using published literature, a Children's PK Database has been compiled which compares PK parameters between children and adults for 45 drugs. This has enabled comparison of child and adult PK function across a number of cytochrome P450 (CYP) pathways, as well as certain Phase II conjugation reactions and renal elimination. These comparisons indicate that premature and full-term neonates tend to have 3 to 9 times longer half-life than adults for the drugs included in the database. This difference disappears by 2-6 months of age. Beyond this age, half-life can be shorter than in adults for specific drugs and pathways. The range of neonate/adult half-life ratios exceeds the 3.16-fold factor commonly ascribed to interindividual PK variability. Thus, this uncertainty factor may not be adequate for certain chemicals in the early postnatal period. The current findings present a PK developmental profile that is relevant to environmental toxicants metabolized and cleared by the pathways represented in the current database. The manner in which this PK information can be applied to the risk assessment of children includes several different approaches: qualitative (e.g., enhanced discussion of uncertainties), semiquantitative (age group-specific adjustment factors), and quantitative (estimation of internal dosimetry in children via physiologically based PK modeling).