Dale Hattis

Physiologically Based Pharmacokinetic (PBPK) Modeling of Caffeine and Theophylline in Neonates and Adults: Implications for Assessing Children's Risks from Environmental Agents

Childrens risks can differ from those in adults for numerous reasons, one being differences in the pharmacokinetic handling of chemicals. Immature metabolism and a variety of other factors in neonates can affect chemical disposition and clearance. These factors can be incorporated into physiologically based pharmacokinetic (PBPK) models that simulate the fate of environmental toxicants in both children and adults. PBPK models are most informative when supported by empirical data, but typically pediatric pharmacokinetic data for toxicants are not available. In contrast, pharmacokinetic data in children are readily available for therapeutic drugs. The current analysis utilizes data for caffeine and theophylline, closely related xanthines that are both cytochrome P-450 (CYP) 1A2 substrates, in developing PBPK models for neonates and adults. Model development involved scale-up of in vitro metabolic parameters to whole liver and adjusting metabolic function for the ontological pattern of CYP1A2 and other CYPs. Model runs were able to simulate the large differences in half-life and clearance between neonates and adults. Further, the models were able to reproduce the faster metabolic clearance of theophylline relative to caffeine in neonates. This differential between xanthines was found to be due primarily to an extra metabolic pathway available to theophylline, back-methylation to caffeine, that is not available to caffeine itself. This pathway is not observed in adults exemplifying the importance of secondary or novel routes of metabolism in the immature liver. Greater CYP2E1 metabolism of theophylline relative to caffeine in neonates also occurs. Neonatal PBPK models developed for these drugs may be adapted to other CYP1A2 substrates (e.g., arylamine toxicants). A stepwise approach for modeling environmental toxicants in children is proposed.

Evaluation of the Cardiovascular Effects of Methylmercury Exposures: Current Evidence Supports Development of a Dose-Response Function for Regulatory Benefits Analysis

Background The U.S. Environmental Protection Agency (U.S. EPA) has estimated the neurological benefits of reductions in prenatal methylmercury (MeHg) exposure in past assessments of rules controlling mercury (Hg) emissions. A growing body of evidence suggests that MeHg exposure can also lead to increased risks of adverse cardiovascular impacts in exposed populations. Data extraction The U.S. EPA assembled the authors of this article to participate in a workshop, where we reviewed the current science concerning cardiovascular health effects of MeHg exposure via fish and seafood consumption and provided recommendations concerning whether cardiovascular health effects should be included in future Hg regulatory impact analyses. Data synthesis We found the body of evidence exploring the link between MeHg and acute myocardial infarction (MI) to be sufficiently strong to support its inclusion in future benefits analyses, based both on direct epidemiological evidence of an MeHg–MI link and on MeHg’s association with intermediary impacts that contribute to MI risk. Although additional research in this area would be beneficial to further clarify key characteristics of this relationship and the biological mechanisms that underlie it, we consider the current epidemiological literature sufficiently robust to support the development of a dose–response function. Conclusions We recommend the development of a dose–response function relating MeHg exposures with MIs for use in regulatory benefits analyses of future rules targeting Hg air emissions.

Assessing cumulative health risks from exposure to environmental mixtures-three fundamental questions

Differential exposure to mixtures of environmental agents, including biological, chemical, physical, and psychosocial stressors, can contribute to increased vulnerability of human populations and ecologic systems. Cumulative risk assessment is a tool for organizing and analyzing information to evaluate the probability and seriousness of harmful effects caused by either simultaneous and/or sequential exposure to multiple environmental stressors. In this article we focus on elucidating key challenges that must be addressed to determine whether and to what degree differential exposure to environmental mixtures contributes to increased vulnerability of exposed populations. In particular, the emphasis is on examining three fundamental and interrelated questions that must be addressed as part of the process to assess cumulative risk: a) Which mixtures are most important from a public health perspective? and b) What is the nature (i.e., duration, frequency, timing) and magnitude (i.e., exposure concentration and dose) of relevant cumulative exposures for the population of interest? c) What is the mechanism (e.g., toxicokinetic or toxicodynamic) and consequence (e.g., additive, less than additive, more than additive) of the mixture’s interactive effects on exposed populations? The focus is primarily on human health effects from chemical mixtures, and the goal is to reinforce the need for improved assessment of cumulative exposure and better understanding of the biological mechanisms that determine toxicologic interactions among mixture constituents.

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.

Genetic Polymorphism in N-Acetyltransferase (NAT): Population Distribution of NAT1 and NAT2 Activity

N-Acetyltransferases (NAT) are key enzymes in the conjugation of certain drugs and other xenobiotics with an arylamine structure. Polymorphisms in NAT2 have long been recognized to modulate toxicity produced by the anti-tubercular drug isoniazid, with molecular epidemiologic studies suggesting a link between acetylator phenotype and increased risk for bladder cancer. Recent evidence indicates that the other major NAT isozyme, NAT1, is also polymorphic. The current analysis characterizes the main polymorphisms in both NAT2 and NAT1 in terms of their effect on enzyme activity and frequency in the population. Multiple NAT2 alleles (NAT2*5, *6, *7, and *14) have substantially decreased acetylation activity and are common in Caucasians and populations of African descent. In these groups, most individuals carry at least one copy of a slow acetylator allele, and less than 10% are homozygous for the wild type (fast acetylator) trait. Incorporation of these data into a Monte Carlo modeling framework led to a population distribution of NAT2 activity that was bimodal and associated with considerable variability in each population assessed. The ratio of the median to the first percentile of NAT2 activity ranged from 7 in Caucasians to 18 in the Chinese population. This variability indicates the need for more quantitative approaches (e.g., physiologically based pharmacokinetic [PBPK] modeling) to assess the full distribution of internal dose and adverse responses to aromatic amines and other NAT2 substrates. Polymorphisms in NAT1 are generally associated with relatively minor effects on acetylation function, with Monte Carlo analysis indicating less interindividual variability than seen in NAT2 analysis.

Genetic Polymorphism in CYP2E1: Population Distribution of CYP2E1 Activity

Cytochrome P-450 2E1 (CYP2E1) is a key enzyme in the metabolic activation of a variety of toxicants including nitrosamines, benzene, vinyl chloride, and halogenated solvents such as trichloroethylene. CYP2E1 is also one of the enzymes that metabolizes ethanol to acetaldehyde, and is induced by recent ethanol ingestion. There is evidence that interindividual variability in the expression and functional activity of this cytochrome (CYP) may be considerable. Genetic polymorphisms in CYP2E1 were identified and linked to altered susceptibility to hepatic cirrhosis induced by ethanol and esophageal and other cancers in some epidemiological studies. Therefore, it is important to evaluate how such polymorphisms affect CYP2E1 function and whether it is possible to construct a population distribution of CYP2E1 activity based upon the known effects of these polymorphisms and their frequency in the population. This analysis is part of the genetic polymorphism database project described in the lead article in this series and followed the approach described in that article (Ginsberg et al., 2009, this issue). Review of the literature found that there are a variety of CYP2E1 variant alleles but the functional significance of these variants is still unclear. Some, but not all, studies suggest that several upstream 5′ flanking mutations affect gene expression and response to inducers such as ethanol or obesity. None of the coding-region variants consistently affects enzyme function. Part of the reason for conflicting evidence regarding genotype effect on phenotype may be due to the wide variety of exposures such as ethanol or dietary factors and physiological factors including body weight or diabetes that modulate CYP2E1 expression. In conclusion, evidence is too limited to support the development of a population distribution of CYP2E1 enzyme activity based upon genotypes. Health risk assessments may best rely upon data reporting interindividual variability in CYP2E1 function for input into physiologically based pharmacokinetic (PBPK) models involving CYP2E1 substrates.

Genetic Polymorphism in Cytochrome P450 2D6 (CYP2D6): Population Distribution of CYP2D6 Activity

Cytochrome P-450 2D6 (CYP2D6) is involved in the metabolism of many therapeutic drugs even though the enzyme represents a small proportion of the total CYP content of human liver. In vivo phenotyping with probe drug substrates such as debrisoquine and dextromethorphan showed a clear separation between poor metabolizers (PM) and extensive metabolizers (EM). This polymorphism may affect susceptibility to environmental disease, as suggested by molecular epidemiologic studies that found an association between CYP2D6 metabolizer phenotype and cancer risk; however, this association is not consistent. There are only a few examples of CYP2D6 involvement in toxicant mechanism of action, but this has not been extensively studied. Gene probe studies documented a number of genetic polymorphisms that underlie CYP2D6 metabolizer phenotypes. The EM group carries the wild-type (*1) or active (*2) variant alleles, while the PM group carries the *3, *4, *5, or *6 alleles, all of which code for a protein that has lower or null CYP2D6 activity. The current analysis characterizes (a) influence of genotype on phenotype based upon in vivo metabolism studies of probe drugs and (b) frequency of the major genotypes in different population groups is also characterized. These data were then incorporated into Monte Carlo modeling to simulate population distributions of CYP2D6 activity. This analysis reproduced the bimodal distributions commonly seen in phenotyping studies of Caucasians and found extensive population variability in enzyme activity, as indicated by the 9- to 56-fold difference between the PM modal median and the total population median CYP2D6 activity. This substantial degree of interindividual variability in CYP function indicates that assessments involving CYP2D6 substrates need to consider the full distribution of enzyme activity in refining estimates of internal dose in health assessments of xenobiotics.

Addressing human variability in next-generation human health risk assessments of environmental chemicals.

Background: Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment. Objective: Our goal was to explore how next-generation human health risk assessments may better characterize variability in the context of the conceptual framework for the source-to-outcome continuum. Methods: This review was informed by a National Research Council workshop titled “Biological Factors that Underlie Individual Susceptibility to Environmental Stressors and Their Implications for Decision-Making.” We considered current experimental and in silico approaches, and emerging data streams (such as genetically defined human cells lines, genetically diverse rodent models, human omic profiling, and genome-wide association studies) that are providing new types of information and models relevant for assessing interindividual variability for application to human health risk assessments of environmental chemicals. Discussion: One challenge for characterizing variability is the wide range of sources of inherent biological variability (e.g., genetic and epigenetic variants) among individuals. A second challenge is that each particular pair of health outcomes and chemical exposures involves combinations of these sources, which may be further compounded by extrinsic factors (e.g., diet, psychosocial stressors, other exogenous chemical exposures). A third challenge is that different decision contexts present distinct needs regarding the identification—and extent of characterization—of interindividual variability in the human population. Conclusions: Despite these inherent challenges, opportunities exist to incorporate evidence from emerging data streams for addressing interindividual variability in a range of decision-making contexts.

Database for Physiologically Based Pharmacokinetic (PBPK) Modeling: Physiological Data for Healthy and Health-Impaired Elderly

Physiologically based pharmacokinetic (PBPK) models have increasingly been employed in chemical health risk assessments. By incorporating individual variability conferred by genetic polymorphisms, health conditions, and physiological changes during development and aging, PBPK models are ideal for predicting chemical disposition in various subpopulations of interest. In order to improve the parameterization of PBPK models for healthy and health-impaired elderly (herein defined as those aged 65 yr and older), physiological parameter values were obtained from the peer-reviewed literature, evaluated, and entered into a Microsoft ACCESS database. Database records include values for key age-specific model inputs such as ventilation rates, organ volumes and blood flows, glomerular filtration rates, and other clearance-related processes. In total, 528 publications were screened for relevant data, resulting in the inclusion of 155 publications comprising 1051 data records for healthy elderly adults and 115 data records for elderly with conditions such as diabetes, chronic obstructive pulmonary disease (COPD), obesity, heart disease, and renal disease. There are no consistent trends across parameters or their associated variance with age; the gross variance in body weight decreased with advancing age, whereas there was no change in variance for brain weight. The database contains some information to inform ethnic and gender differences in parameters; however, the majority of the published data pertain to Asian (mostly Japanese) and Caucasian males. As expected, the number of records tends to decrease with advancing age. In addition to a general lack of data for parameters in the elderly with various health conditions, there is also a dearth of information on blood and tissue composition in all elderly groups. Importantly, there are relatively few records for alveolar ventilation rate; therefore, the relationship between this parameter and cardiac output (usually assumed to be 1:1) in the elderly is not well informed by the database. Despite these limitations, the database represents a potentially useful resource for parameterizing PBPK models for the elderly to facilitate the prediction of dose metrics in older populations for application in risk assessment.

State-of-the-Science Workshop Report: Issues and Approaches in Low-Dose― Response Extrapolation for Environmental Health Risk Assessment

Low-dose extrapolation model selection for evaluating the health effects of environmental pollutants is a key component of the risk assessment process. At a workshop held in Baltimore, Maryland, on 23–24 April 2007, sponsored by U.S. Environmental Protection Agency and Johns Hopkins Risk Sciences and Public Policy Institute, a multidisciplinary group of experts reviewed the state of the science regarding low-dose extrapolation modeling and its application in environmental health risk assessments. Participants identified discussion topics based on a literature review, which included examples for which human responses to ambient exposures have been extensively characterized for cancer and/or noncancer outcomes. Topics included the need for formalized approaches and criteria to assess the evidence for mode of action (MOA), the use of human versus animal data, the use of MOA information in biologically based models, and the implications of interindividual variability, background disease processes, and background exposures in threshold versus nonthreshold model choice. Participants recommended approaches that differ from current practice for extrapolating high-dose animal data to low-dose human exposures, including categorical approaches for integrating information on MOA, statistical approaches such as model averaging, and inference-based models that explicitly consider uncertainty and interindividual variability.