Physiologically-based toxicokinetic modeling of zearalenone and its metabolites: application to the Jersey girl study.

Zearalenone (ZEA), a fungal mycotoxin, and its metabolite zeranol (ZAL) are known estrogen agonists in mammals, and are found as contaminants in food. Zeranol, which is more potent than ZEA and comparable in potency to estradiol, is also added as a growth additive in beef in the US and Canada. This article presents the development and application of a Physiologically-Based Toxicokinetic (PBTK) model for ZEA and ZAL and their primary metabolites, zearalenol, zearalanone, and their conjugated glucuronides, for rats and for human subjects. The PBTK modeling study explicitly simulates critical metabolic pathways in the gastrointestinal and hepatic systems. Metabolic events such as dehydrogenation and glucuronidation of the chemicals, which have direct effects on the accumulation and elimination of the toxic compounds, have been quantified. The PBTK model considers urinary and fecal excretion and biliary recirculation and compares the predicted biomarkers of blood, urinary and fecal concentrations with published in vivo measurements in rats and human subjects. Additionally, the toxicokinetic model has been coupled with a novel probabilistic dietary exposure model and applied to the Jersey Girl Study (JGS), which involved measurement of mycoestrogens as urinary biomarkers, in a cohort of young girls in New Jersey, USA. A probabilistic exposure characterization for the study population has been conducted and the predicted urinary concentrations have been compared to measurements considering inter-individual physiological and dietary variability. The in vivo measurements from the JGS fall within the high and low predicted distributions of biomarker values corresponding to dietary exposure estimates calculated by the probabilistic modeling system. The work described here is the first of its kind to present a comprehensive framework developing estimates of potential exposures to mycotoxins and linking them with biologically relevant doses and biomarker measurements, including a systematic characterization of uncertainties in exposure and dose estimation for a vulnerable population.

Studying permethrin exposure in flight attendants using a physiologically based pharmacokinetic model.

Assessment of potential health risks to flight attendants from exposure to pyrethroid insecticides, used for aircraft disinsection, is limited because of (a) lack of information on exposures to these insecticides, and (b) lack of tools for linking these exposures to biomarker data. We developed and evaluated a physiologically based pharmacokinetic (PBPK) model to assess the exposure of flight attendants to the pyrethroid insecticide permethrin attributable to aircraft disinsection. The permethrin PBPK model was developed by adapting previous models for pyrethroids, and was parameterized using currently available metabolic parameters for permethrin. The human permethrin model was first evaluated with data from published human studies. Then, it was used to estimate urinary metabolite concentrations of permethrin in flight attendants who worked in aircrafts, which underwent residual and pre-flight spray treatments. The human model was also applied to analyze the toxicokinetics following permethrin exposures attributable to other aircraft disinsection scenarios. Predicted levels of urinary 3-phenoxybenzoic acid (3-PBA), a metabolite of permethrin, following residual disinsection treatment were comparable to the measurements made for flight attendants. Simulations showed that the median contributions of the dermal, oral and inhalation routes to permethrin exposure in flight attendants were 83.5%, 16.1% and 0.4% under residual treatment scenario, respectively, and were 5.3%, 5.0% and 89.7% under pre-flight spray scenario, respectively. The PBPK model provides the capability to simulate the toxicokinetic profiles of permethrin, and can be used in the studies on human exposure to permethrin.

Exposure indices for the National Children's Study: application to inhalation exposures in Queens County, NY.

Characterization of environmental exposures to population subgroups within the National Children's Study (NCS), or other large-scale human environmental health studies is essential for developing a high-quality data platform for subsequent investigations. A computational formulation utilizing the tiered exposure ranking framework is presented for calculating inhalation exposure indices (EIs) for population subgroups. This formulation employs a probabilistic approach and combines information from diverse, publicly available exposure-relevant databases and information on biological mechanisms, for ranking study locations or population subgroups with respect to potential for specific end point-related environmental exposures. These EIs capture and summarize, within a set of numerical values/ranges, complex distributions of potential exposures to multiple airborne contaminants. These estimates capture spatial and demographic variability within each study segment, and allow for the relative comparison of study locations based on different statistical metrics of exposures. The EI formulation was applied to characterize and rank segments within Queens County, NY, which is one of the Vanguard centers for the NCS. Inhalation EI estimates relevant to respiratory outcomes, and potentially to pregnancy outcomes (low birth weight and preterm birth rates) were calculated at the study segment level. Results indicate that there is substantial variability across the study segments in Queens County, NY, and within segments, and showed an exposure gradient across the study segments that can help guide and target environmental and personal exposure sampling efforts in this county. The results also serve as an example application of the EI for use in other exposure and outcome studies.

Computational fluid dynamics modeling of transport and deposition of pesticides in an aircraft cabin.

Spraying of pesticides in aircraft cabins is required by some countries as part of a disinsection process to kill insects that pose a public health threat. However, public health concerns remain regarding exposures of cabin crew and passengers to pesticides in aircraft cabins. While large scale field measurements of pesticide residues and air concentrations in aircraft cabins scenarios are expensive and time consuming, Computational Fluid Dynamics (CFD) models provide an effective alternative for characterizing concentration distributions and exposures. This study involved CFD modeling of a twin-aisle 11 row cabin mockup with heated manikins, mimicking a part of a fully occupied Boeing 767 cabin. The model was applied to study the flow and deposition of pesticides under representative scenarios with different spraying patterns (sideways and overhead) and cabin air exchange rates (low and high). Corresponding spraying experiments were conducted in the cabin mockup, and pesticide deposition samples were collected at the manikin's lap and seat top for a limited set of five seats. The CFD model performed well for scenarios corresponding to high air exchange rates, captured the concentration profiles for middle seats under low air exchange rates, and underestimated the concentrations at window seats under low air exchange rates. Additionally, both the CFD and experimental measurements showed no major variation in deposition characteristics between sideways and overhead spraying. The CFD model can estimate concentration fields and deposition profiles at very high resolutions, which can be used for characterizing the overall variability in air concentrations and surface loadings. Additionally, these model results can also provide a realistic range of surface and air concentrations of pesticides in the cabin that can be used to estimate potential exposures of cabin crew and passengers to these pesticides.

Bayesian Analysis of a Lipid-Based Physiologically Based Toxicokinetic Model for a Mixture of PCBs in Rats.

A lipid-based physiologically based toxicokinetic (PBTK) model has been developed for a mixture of six polychlorinated biphenyls (PCBs) in rats. The aim of this study was to apply population Bayesian analysis to a lipid PBTK model, while incorporating an internal exposure-response model linking enzyme induction and metabolic rate. Lipid-based physiologically based toxicokinetic models are a subset of PBTK models that can simulate concentrations of highly lipophilic compounds in tissue lipids, without the need for partition coefficients. A hierarchical treatment of population metabolic parameters and a CYP450 induction model were incorporated into the lipid-based PBTK framework, and Markov-Chain Monte Carlo was applied to in vivo data. A mass balance of CYP1A and CYP2B in the liver was necessary to model PCB metabolism at high doses. The linked PBTK/induction model remained on a lipid basis and was capable of modeling PCB concentrations in multiple tissues for all dose levels and dose profiles.

Impact of biogenic emission uncertainties on the simulated response of ozone and fine particulate matter to anthropogenic emission reductions.

The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM2.5) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NOx). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NOx emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM2.5, the differences were 0.1-0.25 microg/m3 in the summer total organic mass component of DVFs, corresponding to approximately 1-2% of the value of the annual PM2.5 NAAQS of 15 microg/m3. Spatial variations in the ozone and PM2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM2.5 levels are dependent on ambient levels of anthropogenic emissions.

Mathematical model of uptake and metabolism of arsenic(III) in human hepatocytes - Incorporation of cellular antioxidant response and threshold-dependent behavior.

BACKGROUND: Arsenic is an environmental pollutant, potent human toxicant, and oxidative stress agent with a multiplicity of health effects associated with both acute and chronic exposures. A semi-mechanistic cellular-level toxicokinetic (TK) model was developed in order to describe the uptake, biotransformation and clearance of arsenical species in human hepatocytes. Notable features of this model are the incorporation of arsenic-glutathione complex formation and a "switch-like" formulation to describe the antioxidant response of hepatocytes to arsenic exposure. RESULTS: The cellular-level TK model applies mass action kinetics in order to predict the concentrations of trivalent and pentavalent arsenicals in hepatocytes. The model simulates uptake of arsenite (iAsIII) via aquaporin isozymes 9 (AQP9s), glutathione (GSH) conjugation, methylation by arsenic methyltransferase (AS3MT), efflux through multidrug resistant proteins (MRPs) and the induced antioxidant response via thioredoxin reductase (TR) activity. The model was parameterized by optimization of model estimates for arsenite (iAsIII), monomethylated (MMA) and dimethylated (DMA) arsenicals concentrations with time-course experimental data in human hepatocytes for a time span of 48 hours, and dose-response data at 24 hours for a range of arsenite concentrations from 0.1 to 10 µM. Global sensitivity analysis of the model showed that at low doses the transport parameters had a dominant role, whereas at higher doses the biotransformation parameters were the most significant. A parametric comparison of the TK model with an analogous model developed for rat hepatocytes from the literature demonstrated that the biotransformation of arsenite (e.g. GSH conjugation) has a large role in explaining the variation in methylation between rats and humans. CONCLUSIONS: The cellular-level TK model captures the temporal modes of arsenical accumulation in human hepatocytes. It highlighted the key biological processes that influence arsenic metabolism by explicitly modelling the metabolic network of GSH-adducts formation. The parametric comparison with the TK model developed for rats suggests that the variability in GSH conjugation could have an important role in inter-species variability of arsenical methylation. The TK model can be incorporated into larger-scale physiologically based toxicokinetic (PBTK) models of arsenic for improving the estimates of PBTK model parameters.

A generalized physiologically-based toxicokinetic modeling system for chemical mixtures containing metals.

BACKGROUND: Humans are routinely and concurrently exposed to multiple toxic chemicals, including various metals and organics, often at levels that can cause adverse and potentially synergistic effects. However, toxicokinetic modeling studies of exposures to these chemicals are typically performed on a single chemical basis. Furthermore, the attributes of available models for individual chemicals are commonly estimated specifically for the compound studied. As a result, the available models usually have parameters and even structures that are not consistent or compatible across the range of chemicals of concern. This fact precludes the systematic consideration of synergistic effects, and may also lead to inconsistencies in calculations of co-occurring exposures and corresponding risks. There is a need, therefore, for a consistent modeling framework that would allow the systematic study of cumulative risks from complex mixtures of contaminants. METHODS: A Generalized Toxicokinetic Modeling system for Mixtures (GTMM) was developed and evaluated with case studies. The GTMM is physiologically-based and uses a consistent, chemical-independent physiological description for integrating widely varying toxicokinetic models. It is modular and can be directly "mapped" to individual toxicokinetic models, while maintaining physiological consistency across different chemicals. Interaction effects of complex mixtures can be directly incorporated into the GTMM. CONCLUSIONS: The application of GTMM to different individual metals and metal compounds showed that it explains available observational data as well as replicates the results from models that have been optimized for individual chemicals. The GTMM also made it feasible to model toxicokinetics of complex, interacting mixtures of multiple metals and nonmetals in humans, based on available literature information. The GTMM provides a central component in the development of a "source-to-dose-to-effect" framework for modeling population health risks from environmental contaminants. As new data become available on interactions of multiple chemicals, the GTMM can be iteratively parameterized to improve mechanistic understanding of human health risks from exposures to complex mixtures of chemicals.

A unified multiscale field/network/agent based modeling framework for human and ecological health risk analysis.

A conceptual framework is presented for multi-scale field/network/agent-based modeling to support human and ecological health risk assessments. This framework is based on the representation of environmental dynamics in terms of interacting networks, agents that move across different networks, fields representing spatiotemporal distributions of physical properties, rules governing constraints and interactions, and actors that make decisions affecting the state of the system. Different deterministic and stochastic modeling case studies focusing on environmental exposures and associated risks are provided as examples, utilizing the bidirectional mapping between discrete, agent based approaches and continuous, equation based approaches. These examples include problems describing human health risk assessment, ecological risk assessment, and environmentally caused disease.

Using national and local extant data to characterize environmental exposures in the national children's study: Queens County, New York.

OBJECTIVE: The National Children's Study is a long-term epidemiologic study of 100,000 children from 105 locations across the United States. It will require information on a large number of environmental variables to address its core hypotheses. The resources available to collect actual home and personal exposure samples are limited, with most of the home sampling completed on periodic visits and the personal sampling generally limited to biomonitoring. To fill major data gaps, extant data will be required for each study location. The Queens Vanguard Center has examined the extent of those needs and the types of data that are generally and possibly locally available. DATA: In this review we identify three levels of data--national, state and county--and local data and information sets (levels 1-3, respectively), each with different degrees of availability and completeness, that can be used as a starting point for the extant data collection in each study location over time. We present an example on the use of this tiered approach, to tailor the data needs for Queens County and to provide general guidance for application to other NCS locations. CONCLUSIONS: Preexisting and continually evolving databases are available for use in the NCS to characterize exposure. The three levels of data we identified will be used to test a method for developing exposure indices for segments and homes during the pilot phase of NCS, as outlined in this article.

Lack of association between estimated World Trade Center plume intensity and respiratory symptoms among New York City residents outside of Lower Manhattan.

Researchers have reported adverse health effects among rescue/recovery workers and people living near the World Trade Center on September 11, 2001. The authors investigated the occurrence of respiratory symptoms among persons living outside of Lower Manhattan in areas affected by the World Trade Center particulate matter plume. Using a novel atmospheric dispersion model, they estimated relative cumulative plume intensity in areas surrounding the World Trade Center site over a 5-day period following the collapse of the buildings. Using data from a telephone survey of residents (n = 2,755) conducted approximately 6 months after the event, the authors evaluated associations between the estimated plume intensities at individual residence locations and self-reported respiratory symptoms among nonasthmatics, as well as symptoms and nonroutine care among asthmatics. Comparing persons at or above the 75th percentile of cumulative plume intensity with those below it, there was no statistically significant difference in self-reported new-onset wheezing/cough after September 11 (16.1% vs. 13.3%; adjusted odds ratio = 1.0, 95% confidence interval: 0.7, 1.7) and no worsening of asthma from before September 11 to the 4 weeks prior to the survey (13.9% vs. 16.6%; odds ratio = 1.0, 95% confidence interval: 0.3, 2.8). These results suggest that the plume was not strongly associated with respiratory symptoms outside of Lower Manhattan, within the limitations of this retrospective study.

Differential gene expression profiling of mouse skin after sulfur mustard exposure: Extended time response and inhibitor effect.

Sulfur mustard (HD, SM), is a chemical warfare agent that within hours causes extensive blistering at the dermal-epidermal junction of skin. To better understand the progression of SM-induced blistering, gene expression profiling for mouse skin was performed after a single high dose of SM exposure. Punch biopsies of mouse ears were collected at both early and late time periods following SM exposure (previous studies only considered early time periods). The biopsies were examined for pathological disturbances and the samples further assayed for gene expression profiling using the Affymetrix microarray analysis system. Principal component analysis and hierarchical cluster analysis of the differently expressed genes, performed with ArrayTrack showed clear separation of the various groups. Pathway analysis employing the KEGG library and Ingenuity Pathway Analysis (IPA) indicated that cytokine-cytokine receptor interaction, cell adhesion molecules (CAMs), and hematopoietic cell lineage are common pathways affected at different time points. Gene ontology analysis identified the most significantly altered biological processes as the immune response, inflammatory response, and chemotaxis; these findings are consistent with other reported results for shorter time periods. Selected genes were chosen for RT-PCR verification and showed correlations in the general trends for the microarrays. Interleukin 1 beta was checked for biological analysis to confirm the presence of protein correlated to the corresponding microarray data. The impact of a matrix metalloproteinase inhibitor, MMP-2/MMP-9 inhibitor I, against SM exposure was assessed. These results can help in understanding the molecular mechanism of SM-induced blistering, as well as to test the efficacy of different inhibitors.

Reconstructing population exposures to environmental chemicals from biomarkers: challenges and opportunities.

A conceptual/computational framework for exposure reconstruction from biomarker data combined with auxiliary exposure-related data is presented, evaluated with example applications, and examined in the context of future needs and opportunities. This framework employs physiologically based toxicokinetic (PBTK) modeling in conjunction with numerical "inversion" techniques. To quantify the value of different types of exposure data "accompanying" biomarker data, a study was conducted focusing on reconstructing exposures to chlorpyrifos, from measurements of its metabolite levels in urine. The study employed biomarker data as well as supporting exposure-related information from the National Human Exposure Assessment Survey (NHEXAS), Maryland, while the MENTOR-3P system (Modeling ENvironment for TOtal Risk with Physiologically based Pharmacokinetic modeling for Populations) was used for PBTK modeling. Recently proposed, simple numerical reconstruction methods were applied in this study, in conjunction with PBTK models. Two types of reconstructions were studied using (a) just the available biomarker and supporting exposure data and (b) synthetic data developed via augmenting available observations. Reconstruction using only available data resulted in a wide range of variation in estimated exposures. Reconstruction using synthetic data facilitated evaluation of numerical inversion methods and characterization of the value of additional information, such as study-specific data that can be collected in conjunction with the biomarker data. Although the NHEXAS data set provides a significant amount of supporting exposure-related information, especially when compared to national studies such as the National Health and Nutrition Examination Survey (NHANES), this information is still not adequate for detailed reconstruction of exposures under several conditions, as demonstrated here. The analysis presented here provides a starting point for introducing improved designs for future biomonitoring studies, from the perspective of exposure reconstruction; identifies specific limitations in existing exposure reconstruction methods that can be applied to population biomarker data; and suggests potential approaches for addressing exposure reconstruction from such data.