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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 (iAs^III) 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 (iAs^III), 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.</p> <p>Conclusions</p> <p>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.</p

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 scaling and body movement on contaminant transport in airliner cabins&quot;, Atmospheric Environment 45(33

Abstract: Studies of contaminant transport have been conducted using small-scale models. This investigation used validated Computational Fluid Dynamics (CFD) to examine if a small-scale water model could reveal the same contaminant transport characteristics as a fullscale airliner cabin. But due to similarity problems and the difficulty of scaling the geometry, a perfect scale up from a small water model to an actual air model was found to be impossible. The study also found that the seats and passengers tended to obstruct the lateral transport of the contaminants and confine their spread to the aisle of the cabin. The movement of a crew member or a passenger could carry a contaminant in its wake to as many rows as the crew member or passenger passed. This could be the reason why a SARS infected passenger could infect fellow passengers who were seated seven rows away. To accurately simulate the contaminant transport, the shape of the moving body should be a human-like model

Modelling of aerosol processes in plumes

Summarization: A modelling platform for studying photochemical gaseous and aerosol phase processes from localized (e.g., point) sources has been presented. The current approach employs a reactive plume model which extends the regulatory model RPM-IV by incorporating aerosol processes and heterogeneous chemistry. The physics and chemistry of elemental carbon, organic carbon, sulfate, nitrate, ammonium material of aerosols are treated and attributed to the PM size distribution. A modified version of the carbon bond IV chemical mechanism is included to model the formation of organic aerosol. Aerosol dynamics modeled include mechanisms of nucleation, condensation, dry deposition and gas/particle partitioning of organic matter. The model is first applied to a number of case studies involving emissions from point sources and sulfate particle formation in plumes. Model calculations show that homogeneous nucleation is an efficient process for new particle formation in plumes, in agreement with previous field studies and theoretical predictions. In addition, the model is compared with field data from power plant plumes with satisfactory predictions against gaseous species and total sulphate mass measurements. Finally, the plume model is applied to study secondary organic matter formation due to various emission categories such as vehicles and the oil production sector.Presented on: Tellus

Physiologically-Based Toxicokinetic Modeling of Zearalenone and Its Metabolites: Application to the Jersey Girl Study

<div><p>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 <i>in vivo</i> 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 <i>in vivo</i> 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.</p></div