Development and application of a rat PBPK model to elucidate kidney and liver effects induced by ETBE and tert-butanol.

Subchronic and chronic studies in rats of the gasoline oxygenates ethyl tert-butyl ether (ETBE) and tert-butanol (TBA) report similar noncancer kidney and liver effects but differing results with respect to kidney and liver tumors. Because TBA is a major metabolite of ETBE, it is possible that TBA is the active toxic moiety in all these studies, with reported differences due simply to differences in the internal dose. To test this hypothesis, a physiologically-based pharmacokinetic (PBPK) model was developed for ETBE and TBA to calculate internal dosimetrics of TBA following either TBA or ETBE exposure. This model, based on earlier PBPK models of methyl tert-butyl ether (MTBE), was used to evaluate whether kidney and liver effects are consistent across routes of exposure, as well as between ETBE and TBA studies, on the basis of estimated internal dose. The results demonstrate that noncancer kidney effects, including kidney weight changes, urothelial hyperplasia, and chronic progressive nephropathy (CPN), yielded consistent dose-response relationships across routes of exposure and across ETBE and TBA studies using TBA blood concentration as the dose metric. Relative liver weights were also consistent across studies on the basis of TBA metabolism, which is proportional to TBA liver concentrations. However, kidney and liver tumors were not consistent using any dose metric. These results support the hypothesis that TBA mediates the noncancer kidney and liver effects following ETBE administration; however, additional factors besides internal dose are necessary to explain the induction of liver and kidney tumors.

A tiered framework for risk-relevant characterization and ranking of chemical exposures: applications to the National Children's Study (NCS).

A challenge for large-scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co-occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure-relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new "Tiered Exposure Ranking" (TiER) framework, developed to support various aspects of risk-relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both "discovery-driven" and "hypothesis-driven" analyses. "Tier 1" applications focus on "exposomic" pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk-relevant exposure metrics for populations and individuals. In this article, "tier 1" applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A "tier 2" application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk-relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.

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.

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.

Collision coupling, crosstalk, and compartmentalization in G-protein coupled receptor systems: can a single model explain disparate results?

The collision coupling model describes interactions between receptors and G-proteins as first requiring the molecules to find each other by diffusion. A variety of experimental data on G-protein activation have been interpreted as suggesting (or not) the compartmentalization of receptors and/or G-proteins in addition to a collision coupling mechanism. In this work, we use a mathematical model of G-protein activation via collision coupling but without compartmentalization to demonstrate that these disparate observations do not imply the existence of such compartments. In experiments with GTP analogs (commonly GTPgammaS), the extent of G-protein activation is predicted to be a function of both receptor number and the rate of GTP analog hydrolysis. The sensitivity of G-protein activation to receptor number is shown to be dependent upon the assay used, with the sensitivity of phosphate production assays (GTPase) >GTPgammaS-binding assays >cAMP inhibition assays. Finally, the amount of competition or crosstalk between receptor species activating the same type of G-proteins is predicted to depend on receptor and G-protein number, but in some (common) experimental regimes this dependence is expected to be minimal. Taken together, these observations suggest that the collision coupling model, without compartments of receptors and/or G-proteins, is sufficient to explain a variety of observations in literature data.

Diffusion-limited reactions in G-protein activation: unexpected consequences of antagonist and agonist competition.

In this work, we ask whether the simultaneous movement of agonist and antagonist among surface receptors (i.e. continually associating and dissociating from individual receptors according to specified kinetics) has any unexpected consequences for G-protein activation and receptor desensitization. A Monte Carlo model framework is used to track the diffusion and reaction of individual receptors, allowing the requirement for receptors and G-proteins or receptors and kinases to find each other by diffusion (collision coupling) to be implemented explicitly. We find that at constant agonist occupancy the effect of an antagonist on both G-protein activation and the ratio of G-protein activation to receptor desensitization can be modulated by varying the antagonist dissociation kinetics. The explanation for this effect is that antagonist dissociation kinetics influence the ability of agonists to access particular receptors and thus reach G-proteins and kinases near those receptors. Relevant parameter ranges for observation of these effects are identified. These results are useful for understanding experimental and therapeutic situations when both agonist and antagonist are present, and in addition may offer new insights into insurmountable antagonism.

Integrin dimerization and ligand organization: key components in integrin clustering for cell adhesion.

Cell adhesion requires both integrin occupancy and integrin clustering. In this work, we investigate a mechanism based on organizing ligand into islands and integrin dimerization for the initiation of integrin clustering. To study integrin clustering and integrin occupancy we develop a two-dimensional Monte Carlo lattice description of the cell-substrate interface to simulate the diffusion and reaction of integrins. We demonstrate that integrin dimerization can drive integrins into clusters of sizes greater than two. Ligand organization or integrin dimerization alone is unable to increase the number of bound integrins, but when both are present they cooperate to increase both binding and clustering of integrins. In addition, when integrin dimerization and ligand organization are both present large integrin clusters, which may act as nucleation sites for the formation of adhesion complexes, are observed. These results describe a potential mechanism for the clustering of integrin receptors and avidity modulation in cellular adhesion and have implications for the designs of surfaces to control cell responses to external ligands and to manipulate cell adhesion for tissue-engineering applications.

Monte Carlo simulations of receptor dynamics: insights into cell signaling.

Many receptor-level processes involve the diffusion and reaction of receptors with other membrane-localized molecules. Monte Carlo simulation is a powerful technique that allows us to track the motions and discrete reactions of individual receptors, thus simulating receptor dynamics and the early events of signal transduction. In this paper, we discuss simulations of two receptor processes, receptor dimerization and G-protein activation. Our first set of simulations demonstrates how receptor dimerization can create clusters of receptors via partner switching and the relevance of this clustering for receptor cross-talk and integrin signaling. Our second set of simulations investigates the activation and desensitization of G-protein coupled receptors when either a single agonist or both an agonist and an antagonist are present. For G-protein coupled receptor systems in the presence of an agonist alone, the dissociation rate constant of agonist is predicted to affect the ratio of G-protein activation to receptor phosphorylation. Similarly, this ratio is affected by the antagonist dissociation rate constant when both agonist and antagonist are present. The relationship of simulation predictions to experimental findings and potential applications of our findings are also discussed.