A biologically based model to quantitatively assess the role of the nuclear receptors liver X (LXR), and pregnane X (PXR) on chemically induced hepatic steatosis.

Hepatic steatosis is characterized by the intracellular increase of free fatty acids (FFAs) in the form of triglycerides in hepatocytes. This hepatic adverse outcome can be caused by many factors, including exposure to drugs or environmental toxicants. Mechanistically, accumulation of lipids in the liver can take place via several mechanisms such as de novo synthesis and/or uptake of FFAs from serum via high fat content diets. De novo synthesis of FFAs within the liver is mediated by the liver X receptor (LXR), and their uptake into the liver is mediated through the pregnane X receptor (PXR). We investigated the impact of chemical exposure on FFAs hepatic content via activation of LXR and PXR by integrating chemical-specific physiologically based pharmacokinetic (PBPK) models with a quantitative toxicology systems (QTS) model of hepatic lipid homeostasis. Three known agonists of LXR and/or PXR were modeled: T0901317 (antagonist for both receptors), GW3965 (LXR only), and Rifampicin (PXR only). Model predictions showed that T0901317 caused the most FFAs build-up in the liver, followed by Rifampicin and then GW3965. These modeling results highlight the importance of PXR activation for serum FFAs uptake into the liver while suggesting that increased hepatic FAAs de novo synthesis alone may not be enough to cause appreciable accumulation of lipids in the liver under normal environmental exposure levels. Moreover, the overall PBPK-hepatic lipids quantitative model can be used to screen chemicals for their potential to cause in vivo hepatic lipid content buildup in view of their in vitro potential to activate the nuclear receptors and their exposure levels.

Evaluation of a Physiologically Based Pharmacokinetic (PBPK) Model for Inorganic Arsenic Exposure Using Data from Two Diverse Human Populations.

BACKGROUND: Multiple epidemiological studies exist for some of the well-studied health endpoints associated with inorganic arsenic (iAs) exposure; however, results are usually expressed in terms of different exposure/dose metrics. Physiologically based pharmacokinetic (PBPK) models may be used to obtain a common exposure metric for application in dose-response meta-analysis. OBJECTIVE: A previously published PBPK model for inorganic arsenic (iAs) was evaluated using data sets for arsenic-exposed populations from Bangladesh and the United States. METHODS: The first data set was provided by the Health Effects of Arsenic Longitudinal Study cohort in Bangladesh. The second data set was provided by a study conducted in Churchill County, Nevada, USA. The PBPK model consisted of submodels describing the absorption, distribution, metabolism and excretion (ADME) of iAs and its metabolites monomethylarsenic (MMA) and dimethylarsenic (DMA) acids. The model was used to estimate total arsenic levels in urine in response to oral ingestion of iAs. To compare predictions of the PBPK model against observations, urinary arsenic concentration and creatinine-adjusted urinary arsenic concentration were simulated. As part of the evaluation, both water and dietary intakes of arsenic were estimated and used to generate the associated urine concentrations of the chemical in exposed populations. RESULTS: When arsenic intake from water alone was considered, the results of the PBPK model underpredicted urinary arsenic concentrations for individuals with low levels of arsenic in drinking water and slightly overpredicted urinary arsenic concentrations in individuals with higher levels of arsenic in drinking water. When population-specific estimates of dietary intakes of iAs were included in exposures, the predictive value of the PBPK model was markedly improved, particularly at lower levels of arsenic intake. CONCLUSIONS: Evaluations of this PBPK model illustrate its adequacy and usefulness for oral exposure reconstructions in human health risk assessment, particularly in individuals who are exposed to relatively low levels of arsenic in water or food. https://doi.org/10.1289/EHP3096.

Improving the risk assessment of lipophilic persistent environmental chemicals in breast milk.

Lipophilic persistent environmental chemicals (LPECs) have the potential to accumulate within a woman's body lipids over the course of many years prior to pregnancy, to partition into human milk, and to transfer to infants upon breastfeeding. As a result of this accumulation and partitioning, a breastfeeding infant's intake of these LPECs may be much greater than his/her mother's average daily exposure. Because the developmental period sets the stage for lifelong health, it is important to be able to accurately assess chemical exposures in early life. In many cases, current human health risk assessment methods do not account for differences between maternal and infant exposures to LPECs or for lifestage-specific effects of exposure to these chemicals. Because of their persistence and accumulation in body lipids and partitioning into breast milk, LPECs present unique challenges for each component of the human health risk assessment process, including hazard identification, dose-response assessment, and exposure assessment. Specific biological modeling approaches are available to support both dose-response and exposure assessment for lactational exposures to LPECs. Yet, lack of data limits the application of these approaches. The goal of this review is to outline the available approaches and to identify key issues that, if addressed, could improve efforts to apply these approaches to risk assessment of lactational exposure to these chemicals.

Physiologically based pharmacokinetic model use in risk assessment--Why being published is not enough.

A panel of experts in physiologically based pharmacokinetic (PBPK) modeling and relevant quantitative methods was convened to describe and discuss model evaluation criteria, issues, and choices that arise in model application and computational tools for improving model quality for use in human health risk assessments (HHRAs). Although publication of a PBPK model in a peer-reviewed journal is a mark of good science, subsequent evaluation of published models and the supporting computer code is necessary for their consideration for use in HHRAs. Standardized model evaluation criteria and a thorough and efficient review process can reduce the number of review and revision iterations and hence the time needed to prepare a model for application. Efficient and consistent review also allows for rapid identification of needed model modifications to address HHRA-specific issues. This manuscript reports on the workshop where a process and criteria that were created for PBPK model review were discussed along with other issues related to model review and application in HHRA. Other issues include (1) model code availability, portability, and validity; (2) probabilistic (e.g., population-based) PBPK models and critical choices in parameter values to fully characterize population variability; and (3) approaches to integrating PBPK model outputs with other HHRA tools, including benchmark dose modeling. Two specific case study examples are provided to illustrate challenges that were encountered during the review and application process. By considering the frequent challenges encountered in the review and application of PBPK models during the model development phase, scientists may be better able to prepare their models for use in HHRAs.

Employing a Mechanistic Model for the MAPK Pathway to Examine the Impact of Cellular all or None Behavior on Overall Tissue Response.

The mitogen activated protein kinase (MAPK) cascade is a three-tiered phosphorylation cascade that is ubiquitously expressed among eukaryotic cells. Its primary function is to propagate signals from cell surface receptors to various cytosolic and nuclear targets. Recent studies have demonstrated that the MAPK cascade exhibits an all-or-none response to graded stimuli. This study quantitatively investigates MAPK activation in Xenopus oocytes using both empirical and biologically-based mechanistic models. Empirical models can represent overall tissue MAPK activation in the oocytes. However, these models lack description of key biological processes and therefore give no insight into whether the cellular response occurs in a graded or all-or-none fashion. To examine the propagation of cellular MAPK all-or-none activation to overall tissue response, mechanistic models in conjunction with Monte Carlo simulations are employed. An adequate description of the dose response relationship of MAPK activation in Xenopus oocytes is achieved. Furthermore, application of these mechanistic models revealed that the initial receptor-ligand binding rate contributes to the cells' ability to exhibit an all-or-none MAPK activation response, while downstream activation parameters contribute more to the magnitude of activation. These mechanistic models enable us to identify key biological events which quantitatively impact the shape of the dose response curve, especially at low environmentally relevant doses.

Development of a quantitative model of pregnane X receptor (PXR) mediated xenobiotic metabolizing enzyme induction.

The pregnane X receptor plays an integral role in the regulation of hepatic metabolism. It has been shown to regulate CYP3A4, which is the most abundant cytochrome P450 in the human liver. With its large and flexible ligand-binding domain, PXR can be activated by an enormous range of relatively small, hydrophobic, exogenous compounds. Upon activation, PXR partners with the retinoid X receptor (RXR) to form a heterodimer. The newly formed heterodimer binds to an appropriate DNA response element, causing increased transcription. This leads to an induction in the level of CYP3A4. These mechanistic steps are included into a biologically-based mathematical model. The quantitative model predicts fold level inductions of CYP3A4 mRNA and protein in response to PXR activation. Model parameter values have been taken from literature when appropriate. Unknown parameter values are estimated by optimizing the model results to published in vivo and in vitro data sets. A sensitivity analysis is performed to evaluate the model structure and identify future data needs which would be critical to revising the model.

Development of a quantitative model incorporating key events in a hepatotoxic mode of action to predict tumor incidence.

Biologically based dose-response (BBDR) modeling of environmental pollutants can be utilized to inform the mode of action (MOA) by which compounds elicit adverse health effects. Chemicals that produce tumors are typically labeled as either genotoxic or nongenotoxic. Though both the genotoxic and the nongenotoxic MOA may be operative as a function of dose, it is important to note that the label informs but does not define a MOA. One commonly proposed MOA for nongenotoxic carcinogens is characterized by the key events cytotoxicity and regenerative proliferation. The increased division rate associated with such proliferation can cause an increase in the probability of mutations, which may result in tumor formation. We included these steps in a generalized computational pharmacodynamic (PD) model incorporating cytotoxicity as a MOA for three carcinogens (chloroform, CHCl(3); carbon tetrachloride, CCL(4); and N,N-dimethylformamide, DMF). For each compound, the BBDR model is composed of a chemical-specific physiologically based pharmacokinetic model linked to a PD model of cytotoxicity and cellular proliferation. The rate of proliferation is then linked to a clonal growth model to predict tumor incidences. Comparisons of the BBDR simulations and parameterizations across chemicals suggested that significant variation among the models for the three chemicals arises in a few parameters expected to be chemical specific (such as metabolism and cellular injury rate constants). Optimization of model parameters to tumor data for CCL(4) and DMF resulted in similar estimates for all parameters related to cytotoxicity and tumor incidences. However, optimization of the CHCl(3) data resulted in a higher estimate for one parameter (BD) related to death of initiated cells. This implies that additional steps beyond cytotoxicity leading to induced cellular proliferation can be quantitatively different among chemicals that share cytotoxicity as a hypothesized carcinogenic MOA.

Development of an inhalation physiologically based pharmacokinetic (PBPK) model for 2,2, 4-trimethylpentane (TMP) in male Long-Evans rats using gas uptake experiments.

2,2,4-Trimethylpentane (TMP) is a volatile colorless liquid used primarily to increase the octane rating of combustible fuels. TMP is released in the environment through the manufacture, use, and disposal of products associated with the gasoline and petroleum industry. Short-term inhalation exposure to TMP (< 4 h; > 1000 ppm) caused sensory and motor irritations in rats and mice. Like many volatile hydrocarbons, acute exposure to TMP may also be expected to alter neurological functions. To estimate in vivo metabolic kinetics of TMP and to predict its target tissue dosimetry during inhalation exposures, a physiologically based pharmacokinetic (PBPK) model was developed for the chemical in Long-Evans male rats using closed-chamber gas-uptake experiments. Gas-uptake experiments were conducted in which rats (80-90 days old) were exposed to targeted initial TMP concentrations of 50, 100, 500, and 1000 ppm. The model consisted of compartments for the closed uptake chamber, lung, fat, kidney, liver, brain, and rapidly and slowly perfused tissues. Physiological parameters were obtained from literature. Partition coefficients for the model were experimentally determined for air/blood, fat, liver, kidney, muscle, and brain using vial equilibration methods. Common to other hydrocarbons, metabolism of TMP via oxidative reactions is assumed to mainly occur in the liver. The PBPK model simulations of the closed chamber data were used to estimate in vivo metabolic parameters for TMP in male Long-Evans rats.

A physiologically based pharmacokinetic model for intravenous and ingested dimethylarsinic acid in mice.

A physiologically based pharmacokinetic (PBPK) model for the organoarsenical dimethylarsinic acid (DMA(V)) was developed in mice. The model was calibrated using tissue time course data from multiple tissues in mice administered DMA(V) intravenously. The final model structure was based on diffusion limitation kinetics. In general, PBPK models use the assumption of blood flow-limited transport into tissues. This assumption has historically worked for small lipophilic organic solvents. However, the conditions under which flow-limited kinetics occurs and how to distinguish when flow-limited versus diffusion-limited transport is more appropriate, have been rarely evaluated. One important goal of this modeling effort was to systematically evaluate descriptions of flow-limited compared with diffusion-limited tissue distribution for DMA(V), using the relatively extensive pharmacokinetic data available in mice. The diffusion-limited model consistently provided an improved fit over flow-limited simulations when compared with tissue time course iv experimental data. After model calibration, an independent data set obtained by oral gavage of DMA(V), was used to further test model structure. Sensitivity analysis of the two PBPK model structures showed the importance of early time course data collection, and the impact of diffusion for kidney time course data description. In summary, this modeling effort suggests the importance of availability of organ specific time course data sets necessary for the discernment of PBPK modeling structure, motivated by knowledge of biology, and providing necessary feedback between experimental design and biological modelers.

Development of a human physiologically based pharmacokinetic (PBPK) model for inorganic arsenic and its mono- and di-methylated metabolites.

A physiologically-based pharmacokinetic (PBPK) model was developed to estimate levels of arsenic and its metabolites in human tissues and urine after oral exposure to arsenate (As(V)), arsenite (As(III)) or organoarsenical pesticides. The model consists of interconnected individual PBPK models for inorganic arsenic (As(V) and As(III)), monomethylarsenic acid (MMA(V)), and, dimethylarsenic acid (DMA(V)). Reduction of MMA(V) and DMA(V) to their respective trivalent forms also occurs in the lung, liver, and kidney including excretion in urine. Each submodel was constructed using flow limited compartments describing the mass balance of the chemicals in GI tract (lumen and tissue), lung, liver, kidney, muscle, skin, heart, and brain. The choice of tissues was based on physiochemical properties of the arsenicals (solubility), exposure routes, target tissues, and sites for metabolism. Metabolism of inorganic arsenic in liver was described as a series of reduction and oxidative methylation steps incorporating the inhibitory influence of metabolites on methylation. The inhibitory effects of As(III) on the methylation of MMA(III) to DMA, and MMA(III) on the methylation of As(III) to MMA were modeled as noncompetitive. To avoid the uncertainty inherent in estimation of many parameters from limited human data, a priori independent parameter estimates were derived using data from diverse experimental systems with priority given to data derived using human cells and tissues. This allowed the limited data for human excretion of arsenicals in urine to be used to estimate only parameters that were most sensitive to this type of data. Recently published urinary excretion data, not previously used in model development, are also used to evaluate model predictions.

Research toward the development of a biologically based dose response assessment for inorganic arsenic carcinogenicity: a progress report.

Cancer risk assessments for inorganic arsenic have been based on human epidemiological data, assuming a linear dose response below the range of observation of tumors. Part of the reason for the continued use of the linear approach in arsenic risk assessments is the lack of an adequate biologically based dose response (BBDR) model that could provide a quantitative basis for an alternative nonlinear approach. This paper describes elements of an ongoing collaborative research effort between the CIIT Centers for Health Research, the U.S. Environmental Protection Agency, ENVIRON International, and EPRI to develop BBDR modeling approaches that could be used to inform a nonlinear cancer dose response assessment for inorganic arsenic. These efforts are focused on: (1) the refinement of physiologically based pharmacokinetic (PBPK) models of the kinetics of inorganic arsenic and its metabolites in the mouse and human; (2) the investigation of mathematical solutions for multi-stage cancer models involving multiple pathways of cell transformation; (3) the review and evaluation of the literature on the dose response for the genomic effects of arsenic; and (4) the collection of data on the dose response for genomic changes in the urinary bladder (a human target tissue for arsenic carcinogenesis) associated with in vivo drinking water exposures in the mouse as well as in vitro exposures of both mouse and human cells. An approach is proposed for conducting a biologically based margin of exposure risk assessment for inorganic arsenic using the in vitro dose response for the expression of genes associated with the obligatory precursor events for arsenic tumorigenesis.

Experimental and mathematical modeling methods for the investigation of toxicological interactions.

While procedures have been developed and used for many years to assess risk and determine acceptable exposure levels to individual chemicals, most cases of environmental contamination can result in concurrent or sequential exposure to more than one chemical. Toxicological predictions of such combinations must be based on an understanding of the mechanisms of action and interaction of the components of the mixtures. Statistical and experimental methods test the existence of toxicological interactions in a mixture. However, these methods are limited to experimental data ranges for which they are derived, in addition to limitations caused by response differences from experimental animals to humans. Empirical methods such as isobolograms, median-effect principle and response surface methodology (RSM) are based on statistical experimental design and regression of data. For that reason, the predicted response surfaces can be used for extrapolation across dose regions where interaction mechanisms are not anticipated to change. In general, using these methods for predictions can be problematic without including biologically based mechanistic descriptions that can account for dose and species differences. Mechanistically based models, such as physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, include explicit descriptions of interaction mechanisms which are related to target tissues levels. These models include dose-dependent mechanistic hypotheses of toxicological interactions which can be tested by model-directed experimental design and used to identify dose regions where interactions are not significant.

Predicting blood:air partition coefficients using theoretical molecular descriptors.

Three regression methods, namely ridge regression (RR), partial least squares (PLS), and principal components regression (PCR), were used to develop models for the prediction of rat blood:air partition coefficient for increasingly diverse data sets. Initially, modeling was performed for a set of 13 chlorocarbons. To this set, 10 additional hydrophobic compounds were added, including aromatic and non-aromatic hydrocarbons. A set of 16 hydrophilic compounds was also modeled separately. Finally, all 39 compounds were combined into one data set for which comprehensive models were developed. A large set of diverse, theoretical molecular descriptors was calculated for use in the current study. The topostructural (TS), topochemical (TC), and geometrical or 3-dimensional (3D) indices were used hierarchically in model development. In addition, single-class models were developed using the TS, TC, and 3D descriptors. In most cases, RR outperformed PLS and PCR, and the models developed using TC indices were superior to those developed using other classes of descriptors.

Application of physiologically-based pharmacokinetic modeling to investigate the toxicological interaction between chlorpyrifos and parathion in the rat.

Environmental exposure is usually due to the presence of multiple chemicals. In most cases, these chemicals interact with each other at both pharmacokinetic and pharmacodynamic toxicity mechanisms. In the absence of data, joint toxicity assessment of a mixture is based on default dose or response additivity. Although, the concept of additivity is mostly accepted at low dose levels, these levels need to be determined quantitatively to validate the use of additivity as an absence of any possible synergistic or antagonistic interactions at low environmental exposure levels. The doses at which interaction becomes significant define the interaction threshold. In most cases, estimation of these low-dose interaction thresholds experimentally is economically costly and challenging because of the need to use a large number of laboratory animals. Computational toxicology methods provide a feasible alternative to establish interaction thresholds. For example, a physiologically based pharmacokinetic (PBPK) model was developed to estimate an interaction threshold for the joint toxicity between chlorpyrifos and parathion in the rat. Initially, PBPK models were developed for each chemical to estimate the blood concentrations of their respective metabolite. The metabolite concentrations in blood out-put was then linked to acetylcholinesterase kinetics submodel. The resulting overall PBPK model described interactions between these pesticides at two levels in the organism: (a) the P450 enzymatic bioactivation site, and (b) acetylcholinesterase binding sites. Using the overall model, a response surface was constructed at various dose levels of each chemical to investigate the mechanism of interaction and to calculate interaction threshold doses. The overall model simulations indicated that additivity is obtained at oral dose levels below 0.08mg/kg of each chemical. At higher doses, antagonism by enzymatic competitive inhibition is the mode of interaction.

Chemical mixture toxicology: from descriptive to mechanistic, and going on to in silico toxicology.

Because of the pioneering vision of certain leaders in the biomedical field, the last two decades witnessed rapid advances in the area of chemical mixture toxicology. Earlier studies utilized conventional toxicology protocol and methods, and they were mainly descriptive in nature. Two good examples might be the parallel series of studies conducted by the U.S. National Toxicology Program and TNO in The Netherlands, respectively. As a natural course of progression, more and more sophistication was incorporated into the toxicology studies of chemical mixtures. Thus, at least the following seven areas of scientific achievements in chemical mixture toxicology are evident in the literature: (a) the application of better and more robust statistical methods; (b) the exploration and incorporation of mechanistic bases for toxicological interactions; (c) the application of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling; (d) the studies on more complex chemical mixtures; (e) the use of science-based risk assessment approaches; (f) the utilization of functional genomics; and (g) the application of technology. Examples are given for the discussion of each of these areas. Two important concepts emerged from these studies and they are: (1) dose-dependent toxicologic interactions; and (2) "interaction thresholds". Looking into the future, one of the most challenging areas in chemical mixture research is finding the answer to the question "when one tries to characterize the health effects of chemical mixtures, how does one deal with the infinite number of combination of chemicals, and other possible stressors?" Undoubtedly, there will be many answers from different groups of researchers. Our answer, however, is first to focus on the finite (biological processes) rather than the infinite (combinations of chemical mixtures and multiple stressors). The idea is that once we know a normal biological process(es), all stimuli and insults from external stressors are merely perturbations of the normal biological process(es). The next step is to "capture" the biological process(es) by integrating the recent advances in computational technology and modern biology. Here, the computer-assisted Reaction Network Modeling, linked with PBPK modeling, offers a ray of hope to dealing with the complex biological systems.

Prediction of human blood: air partition coefficient: a comparison of structure-based and property-based methods.

In recent years, there has been increased interest in the development and use of quantitative structure-activity/property relationship (QSAR/QSPR) models. For the most part, this is due to the fact that experimental data is sparse and obtaining such data is costly, while theoretical structural descriptors can be obtained quickly and inexpensively. In this study, three linear regression methods, viz. principal component regression (PCR), partial least squares (PLS), and ridge regression (RR), were used to develop QSPR models for the estimation of human blood:air partition coefficient (logPblood:air) for a group of 31 diverse low-molecular weight volatile chemicals from their computed molecular descriptors. In general, RR was found to be superior to PCR or PLS. Comparisons were made between models developed using parameters based solely on molecular structure and linear regression (LR) models developed using experimental properties, including saline:air partition coefficient (logPsaline:air) and olive oil:air partition coefficient (logPolive oil:air), as independent variables, indicating that the structure-property correlations are comparable to the property-property correlations. The best models, however, were those that used rat logPblood:air as the independent variable. Haloalkane subgroups were modeled separately for comparative purposes and, although models based on the congeneric compounds were superior, the models developed on the complete set of diverse compounds were of acceptable quality. The structural descriptors were placed into one of three classes based on level of complexity: topostructural (TS), topochemical (TC), or three-dimensional/geometrical (3D). Modeling was performed using the structural descriptor classes both in a hierarchical fashion and separately. The results indicate that highest quality structure-based models, in terms of descriptor classes, were those derived using TC descriptors.

Cytochrome P450 2E1 (CYP2E1) is the principal enzyme responsible for urethane metabolism: comparative studies using CYP2E1-null and wild-type mice.

Urethane ([carbonyl-(14)C]ethyl carbamate) is a fermentation by-product in alcoholic beverages and foods and is classified as reasonably anticipated to be a human carcinogen. Early studies indicated that while CYP2E1 is involved, esterases are the primary enzymes responsible for urethane metabolism. Using CYP2E1-null (KO) mice, current studies were undertaken to elucidate CYP2E1's contribution to urethane metabolism. [Carbonyl-(14)C]urethane was administered by gavage to male CYP2E1-null and wild-type mice at 10 or 100 mg/kg and its metabolism and disposition were investigated. CO(2) was confirmed as the main metabolite of urethane. Significant inhibition of urethane metabolism to CO(2) occurred in CYP2E1-null versus wild-type mice. Pharmacokinetic modeling of (14)CO(2) exhalation data revealed that CYP2E1 is responsible for approximately 96% of urethane metabolism to CO(2) in wild-type mice. The contributions of other enzymes to urethane metabolism merely account for the remaining 4%. The half-life of urethane in wild-type and CYP2E1-null mice was estimated at 0.8 and 22 h, respectively. Additionally, the concentration of urethane-derived radioactivity in blood and tissues was dose-dependent and significantly higher in CYP2E1-null mice. High-performance liquid chromatography analysis showed only urethane in the plasma and liver extracts of CYP2E1-null mice. Because the lack of CYP2E1 did not completely inhibit urethane metabolism, the disposition of 10 mg/kg urethane was compared in mice pretreated with the P450 inhibitor, 1-aminobenzotriazole or the esterase inhibitor, paraoxon. Unlike paraoxon, 1-aminobenzotriazole resulted in significant inhibition of urethane metabolism to CO(2) in both genotypes. In conclusion, this work demonstrated that CYP2E1, not esterase, is the principal enzyme responsible for urethane metabolism.

Applications of computational toxicology methods at the Agency for Toxic Substances and Disease Registry.

In its efforts to provide consultations to state and local health departments, other federal agencies, health professionals, and the public on the health effects of environmental pollutants, the Agency for Toxic Substances and Disease Registry relies on the latest advances in computational toxicology. The computational toxicology laboratory at the agency is continually engaged in developing and applying models for decision-support tools such as physiologically based pharmacokinetic (PBPK) models, benchmark dose (BMD) models, and quantitative structure-activity relationship (QSAR) models. PBPK models are suitable for connecting exposure scenarios to biological indicators such as tissue dose or end point response. The models are used by the agency to identify the significance of exposure routes in producing tissue levels of possible contaminants for people living near hazardous waste sites. Additionally, PBPK models provide a credible scientific methodology for route-to-route extrapolations of health guidance values, which are usually determined from a very specific set of experiments. Also, scientists at the computational toxicology laboratory are using PBPK models for advancing toxicology research in such areas as joint toxicity assessment and child-based toxicity assessments. With BMD modeling, all the information embedded in an experimentally determined dose-response relationship is used to estimate, with minimum extrapolations, human health guidance values for environmental substances. Scientists in the laboratory also rely on QSAR models in the many cases where consultations from the agency are reported for chemicals that lack adequate experimental documentation.