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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.