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A physiologically based pharmacokinetic model for trichloroethylene (TCE) in rodents and humans was calibrated with published toxicokinetic data sets. A Bayesian statistical framework was used to combine previous information about the model parameters with the data likelihood, to yield posterior parameter distributions. The use of the hierarchical statistical model yielded estimates of both variability between experimental groups and uncertainty in TCE toxicokinetics. After adjustment of the model by Markov chain Monte Carlo sampling, estimates of variability for the animal or human metabolic parameters ranged from a factor of 1.5-2 (geometric standard deviation [GSD]). Uncertainty was of the same order as variability for animals and higher than variability for humans. The model was used to make posterior predictions for several measures of cancer risk. These predictions were affected by both uncertainties and variability and exhibited GSDs ranging from 2 to 6 in mice and rats and from 2 to 10 for humans.

Two physiologically based pharmacokinetic models for trichloroethylene (TCE) in mice and humans were calibrated with new toxicokinetic data sets. Calibration is an important step in model development, essential to a legitimate use of models for research or regulatory purposes. A Bayesian statistical framework was used to combine prior information about the model parameters with the data likelihood to yield posterior parameter distributions. For mice, these distributions represent uncertainty. For humans, the use of a population statistical model yielded estimates of both variability and uncertainty in human toxicokinetics of TCE. After adjustment of the models by Markov chain Monte Carlo sampling, the mouse model agreed with a large part of the data. Yet, some data on secondary metabolites were not fit well. The posterior parameter distributions obtained for mice were quite narrow (coefficient of variation [CV] of about 10 or 20%), but these CVs might be underestimated because of the incomplete fit of the model. The data fit, for humans, was better than for mice. Yet, some improvement of the model is needed to correctly describe trichloroethanol concentrations over long time periods. Posterior uncertainties about the population means corresponded to 10-20% CV. In terms of human population variability, volumes and flows varied across subject by approximately 20% CV. The variability was somewhat higher for partition coefficients (between 30 and 40%) and much higher for the metabolic parameters (standard deviations representing about a factor of 2). Finally, the analysis points to differences between human males and females in the toxicokinetics of TCE. The significance of these differences in terms of risk remains to be investigated.

A physiologically based pharmacokinetic (PBPK) model was developed that provides a comprehensive description of the kinetics of trichloroethylene (TCE) and its metabolites, trichloroethanol (TCOH), trichloroacetic acid (TCA), and dichloroacetic acid (DCA), in the mouse, rat, and human for both oral and inhalation exposure. The model includes descriptions of the three principal target tissues for cancer identified in animal bioassays: liver, lung, and kidney. Cancer dose metrics provided in the model include the area under the concentration curve (AUC) for TCA and DCA in the plasma, the peak concentration and AUC for chloral in the tracheobronchial region of the lung, and the production of a thioacetylating intermediate from dichlorovinylcysteine in the kidney. Additional dose metrics provided for noncancer risk assessment include the peak concentrations and AUCs for TCE and TCOH in the blood, as well as the total metabolism of TCE divided by the body weight. Sensitivity and uncertainty analyses were performed on the model to evaluate its suitability for use in a pharmacokinetic risk assessment for TCE. Model predictions of TCE, TCA, DCA, and TCOH concentrations in rodents and humans are in good agreement with a variety of experimental data, suggesting that the model should provide a useful basis for evaluating cross-species differences in pharmacokinetics for these chemicals. In the case of the lung and kidney target tissues, however, only limited data are available for establishing cross-species pharmacokinetics. As a result, PBPK model calculations of target tissue dose for lung and kidney should be used with caution.

Carcinogenic agents can influence the carcinogenic process either by mutating critical target genes or by increasing the number of cells at risk for mutations. Cytogenetic and molecular studies of asbestos-related cancers indicate that inactivation or loss of multiple tumor suppressor genes occurs during lung cancer development. Aneuploidy and other chromosomal changes induced by asbestos fibers may be involved in genetic alterations in asbestos-related cancers. Furthermore, asbestos fibers may influence the carcinogenic process by inducing cell proliferation, free radicals, or other promotional mechanisms. Therefore, asbestos fibers may act at multiple stages of the carcinogenic process by both genetic and epigenetic mechanisms. Biopersistence is undoubtedly important in fiber carcinogenicity. However, the time required for a fiber to remain in the lung to exert a cancer-related effect is difficult to specify.

This paper describes a physiologically based toxicokinetic model for 1,3-butadiene uptake, distribution, and metabolic clearance in mice. Model parameters for metabolic activity were estimated from the correspondence between computer simulation studies and experimental results as published in the literature. The parameterized model was validated with independent literature data. With the resulting model, the relative importance of lung metabolism as compared to metabolism in the liver increased with decreasing ambient air concentrations. This was due to saturation of metabolism in the alveolar area of the lung, which occurred in the simulations at ambient air concentrations well below current threshold limit values. At higher air concentration, liver metabolism became relatively more important. The tendency toward increased importance of lung metabolism at low doses indicates the necessity of careful extrapolation of in vivo results to low doses. Moreover, this trend may also contribute to species difference in susceptibility to the carcinogenic activity of butadiene.

There are two reasons to connect discussions of biological markers and pharmacokinetics. First, both tend to open up the black box between exposure and effect. Doing this promises more complete scientific understanding than simple input-output analysis, the possibility of better mechanism-based projection of risk beyond the range of possible direct observations, and the possibility of greater sensitivity of analysis, in some cases going from the organism to the cell as the unit of analysis. Second, pharmacokinetic (or similar pharmacodynamic) analysis will often be essential for appropriate interpretation of biological marker information. One needs some sort of dynamic model of the generation and loss of the marker in relation to exposure in order to use a biological marker, either to form a better measure of dosage (either accumulated past dose, or biologically relevant dose), or to make an improved prediction of effect. (For example, the use of a blood cadmium level alone to predict kidney effects might be inferior to predictions based on aggregate past accumulation of cadmium in the kidney, based on the past history of cadmium blood levels x time). Several examples will be discussed of the use of biomarkers and pharmacokinetics in risk assessments for both carcinogenesis and other effects.