A regression analysis using simple descriptors for multiple dermal datasets: Going from individual membranes to the full skin.

In silico methods to estimate and/or quantify skin absorption of chemicals as a function of chemistry are needed to realistically predict pharmacological, occupational, and environmental exposures. The Potts-Guy equation is a well-established approach, using multi-linear regression analysis describing skin permeability (Kp) in terms of the octanol/water partition coefficient (logP) and molecular weight (MW). In this work, we obtained regression equations for different human datasets relevant to environmental and cosmetic chemicals. Since the Potts-Guy equation was published in 1992, we explored recent datasets that include different skin layers, such as dermatomed (including dermis to a defined thickness) and full skin. Our work was consistent with others who have observed that fits to the Potts-Guy equation are stronger for experiments focused on the epidermis. Permeability estimates for dermatomed skin and full skin resulted in low regression coefficients when compared to epidermis datasets. An updated regression equation uses a combination of fitted permeability values obtained with a published 2D compartmental model previously evaluated. The resulting regression equation was: logKp = -2.55 + 0.65logP - 0.0085MW, R2 = 0.91 (applicability domain for all datasets: MW ranges from 18 to >584 g/mol and -4 to >5 for logP). This approach demonstrates the advantage of combining mechanistic with structural activity relationships in a single modeling approach. This combination approach results in an improved regression fit when compared to permeability estimates obtained using the Potts-Guy approach alone. The analysis presented in this work assumes a one-compartment skin absorption route; future modeling work will consider adding multiple compartments.

Global optimization of the Michaelis-Menten parameters using physiologically-based pharmacokinetic (PBPK) modeling and chloroform vapor uptake data in F344 rats.

Objective: To quantify metabolism, a physiologically based pharmacokinetic (PBPK) model for a volatile compound can be calibrated with the closed chamber (i.e. vapor uptake) inhalation data. Here, we introduce global optimization as a novel component of the predictive process and use it to illustrate a procedure for metabolic parameter estimation.Materials and methods: Male F344 rats were exposed in vapor uptake chambers to initial concentrations of 100, 500, 1000, and 3000 ppm chloroform. Chamber time-course data from these experiments, in combination with optimization using a chemical-specific PBPK model, were used to estimate Michaelis-Menten metabolic constants. Matlab® simulation software was used to integrate the mass balance equations and to perform the global optimizations using MEIGO (MEtaheuristics for systems biology and bIoinformatics Global Optimization - Version 64 bit, R2016A), a toolbox written for Matlab®. The cost function used the chamber time-course data and least squares to minimize the difference between data and simulation values.Results and discussion: The final values estimated for Vmax (maximum metabolic rate) and Km (affinity constant) were 1.2 mg/h and a range between 0.0005 and 0.6 mg/L, respectively. Also, cost function plots were used to analyze the dose-dependent capacity to estimate Vmax and Km within the experimental range used. Sensitivity analysis was used to assess identifiability for both parameters and show these kinetic data may not be sufficient to identify Km.Conclusion: In summary, this work should help toxicologists interested in optimization techniques understand the overall process employed when calibrating metabolic parameters in a PBPK model with inhalation data.

A physiologically based pharmacokinetic model of vitamin D.

Despite the plethora of studies discussing the benefits of vitamin D on physiological functioning, few mathematical models of vitamin D predict the response of the body on low-concentration supplementation of vitamin D under sunlight-restricted conditions. This study developed a physiologically based pharmacokinetic (PBPK) model utilizing published human data on the metabolic cascade of orally derived, low-concentration (placebo, 5 µg and 10 µg) supplementation of vitamin D over the course of 28 days in the absence of sunlight. Vitamin D and its metabolites are highly lipophilic and binding assays of these compounds in serum may not account for binding by lipids and additional proteins. To compensate for the additional bound amounts, this study allowed the effective adipose-plasma partition coefficient to vary dynamically with the concentration of each compound in serum utilizing the Hill equation for binding. Through incorporating the optimized parameters with the adipose partition coefficient adaptation to the PBPK model, this study was able to fit serum concentration data for circulating vitamin D at all three supplementation concentrations within confidence intervals of the data. Copyright © 2017 John Wiley & Sons, Ltd.

Development of a human physiologically based pharmacokinetic (PBPK) model for dermal permeability for lindane.

Lindane is a neurotoxicant used for the treatment of lice and scabies present on human skin. Due to its pharmaceutical application, an extensive pharmacokinetic database exists in humans. Mathematical diffusion models allow for calculation of lindane skin permeability coefficients using human kinetic data obtained from in vitro and in vivo experimentation as well as a default compound-specific calculation based on physicochemical characteristics used in the absence of kinetic data. A dermal model was developed to describe lindane diffusion into the skin, where the skin compartment consisted of homogeneous dermal tissue. This study utilized Fick's law of diffusion along with chemical binding to protein and lipids to determine appropriate dermal absorption parameters which were then incorporated into a physiologically based pharmacokinetic (PBPK) model to describe in vivo kinetics. The estimation of permeability coefficients using chemical binding in combination with in vivo data demonstrates the advantages of combining physiochemical properties with a PBPK model to predict dermal absorption.

Improving in vitro to in vivo extrapolation by incorporating toxicokinetic measurements: a case study of lindane-induced neurotoxicity.

Approaches for extrapolating in vitro toxicity testing results for prediction of human in vivo outcomes are needed. The purpose of this case study was to employ in vitro toxicokinetics and PBPK modeling to perform in vitro to in vivo extrapolation (IVIVE) of lindane neurotoxicity. Lindane cell and media concentrations in vitro, together with in vitro concentration-response data for lindane effects on neuronal network firing rates, were compared to in vivo data and model simulations as an exercise in extrapolation for chemical-induced neurotoxicity in rodents and humans. Time- and concentration-dependent lindane dosimetry was determined in primary cultures of rat cortical neurons in vitro using "faux" (without electrodes) microelectrode arrays (MEAs). In vivo data were derived from literature values, and physiologically based pharmacokinetic (PBPK) modeling was used to extrapolate from rat to human. The previously determined EC50 for increased firing rates in primary cultures of cortical neurons was 0.6µg/ml. Media and cell lindane concentrations at the EC50 were 0.4µg/ml and 7.1µg/ml, respectively, and cellular lindane accumulation was time- and concentration-dependent. Rat blood and brain lindane levels during seizures were 1.7-1.9µg/ml and 5-11µg/ml, respectively. Brain lindane levels associated with seizures in rats and those predicted for humans (average=7µg/ml) by PBPK modeling were very similar to in vitro concentrations detected in cortical cells at the EC50 dose. PBPK model predictions matched literature data and timing. These findings indicate that in vitro MEA results are predictive of in vivo responses to lindane and demonstrate a successful modeling approach for IVIVE of rat and human neurotoxicity.

Scientific considerations for evaluating cancer bioassays conducted by the Ramazzini Institute.

BACKGROUND: The Ramazzini Institute (RI) has completed nearly 400 cancer bioassays on > 200 compounds. The European Food Safety Authority (EFSA) and others have suggested that study design and protocol differences between the RI and other laboratories by may contribute to controversy regarding cancer hazard findings, principally findings on lymphoma/leukemia diagnoses. OBJECTIVE: We aimed to evaluate RI study design, protocol differences, and accuracy of tumor diagnoses for their impact on carcinogenic hazard characterization. METHODS: We analyzed the findings from a recent Pathology Working Group (PWG) review of RI procedures and tumor diagnoses, evaluated consistency of RI and other laboratory findings for chemicals identified by the RI as positive for lymphoma/leukemia, and examined evidence for a number of other issues raised regarding RI bioassays. The RI cancer bioassay design and protocols were evaluated in the context of relevant risk assessment guidance from international authorities. DISCUSSION: Although the PWG identified close agreement with RI diagnoses for most tumor types, it did not find close agreement for lymphoma/leukemia of the respiratory tract or for neoplasms of the inner ear and cranium. Here we discuss a) the implications of the PWG findings, particularly lymphoma diagnostic issues; b) differences between RI studies and those from other laboratories that are relevant to evaluating RI cancer bioassays; and c) future work that may help resolve some concerns. CONCLUSIONS: We concluded that a) issues related to respiratory tract infections have complicated diagnoses at that site (i.e., lymphoma/leukemia), as well as for neoplasms of the inner ear and cranium, and b) there is consistency and value in RI studies for identification of other chemical-related neoplasia.

Human health effects of trichloroethylene: key findings and scientific issues.

BACKGROUND: In support of the Integrated Risk Information System (IRIS), the U.S. Environmental Protection Agency (EPA) completed a toxicological review of trichloroethylene (TCE) in September 2011, which was the result of an effort spanning > 20 years. OBJECTIVES: We summarized the key findings and scientific issues regarding the human health effects of TCE in the U.S. EPA's toxicological review. METHODS: In this assessment we synthesized and characterized thousands of epidemiologic, experimental animal, and mechanistic studies, and addressed several key scientific issues through modeling of TCE toxicokinetics, meta-analyses of epidemiologic studies, and analyses of mechanistic data. DISCUSSION: Toxicokinetic modeling aided in characterizing the toxicological role of the complex metabolism and multiple metabolites of TCE. Meta-analyses of the epidemiologic data strongly supported the conclusions that TCE causes kidney cancer in humans and that TCE may also cause liver cancer and non-Hodgkin lymphoma. Mechanistic analyses support a key role for mutagenicity in TCE-induced kidney carcinogenicity. Recent evidence from studies in both humans and experimental animals point to the involvement of TCE exposure in autoimmune disease and hypersensitivity. Recent avian and in vitro mechanistic studies provided biological plausibility that TCE plays a role in developmental cardiac toxicity, the subject of substantial debate due to mixed results from epidemiologic and rodent studies. CONCLUSIONS: TCE is carcinogenic to humans by all routes of exposure and poses a potential human health hazard for noncancer toxicity to the central nervous system, kidney, liver, immune system, male reproductive system, and the developing embryo/fetus.

Cutting Edge PBPK Models and Analyses: Providing the Basis for Future Modeling Efforts and Bridges to Emerging Toxicology Paradigms.

Physiologically based Pharmacokinetic (PBPK) models are used for predictions of internal or target dose from environmental and pharmacologic chemical exposures. Their use in human risk assessment is dependent on the nature of databases (animal or human) used to develop and test them, and includes extrapolations across species, experimental paradigms, and determination of variability of response within human populations. Integration of state-of-the science PBPK modeling with emerging computational toxicology models is critical for extrapolation between in vitro exposures, in vivo physiologic exposure, whole organism responses, and long-term health outcomes. This special issue contains papers that can provide the basis for future modeling efforts and provide bridges to emerging toxicology paradigms. In this overview paper, we present an overview of the field and introduction for these papers that includes discussions of model development, best practices, risk-assessment applications of PBPK models, and limitations and bridges of modeling approaches for future applications. Specifically, issues addressed include: (a) increased understanding of human variability of pharmacokinetics and pharmacodynamics in the population, (b) exploration of mode of action hypotheses (MOA), (c) application of biological modeling in the risk assessment of individual chemicals and chemical mixtures, and (d) identification and discussion of uncertainties in the modeling process.

Characterizing uncertainty and population variability in the toxicokinetics of trichloroethylene and metabolites in mice, rats, and humans using an updated database, physiologically based pharmacokinetic (PBPK) model, and Bayesian approach.

We have developed a comprehensive, Bayesian, PBPK model-based analysis of the population toxicokinetics of trichloroethylene (TCE) and its metabolites in mice, rats, and humans, considering a wider range of physiological, chemical, in vitro, and in vivo data than any previously published analysis of TCE. The toxicokinetics of the "population average," its population variability, and their uncertainties are characterized in an approach that strives to be maximally transparent and objective. Estimates of experimental variability and uncertainty were also included in this analysis. The experimental database was expanded to include virtually all available in vivo toxicokinetic data, which permitted, in rats and humans, the specification of separate datasets for model calibration and evaluation. The total combination of these approaches and PBPK analysis provides substantial support for the model predictions. In addition, we feel confident that the approach employed also yields an accurate characterization of the uncertainty in metabolic pathways for which available data were sparse or relatively indirect, such as GSH conjugation and respiratory tract metabolism. Key conclusions from the model predictions include the following: (1) as expected, TCE is substantially metabolized, primarily by oxidation at doses below saturation; (2) GSH conjugation and subsequent bioactivation in humans appear to be 10- to 100-fold greater than previously estimated; and (3) mice had the greatest rate of respiratory tract oxidative metabolism as compared to rats and humans. In a situation such as TCE in which there is large database of studies coupled with complex toxicokinetics, the Bayesian approach provides a systematic method of simultaneously estimating model parameters and characterizing their uncertainty and variability. However, care needs to be taken in its implementation to ensure biological consistency, transparency, and objectivity.

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.

Difficulty of mode of action determination for trichloroethylene: An example of complex interactions of metabolites and other chemical exposures.

The mode(s) of action (MOA) of a pollutant for adverse health effects may be dependent on the mixture of metabolites resulting from exposure to a single agent and may also be affected by coexposure to pollutants that have similar targets or affected pathways. Trichloroethylene (TCE) can be an useful example for illustration of the complexity coexposure can present to elucidation of the MOA of an agent. TCE exposure has been associated with increased risk of liver and kidney cancer in both laboratory animal and epidemiologic studies. There are a number of TCE metabolites that could play a role in the induction of these effects. Coexposures of other chemicals with TCE typically occurs as a result of environmental cocontamination that include its own metabolites, such as trichloroacetic acid, dichloroacetic acid, and other pollutants with similar metabolites such as perchloroethylene. Behaviors such as alcohol consumption can also potentially modify TCE toxicity through similar MOAs. The U.S. Environmental Protection Agency (EPA)'s 2001 draft TCE risk assessment, Trichloroethylene (TCE) Health Risk Assessment: Synthesis and Characterization, concluded that it was difficult to determine which of the metabolites of TCE may be responsible for these effects, what key events in their hypothesized MOAs are involved, and the relevance of some of the hypothesized MOAs to humans. Since the publication of U.S. EPA's draft TCE assessment, several studies have been conducted to understand the effects of coexposures to TCE. They cover both pharmacodynamic and pharmacokinetic considerations. This article highlights some of the recently published scientific literature on toxicological interactions between TCE, its metabolites, and other coexposures, including solvents, haloacetates, and ethanol. These studies give insight into both the potential MOAs of TCE exposure itself and putative modulators of TCE toxicity, and illustrate the difficulties encountered in determining the MOAs and modulators of toxicity for pollutants with such complex metabolism and coexposures.

An example of model structure differences using sensitivity analyses in physiologically based pharmacokinetic models of trichloroethylene in humans.

Trichloroethylene (TCE) is an industrial chemical and an environmental contaminant. TCE and its metabolites may be carcinogenic and affect human health. Physiologically based pharmacokinetic (PBPK) models that differ in compartmentalization are developed for TCE metabolism in humans, and the focus of this investigation is to evaluate alternative models. The two models formulated differ in the compartmentalization of metabolites; more specifically, one model has compartments for all chemicals and the other model has only a generalized body compartment for each the metabolites and contains multiple compartments for the parent, TCE. The models are compared through sensitivity analyses in order to selectively discriminate with regards to model structure. Sensitivities to a parameter of cardiac output (Qcc) are calculated, and the more compartmentalized model predictions for excretion show lower sensitivity to changes in this parameter. Values of Qcc used in the sensitivity analyses are specifically chosen to be applicable to adults of ages into the low 60s. Since information about cardiac output across a population is not often incorporated into a PBPK model, the more compartmentalized ("full") model is probably a more appropriate mathematical description of TCE metabolism, but further study may be necessary to decide which model is a more reasonable option if distributional information about Qcc is used. The study is intended to illustrate how sensitivity analysis can be used in order to make appropriate decisions about model development when considering physiological parameters than vary across the population.

Issues in the pharmacokinetics of trichloroethylene and its metabolites.

Much progress has been made in understanding the complex pharmacokinetics of trichloroethylene (TCE) . Qualitatively, it is clear that TCE is metabolized to multiple metabolites either locally or into systemic circulation. Many of these metabolites are thought to have toxicologic importance. In addition, efforts to develop physiologically based pharmacokinetic (PBPK) models have led to a better quantitative assessment of the dosimetry of TCE and several of its metabolites. As part of a mini-monograph on key issues in the health risk assessment of TCE, this article is a review of a number of the current scientific issues in TCE pharmacokinetics and recent PBPK modeling efforts with a focus on literature published since 2000. Particular attention is paid to factors affecting PBPK modeling for application to risk assessment. Recent TCE PBPK modeling efforts, coupled with methodologic advances in characterizing uncertainty and variability, suggest that rigorous application of PBPK modeling to TCE risk assessment appears feasible at least for TCE and its major oxidative metabolites trichloroacetic acid and trichloroethanol. However, a number of basic structural hypotheses such as enterohepatic recirculation, plasma binding, and flow- or diffusion-limited treatment of tissue distribution require additional evaluation and analysis. Moreover, there are a number of metabolites of potential toxicologic interest, such as chloral, dichloroacetic acid, and those derived from glutathione conjugation, for which reliable pharmacokinetic data is sparse because of analytical difficulties or low concentrations in systemic circulation. It will be a challenge to develop reliable dosimetry for such cases.

Comments on article "Applying mode-of-action and pharmacokinetic considerations in contemporary cancer risk assessments: an example with trichloroethylene" by Clewell and Andersen.

In their 2004 article, Clewell and Andersen provide their perspective on the application of mode-of-action (MOA) and pharmacokinetic considerations in contemporary cancer risk assessment using trichloroethylene (TCE) as a case example. TCE is a complex chemical toxicologically, with multiple metabolites, multiple sites of observed toxicity, and multiple potential MOAs. As scientists who are responsible for revising the U.S. Environmental Protection Agency's draft risk assessment of TCE, we welcome input of the quality to which the Agency is held accountable. However, in our view, Clewell and Andersen do not present a sufficiently current, complete, accurate, and transparent review of the pertinent scientific literature. In particular, their article would need to incorporate substantial recently published scientific information, better support its conclusions about MOA and choice of linear or nonlinear dose-response extrapolation, and increase its transparency as to quantitative analyses in order to make a significant contribution to the scientific discussion of TCE health risks.

Moving from external exposure concentration to internal dose: duration extrapolation based on physiologically based pharmacokinetic derived estimates of internal dose.

The potential human health risk(s) from chemical exposure must frequently be assessed under conditions for which adequate human or animal data are not available. The default method for exposure-duration adjustment, based on Haber's rule, C (external exposure concentration) or C(n) (the ten Berge modification) x t (exposure duration) = K (a constant toxic effect), has been criticized for prediction errors. A promising alternative approach to duration adjustment is based on equivalence of internal dose, that is, target-tissue dose levels, across different exposure durations. A proposed methodology for dose-duration adjustments for acute exposure guideline levels (AEGLs) based on physiologically based pharmacokinetic (PBPK) estimates of dose is illustrated with trichloroethylene (TCE). Steps in this methodology include: (1) selection and evaluation, or development and evaluation, of an appropriate PBPK model; (2) determination of an appropriate measure of internal dose; (3) estimation with the PBPK model of the tissue dose (the target tissue dose) resulting from the external exposure conditions (concentration, duration) of the critical effect; (4) estimation of the external exposure concentrations required to achieve tissue doses equivalent to the target tissue dose at exposure durations of interest; and (5) evaluation of sources of variability and uncertainty. For TCE, this PBPK modeling approach has allowed determination of dose metrics predictive of the acute neurotoxic effects of TCE and dose-duration adjustments based on estimates of internal dose.

Duration adjustment of acute exposure guideline level values for trichloroethylene using a physiologically-based pharmacokinetic model.

Acute Exposure Guideline Level (AEGL) recommendations are developed for 10-minute, 30-minute, 1-hour, 4-hours, and 8-hours exposure durations and are designated for three levels of severity: AEGL-1 represents concentrations above which acute exposures may cause noticeable discomfort including irritation; AEGL-2 represents concentrations above which acute exposure may cause irreversible health effects or impaired ability to escape; and AEGL-3 represents concentrations above which exposure may cause life-threatening health effects or death. The default procedure for setting AEGL values across durations when applicable data are unavailable involves estimation based on Haber's rule, which has an underlying assumption that cumulative exposure is the determinant of toxicity. For acute exposure to trichloroethylene (TCE), however, experimental data indicate that momentary tissue concentration, and not the cumulative amount of exposure, is important. We employed an alternative approach to duration adjustments in which a physiologically-based pharmacokinetic (PBPK) model was used to predict the arterial blood concentrations [TCE(a)] associated with adverse outcomes appropriate for AEGL-1, -2, or -3-level effects. The PBPK model was then used to estimate the atmospheric concentration that produces equivalent [TCE(a)] at each of the AEGL-specific exposure durations. This approach yielded [TCE(a)] values of 4.89 mg/l for AEGL-1, 18.7 mg/l for AEGL-2, and 310 mg/l for AEGL-3. Duration adjustments based on equivalent target tissue doses should provide similar degrees of toxicity protection at different exposure durations.

Genotoxicity and metabolism of the source-water contaminant 1,1-dichloropropene: activation by GSTT1-1 and structure-activity considerations.

1,1-Dichloropropene (1,1-DCPe) is a contaminant of some source waters used to make drinking water. Because of this and the fact that no toxicological data were available for this compound, which is structurally similar to the rodent carcinogen 1,3-dichloropropene (1,3-DCPe), 1,1-DCPe was placed on the Contaminant Candidate List of the US Environmental Protection Agency. Consequently, we have performed a hazard characterization of 1,1-DCPe by evaluating its mutagenicity in the Salmonella assay and its DNA damaging (comet assay) and apoptotic (caspase assay) activities in human lymphoblastoid cells. In Salmonella, 1,1-DCPe was not mutagenic in strains TA98, TA100, TA1535, or TA104 +/-S9 mix. However, it was clearly mutagenic in strain RSJ100, which expresses the rat GSTT1-1 gene. 1,1-DCPe did not induce DNA damage in GSTT1-1-deficient human lymphoblastoid cells, and it induced apoptosis in these cells only at 5 mM. Consistent with its mutagenesis in RSJ100, 1,1-DCPe reacted with glutathione (GSH) in vitro, suggesting an addition-elimination mechanism to account for the detected GSH conjugate. 1,1-DCPe was approximately 5000 times more mutagenic than its ethene congener 1,1-dichloroethylene (1,1-DCE or vinylidene chloride). Neither 1,1-DCE nor 1,3-DCPe showed enhanced mutagenicity in strain RSJ100, indicating a lack of activation of these congeners by GSTT1-1. Thus, 1,1-DCPe is a base-substitution mutagen requiring activation by GSTT1-1, possibly involving the production of a reactive episulfonium ion. This bioactivation mechanism of 1,1-DCPe is different from that of its congeners 1,1-DCE and 1,3-DCPe. The presence of 1,1-DCPe in source waters could pose an ecological or human health risk. Occurrence data for 1,1-DCPe in finished drinking water are needed to estimate human exposure to, and possible health risks from, this mutagenic compound.

Visualization-based analysis for a mixed-inhibition binary PBPK model: determination of inhibition mechanism.

A physiologically based pharmacokinetic (PBPK) model incorporating mixed enzyme inhibition was used to determine the mechanism of metabolic interactions occurring during simultaneous exposures to the organic solvents chloroform and trichloroethylene (TCE). Visualization-based sensitivity and identifiability analyses of the model were performed to determine the conditions under which four inhibitory parameters describing inhibitor binding could be estimated. The sensitivity methods were used to reduce the 4-parameter estimation problem into two distinct 2-parameter problems. The inhibitory parameters were then estimated from multiple closed-chamber gas-uptake experiments using graphical methods. The estimated values of the four inhibitory parameters predicted that chloroform and TCE interact in a competitive manner. Based on the model analysis, we present recommendations for the design of experiments for determination of inhibition mechanism in binary chemical mixtures. We assert that a thorough analysis of the parameter-dependent sensitivity and identifiability characteristics can be used to plan efficient experimental protocols for the quantitative analysis of inhalation pharmacokinetics.

The metabolic rate constants and specific activity of human and rat hepatic cytochrome P-450 2E1 toward toluene and chloroform.

Chloroform (CHCl3) is a near-ubiquitous environmental contaminant, a by-product of the disinfection of drinking water sources and a commercially important compound. Standards for safe exposure have been established based on information defining its toxicity, which is mediated by metabolites. The metabolism of CHCl3 is via cytochrome P-450 2E1 (CYP2E1)-mediated oxidation to phosgene, which is known to obey a saturable mechanism. CYP2E1 is a highly conserved form, expressed in all mammalian systems studied, and is responsible for the metabolism of a great many low-molecular-weight (halogenated) compounds. However, the Michaelis-Menten rate constants for CHCl3 oxidation have not been derived in vitro, and the specific activity of CYP2E1 toward CHCl3 has not been reported. In this investigation with microsomal protein (MSP), apparent Vmax values of 27.6 and 28.3 nmol/h/mg MSP and apparent K(m) values of 1 and 0.15 microM in rats and human organ donors, respectively, were demonstrated. The specific activity of CYP2E1 toward CHCl3 in rats and humans was 5.29 and 5.24 pmol/min/pmol CYP2E1, respectively. Toluene metabolism to benzyl alcohol (BA), another CYP2E1-dependent reaction, was also highly dependent on CYP2E1 content in humans, and was more efficient than was CHCl3 metabolism. The specific activity of human CYP2E1 toward toluene metabolism in human MSP was 23 pmol/min/pmol CYP2E1. These results demonstrate that differences in CYP2E1 content of MSP among individuals and between species are largely responsible for observed differences in toluene and CHCl3 metabolism in vitro.