Physiologically based pharmacodynamic modeling of an interaction threshold between trichloroethylene and 1,1-dichloroethylene in fischer 344 rats

Modeling acetylcholine esterase inhibition resulting from exposure to a mixture of atrazine and chlorpyrifos using a physiologically-based kinetic model in fish

Aquatic organisms are exposed to mixtures of chemicals that may interact. Mixtures of atrazine (ATR) and chlorpyrifos (CPF) may elicit synergic effects on the permanent inhibition of acetylcholinesterase (AChE) in certain aquatic organisms, causing severe damage. Mechanistic mathematical models of toxicokinetics and toxicodynamics (TD) may be used to better characterize and understand the interactions of these two chemicals. In this study, a previously published generic physiologically-based toxicokinetic (PBTK) model for fish was adapted to ATR and CPF. A sub-model of the kinetics of one of the main metabolites of CPF, chlorpyrifos-oxon (CPF-oxon), was included, as well as a TD model. Inhibition of two esterases, AChE and carboxylesterase, by ATR, CPF and CPF-oxon, was modeled using TD modeling of quantities of total and inactive esterases. Specific attention was given to the parameterization and calibration of the model to accurately predict the concentration and effects observed in the fish using Bayesian inference and published data from fathead minnow (Pimephales promelas), zebrafish (Danio rerio) and common carp (Cyprinus carpio L.). A PBTK-TD for mixtures was used to predict dose-response relationships for comparison with available adult fish data. Synergistic effects of a joint exposure to ATR and CPF could not be demonstrated in adult fish.

Application of Biologically Based Lumping To Investigate the Toxicokinetic Interactions of a Complex Gasoline Mixture

People are often exposed to complex mixtures of environmental chemicals such as gasoline, tobacco smoke, water contaminants, or food additives. We developed an approach that applies chemical lumping methods to complex mixtures, in this case gasoline, based on biologically relevant parameters used in physiologically based pharmacokinetic (PBPK) modeling. Inhalation exposures were performed with rats to evaluate the performance of our PBPK model and chemical lumping method. There were 109 chemicals identified and quantified in the vapor in the chamber. The time-course toxicokinetic profiles of 10 target chemicals were also determined from blood samples collected during and following the in vivo experiments. A general PBPK model was used to compare the experimental data to the simulated values of blood concentration for 10 target chemicals with various numbers of lumps, iteratively increasing from 0 to 99. Large reductions in simulation error were gained by incorporating enzymatic chemical interactions, in comparison to simulating the individual chemicals separately. The error was further reduced by lumping the 99 nontarget chemicals. The same biologically based lumping approach can be used to simplify any complex mixture with tens, hundreds, or thousands of constituents.

The impact of CYP2E1 genetic variability on risk assessment of VOC mixtures

Humans are simultaneously exposed to multiple chemicals in the environment. Many of the chemicals use the same enzymes in their metabolic pathways. Competitive inhibition may occur as one of the possible interactions between the xenobiotics in human body. For example, many volatile organic compounds (VOCs) are metabolized using P450 enzymes, specifically CYP2E1. Inheritable gene alterations may result in changes of function of the enzymes in different human subpopulations. Variations in quantity and/or quality of particular isoenzymes may cause differences in the metabolism of VOCs. These variations may cause higher sensitivity in certain populations. Using examples of three different mixtures, this review paper outlines the variances in CYP2E1 isoenzymes, effect of exposure to such mixtures on sensitive populations, and approaches to mixtures risk assessment.

Evidence for dose-additive effects of pyrethroids on motor activity in rats

BACKGROUND

Pyrethroids are neurotoxic insecticides used in a variety of indoor and outdoor applications. Previous research characterized the acute dose-effect functions for 11 pyrethroids administered orally in corn oil (1 mL/kg) based on assessment of motor activity.

OBJECTIVES

We used a mixture of these 11 pyrethroids and the same testing paradigm used in single-compound assays to test the hypothesis that cumulative neurotoxic effects of pyrethroid mixtures can be predicted using the default dose-addition theory.

METHODS

Mixing ratios of the 11 pyrethroids in the tested mixture were based on the ED30 (effective dose that produces a 30% decrease in response) of the individual chemical (i.e., the mixture comprised equipotent amounts of each pyrethroid). The highest concentration of each individual chemical in the mixture was less than the threshold for inducing behavioral effects. Adult male rats received acute oral exposure to corn oil (control) or dilutions of the stock mixture solution. The mixture of 11 pyrethroids was administered either simultaneously (2 hr before testing) or after a sequence based on times of peak effect for the individual chemicals (4, 2, and 1 hr before testing). A threshold additivity model was fit to the single-chemical data to predict the theoretical dose-effect relationship for the mixture under the assumption of dose additivity.

RESULTS

When subthreshold doses of individual chemicals were combined in the mixtures, we found significant dose-related decreases in motor activity. Further, we found no departure from the predicted dose-additive curve regardless of the mixture dosing protocol used.

CONCLUSION

In this article we present the first in vivo evidence on pyrethroid cumulative effects supporting the default assumption of dose addition.

Pitfalls and Related Improvements of In Vivo Gas Uptake Pharmacokinetic Experimental Systems

Gas uptake chamber studies have been widely used to study inhalation pharmacokinetics (PKs) in rodents, often for the ultimate purpose of developing physiologically-based pharmacokinetic (PBPK) models that can be used to describe human PKs and to support risk assessment for the chemical. In the course of our studies of gasoline PKs, we revisited several important issues heretofore not thoroughly addressed. Here, we report several refinements which will significantly improve future studies with this type of system, relating to the understanding of loss rates, the importance of carbon dioxide removal, and sampling of blood and chamber air at the same time. Losses of chemicals in gas uptake systems consist of leakage, adsorption to system components, and adsorption to the hair and skin (fur) of experimental animals. The loss rates were experimentally determined for a series of chemicals and mixtures including n-hexane, benzene, toluene, ethylbenzene, o-xylene, gasoline, and other gasoline components. The rate of loss to the animals' fur was similar to loss rates to system components and involved absorption to both hair and skin. Most of the absorption to fur was reversible when the chamber concentration was low enough. The amount of chemical that desorbed from the animal after an experiment was significant when compared to the amount of chemical in the chamber at the end of a gas uptake experiment, indicating that the rate of decline in concentrations can be influenced by a decrease in the fur absorption rate or desorption of chemicals. A modified gas uptake system design is described in which a steel ring improved the connections to an autosampler and allowed insertion of probes to monitor gases, such as carbon dioxide (CO2), in the chamber. When CO2 absorbent efficiency was inadequate, CO2 concentrations rose to levels that significantly affected the animals' ventilation rate. Using a real-time CO2 probe, an absorbent system was developed that adequately controlled CO2 levels in the chamber. Attention to details of absorptive loss and CO2 scrubbing can improve the reliability of kinetic constants inferred from closed chamber studies. We then describe a method for extending gas uptake experiments by simultaneously collecting blood to be analyzed for chemicals and/or metabolites.

Metabolism and tissue distribution of orally administered trichloroethylene in male and female rats: identification of glutathione- and cytochrome P-450-derived metabolites in liver, kidney, blood, and urine

Male and female Fischer 344 rats were administered trichloroethylene (TRI) (2, 5, or 15 mmol/kg body weight) in corn oil by oral gavage, and TRI and its metabolites were measured at times up to 48 h in liver, kidneys, blood, and urine. Studies tested the hypothesis that gender-dependent differences in distribution and metabolism of TRI could help explain differences in toxicity. Higher levels of TRI were generally observed in tissues of males at lower doses. Complex patterns of TRI concentration, sometimes with multiple peaks, were observed in liver, kidneys, and blood of both males and females, consistent with enterohepatic recirculation. Higher concentrations of cytochrome P-450 (P450)-derived metabolites were observed in livers of males than in females, whereas the opposite pattern was observed in kidneys. Trichloroacetate was the primary P450-derived metabolite in blood and urine, although it generally appeared at later times than chloral hydrate. Trichloroethanol was also a significant metabolite in urine. S-(1,2-Dichlorovinyl)glutathione (DCVG) was recovered in liver and kidneys of female rats only and in blood of both males and females, with generally higher amounts found in females. S-(1,2-Dichlorovinyl)-L-cysteine (DCVC), the penultimate nephrotoxic metabolite, was recovered in male and female liver, female kidneys, male blood, and in urine of both males and females. The relationship between gender-dependent differences in distribution and metabolism of TRI and susceptibility to TRI-induced toxicity is discussed.

Analysis of an interaction threshold in a mixture of drugs and/or chemicals

Increasingly, humans are exposed to drug/chemical mixtures. These exposures can result from therapeutic interventions or environmental sources. Of interest is the interaction that may occur among the components of these mixtures. Since interaction can be dose-dependent, it is important to determine exposure levels to either exploit the benefits of the interaction in a therapeutic application or to avoid the effect of the interaction in the case of an environmental risk assessment. We propose generalized linear models that permit the estimation of interaction threshold boundaries. The methods developed are applied to the combination of ethanol and chloral hydrate.

Framework for evaluation of physiologically-based pharmacokinetic models for use in safety or risk assessment

Proposed applications of increasingly sophisticated biologically-based computational models, such as physiologically-based pharmacokinetic models, raise the issue of how to evaluate whether the models are adequate for proposed uses, including safety or risk assessment. A six-step process for model evaluation is described. It relies on multidisciplinary expertise to address the biological, toxicological, mathematical, statistical, and risk assessment aspects of the modeling and its application. The first step is to have a clear definition of the purpose(s) of the model in the particular assessment; this provides critical perspectives on all subsequent steps. The second step is to evaluate the biological characterization described by the model structure based on the intended uses of the model and available information on the compound being modeled or related compounds. The next two steps review the mathematical equations used to describe the biology and their implementation in an appropriate computer program. At this point, the values selected for the model parameters (i.e., model calibration) must be evaluated. Thus, the fifth step is a combination of evaluating the model parameterization and calibration against data and evaluating the uncertainty in the model outputs. The final step is to evaluate specialized analyses that were done using the model, such as modeling of population distributions of parameters leading to population estimates for model outcomes or inclusion of early pharmacodynamic events. The process also helps to define the kinds of documentation that would be needed for a model to facilitate its evaluation and implementation.

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.

Mechanistic approaches for mixture risk assessments-present capabilities with simple mixtures and future directions

Mechanistic studies with simple mixtures have provided insights into the nature of interactions among chemicals that lead to non-additive effects and have elucidated the exposure conditions under which interactions are likely to occur. This paper discusses studies on four mixtures: (1) 1,1-dichloroethylene and trichloroethylene, (2) carbon tetrachloride and Kepone, (3) hexane and methyl-n-butylketone, and (4) coplanar and non-coplanar polychlorinated biphenyls. These mechanistic studies show that interactions should be described at the level of target tissue dose and are best categorized as either pharmacokinetic (PK) or pharmacodynamic (PD) interactions. In PK interactions the presence of a second chemical alters the kinetics such that a unit of administered dose no longer produces a unit of dose at the target tissue. In PD interactions, the presence of other compounds alters the PDs such that a unit tissue dose no longer produces a unit of response. Physiologically based pharmacokinetic (PBPK) models for mixtures have become important tools for predicting conditions under which interactions are likely to alter the assumption of additivity and have permitted calculation of interaction thresholds with more confidence. New cumulative risk assessment approaches have provided opportunities to classify compounds on the basis of similar chemistry-based modes of action (cholinesterase inhibitors) or similar physiological modes of action (diverse chemicals that alter a common biological outcome, such as defeminization of the developing nervous system). The latter examples present challenges for expanding our risk assessment paradigm to focus on the biology of responses more than on the kinetics of the xenobiotics. Some of the future advances in mixture research will depend on progress in systems biology, a discipline that integrates information across multiple level of biological organization producing PD models of normal function and assessing conditions under which exposures to chemicals lead to the perturbations sufficiently great to produce toxicity and disease. We describe briefly the elements of a systems biology approach for assessing the interactions between various PCB congeners.

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.

An in vitro model for evaluation of vaporous toxicity of trichloroethylene and tetrachloroethylene to CHO-K1 cells

Toxicokinetics of trichloroethylene (TCE) and tetrachloroethylene (PER) in culture medium and their toxicity to CHO-K1 cells were investigated by employing an in vitro vapor exposure system. Cells were cultured in a 60 mm petri dish with a 25 mm glass dish glued in the central area. TCE or PER was added to the central glass dish so that it would evaporate and dissolve in the surrounding medium in which cells were growing. The results showed that the concentration of TCE or PER in medium increased significantly within 20 min and then decreased very rapidly with time. After a 24 h incubation, the residual of TCE or PER in the medium was very low, but was displayed in a dose-dependent manner. Treatment of cells with either TCE or PER resulted in a dose- and time-dependent inhibition of cell growth. A significantly increase in the frequency of micronuclei (MN) was also observed with either TCE or PER treatment. Low doses of TCE (5-20 microl) or PER (1-5 microl) significantly enhanced the intracellular glutathione (GSH) level. However, the level of GSH rapidly decreased with higher doses of TCE (40-80 microl) or PER (10-20 microl). Depletion of cellular GSH showed no effect on the sensitivity of cells to TCE or PER treatment. GSH-conjugation has been proposed as an activation mechanism to account for the nephrotoxicity of TCE and PER, however the toxicity of TCE and PER to CHO-K1 cells is probably mediated through a distinct mechanism.

Physiological modeling of the toxicokinetic interactions in a quaternary mixture of aromatic hydrocarbons

The available data on binary interactions are yet to be considered within the context of mixture risk assessments because of our inability to predict the effect of a third or fourth chemical in the mixture on the interacting binary pairs. Physiologically based toxicokinetic (PBTK) models represent a framework that can be potentially used for predicting the impact of multiple interactions on component kinetics at any level of complexity. The objective of this study was to develop and validate an interaction-based PBTK model for simulating the toxicokinetics of the components of a quaternary mixture of aromatic hydrocarbons [benzene (B), toluene (T), ethylbenzene (E), m-xylene (X)] in the rat. The methodology consisted of: (1) obtaining and refining the validated individual chemical PBTK models from the literature, (2) interconnecting all individual chemical PBTK models at the level of liver on the basis of the mechanism of binary chemical interactions (e.g., competitive, noncompetitive, or uncompetitive metabolic inhibition), and (3) comparing the a priori predictions of the interaction-based model to corresponding experimental data on venous blood concentrations of B, T, E, and X during mixture exposures. The analysis of blood kinetics data from inhalation exposures (4 h, 50-200 ppm each) of rats to all binary combinations of B, T, E, and X was suggestive of competitive metabolic inhibition as the plausible interaction mechanism. The metabolic inhibition constant (K(i)) for each binary combination was quantified and incorporated within the mixture PBTK model. The binary interaction-based PBTK model predicted adequately the inhalation toxicokinetics of all four components in rats following exposure to mixtures of BTEX (50 ppm each of B, T, E, and X, 4 h; 100 ppm each of B, T, E and X, 4 h; 100 ppm B + 50 ppm each of T, E, and X, 4 h). The results of the present study suggest that data on interactions at the binary level alone are required and sufficient for predicting the kinetics of components in complex mixtures.

Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling

Systematic toxicity testing, using conventional toxicology methodologies, of single chemicals and chemical mixtures is highly impractical because of the immense numbers of chemicals and chemical mixtures involved and the limited scientific resources. Therefore, the development of unconventional, efficient, and predictive toxicology methods is imperative. Using carcinogenicity as an end point, we present approaches for developing predictive tools for toxicologic evaluation of chemicals and chemical mixtures relevant to environmental contamination. Central to the approaches presented is the integration of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) and quantitative structure--activity relationship (QSAR) modeling with focused mechanistically based experimental toxicology. In this development, molecular and cellular biomarkers critical to the carcinogenesis process are evaluated quantitatively between different chemicals and/or chemical mixtures. Examples presented include the integration of PBPK/PD and QSAR modeling with a time-course medium-term liver foci assay, molecular biology and cell proliferation studies. Fourier transform infrared spectroscopic analyses of DNA changes, and cancer modeling to assess and attempt to predict the carcinogenicity of the series of 12 chlorobenzene isomers. Also presented is an ongoing effort to develop and apply a similar approach to chemical mixtures using in vitro cell culture (Syrian hamster embryo cell transformation assay and human keratinocytes) methodologies and in vivo studies. The promise and pitfalls of these developments are elaborated. When successfully applied, these approaches may greatly reduce animal usage, personnel, resources, and time required to evaluate the carcinogenicity of chemicals and chemical mixtures.

In vitro to in vivo extrapolation for trichloroethylene metabolism in humans

The use of in vitro systems in the assessment of xenobiotic metabolism has distinct advantages and disadvantages. While isolated hepatocytes and microsomes prepared from human liver may be used to generate data for comparisons among species and in vitro systems, such comparisons are generally performed on the basis of microsomal protein or million (viable) hepatocytes. Recently, in vitro data have been investigated for their value as quantitative predictors of in vivo metabolic capacity. Because of the existence of large amounts of trichloroethylene (TRI) data in the human, we have examined the metabolism of TRI as a case study in the development of a method to compare metabolism across species using in vitro systems and for extrapolation of metabolic rates from in vitro to in vivo. TRI is well metabolized by human hepatocytes in culture with a K(m) of 266 +/- 202 ppm (mean +/- SD) in headspace and a Vmax of 16.1 +/- 12.9 nmol/h/10(6) viable hepatocytes. We determined that human liver contains approximately 116 x 10(6) hepatocytes and 20.8 mg microsomal protein/g, based on DNA recovery and glucose-6-phosphatase activity, respectively. Thus, the microsomal protein content of hepatocytes is 179 micrograms microsomal protein/10(6) isolated hepatocytes. The microsomal apparent Vmax value of 1589 pmol/min/mg microsomal protein extrapolates to 17.07 nmol/h/10(6) hepatocytes. The combination of protein recovery and metabolic rate predicted a Vmax of approximately 1400 nmol/h/g human liver, which, when extrapolated and incorporated into an existing physiologically based pharmacokinetic (PBPK) model for TRI, slightly underpredicted TRI metabolism in the intact human. The quantitation, extrapolation, and inclusion of extrahepatic and cytochrome P450 (CYP)-independent TRI metabolism may increase the predictive value of this approach.

Some current approaches for studying combination toxicology in chemical mixtures

Using conventional toxicology methodologies and at the mode and rate of studying chemicals in the last 25 yr, it is doubtful that society will ever have adequate toxicology information on the majority of the chemicals we used now on in the future. Considering further the issue of health effects of chemical mixture exposure (i.e. real-world issues), the problem of not being able to obtain adequate toxicology information is amplified. From a different perspective, concerns over animal rights have raised the consciousness of many biomedical researchers regarding animal experimentation. As many as 17-100 million animals are estimated to be killed for biomedical research in the US alone each year; therefore, minimizing animal usage judiciously in toxicological research should be in the mind of every responsible toxicologist. From these considerations, it is apparent that new, alternative, less animal-intensive, shorter-term and less expensive toxicology methods must be developed if there is to be a reasonable chance to deal with the hundreds of thousand of chemicals, as well as the near-infinite number of chemical mixtures, in the environment. Some of the recent advances indeed are heading towards that direction. In this article, a number of approaches for research work on combination toxicology of chemical mixtures are given; the examples are selected based on one or more of the following criteria: (1) minimizing animal usage; (2) shortening experimental durations; (3) studying environmentally realistic concentrations; (4) utilizing statistical/mathematical modelling; (5) advancing efficient experimental designs and (6) developing predictive toxicology.