Exploration of an interaction threshold for the joint toxicity of trichloroethylene and 1,1-dichloroethylene: utilization of a PBPK model

Concentration responses to organochlorines in Phragmites australis

Phragmites australis shows potential for the phytoremediation of chlorinated chemicals. Also there has been some attempt to determine the phytotoxic effects of organochlorines (OC). This study reports for lindane (HCH), monochlorobenzene (MCB), 1,4-dichlorobenzene (DCB) and 1,2,4-trichlorobenzene (TCB), a no-observed-effect-concentration (NOEC(7d)) that was 1000-300,000 times higher than environmental concentrations. Nevertheless, the combined OC mixture (NOEC(7d) level of each congener) induced a synergistic toxic effect, causing a severe drop (70%) in chlorophyll concentration. The mixture 0.2 mg L(-1) MCB+0.2 mg L(-1) DCB+2.5 mg L(-1) TCB+0.175 mg L(-1) HCH, that was 15 times more concentrated than environmental OC mixture, did not cause phytotoxicity during 21 days. Antioxidant enzymes were affected immediately after the start of exposure (3 days), but the plants showed no signs of stress thereafter. These data suggest that environmental OC mixtures do not pose a significant risk to P. australis.

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.

Physiologically based modelling and prediction of drug interactions

A major challenge for drug development and environmental or occupational health is the prediction of pharmacokinetic and pharmacodynamic interactions between drugs, natural chemicals or environmental contaminants. This article reviews briefly past developments in the area of physiologically based pharmacokinetic (PBPK) modelling of interactions. It also demonstrates a systems biology approach to the question, and the capabilities of new software tools to facilitate that development. Individual Systems Biology Markup Language models of metabolic pathways can now be automatically merged and coupled to a template PBPK pharmacokinetic model, using for example the GNU MCSim software. The global model generated is very efficient and able to simulate the interactions between a theoretically unlimited number of substances. Development time and the number of model parameter increase only linearly with the number of substances considered, even though the number of possible interactions increases exponentially.

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.

Binary weight-of-evidence evaluations of chemical interactions--15 years of experience

The paper reflects on the last 15years of experience in the field of mixtures risk assessment. It summarizes results found in various documents developed by the Agency for Toxic Substances and Disease Registry (ATSDR) of the weight-of-evidence (WOE) approach applied to 380 binary combinations of chemicals. Of these evaluations, 156 assessments indicated possible additivity of effects [=], 76 indicated synergism (greater-than-additive effects [>]), and 57 indicated antagonism (less-than-additive effects [<]). However, 91 combinations lacked the minimum information needed for making any assessments and, hence, were undetermined. The paper provides examples of the rationale behind some of the WOE decisions and discusses the importance of expert judgments in risk assessment evaluations. Examples are given regarding the importance of human variability in mixtures' ability to affect human health and regarding the dose versus effect relationships.

Evaluation of potential toxicity from co-exposure to three CNS depressants (toluene, ethylbenzene, and xylene) under resting and working conditions using PBPK modeling

Under OSHA and American Conference of Governmental Industrial Hygienists (ACGIH) guidelines, the mixture formula (unity calculation) provides a method for evaluating exposures to mixtures of chemicals that cause similar toxicities. According to the formula, if exposures are reduced in proportion to the number of chemicals and their respective exposure limits, the overall exposure is acceptable. This approach assumes that responses are additive, which is not the case when pharmacokinetic interactions occur. To determine the validity of the additivity assumption, we performed unity calculations for a variety of exposures to toluene, ethylbenzene, and/or xylene using the concentration of each chemical in blood in the calculation instead of the inhaled concentration. The blood concentrations were predicted using a validated physiologically based pharmacokinetic (PBPK) model to allow exploration of a variety of exposure scenarios. In addition, the Occupational Safety and Health Administration and ACGIH occupational exposure limits were largely based on studies of humans or animals that were resting during exposure. The PBPK model was also used to determine the increased concentration of chemicals in the blood when employees were exercising or performing manual work. At rest, a modest overexposure occurs due to pharmacokinetic interactions when exposure is equal to levels where a unity calculation is 1.0 based on threshold limit values (TLVs). Under work load, however, internal exposure was 87%higher than provided by the TLVs. When exposures were controlled by a unity calculation based on permissible exposure limits (PELs), internal exposure was 2.9 and 4.6 times the exposures at the TLVs at rest and workload, respectively. If exposure was equal to PELs outright, internal exposure was 12.5 and 16 times the exposure at the TLVs at rest and workload, respectively. These analyses indicate the importance of (1) selecting appropriate exposure limits, (2) performing unity calculations, and (3) considering the effect of work load on internal doses, and they illustrate the utility of PBPK modeling in occupational health risk assessment.

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.

Toxicology of chemical mixtures: a challenging quest along empirical sciences

This paper describes the "quest" of our institute trying to assess the toxicology of chemical mixtures. In this overview, we will discuss some critical developments in hazard identification and risk assessment of chemical mixtures during these past 15 years. We will stand still at empirical and mechanistic modeling. "Empirical" means that only information on doses or concentrations and effects is available in addition to an often empirically selected quantitative dose-response relationship. Empirical models have played a dominant role in the last decade to identify health and safety characteristics of chemical mixtures. Many of these models are based on the work of pioneers in mixture toxicology who defined three basic types of action for combinations of chemicals: simple similar action, simple dissimilar action and interaction. Nowadays, empirical models are mainly based on response-surface analysis and make use of advanced statistical designs. However, possible interactions between components in a mixture can also be given in terms of mechanistic models. In terms of "mechanistic" (or biological) understanding, interactions between compounds may occur in the kinetic phase (processes of uptake, distribution, metabolism and excretion) or in the dynamic phase (effects of chemicals on the receptor, cellular target or organ). A biological phenomenon such as competitive agonism as described for mixtures of drugs (biotransformation enzymes) or sensory irritants (nerve receptors) can accurately predict the effect of any of these mixtures. Thus, far mechanistic and empirical analyses of interactions are usually unrelated. It is one of the future challenges for mixtures research to combine information from both approaches. Also, our current biology-based models have their limitations, since they cannot integrate every relevant biological mechanism. In this respect, mechanistic modeling of mixtures may benefit from the developments coming from the arena of molecular biology (toxicogenomics) which offers an in-depth analysis of several involved enzymatic pathways in parallel through the use of a systems biology approach. This was illustrated with mixtures of food additives known to affect the liver. Key to further maturation of mixture toxicology is collaboration of experimental toxicologists, biomathematicians, biologists, pharmacologists, model developers, molecular biologists and bioinformaticians to ensure parallel and coordinated research in this challenging area of toxicology. For this reason, the next sequel will be even more challenging and exciting to that first 15 years of empirical testing.

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.

Implications of chemical mixtures in public health practice

The Agency for Toxic Substances and Disease Registry (ATSDR) is a federal public health agency that investigates and strives to prevent human health problems produced by exposure to toxic chemicals and their mixtures in the environment. Most human exposures involving toxic chemicals or mixtures are thought to originate from environmental and occupational sources; however, concurrent exposures are also likely from other sources, such as prescription and nonprescription drugs, indoor air pollutants, alcohol, and tobacco smoke. Thus, in evaluating the potential hazard following exposure to environmental mixtures, ATSDR not only considers the inherent joint toxicity of the mixture but also the influence of environmental, demographic, occupational, and lifestyle factors. To foster these goals, ATSDR has pursued a Mixtures Research and Assessment Program that consists of three component efforts: trend analysis, joint toxicity assessment, and experimental testing. Through trend analysis, ATSDR sets priorities for environmental mixtures of concern for which joint toxicity assessments are conducted as needed. If data are not available to conduct appropriate assessments, a research agenda is pursued through established extramural mechanisms. Ultimately, the data generated are used to support ATSDR's work at sites involving exposure to chemical mixtures. This pragmatic approach allows testable hypotheses or research needs to be identified and resolved and enhances our understanding of the mechanisms of joint toxicity. Several collaborative and cooperative efforts with national and international organizations such as the Toxicology and Nutrition Office, the Netherlands, and the Department of Energy are being pursued as part of these activities. ATSDR also develops guidance manuals to consistently and accurately apply current methodologies for the joint toxicity assessment of chemicals. Further, expert panels often are assembled to resolve outstanding scientific issues or obtain expert advice on pertinent issues. Recently, the need for studies on chemical mixtures has been proposed as one of the six priority areas the agency identified in its agenda for public health environmental research. This has been reinforced through the agency's close work with communities whose leaders have spoken passionately about their concern for information on exposures to chemical mixtures. The five other priority research areas the agency identified are exposure, susceptible populations, communities and tribal involvement, evaluation/surveillance of health effects, and health promotion/prevention.

PBPK modeling of the metabolic interactions of carbon tetrachloride and tetrachloroethylene in B6C3F1 mice

Potential exists for widespread human exposure to low levels of carbon tetrachloride (CT) and tetrachloroethylene (TET). These halocarbons are metabolized by the cytochrome P450 system. CT is known to inhibit its own metabolism (suicide inhibition) and to cause liver injury by generation of metabolically derived free radicals. The objective of this research was to use develop a physiologically based pharmacokinetic (PBPK) model to forcast the metabolic interactions between orally administered CT and TET in male B6C3F1 mice. Trichloroacetic acid (TCA), a stable metabolite of TET, was used as a biomarker to assess inhibition of the cytochrome P450 system by CT. Metabolic constants utilized for CT were 1.0mg/kg/h for Vmaxc_CT and 0.3 for Km_CT (mg/l). Values for TET (based in TCA production), were 6.0mg/kg/h for Vmaxc_TET was 3.0mg/l for Km_TET. The rate of loss of metabolic capacity for CT (suicide inhibition) was describe as: Vmaxloss ( mg / h )=- Kd ( RAM × RAM ) , where Kd (h/kg) is a second-order rate constant, and RAM (mg/h) is the Michaelis-Menten description of the rate of metabolism of CT. For model simplicity, CT was assumed to damage the primary enzymes responsible for metabolism of CT (CYP2E1) and TET (CYP2B2) in an equal fashion. Thus, the calculated fractional loss of TET metabolic capacity was assumed to be equivalent to the calculated loss in metabolic capacity of CT. Use of a Kd value of 400h/kg successfully described serum TCA levels in mice dosed orally with 5-100mg/kg of CT. We report, for the first time, suicide inhibition at a very low dose of CT (1mg/kg). The PBPK model under-predicted the degree of metabolic inhibition in mice administered 1mg/kg of CT. This PBPK model is one of only a few physiological models available to predict the metabolic interactions of chemical mixtures involving suicide inhibition. The success of this PBPK model demonstrates that PBPK models are useful tools for examining the nature of metabolic interactions of chemical mixtures, including suicide inhibition. Further research is required to compare the inhibitory effects of inhaled CT vapors with CT administered by oral bolus dosing and determine the interaction threshold for CT-induced metabolic inhibition.

The use of in vitro metabolic parameters and physiologically based pharmacokinetic (PBPK) modeling to explore the risk assessment of trichloroethylene

A physiologically based pharmacokinetic (PBPK) model has been developed for trichloroethylene (1,1,2-trichloroethene, TRI) for rat and humans, based on in vitro metabolic parameters. These were obtained using individual cytochrome P450 and glutathione S-transferase enzymes. The main enzymes involved both for rats and humans are CYP2E1 and the μ- and π-class glutathione S-transferases. Validation experiments were performed in order to test the predictive value of the enzyme kinetic parameters to describe 'whole-body' disposition. Male Wistar rats were dosed orally or intravenously with different doses of trichloroethylene. Obtained exhaled radioactivity, excreted radioactivity in urine, and obtained blood concentration-time curves of trichloroethylene for all dosing groups were compared to predictions from the PBPK model. Subsequently, using the scaling factor derived from the rat experiments predictions were made for the extreme cases to be expected in humans, based on interindividual variations of the key enzymes involved. On comparing these predictions with literature data a very close match was found. This illustrates the potential application of in vitro metabolic parameters in risk assessment, through the use of PBPK modeling as a tool to understand and predict in vivo data. From a hypothetical 8 h exposure scenario to 35 ppm trichloroethylene in rats and humans, and assuming that the glutathione S-transferase pathway is responsible for the toxicity of trichloroethylene, it was concluded that humans are less sensitive for trichloroethylene toxicity than rats.

Synergy and other ineffective mixture risk definitions

A substantial effort has been spent over the past few decades to label toxicologic interaction outcomes as synergistic, antagonistic, or additive. Although useful in influencing the emotions of the public and the press, these labels have contributed fairly little to our understanding of joint toxic action. Part of the difficulty is that their underlying toxicological concepts are only defined for two chemical mixtures, while most environmental and occupational exposures are to mixtures of many more chemicals. Furthermore, the mathematical characterizations of synergism and antagonism are inextricably linked to the prevailing definition of 'no interaction,' instead of some intrinsic toxicological property. For example, the US EPA has selected dose addition as the no-interaction definition for mixture risk assessment, so that synergism would represent toxic effects that exceed those predicted from dose addition. For now, labels such as synergism are useful to regulatory agencies, both for qualitative indications of public health risk as well as numerical decision tools for mixture risk characterization. Efforts to quantify interaction designations for use in risk assessment formulas, however, are highly simplified and carry large uncertainties. Several research directions, such as pharmacokinetic measurements and models, and toxicogenomics, should promote significant improvements by providing multi-component data that will allow biologically based mathematical models of joint toxicity to replace these pairwise interaction labels in mixture risk assessment procedures.

Toxicology of simple and complex mixtures

Humans are exposed to mixtures of chemicals, rather than to individual chemicals. From a public health point of view, it is most relevant to answer the question of whether or not the components in a mixture interact in a way that results in an increase in their overall effect compared with the sum of the effects of the individual components. In this article, options for the hazard identification and risk assessment of simple and complex chemical mixtures will be discussed. In addition, key research needed to continue the development of hazard characterization of chemical mixtures will be described. Clearly, more collaboration among toxicologists, model developers and pharmacologists will be necessary.

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.

Physiologically based pharmacokinetic modeling of a ternary mixture of alkyl benzenes in rats and humans

The objective of the present study was to develop a physiologically based pharmacokinetic (PBPK) model for a ternary mixture of alkyl benzenes [toluene (TOL), m-xylene (XYL), and ethylbenzene (EBZ)] in rats and humans. The approach involved the development of the mixture PBPK model in the rat and extrapolation to humans by substituting rat physiological parameters and blood:air partition coefficients in the model with those of humans, scaling maximal velocity for metabolism on the basis of body weight0.75 and keeping all other model parameters species-invariant. The development of the PBPK model for the ternary mixture in the rat was accomplished by initially validating or refining the existing PBPK models for TOL, XYL, and EBZ and linking the individual chemical models via the hepatic metabolism term. Accordingly, the Michaelis-Menten equation for each solvent was modified to test four possible mechanisms of metabolic interaction (i.e., no interaction, competitive inhibition, noncompetitive inhibition, and uncompetitive inhibition). The metabolic inhibition constant (Ki) for each binary pair of alkyl benzenes was estimated by fitting the binary chemical PBPK model simulations to previously published data on blood concentrations of TOL, XYL, and EBZ in rats exposed for 4 hr to a binary combination of 100 or 200 ppm of each of these solvents. Competitive metabolic inhibition appeared to be the most plausible mechanism of interaction at relevant exposure concentrations for all binary mixtures of alkyl benzenes in the rat (Ki,TOL-XYL = 0.17; Ki,TOL-EBZ = 0.79; Ki,XYL-TOL = 0.77; Ki,XYL-EBZ = 1.50; Ki,EBZ-TOL = 0.33; Ki,EBZ-XYL = 0.23 mg/L). Incorporating the Ki values obtained with the binary chemical mixtures, the PBPK model for the ternary mixture simulated adequately the time course of the venous blood concentrations of TOL, XYL, and EBZ in rats exposed to a mixture containing 100 ppm each of these solvents. Following the validation of the ternary mixture model in the rat, it was scaled to predict the kinetics of TOL, XYL, and EBZ in blood and alveolar air of human volunteers exposed for 7 hr to a combination of 17, 33, and 33 ppm, respectively, of these solvents. Model simulations and experimental data obtained in humans indicated that exposure to atmospheric concentrations of TOL, XYL, and EBZ that remain within the permissible concentrations for a mixture would not result in biologically significant modifications of their pharmacokinetics. Overall, this study demonstrates the utility of PBPK models in the prediction of the kinetics of components of chemical mixtures, by accounting for mechanisms of binary chemical interactions.