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The inter-relationship of "Thresholds" between chemical mixtures and their respective component single chemicals was studied using three sets of data and two types of analyses. Two in vitro data sets involve cytotoxicity in human keratinocytes from treatment of metals and a metal mixture [Bae, D.S., Gennings, C., Carter, Jr., W.H., Yang, R.S.H., Campain, J.A., 2001. Toxicological interactions among arsenic, cadmium, chromium, and lead in human keratinocytes. Toxicol. Sci. 63, 132-142; Gennings, C., Carter, Jr., W.H., Campain, J.A., Bae, D.S., Yang, R.S.H., 2002. Statistical analysis of interactive cytotoxicity in human epidermal keratinocytes following exposure to a mixture of four metals. J. Agric. Biol. Environ. Stat. 7, 58-73], and induction of estrogen receptor alpha (ER-alpha) reporter gene in MCF-7 human breast cancer cells by estrogenic xenobiotics [Gennings, C., Carter, Jr., W.H., Carney, E.W., Charles, G.D., Gollapudi, B.B., Carchman, R.A., 2004. A novel flexible approach for evaluating fixed ratio mixtures of full and partial agonists. Toxicol. Sci. 80, 134-150]. The third data set came from PBPK modeling of gasoline and its components in the human. For in vitro cellular responses, we employed Benchmark Dose Software (BMDS) to obtain BMD01, BMD05, and BMD10. We then plotted these BMDs against exposure concentrations for the chemical mixture and its components to assess the ranges and slopes of these BMD-concentration lines. In doing so, we consider certain BMDs to be "Interaction Thresholds" or "Thresholds" for mixtures and their component single chemicals and the slope of the line must be a reflection of the potency of the biological effects. For in vivo PBPK modeling, we used 0.1x TLVs, TLVs, and 10x TLVs for gasoline and six component markers as input dosing for PBPK modeling. In this case, the venous blood levels under the hypothetical exposure conditions become our designated "Interaction Thresholds" or "Thresholds" for gasoline and its component single chemicals. Our analyses revealed that the mixture "Interaction Thresholds" appear to stay within the bounds of the "Thresholds" of its respective component single chemicals. Although such a trend appears to be emerging, nevertheless, it should be emphasized that our analyses are based on limited data sets and further analyses on data sets, preferably the more comprehensive experimental data sets, are needed before a definitive conclusion can be drawn.

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.

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.

Many cellular responses are quantal; that is, they either take place or they do not. Examples of "either-or" responses include cell replication, differentiation and apoptosis. Surprisingly, induction of suites of genes and coordinated phenotypic changes in cells are also often quantal, where embedded molecular circuitry creates on-off switches. Mechanistic incidence-dose (ID) models need to account for the quantal characteristics of cellular switches that contribute, in turn, to dose thresholds and to the incidence of biological responses in individuals. Interdisciplinary systems biology approaches create mechanistic ID models based on: (i) detailed knowledge of the cellular circuitry controlling signal transduction; (ii) evolving biological modeling tools describing cellular circuits and their perturbations by chemicals and (iii) high throughput, high coverage "omic" screens for examining cell signaling pathways and biological responses. These interdisciplinary approaches should produce novel, quantitative ID models for biological responses and greatly improve the biological basis of safety and risk assessments.

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.

Physiologically based pharmacokinetic (PBPK) models have often been used to describe the absorption, distribution, metabolism, and excretion of chemicals in animals but have been limited to single chemicals and simple mixtures due to the numerous parameters required in the models. To overcome the barrier to modeling more complex mixtures, we used a chemical lumping approach, used in the past in chemical engineering but not in pharmacokinetic modeling, in a rat PBPK model for gasoline hydrocarbons. Our previous gasoline model consisted of five individual components (benzene, toluene, ethylbenzene, xylene, and hexane) and a lumped chemical that included all remaining components of whole gasoline. Despite being comprised of hundreds of components, the lumped component could be described using a single set of chemical parameters that depended on the blend of gasoline. In the present study, we extend this approach to evaporative fractions of gasoline. The PBPK model described the pharmacokinetics of all of the volatility-weighted fractions of gasoline when differences in partitioning and metabolism between fractions were taken into account. Adjusting the ventilation rate parameter to account for respiratory depression at high exposures also allowed a much improved description of the data. At high exposure levels, gasoline components competitively inhibit each other's metabolism, and the model successfully accounted for binary interactions of this type, including between the lumped component and the five other chemicals. The model serves as a first example of how the engineering concept of chemical lumping can be used in pharmacokinetics.

Individuals are exposed to mixtures, and never to single chemicals. Depending on the composition of the elements of mixtures, significant toxicological interactions between the components may occur. These interactions are complex and often difficult to predict, ranging from synergistic to additive and subadditive interactions. The nature of the interactions needs to be evaluated as the target tissue dose of the active form of each chemical. PBPK modeling is an effective tool for determining the target tissue dose and evaluating these interactions when data are available for model development. Some of the interactions are pharmacokinetic in nature, affecting the disposition of other chemicals in the body. Other interactions can be pharmacodynamic in nature, altering the effects that other chemicals have on the organism. For many organic solvents, these interactions occur principally at the level of the metabolizing enzyme, cytochrome P-450 2E1 (CYP2E1). Many solvents are known to induce or inhibit CYP2E1, or both. Mixtures may be comprised of concomitant exposures to chemicals or from components encountered separately on-the-job, off-the-job, through the diet, and otherwise. Examples of mixtures where the exposure to separate components occurs off the job will be discussed, with special emphasis on ethanol consumption as a modifier of solvent pharmacokinetics. The present practice of the linear extrapolation of the toxicity of individual mixture components in the interpretation of occupational exposure limits will also be critiqued.

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.

Petroleum hydrocarbon mixtures such as gasoline, diesel fuel, aviation fuel, and asphalt liquids typically contain hundreds of compounds. These compounds include aliphatic and aromatic hydrocarbons within a specific molecular weight range and sometimes lesser amounts of additives, and often exhibit qualitatively similar pharmacokinetic (PK) and pharmacodynamic properties. However, there are some components that exhibit specific biological effects, such as methyl t-butyl ether and benzene in gasoline. One of the potential pharmacokinetic interactions of many components in such mixtures is inhibition of the metabolism of other components. Due to the complexity of the mixtures, a quantitative description of the pharmacokinetics of each component, particularly in the context of differing blends of these mixtures, has not been available. We describe here a physiologically-based pharmacokinetic (PBPK) modeling approach to describe the PKs of whole gasoline. The approach simplifies the problem by isolating specific components for which a description is desired and treating the remaining components as a single lumped chemical. In this manner, the effect of the non-isolated components (i.e. inhibition) can be taken into account. The gasoline model was based on PK data for the single chemicals, for simple mixtures of the isolated chemicals, and for the isolated and lumped chemicals during gas uptake PK experiments in rats exposed to whole gasoline. While some sacrifice in model accuracy must be made when a chemical lumping approach is used, our lumped PK model still permitted a good representation of the PKs of five isolated chemicals (n-hexane, benzene, toluene, ethylbenzene, and o-xylene) during exposure to various levels of two different blends of gasoline. The approach may be applicable to other hydrocarbon mixtures when appropriate PK data are available for model development.

Gasoline consists of a few toxicologically significant components and a large number of other hydrocarbons in a complex mixture. By using an integrated, physiologically based pharmacokinetic (PBPK) modeling and lumping approach, we have developed a method for characterizing the pharmacokinetics (PKs) of gasoline in rats. The PBPK model tracks selected target components (benzene, toluene, ethylbenzene, o-xylene [BTEX], and n-hexane) and a lumped chemical group representing all nontarget components, with competitive metabolic inhibition between all target compounds and the lumped chemical. PK data was acquired by performing gas uptake PK studies with male F344 rats in a closed chamber. Chamber air samples were analyzed every 10-20 min by gas chromatography/flame ionization detection and all nontarget chemicals were co-integrated. A four-compartment PBPK model with metabolic interactions was constructed using the BTEX, n-hexane, and lumped chemical data. Target chemical kinetic parameters were refined by studies with either the single chemical alone or with all five chemicals together. o-Xylene, at high concentrations, decreased alveolar ventilation, consistent with respiratory irritation. A six-chemical interaction model with the lumped chemical group was used to estimate lumped chemical partitioning and metabolic parameters for a winter blend of gasoline with methyl t-butyl ether and a summer blend without any oxygenate. Computer simulation results from this model matched well with experimental data from single chemical, five-chemical mixture, and the two blends of gasoline. The PBPK model analysis indicated that metabolism of individual components was inhibited up to 27% during the 6-h gas uptake experiments of gasoline exposures.

Signaling motifs (nuclear transcriptional receptors, kinase/phosphatase cascades, G-coupled protein receptors, etc.) have composite dose-response behaviors in relation to concentrations of protein receptors and endogenous signaling molecules. "Molecular circuits" include the biological components and their interactions that comprise the workings of these signaling motifs. Many of these molecular circuits have nonlinear dose-response behaviors for endogenous ligands and for exogenous toxicants, acting as switches with "all-or-none" responses over a narrow range of concentration. In turn, these biological switches regulate large-scale cellular processes, e.g., commitment to cell division, cell differentiation, and phenotypic alterations. Biologically based dose-response (BBDR) models accounting for these biological switches would improve risk assessment for many nonlinear processes in toxicology. These BBDR models must account for normal control of the signaling motifs and for perturbations by toxic compounds. We describe several of these biological switches, current tools available for constructing BBDR models of these processes, and the potential value of these models in risk assessment.

The complexity and the astronomic number of possible chemical mixtures preclude any systematic experimental assessment of toxicology of all potentially troublesome chemical mixtures. Thus, the use of computer modeling and mechanistic toxicology for the development of a predictive tool is a promising approach to deal with chemical mixtures. In the past 15 years or so, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been applied to the toxicologic interactions of chemical mixtures. This approach is promising for relatively simple chemical mixtures; the most complicated chemical mixtures studied so far using this approach contained five or fewer component chemicals. In this presentation we provide some examples of the utility of PBPK/PD modeling for toxicologic interactions in chemical mixtures. The probability of developing predictive tools for simple mixtures using PBPK/PD modeling is high. Unfortunately, relatively few attempts have been made to develop paradigms to consider the risks posed by very complex chemical mixtures such as gasoline, diesel, tobacco smoke, etc. However, recent collaboration between scientists at Colorado State University and engineers at Rutgers University attempting to use reaction network modeling has created hope for the possible development of a modeling approach with the potential of predicting the outcome of toxicology of complex chemical mixtures. We discuss the applications of reaction network modeling in the context of petroleum refining and its potential for elucidating toxic interactions with mixtures.

Two fundamental concepts have emerged to organize contemporary approaches to chemical risk assessment - mode of action and tissue dosimetry. Mode of action specifies the nature of the interactions between the chemical and the body that lead to toxic responses and should, under optimal circumstances, also specify the form of the tissue dose that leads to these effects. This paper highlights recent development of biologically based dose response (BBDR) models for specific toxic endpoints that use knowledge on mode of action to specify measures of dose. These dose measures then are used to support low dose and interspecies extrapolations. We first focus on a series of dose response models developed for several compounds that produce nasal toxicity. These examples demonstrate a range of model structures from simple dosimetry models (methylmethacrylate) to linkage of dosimetry with specific biological processes involved in carcinogenesis (formaldehyde). Two BBDR models with dioxin illustrate the organization of biological and dosimetry information into specific testable hypotheses that could distinguish these different models and lead to a more uniform approach to risk assessment for this compound. A final section discusses the impact of molecular biology and the genomic revolution in relation to development of BBDR models for specific toxic endpoints.