Physiologically based modeling of the maximal effect of metabolic interactions on the kinetics of components of complex chemical mixtures

Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds

The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.

Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology

Mixtures risk assessment needs an efficient integration of in vivo, in vitro, and in silico data with epidemiology and human studies data. This involves several approaches, some in current use and others under development. This work extends the Agency for Toxic Substances and Disease Registry physiologically based pharmacokinetic (PBPK) toolkit, available for risk assessors, to include a mixture PBPK model of benzene, toluene, ethylbenzene, and xylenes. The recoded model was evaluated and applied to exposure scenarios to evaluate the validity of dose additivity for mixtures. In the second part of this work, we studied toluene, ethylbenzene, and xylene (TEX)-gene-disease associations using Comparative Toxicogenomics Database, pathway analysis and published microarray data from human gene expression changes in blood samples after short- and long-term exposures. Collectively, this information was used to establish hypotheses on potential linkages between TEX exposures and human health. The results show that 236 genes expressed were common between the short- and long-term exposures. These genes could be central for the interconnecting biological pathways potentially stimulated by TEX exposure, likely related to respiratory and neuro diseases. Using publicly available data we propose a conceptual framework to study pathway perturbations leading to toxicity of chemical mixtures. This proposed methodology lends mechanistic insights of the toxicity of mixtures and when experimentally validated will allow data gaps filling for mixtures' toxicity assessment. This work proposes an approach using current knowledge, available multiple stream data and applying computational methods to advance mixtures risk assessment.

The genotoxicity of an organic solvent mixture: A human biomonitoring study and translation of a real-scenario exposure to in vitro

This study aimed to evaluate occupational exposure to a styrene and xylene mixture through environmental exposure assessment and identify the potential genotoxic effects through biological monitoring. Secondly, we also exposed human peripheral blood cells in vitro to both xylene and styrene either alone or in mixture at concentrations found in occupational settings in order to understand their mechanism of action. The results obtained by air monitoring were below the occupational exposure limits for both substances. All biomarkers of effect, except for nucleoplasmic bridges, had higher mean values in workers (N = 17) compared to the corresponding controls (N = 17). There were statistically significant associations between exposed individuals and the presence of nuclear buds and oxidative damage. As for in vitro results, there was no significant influence on primary DNA damage in blood cells as evaluated by the comet assay. On the contrary, we did observe a significant increase of micronuclei and nuclear buds, but not nucleoplasmic bridges upon in vitro exposure. Taken together, both styrene and xylene have the potential to induce genomic instability either alone or in combination, showing higher effects when combined. The obtained data suggested that thresholds for individual chemicals might be insufficient for ensuring the protection of human health.

Development of a generic lifelong physiologically based biokinetic model for exposome studies

The current study within the frame of the HEALS project aims at the development of a lifelong physiologically based biokinetic (PBBK) model for exposome studies. The aim was to deliver a comprehensive modelling framework for addressing a large chemical space. Towards this aim, the delivered model can easily adapt parameters from existing ad-hoc models or complete the missing compound specific parameters using advanced quantitative structure activity relationship (QSAR). All major human organs are included, as well as arterial, venous, and portal blood compartments. Xenobiotics and their metabolites are linked through the metabolizing tissues. This is mainly the liver, but also other sites of metabolism might be considered (intestine, brain, skin, placenta) based on the presence or not of the enzymes involved in the metabolism of the compound of interest. Each tissue is described by three mass balance equations for (a) red blood cells, (b) plasma and interstitial tissue and (c) cells respectively. The anthropometric parameters of the models are time dependent, so as to provide a lifetime internal dose assessment, as well as to describe the continuously changing physiology of the mother and the developing fetus. An additional component of flexibility is that the biokinetic processes that relate to metabolism are related with either Michaelis-Menten kinetics, as well as intrinsic clearance kinetics. The capability of the model is demonstrated in the assessment of internal exposure and the prediction of expected biomonitored levels in urine for three major compounds within the HEALS project, namely bisphenol A (BPA), Bis(2-ethylhexyl) phthalate (DEHP) and cadmium (Cd). The results indicated that the predicted urinary levels fit very well with the ones from human biomonitoring (HBM) studies; internal exposure to plasticizers is very low (in the range of ng/L), while internal exposure to Cd is in the range of μg/L.

Toluene and methylethylketone: effect of combined exposure on their metabolism in rat

1. Multiple exposures are ubiquitous in industrial environments. In this article, we highlight the risks faced by workers and complete the data available on the metabolic impact of a common mixture: toluene (TOL) and methylethylketone (MEK). 2. Rats were exposed by inhalation under controlled conditions either to each solvent individually, or to mixtures of the two. How the interaction between the two solvents affected their fate in the blood and brain, their main relevant urinary metabolites (o-cresol, benzylmercapturic acid for TOL and 2,3-butanediols for MEK) and their hepatic metabolism were investigated. 3. Although the cytochrome P450 concentration was unchanged, and the activities of CYP1A2 and CYP2E1 isoforms were not additively or synergistically induced by co-exposure, TOL metabolism was inhibited by the presence of MEK (and vice versa). Depending on the relative proportions of each compound in the mixture, this sometimes resulted in a large increase in blood and brain concentrations. Apart from extreme cases (unbalanced mixtures), the amount of o-cresol and benzylmercapturic acid (and to a lesser extent 2,3-butanediols) excreted were proportional to the blood solvent concentrations. 4. In a co-exposure context, ortho-cresol and benzylmercapturic acid can be used as urinary biomarkers in biomonitoring for employees to relatively accurately assess TOL exposure.

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.

Assessing human variability in kinetics for exposures to multiple environmental chemicals: a physiologically based pharmacokinetic modeling case study with dichloromethane, benzene, toluene, ethylbenzene, and m-xylene

The objective of this study was to compare the magnitude of interindividual variability in internal dose for inhalation exposure to single versus multiple chemicals. Physiologically based pharmacokinetic models for adults (AD), neonates (NEO), toddlers (TODD), and pregnant women (PW) were used to simulate inhalation exposure to "low" (RfC-like) or "high" (AEGL-like) air concentrations of benzene (Bz) or dichloromethane (DCM), along with various levels of toluene alone or toluene with ethylbenzene and xylene. Monte Carlo simulations were performed and distributions of relevant internal dose metrics of either Bz or DCM were computed. Area under the blood concentration of parent compound versus time curve (AUC)-based variability in AD, TODD, and PW rose for Bz when concomitant "low" exposure to mixtures of increasing complexities occurred (coefficient of variation (CV) = 16-24%, vs. 12-15% for Bz alone), but remained unchanged considering DCM. Conversely, AUC-based CV in NEO fell (15 to 5% for Bz; 12 to 6% for DCM). Comparable trends were observed considering production of metabolites (AMET), except for NEO's CYP2E1-mediated metabolites of Bz, where an increased CV was observed (20 to 71%). For "high" exposure scenarios, Cmax-based variability of Bz and DCM remained unchanged in AD and PW, but decreased in NEO (CV= 11-16% to 2-6%) and TODD (CV= 12-13% to 7-9%). Conversely, AMET-based variability for both substrates rose in every subpopulation. This study analyzed for the first time the impact of multiple exposures on interindividual variability in toxicokinetics. Evidence indicates that this impact depends upon chemical concentrations and biochemical properties, as well as the subpopulation and internal dose metrics considered.

Translational research to develop a human PBPK models tool kit-volatile organic compounds (VOCs)

Toxicity and exposure evaluations remain the two of the key components of human health assessment. While improvement in exposure assessment relies on a better understanding of human behavior patterns, toxicity assessment still relies to a great extent on animal toxicity testing and human epidemiological studies. Recent advances in computer modeling of the dose-response relationship and distribution of xenobiotics in humans to important target tissues have advanced our abilities to assess toxicity. In particular, physiologically based pharmacokinetic (PBPK) models are among the tools than can enhance toxicity assessment accuracy. Many PBPK models are available to the health assessor, but most are so difficult to use that health assessors rarely use them. To encourage their use these models need to have transparent and user-friendly formats. To this end the Agency for Toxic Substances and Disease Registry (ATSDR) is using translational research to increase PBPK model accessibility, understandability, and use in the site-specific health assessment arena. The agency has initiated development of a human PBPK tool-kit for certain high priority pollutants. The tool kit comprises a series of suitable models. The models are recoded in a single computer simulation language and evaluated for use by health assessors. While not necessarily being state-of-the-art code for each chemical, the models will be sufficiently accurate to use for screening purposes. This article presents a generic, seven-compartment PBPK model for six priority volatile organic compounds (VOCs): benzene (BEN), carbon tetrachloride (CCl(4)), dichloromethane (DCM), perchloroethylene (PCE), trichloroethylene (TCE), and vinyl chloride (VC). Limited comparisons of the generic and original model predictions to published kinetic data were conducted. A goodness of fit was determined by calculating the means of the sum of the squared differences (MSSDs) for simulation vs. experimental kinetic data using the generic and original models. Using simplified solvent exposure assumptions for oral ingestion and inhalation, steady-state blood concentrations of each solvent were simulated for exposures equivalent to the ATSDR Minimal Risk Levels (MRLs). The predicted blood levels were then compared to those reported in the National Health and Nutrition Examination Survey (NHANES). With the notable exception of BEN, simulations of combined oral and inhalation MRLs using our generic VOC model yielded blood concentrations well above those reported for the 95th percentile blood concentrations for the U.S. populations, suggesting no health concerns. When the PBPK tool kit is fully developed, risk assessors will have a readily accessible tool for evaluating human exposure to a variety of environmental pollutants.

An integrated QSAR-PBPK modelling approach for predicting the inhalation toxicokinetics of mixtures of volatile organic chemicals in the rat

The objective of this study was to predict the inhalation toxicokinetics of chemicals in mixtures using an integrated QSAR-PBPK modelling approach. The approach involved: (1) the determination of partition coefficients as well as V(max) and K(m) based solely on chemical structure for 53 volatile organic compounds, according to the group contribution approach; and (2) using the QSAR-driven coefficients as input in interaction-based PBPK models in the rat to predict the pharmacokinetics of chemicals in mixtures of up to 10 components (benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene, and styrene). QSAR-estimated values of V(max) varied compared with experimental results by a factor of three for 43 out of 53 studied volatile organic compounds (VOCs). K(m) values were within a factor of three compared with experimental values for 43 out of 53 VOCs. Cross-validation performed as a ratio of predicted residual sum of squares and sum of squares of the response value indicates a value of 0.108 for V(max) and 0.208 for K(m). The integration of QSARs for partition coefficients, V(max) and K(m), as well as setting the K(m) equal to K(i) (metabolic inhibition constant) within the mixture PBPK model allowed to generate simulations of the inhalation pharmacokinetics of benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene and styrene in various mixtures. Overall, the present study indicates the potential usefulness of the QSAR-PBPK modelling approach to provide first-cut evaluations of the kinetics of chemicals in mixtures of increasing complexity, on the basis of chemical structure.

Estimation of absorption of aromatic hydrocarbons diffusing from interior materials in automobile cabins by inhalation toxicokinetic analysis in rats

Aromatic hydrocarbons, as well as aliphatic hydrocarbons, diffusing from interior materials in automotive cabins are the most common compounds contributing to interior air pollution. In this study, the amounts of seven selected aromatic hydrocarbons absorbed by a car driver were estimated by evaluating their inhalation toxicokinetics in rats. Measured amounts of these substances were injected into a closed chamber system containing a rat, and the concentration changes in the chamber were examined. The toxicokinetics of the substances were evaluated on the basis of the concentration-time course using a nonlinear compartment model. The amounts absorbed in humans at actual concentrations in automobile cabins without ventilation were extrapolated from the results obtained from rats. The absorbed amounts estimated for a driver during a 2 h drive were as follows (per 60 kg of human body weight): 30 microg for toluene (interior median concentration, 40 microg m(-3) in our previous study), 10 microg for ethylbenzene (12 microg m(-3)), 6 microg for o-xylene (10 microg m(-3)), 8 microg for m-xylene (11 microg m(-3)), 9 microg for p-xylene (11 microg m(-3)), 11 microg for styrene (11 microg m(-3)) and 27 microg for 1,2,4-trimethylbenzene (24 microg m(-3)). Similarly, in a cabin where air pollution was marked, the absorbed amount of styrene (654 microg for 2 h in a cabin with an interior maximum concentration of 675 microg m(-3)) was estimated to be much higher than those of other substances. This amount (654 microg) was approximately 1.5 times the tolerable daily intake of styrene (7.7 microg kg(-1) per day) recommended by the World Health Organization.

Derivation of Biomonitoring Equivalents for di-n-butyl phthalate (DBP), benzylbutyl phthalate (BzBP), and diethyl phthalate (DEP)

Recent efforts worldwide have resulted in a growing database of measured concentrations of chemicals in blood and urine samples taken from the general population. However, few tools exist to assist in the interpretation of the measured values in a health risk context. Biomonitoring Equivalents (BEs) are defined as the concentration or range of concentrations of a chemical or its metabolite in a biological medium (blood, urine, or other medium) that is consistent with an existing health-based exposure guideline, and are derived by integrating available data on pharmacokinetics with existing chemical risk assessments. This study reviews available health-based exposure guidance values for di-n-butyl phthalate (DBP), benzylbutyl phthalate (BzBP), and diethyl phthalate (DEP) from Health Canada, the United States Environmental Protection Agency (U.S. EPA), the Agency for Toxic Substances and Disease Registry (ATSDR), and the European Food Safety Authority (EFSA). BE values corresponding to the oral reference dose (RfD), minimal risk level (MRL) or tolerable daily intake (TDI) estimates from these agencies were derived for each compound based on data on excretion fractions of key urinary metabolites. These values may be used as screening tools for evaluation of biomonitoring data for metabolites of these three phthalate compounds in the context of existing risk assessments and for prioritization of the potential need for additional risk assessment efforts for each of these compounds relative to other chemicals.

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.

Review of the toxicity of chemical mixtures: Theory, policy, and regulatory practice

An analysis of current mixture theory, policy, and practice was conducted by examining standard reference texts, regulatory guidance documents, and journal articles. Although this literature contains useful theoretical concepts, clear definitions of most terminology, and well developed protocols for study design and statistical analysis, no general theoretical basis for the mechanisms and interactions of mixture toxicity could be discerned. There is also a poor understanding of the relationship between exposure-based and internal received dose metrics. This confounds data interpretation and limits reliable determinations of the nature and extent of additivity. The absence of any generally accepted classification scheme for either modes/mechanisms of toxic action or of mechanisms of toxicity interactions is problematic as it produces a cycle in which research and policy are interdependent and mutually limiting. Current regulatory guidance depends heavily on determination of toxicological similarity concluded from the presence of a few prominent constituents, assumed from a common toxicological effect, or presumed from an alleged similar toxic mode/mechanism. Additivity, or the lack of it, is largely based on extrapolation of existing knowledge for single chemicals in this context. Thus, regulatory risk assessment protocols lack authoritative theoretical underpinnings, creating substantial uncertainty. Development of comprehensive classification schemes for modes/mechanisms of toxic action and mechanisms of interaction is needed to ensure a sound theoretical foundation for mixture-related regulatory activity and provide a firm basis for iterative hypothesis development and experimental testing.

A human PBPK model for ethanol describing inhibition of gastric motility

A physiologically based pharmacokinetic model for investigating inter-individual and inter-racial variability in ethanol pharmacokinetics is presented. The model is a substantial modification of an existing model which described some genetic polymorphisms in the hepatic alcohol dehydrogenase enzymes. The model was modified to incorporate a description of ethanol absorption from the stomach and gastro-intestinal tract and the retardation of gastric emptying due to a concentration-dependent inhibition of gastric peristalsis. In addition, intra-venous and intra-arterial routes of administration were added to investigate whether the biological structure of the model provided a core which may be easily adapted for any route of exposure. The model is proposed as suitable for the investigation of the effects of both acute and chronic ethanol exposure.

Liver regeneration: a critical toxicodynamic response in predictive toxicology

The objective of the present review is to discuss the importance tissue repair in the mixture risk assessment. Studies have revealed the existence of two stages of toxicity: an inflictive stage (stage I) and progressive or regressive stage (stage II). While much is known about mechanisms by which injury is inflicted (stage I), very little is known about the mechanisms that lead to progression or regression of injury. A wide variety of additional experimental evidence suggests that tissue repair impacts decisively on the final toxic outcome and any modulation in this response has profound impact in the final outcome of toxicity. We designed the present research to investigate the importance of tissue repair in the final acute hepatotoxic outcome upon exposures to mixture of toxicants comprising thioacetamide (TA), allyl alcohol (AA), chloroform (CHCl(3)) and trichloroethylene (TCE). Dose response studies with individual compounds, binary mixtures (BM), ternary (TM) and quaternary mixtures (QM) have been conducted. Results of CHCl(3) + AA BM [Anand, S.S., Murthy, S.N., Vishal, V.S., Mumtaz, M.M., Mehendale, H.M., 2003. Tissue repair plays pivotal role in final outcome of supra-additive liver injury after chloroform and allyl alcohol binary mixture. Food Chem. Toxicol. 41, 1123] and CHCl(3) + AA + TA +TCE QM [Soni, M.G., Ramaiah, S.K., Mumtaz, M.M., Clewell, H., Mehendale, H.M., 1999. Toxicant-inflicted injury and stimulated tissue repair are opposing toxicodynamic forces in predictive toxicology. Regul. Phramcol. Toxicol. 19, 165], and two representative individual compounds (TA and AA) [Mangipudy, R.S., Chanda, S., Mehendale, H.M., 1995a. Tissue repair response as a function of dose in thioacetamide hepatotoxicity. Environ. Health Perspect. 103, 260; Soni, M.G., Ramaiah, S.K., Mumtaz, M.M., Clewell, H., Mehendale, H.M., 1999. Toxicant-inflicted injury and stimulated tissue repair are opposing toxicodynamic forces in predictive toxicology. Regul. Phramcol. Toxicol. 19, 165] are described in this review. In addition, modulation of tissue repair in the outcome of hepatotoxicity and its implications in the risk assessment have been discussed. Male Sprague-Dawley (S-D) rats (250-300g) received a single i.p. injection of individual toxicants as well as mixtures. Liver injury was assessed by plasma alanine amino transferase (ALT) and histopathology. Tissue regeneration response was measured by [(3)H]-thymidine ((3)H-T) incorporation into hepatocellular nuclear DNA and PCNA. Only ALT and (3)H-T data have been presented in this review for the sake of simplicity. Studies with individual hepatotoxicants showed a dose-related increase in injury as well as tissue repair up to a threshold dose. Beyond this threshold, tissue repair was inhibited, and liver injury progressed leading to mortality. Since the highest dose of individual compounds resulted in mortality, this dose was not employed for mixture studies. While CHCl(3) + AA BM caused supra-additive liver injury, QM caused additive liver injury. Due to the prompt and robust compensatory tissue repair, all the rats exposed to BM survived. With QM, the rats receiving the highest dose combination experienced some mortality consequent to the progression of liver injury attendant to suppressed tissue repair. These findings suggest that liver tissue repair, the opposing biological response that restores tissue lost to injury, may play a critical and determining role in the outcome of liver injury regardless of the number of toxicants in the mixture or the mechanism of initiation of injury. These data suggest that inclusion of this response in risk assessment might help in fine-tuning the prediction of toxic outcomes.

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.