Improving the risk assessment of lipophilic persistent environmental chemicals in breast milk.

Lipophilic persistent environmental chemicals (LPECs) have the potential to accumulate within a woman's body lipids over the course of many years prior to pregnancy, to partition into human milk, and to transfer to infants upon breastfeeding. As a result of this accumulation and partitioning, a breastfeeding infant's intake of these LPECs may be much greater than his/her mother's average daily exposure. Because the developmental period sets the stage for lifelong health, it is important to be able to accurately assess chemical exposures in early life. In many cases, current human health risk assessment methods do not account for differences between maternal and infant exposures to LPECs or for lifestage-specific effects of exposure to these chemicals. Because of their persistence and accumulation in body lipids and partitioning into breast milk, LPECs present unique challenges for each component of the human health risk assessment process, including hazard identification, dose-response assessment, and exposure assessment. Specific biological modeling approaches are available to support both dose-response and exposure assessment for lactational exposures to LPECs. Yet, lack of data limits the application of these approaches. The goal of this review is to outline the available approaches and to identify key issues that, if addressed, could improve efforts to apply these approaches to risk assessment of lactational exposure to these chemicals.

A physiologically based pharmacokinetic model of rifampin in mice.

One problem associated with regimen-based development of antituberculosis (anti-TB) drugs is the difficulty of a systematic and thorough in vivo evaluation of the large number of possible regimens that arise from consideration of multiple drugs tested together. A mathematical model capable of simulating the pharmacokinetics and pharmacodynamics of experimental combination chemotherapy of TB offers a way to mitigate this problem by extending the use of available data to investigate regimens that are not initially tested. In order to increase the available mathematical tools needed to support such a model for preclinical anti-TB drug development, we constructed a preliminary whole-body physiologically based pharmacokinetic (PBPK) model of rifampin in mice, using data from the literature. Interindividual variability was approximated using Monte Carlo (MC) simulation with assigned probability distributions for the model parameters. An MC sensitivity analysis was also performed to determine correlations between model parameters and plasma concentration to inform future model development. Model predictions for rifampin concentrations in plasma, liver, kidneys, and lungs, following oral administration, were generally in agreement with published experimental data from multiple studies. Sensitive model parameters included those descriptive of oral absorption, total clearance, and partitioning of rifampin between blood and muscle. This PBPK model can serve as a starting point for the integration of rifampin pharmacokinetics in mice into a larger mathematical framework, including the immune response to Mycobacterium tuberculosis infection, and pharmacokinetic models for other anti-TB drugs.

Application of Bayesian population physiologically based pharmacokinetic (PBPK) modeling and Markov chain Monte Carlo simulations to pesticide kinetics studies in protected marine mammals: DDT, DDE, and DDD in harbor porpoises.

Physiologically based pharmacokinetic (PBPK) modeling in marine mammals is a challenge because of the lack of parameter information and the ban on exposure experiments. To minimize uncertainty and variability, parameter estimation methods are required for the development of reliable PBPK models. The present study is the first to develop PBPK models for the lifetime bioaccumulation of p,p'-DDT, p,p'-DDE, and p,p'-DDD in harbor porpoises. In addition, this study is also the first to apply the Bayesian approach executed with Markov chain Monte Carlo simulations using two data sets of harbor porpoises from the Black and North Seas. Parameters from the literature were used as priors for the first "model update" using the Black Sea data set, the resulting posterior parameters were then used as priors for the second "model update" using the North Sea data set. As such, PBPK models with parameters specific for harbor porpoises could be strengthened with more robust probability distributions. As the science and biomonitoring effort progress in this area, more data sets will become available to further strengthen and update the parameters in the PBPK models for harbor porpoises as a species anywhere in the world. Further, such an approach could very well be extended to other protected marine mammals.

A non-invasive approach to study lifetime exposure and bioaccumulation of PCBs in protected marine mammals: PBPK modeling in harbor porpoises.

In the last decade, physiologically based pharmacokinetic (PBPK) models have increasingly been developed to explain the kinetics of environmental pollutants in wildlife. For marine mammals specifically, these models provide a new, non-destructive tool that enables the integration of biomonitoring activities and in vitro studies. The goals of the present study were firstly to develop PBPK models for several environmental relevant PCB congeners in harbor porpoises (Phocoena phocoena), a species that is sensitive to pollution because of its limited metabolic capacity for pollutant transformation. These models were tested using tissue data of porpoises from the Black Sea. Secondly, the predictive power of the models was investigated for time trends in the PCB concentrations in North Sea harbor porpoises between 1990 and 2008. Thirdly, attempts were made to assess metabolic capacities of harbor porpoises for the investigated PCBs. In general, results show that parameter values from other species (rodents, humans) are not always suitable in marine mammal models, most probably due to differences in physiology and exposure. The PCB 149 levels decrease the fastest in male harbor porpoises from the North Sea in a time period of 18years, whereas the PCB 101 levels decrease the slowest. According to the models, metabolic breakdown of PCB 118 is probably of lesser importance compared to other elimination pathways. For PCB 101 and 149 however, the presence of their metabolites can be attributed to bioaccumulation of metabolites from the prey and to metabolic breakdown of the parent compounds in the harbor porpoises.

Physiologically based pharmacokinetic (PBPK) models for lifetime exposure to PCB 153 in male and female harbor porpoises (Phocoena phocoena): model development and evaluation.

Physiologically based pharmacokinetic (PBPK) models were developed for the most persistent polychlorinated biphenyl (PCB 153) in male and female harbor porpoises (Phocoena phocoena) to elucidate processes such as uptake, distribution, and elimination. Due to its limited metabolic capacities, long life span, and top position in marine food chains, this species is highly sensitive to pollution. The models consist of 5 compartments, liver, blubber, kidney, brain, and a compartment which accounts for the rest of the body, all connected through blood. All physiological and biochemical parameters were extracted from the literature, except for the brain/blood partition coefficient and rate of excretion, which were both fitted to data sets used for validation of the models. These data sets were compiled from our own analyses performed with GC-MS on tissue samples of harbor porpoises. The intake of PCB 153 was from milk from birth to 4 months, and after weaning fish was the main food source. Overall, these models reveal that concentrations of PCB 153 in males increase with age but suggest that, as the animals grow older, metabolic transformation can be a possible pathway for elimination as well. In contrast, the model for females confirms that gestation and lactation are key processes for eliminating PCB 153 as body burdens decrease with age. These PBPK models are capable of simulating the bioaccumulation of PCB 153 during the entire life span of approximately 20 years of the harbor porpoises.

Pharmacokinetics and physiologically-based pharmacokinetic modeling of nanoparticles.

The worldwide commerce involving nanoparticles will soon reach $1 trillion and already we have more than 600 commercial products containing nanoparticles. Because nanoparticles are invisible and little is known about their toxicities, there has been concern about health effects in humans. As toxicology is a continuum of pharmacokinetics and pharmacodynamics, this is a review of recent advances on pharmacokinetics and physiologically-based pharmacokinetic (PBPK) modeling involving nanoparticles. We provide a synopsis of the state-of-the-science on the absorption, distribution, metabolism, and excretion (ADME) of nanoparticles in mammals, as well as some of the unique applications of pharmacokinetics to nanotechnology. Earlier, the main emphasis of pharmacokinetics of nanoparticles centered around the "control release" of drugs. Thus, drugs encapsulated by lipidic nanoparticles or bound to nano-particles form a controlled-release mechanism. The end results included, among others, enhancement of therapeutic duration and reversion of multidrug resistance. As the science advances in this area, the resulting achievements included: (1) utilizing nanoparticles as delivery vehicle for drugs, drug combinations, or genetic materials; (2) capitalizing on physico-chemical properties and tissue affinity of nanoparticles for medical imaging; (3) potentiating drug effects on immunotoxin and anticancer drugs; and (4) creating "stealth" capability from body's defense system. More recently, the application of biologically-based computer modeling to nanoparticles made it possible not only for inter-species, inter-routes, and inter-dose extrapolations but also for the integration of the modern tumor biology and computational technology for the possible improvement of cancer chemotherapy. Although pharmacokinetics and PBPK modeling of nanoparticles are still in their infancy, impressive innovations have already been demonstrated in their applications to medical sciences. Nanotoxicology is one of the most promising and fertile areas of science given the importance of nanoparticles to the economy of the 21st century, their possible environmental fates, as well as the potential health concerns of these particles.

Effect of PCBs on the lactational transfer of methyl mercury in mice: PBPK modeling.

MeHg and PCB exposure to lactating mice were analyzed and a physiologically-based pharmacokinetic (PBPK) model was developed to describe the lactational transfer of MeHg in mice. The influence of albumin on the lactational transfer of MeHg was incorporated into the PBPK model. Experimental results with lactating mice and their pups showed that co-exposure with PCB congeners increased the lactational transfer of MeHg to the pups, which was associated with the rise of albumin levels in maternal blood. Observed results were matched with PBPK model simulations conducted under the assumptions that (1) MeHg bound to plasma albumin is transferred to maternal milk, and (2) PCB congeners may increase the lactational transfer of MeHg by escalating albumin levels in maternal blood. Further refinement of PBPK model quantitatively described the pharmacokinetic changes of MeHg by co-exposure with PCBs in pup's tissues.

Computational toxicology of chloroform: reverse dosimetry using Bayesian inference, Markov chain Monte Carlo simulation, and human biomonitoring data.

BACKGROUND: One problem of interpreting population-based biomonitoring data is the reconstruction of corresponding external exposure in cases where no such data are available. OBJECTIVES: We demonstrate the use of a computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of environmental chloroform source concentrations consistent with human biomonitoring data. The biomonitoring data consist of chloroform blood concentrations measured as part of the Third National Health and Nutrition Examination Survey (NHANES III), and for which no corresponding exposure data were collected. METHODS: We used a combined PBPK and shower exposure model to consider several routes and sources of exposure: ingestion of tap water, inhalation of ambient household air, and inhalation and dermal absorption while showering. We determined posterior distributions for chloroform concentration in tap water and ambient household air using U.S. Environmental Protection Agency Total Exposure Assessment Methodology (TEAM) data as prior distributions for the Bayesian analysis. RESULTS: Posterior distributions for exposure indicate that 95% of the population represented by the NHANES III data had likely chloroform exposures < or = 67 microg/L [corrected] in tap water and < or = 0.02 microg/L in ambient household air. CONCLUSIONS: Our results demonstrate the application of computer simulation to aid in the interpretation of human biomonitoring data in the context of the exposure-health evaluation-risk assessment continuum. These results should be considered as a demonstration of the method and can be improved with the addition of more detailed data.

Population physiologically based pharmacokinetic modeling for the human lactational transfer of PCB-153 with consideration of worldwide human biomonitoring results.

BACKGROUND: One of the most serious human health concerns related to environmental contamination with polychlorinated biphenyls (PCBs) is the presence of these chemicals in breast milk. OBJECTIVES: We developed a physiologically based pharmacokinetic model of PCB-153 in women, and predict its transfer via lactation to infants. The model is the first human, population-scale lactational model for PCB-153. Data in the literature provided estimates for model development and for performance assessment. METHODS: We used physiologic parameters from a cohort in Taiwan and reference values given in the literature to estimate partition coefficients based on chemical structure and the lipid content in various body tissues. Using exposure data from Japan, we predicted acquired body burden of PCB-153 at an average childbearing age of 25 years and compared predictions to measurements from studies in multiple countries. We attempted one example of reverse dosimetry modeling using our PBPK model for possible exposure scenarios in Canadian Inuits, the population with the highest breast milk PCB-153 level in the world. RESULTS: Forward-model predictions agree well with human biomonitoring measurements, as represented by summary statistics and uncertainty estimates. CONCLUSION: The model successfully describes the range of possible PCB-153 dispositions in maternal milk, suggesting a promising option for back-estimating doses for various populations.

Persistent tissue kinetics and redistribution of nanoparticles, quantum dot 705, in mice: ICP-MS quantitative assessment.

BACKGROUND: Quantum dots (QDs) are autofluorescent semiconductor nanocrystals that can be used for in vivo biomedical imaging. However, we know little about their in vivo disposition and health consequences. OBJECTIVES: We assessed the tissue disposition and pharmacokinetics of QD705 in mice. METHODS: We determined quantitatively the blood and tissue kinetics of QD705 in mice after single intravenous (iv) injection at the dose of 40 pmol for up to 28 days. Inductively coupled plasma-mass spectrometry (ICP-MS) measurement of cadmium was the primary method of quantification of QD705. Fluorescence light microscopy revealed the localization of QD705 in tissues. RESULTS: Plasma half-life of QD705 in mice was short (18.5 hr), but ICP-MS analyses revealed QD705 persisted and even continued to increase in the spleen, liver, and kidney 28 days after an iv dose. Considerable time-dependent redistribution from body mass to liver and kidney was apparent between 1 and 28 days postdosing. The recoveries at both time points were near 100%; all QD705s reside in the body. Neither fecal nor urinary excretion of QD705 was detected appreciably in 28 days postdosing. Fluorescence microscopy demonstrated deposition of QD705 in the liver, spleen, and kidneys. CONCLUSION: Judging from the continued increase in the liver (29-42% of the administered dose), kidney (1.5-9.2%), and spleen (4.8-5.2%) between 1 and 28 days without any appreciable excretion, QD705 has a very long half-life, potentially weeks or even months, in the body and its health consequences deserve serious consideration.

Hepatic sequestration of chlordecone and hexafluoroacetone evaluated by pharmacokinetic modeling.

Chlordecone (CD) and mirex (M) differ by a single carbonyl group in CD in place of two chlorines in M. Although both compounds are lipophilic, their tissue distributions differ markedly: CD concentrations are highest in liver; M concentrations are highest in fat. We used tissue time course data in rats from our laboratory for CD and M and literature data from monkeys to develop PBPK models to study differences in liver and fat partitioning. The PK model for M had partitioning in tissue without specific hepatic binding. The CD model had partitioning similar to M, and also included liver binding: the maximal binding (B(max)) and binding affinity constant (Kd) required to describe the rat data were 370 nmol/g liver and 100 nM, respectively. To see if other ketones with electron withdrawing constituents at the alpha carbon were also preferentially distributed to liver, we developed a PBPK description for tissue distribution of hexafluoroacetone (HFA). Compared to acetone, HFA is known to be preferentially sequestered in liver and more slowly excreted unchanged from the body. Acetone is more equally distributed to tissues. HFA distribution was evaluated with a PBPK model that included hepatic binding. B(max) and Kd were 1.58 micromol/g liver and 301 microM. In summary, liver sequestration of CD and HFA most likely represents relatively high-affinity but reversible binding of activated carbonyls in these compounds (activated by the presence of electron withdrawing substituents on the alpha-carbons) with glutathione and glutathione transferases, that are present at much higher concentrations in liver than in other tissues. Strong, but reversible hemithioketal formation with active sulfhydryls may also be associated with the toxic responses to CD and HFA.

A physiologically-based pharmacokinetic model of methotrexate incorporating hepatic excretion via multidrug-resistance-associated protein 2 (Mrp2) in mice, rats, dogs, and humans.

An updated physiologically-based pharmacokinetic (PBPK) model of methotrexate (MTX) was built based on an earlier model developed by Bischoff et al. (1971). MTX has been known to be a substrate of multidrug-resistance-associated protein 2 (Mrp2). A three-dimensional quantitative structure-activity relationship model (3D-QSAR) of Mrp2 was developed by Hirono et al. (2005). In our updated PBPK model of MTX, using the computational chemistry-derived binding affinity (Km), a Mrp2-mediated biliary excretion process was incorporated as the MTX excretory pathway. Our model simulation results are consistent with numerous datasets obtained from mice, rats, dogs, and humans, at a variety of dose levels. Comparisons were made between our updated PBPK model and the earlier one from Bischoff et al. using a PBPK Index approach. Our new PBPK model was further verified against additional pharmacokinetic datasets from rats under special experimental conditions (cannulated bile duct) and Eisai hyperbirilubinemic rats.

Performance Assessment and Translation of Physiologically Based Pharmacokinetic Models From acslX to Berkeley Madonna, MATLAB, and R Language: Oxytetracycline and Gold Nanoparticles As Case Examples.

Many physiologically based pharmacokinetic (PBPK) models for environmental chemicals, drugs, and nanomaterials have been developed to aid risk and safety assessments using acslX. However, acslX has been rendered sunset since November 2015. Alternative modeling tools and tutorials are needed for future PBPK applications. This forum article aimed to: (1) demonstrate the performance of 4 PBPK modeling software packages (acslX, Berkeley Madonna, MATLAB, and R language) tested using 2 existing models (oxytetracycline and gold nanoparticles); (2) provide a tutorial of PBPK model code conversion from acslX to Berkeley Madonna, MATLAB, and R language; (3) discuss the advantages and disadvantages of each software package in the implementation of PBPK models in toxicology, and (4) share our perspective about future direction in this field. Simulation results of plasma/tissue concentrations/amounts of oxytetracycline and gold from different models were compared visually and statistically with linear regression analyses. Simulation results from the original models were correlated well with results from the recoded models, with time-concentration/amount curves nearly superimposable and determination coefficients of 0.86-1.00. Step-by-step explanations of the recoding of the models in different software programs are provided in the Supplementary Data. In summary, this article presents a tutorial of PBPK model code conversion for a small molecule and a nanoparticle among 4 software packages, and a performance comparison of these software packages in PBPK model implementation. This tutorial helps beginners learn PBPK modeling, provides suggestions for selecting a suitable tool for future projects, and may lead to the transition from acslX to alternative modeling tools.

An updated physiologically based pharmacokinetic model for hexachlorobenzene: incorporation of pathophysiological states following partial hepatectomy and hexachlorobenzene treatment.

Physiologically based pharmacokinetic (PBPK) modeling is generally used for describing xenobiotic disposition in animals and humans with normal physiological conditions. We describe here an updated PBPK model for hexachlorobenzene (HCB) in male F344 rats with the incorporation of pathophysiological conditions. Two more features contribute to the distinctness of this model from the earlier published versions. This model took erythrocyte binding into account, and a particular elimination process of HCB, the plasma-to-gastrointestinal (GI) lumen passive diffusion (i.e., exsorption), was incorporated. Our PBPK model was developed using data mined from multiple pharmacokinetic studies in the literature, and then modified to simulate HCB disposition under the conditions of our integrated pharmacokinetics/liver foci bioassay. This model included plasma, erythrocytes, liver, fat, rapidly and slowly perfused compartments, and GI lumen. To account for the distinct characteristics of HCB absorption, the GI lumen was split into an upper and a lower part. HCB was eliminated through liver metabolism and the exsorption process. The pathophysiological changes after partial hepatectomy, such as alterations in the liver and body weights and fat volume, were incorporated in our model. With adjustment of the transluminal diffusion-related parameters, the model adequately described the data from the literature and our bioassay. Our PBPK model simulation suggests that HCB absorption and exsorption processes depend on exposure conditions; different exposure conditions dictate different absorption and exsorption rates. This model forms a foundation for our further exploration of the quantitative relationship between HCB exposure and development of preneoplastic liver foci.

A novel, sensitive method for determining benzo[a]pyrene-diones using high-performance liquid chromatography with post-column zinc reduction.

A novel and sensitive high-performance liquid chromatography (HPLC) method was developed to analyze dione metabolites of benzo[a]pyrene (BaP). Because BaP-diones do not fluoresce, detection of low concentrations is difficult to achieve when analyzing these chemicals with a simple HPLC system. We developed a method to increase the detection sensitivities for BaP-diones using reduction by zinc after the chromatographic separation. A post-column zinc reducer was used to convert BaP-diones, in-line, to their corresponding fluorescent BaP-hydroquinones, which can be measured by fluorescence detection with high sensitivity. With 20-muL injections, the limits of detection for the BaP-diones tested (BaP-1,6-dione, BaP-3,6-dione, and BaP-6,12-dione) were all below 1.0 nM. In addition to the high detection sensitivity, this HPLC method provides a wide linear dynamic range for BaP-dione detection (1.0-220 nM). We also studied the extraction recovery of BaP-diones from recombinant human cytochrome P450 and epoxide hydrolase. To demonstrate the application of this method, the kinetics of BaP-dione formation was studied by incubating BaP with these recombinant enzymes. The present method enhances the detection sensitivity for BaP-diones by more than two orders of magnitude compared with traditional ultraviolet detection. Moreover, the method avoids the time-consuming derivatization or reduction steps required by other methods.

In silico toxicology: simulating interaction thresholds for human exposure to mixtures of trichloroethylene, tetrachloroethylene, and 1,1,1-trichloroethane.

In this study, we integrated our understanding of biochemistry, physiology, and metabolism of three commonly used organic solvents with computer simulation to present a new approach that we call "in silico" toxicology. Thus, we developed an interactive physiologically based pharmacokinetic (PBPK) model to predict the individual kinetics of trichloroethylene (TCE), perchloroethylene (PERC), and methylchloroform (MC) in humans exposed to differently constituted chemical mixtures of the three solvents. Model structure and parameterization originate from the literature. We calibrated the single-compound PBPK models using published data and described metabolic interactions within the chemical mixture using kinetic constants estimated in rats. The mixture model was used to explore the general pharmacokinetic profile of two common biomarkers of exposure, peak TCE blood levels and total amount of TCE metabolites generated, in rats and humans. Assuming that a 10% change in the biomarkers corresponds to a significant health effect, we calculated interaction thresholds for binary and ternary mixtures of TCE, PERC, and MC. Increases in the TCE blood levels led to higher availability of the parent compound for glutathione conjugation, a metabolic pathway associated with kidney toxicity/carcinogenicity. The simulated change in production rates of toxic conjugative metabolites exceeded 17% for a corresponding 10% increase in TCE blood concentration, indicating a nonlinear risk increase due to combined exposures to TCE. Evaluation of metabolic interactions and their thresholds illustrates a unique application of PBPK modeling in risk assessment of occupational exposures to chemical mixtures.

Characterization of gene expression changes associated with MNNG, arsenic, or metal mixture treatment in human keratinocytes: application of cDNA microarray technology.

The identification of molecular markers related to critical biological processes during carcinogenesis may aid in the evaluation of carcinogenic potentials of chemicals and chemical mixtures. Work from our laboratory demonstrated that a single treatment with N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) enhanced spontaneous malignant transformation of the human keratinocyte cell line RHEK-1. In contrast, chronic low-level exposure of cells to arsenic alone or in a mixture containing arsenic, cadmium, chromium, and lead inhibited malignant conversion. To identify changes in gene expression that influence these different outcomes, cDNA microarray technology was used. Analysis of multiple human arrays in MNNG-transformed RHEK-1 cells, designated OM3, and those treated with arsenic or the arsenic-containing metal mixture showed unique patterns of gene expression. Genes that were overexpressed in OM3 included oncogenes, cell cycle regulators, and those involved in signal transduction, whereas genes for DNA repair enzymes and inhibitors of transformation and metastasis were suppressed. In arsenic-treated cells, multiple DNA repair proteins were overexpressed. Mixture-treated cells showed increased expression of a variety of genes including metallothioneins and integrin 4. These cells showed decreased expression of oncogenes, DNA repair proteins, and genes involved in the mitogen-activated protein kinase pathway. For comparison we are currently analyzing gene expression changes in RHEK-1 cells transformed by other means. The goal of these studies is to identify common batteries of genes affected by chemical modulators of the carcinogenic process. Mechanistic studies may allow us to correlate alterations in their expression with sequential stages in the carcinogenic process and may aid in the risk assessment of other xenobiotics.

Molecular circuits, biological switches, and nonlinear dose-response relationships.

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.

Application of biologically based computer modeling to simple or complex mixtures.

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.

BioMOL: a computer-assisted biological modeling tool for complex chemical mixtures and biological processes at the molecular level.

A chemical engineering approach for the rigorous construction, solution, and optimization of detailed kinetic models for biological processes is described. This modeling capability addresses the required technical components of detailed kinetic modeling, namely, the modeling of reactant structure and composition, the building of the reaction network, the organization of model parameters, the solution of the kinetic model, and the optimization of the model. Even though this modeling approach has enjoyed successful application in the petroleum industry, its application to biomedical research has just begun. We propose to expand the horizons on classic pharmacokinetics and physiologically based pharmacokinetics (PBPK), where human or animal bodies were often described by a few compartments, by integrating PBPK with reaction network modeling described in this article. If one draws a parallel between an oil refinery, where the application of this modeling approach has been very successful, and a human body, the individual processing units in the oil refinery may be considered equivalent to the vital organs of the human body. Even though the cell or organ may be much more complicated, the complex biochemical reaction networks in each organ may be similarly modeled and linked in much the same way as the modeling of the entire oil refinery through linkage of the individual processing units. The integrated chemical engineering software package described in this article, BioMOL, denotes the biological application of molecular-oriented lumping. BioMOL can build a detailed model in 1-1,000 CPU sec using standard desktop hardware. The models solve and optimize using standard and widely available hardware and software and can be presented in the context of a user-friendly interface. We believe this is an engineering tool with great promise in its application to complex biological reaction networks.