Chromatographic Methods for the Analysis of Hexachlorobenzene and Possible Metabolites in Monkey Fecal Samples

The separation and identification of hexachlorobenzene (HCB), pentachlorobenzene (QCB), and pentachlorophenol (PCP) by thin layer, high-pressure liquid, and gas chromatography are reported. Satisfactory results were obtained with reverse phase thin layer and highpressure liquid chromatography. The 2 gas chromatographic columns tested gave adequate separation of HCB and QCB. A PCP methylation process using dimethyl sulfate was attempted. Procedures are presented for extraction, cleanup, separation, and identification of HCB and possible metabolites from feces of a rhesus monkey treated with 14C-HCB. Recovery of radioactivity in excess of 96% was attained in extracts from feces by these methods. Preliminary evidence suggests the formation of PCP and possibly other polar metabolites from HCB in the rhesus monkey.

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 (K_m), 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.

Lifetime PCB 153 bioaccumulation and pharmacokinetics in pilot whales: Bayesian population PBPK modeling and Markov chain Monte Carlo simulations

Physiologically based pharmacokinetic (PBPK) models for wild animal populations such as marine mammals typically have a high degree of model uncertainty and variability due to the scarcity of information and the embryonic nature of this field. Parameters values used in marine mammals models are usually taken from other mammalian species (e.g. rats or mice) and might not be entirely suitable to properly explain the kinetics of pollutants in marine mammals. Therefore, several parameters for a PBPK model for the bioaccumulation and pharmacokinetics of PCB 153 in long-finned pilot whales were estimated in the present study using the Bayesian approach executed with Markov chain Monte Carlo (MCMC) simulations. This method uses 'prior' information of the parameters, either from the literature or from previous model runs. The advantage is that this method uses such 'prior' parameters to calculate probability distributions to determine 'posterior' values that best explain the field observations. Those field observations or datasets were PCB 153 concentrations in blubber of long-finned pilot whales from Sandy Cape and Stanley, Tasmania, Australia. The model predictions showed an overall decrease in PCB 153 levels in blubber over the lifetime of the pilot whales. All parameters from the Sandy Cape model were updated using the Stanley dataset, except for the concentration of PCB 153 in the milk. The model presented here is a promising and preliminary start to PBPK modeling in long-finned pilot whales that would provide a basis for non-invasive studies in these protected marine mammals.

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.

Computational toxicology: Physiologically based pharmacokinetic models (PBPK) for lifetime exposure and bioaccumulation of polybrominated diphenyl ethers (PBDEs) in marine mammals

Due to migration of harbour porpoises towards more polluted areas like the North Sea and their sensitivity towards pollution, there is a need for proper conservation measures for this species. As a consequence, knowledge about the pollutant's kinetics is required. The present study is the first to investigate the kinetics of PBDEs in marine mammals using PBPK modeling as a non-destructive tool for describing the chemical's kinetics in a protected animal species. The models were developed and parameterized using data from the literature and Black Sea harbour porpoises through computer optimization. The predictability of these models in time was assessed by reverse dosimetry modeling using data from North Sea porpoises (1990-2008). From these predictions, PBDE 99 levels were found to decrease the fastest, followed by PBDE 153, 47 and 100. Results show that the PBPK models can be applied for harbour porpoises from different regions and also simulate time trends.

An in silico approach for evaluating a fraction-based, risk assessment method for total petroleum hydrocarbon mixtures

Both the Massachusetts Department of Environmental Protection (MADEP) and the Total Petroleum Hydrocarbon Criteria Working Group (TPHCWG) developed fraction-based approaches for assessing human health risks posed by total petroleum hydrocarbon (TPH) mixtures in the environment. Both organizations defined TPH fractions based on their expected environmental fate and by analytical chemical methods. They derived toxicity values for selected compounds within each fraction and used these as surrogates to assess hazard or risk of exposure to the whole fractions. Membership in a TPH fraction is generally defined by the number of carbon atoms in a compound and by a compound's equivalent carbon (EC) number index, which can predict its environmental fate. Here, we systematically and objectively re-evaluate the assignment of TPH to specific fractions using comparative molecular field analysis and hierarchical clustering. The approach is transparent and reproducible, reducing inherent reliance on judgment when toxicity information is limited. Our evaluation of membership in these fractions is highly consistent (˜80% on average across various fractions) with the empirical approach of MADEP and TPHCWG. Furthermore, the results support the general methodology of mixture risk assessment to assess both cancer and noncancer risk values after the application of fractionation.

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.

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.

Predicting activation enthalpies of cytochrome-P450-mediated hydrogen abstractions. 2. Comparison of semiempirical PM3, SAM1, and AM1 with a density functional theory method

Predicting the biotransformation of xenobiotics is important in the chemical and pharmaceutical industries, as well as in toxicology. Here, we extend and evaluate the rapid methodology of Korzekwa, Jones, and Gillette (J. Am. Chem. Soc. 1990, 112, 7042-7046 ) to estimate the activation enthalpy (DeltaH) of hydrogen-abstraction by cytochrome P450 (CYP) enzymes, using the p-nitrosophenoxy radical (PNPO) as a simple surrogate for the CYP active oxygen species. The DeltaH is estimated with a linear regression model using the reaction enthalpy and ionization energy (of the substrate radical) as predictor variables, calculated by semiempirical (SE) methods. While Korzekwa et al. used the SE method AM1, we applied PM3 and SAM1 and compared the results of the three methods. For 24 substrates, the AM1-, PM3-, and SAM1-derived regression models showed R(2) values of 0.89, 0.90, and 0.93, respectively, for the correlation between calculated and predicted DeltaH. Furthermore, we compared the DeltaH() calculated semiempirically using PNPO radical with density functional theory (DFT) B3LYP activation energies calculated by Olsen et al. (J. Med. Chem. 2006, 49, 6489-6499 ) using a more realistic iron-oxo-porphine model, and the results revealed limitations of the PNPO radical model. Thus, predictive models developed using SE predictors provide rapid and generally internally consistent results, but they should be interpreted and used cautiously.

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.

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

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

Computational and ultrastructural toxicology of a nanoparticle, Quantum Dot 705, in mice

We conducted pharmacokinetic and toxicology studies on Quantum Dot 705 (QD705) in male ICR mice for up to 6 months after a single intravenous dose. Time-course sacrifices were carried out at 1, 4, and 24 h; 3, 7, 14, and 28 days; and 6 months on groups of six mice per time point. Mass balance studies were also carried out at 24 h, 28 days, and 6 months. Using inductively coupled plasma mass spectrometry, various tissues, urine, and feces were analyzed for cadmium (Cd111), which is a major (46%) component of QD705. On the basis of these experimental studies, a physiologically based pharmacokinetic computer simulation model was developed with excellent predictive capability for the time-dependent kinetic and distributional changes of QD705 in tissues. QD705 persisted and accumulated in the spleen, liver, and kidneys for at least 28 days with little or no disposition but was gradually and partially eliminated by 6 months. Although histological alterations of the spleen, liver, and kidney by light microscopy are unremarkable, investigation using electron microscopy on numerous renal samples revealed definitive mitochondrial alterations in renal tubular epithelial cells at 28 days and 6 months postdosing. Health implications and potential beneficial applications of QD705 are suggested.

A possible role of multidrug resistance-associated protein 2 (Mrp2) in hepatic excretion of PCB126, an environmental contaminant: PBPK/PD modeling

3,3',4,4',5'-Pentachlorobiphenyl (PCB126) is a carcinogenic environmental pollutant and its toxicity is mediated through binding with aryl hydrocarbon receptor (AhR). Earlier, we found that PCB126 treated F344 rats had 110-400 times higher PCB126 concentration in the liver than in the fat. Protein binding was suspected to be a major factor for the high liver concentration of PCB126 despite its high lipophilicity. In this research, we conducted a combined pharmacokinetic/pharmacodynamic study in male F344 rats. In addition to blood and tissue pharmacokinetics, we use the development of hepatic preneoplastic foci (glutathione-S-transferase placental form [GSTP]) as a pharmacodynamic endpoint. Experimental data were utilized for building a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model. PBPK/PD modeling was consistent with the experimental PK and PD data. Salient features of this model include: (1) bindings between PCB126 and hepatic proteins, particularly the multidrug resistance-associated protein (Mrp2), a protein transporter; (2) Mrp2-mediated excretion; and (3) a relationship between area under the curve of PCB126 in the livers and % volume of GSTP foci. Mrp2 involvement in PCB126 pharmacokinetics is supported by computational chemistry calculation using a three-dimensional quantitative structure-activity relationship model of Mrp2 developed by S. Hirono et al. (2005, Pharm. Res. 22, 260-269). This work, for the first time, provided a plausible role of a versatile hepatic transporter for drugs, Mrp2, in the disposition of an important environmental pollutant, PCB126.

Quantitative analysis of liver GST-P foci promoted by a chemical mixture of hexachlorobenzene and PCB 126: implication of size-dependent cellular growth kinetics

The objectives of this study were twofold: (1) evaluating the carcinogenic potential of the mixture of two persistent environmental pollutants, hexachlorobenzene (HCB) and 3,3',4,4',5-pentachlorobiphenyl (PCB 126), in an initiation-promotion bioassay involving the development of pi glutathione S-transferase (GST-P) liver foci, and (2) analyzing the GST-P foci data using a biologically-based computer model (i.e., clonal growth model) with an emphasis on the effect of focal size on the growth kinetics of initiated cells. The 8-week bioassay involved a series of treatments of initiator, two-thirds partial hepatectomy, and daily oral gavage of the mixture of two doses in male F344 rats. The mixture treatment significantly increased liver GST-P foci development, indicating carcinogenic potential of this mixture. Our clonal growth model was developed to simulate the appearance and development of initiated GST-P cells in the liver over time. In the model, the initiated cells were partitioned into two subpopulations with the same division rate but different death rates. Each subpopulation was further categorized into single cells, mini- (2-11 cells), medium- (12-399 cells), and large-foci (>399 cells) with different growth kinetics. Our modeling suggested that the growth of GST-P foci is size-dependent; in general, the larger the foci, the higher the rate constants of division and death. In addition, the modeling implied that the two doses promoted foci development in different manners even though the experimental foci data appeared to be similar between the two doses. This study further illustrated how clonal growth modeling may facilitate our understanding in chemical carcinogenic process.

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.

Initial analyses of the relationship between "Thresholds" of toxicity for individual chemicals and "Interaction Thresholds" for chemical mixtures

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.

A physiologically based pharmacokinetic model for lactational transfer of PCB 153 with or without PCB 126 in mice

Chemical exposure via breast milk is one of the great concerns in public health. Previously, we demonstrated that most body burden of PCB 153 can be transferred from the mother to the pups in mice during lactational period. Here we present a physiologically based pharmacokinetic (PBPK) model to describe the lactational transfer of PCB 153 with or without PCB 126 in mice. The model incorporated physiological changes on the volume and the blood flow into mammary tissues, and considered mechanistic information on the movement of PCB 153 from adipose tissue to the mammary gland during lactational period. The mechanistic consideration includes fat volume changes, binding of PCB 153 to very low density lipoprotein (VLDL) and increased uptake of VLDL in mammary tissues. Model parameters depicting physiological changes were obtained from research articles dealing with chemical transfer during lactational period in rodents. Chemical-specific parameters were derived from previous PBPK models focusing on the PCB disposition in rodents. The developed model adequately described the lactational transfer of PCB 153 with or without PCB 126 in mice. Our model will provide a useful mechanistic tool to estimate the disposition of PCBs in diverse experimental designs regarding PCB effects during developmental period and to improve quantitative risk assessment of PCBs in the developing organisms.

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.

Biochemical reaction network modeling: predicting metabolism of organic chemical mixtures

All organisms are exposed to multiple xenobiotics, through food, environmental contamination, and drugs. These xenobiotics often undergo biotransformation, a complex process that plays a critical role in xenobiotic elimination or bioactivation to toxic metabolites. Here we describe the results of a new computer-based simulation tool that predicts metabolites from exposure to multiple chemicals and interconnects their metabolic pathways, using four common drinking water pollutants (trichloroethylene, perchloroethylene, methylchloroform, and chloroform) as a test case. The simulation tool interconnected the metabolic pathways for these compounds, predicted reactive intermediates, such as epoxides and acid chlorides, and uncovered points in the metabolic pathways where typical endogenous compounds, such as glutathione or carbon dioxide, are consumed or generated. Moreover, novel metabolites, not previously reported, were predicted via this methodology. Metabolite prediction is based on a reaction-mechanism-based methodology, which applies fundamental organic and enzyme chemistry. The tool can be used to (a) complement experimental studies of chemical mixtures, (b) aid in risk assessment, and (c) help understand the effects of complex chemical mixtures. Our results indicate that this tool is useful for predictive xenobiotic metabolomics, providing new and important insights into metabolites and the interrelationship between diverse chemicals that hitherto may have remained unnoticed.

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.

Development of a physiologically based pharmacokinetic model for volatile fractions of gasoline using chemical lumping analysis

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.

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.

A reaction network model for CYP2E1-mediated metabolism of toxicant mixtures

In this paper, we describe a modeling approach to predict the interlinked pathways and kinetics resulting from CYP2E1-mediated metabolism of both pure species and chemical mixtures. This approach is based on the concept of chemical reaction networks, an idea that has formed the basis for simulation tools that have shown good predictive capabilities in the petroleum industry, but also an idea that has heretofore seen minimal application in the biomedical research arena. Although the initial target for developing this reaction network approach was cytochrome P450 2E1 (CYP2E1) and its over 200 substrates, this technology has been used for other families of CYP enzymes and their substrates in our laboratory. Utilizing this approach, we have produced a modular 'predictive metabolomics' simulation framework comprising interdependent software components that perform such tasks as testing of substrate binding feasibility, performing virtual chemistry, formulating reaction-rate equations, computing reaction kinetics and predicting time-dependent species concentrations. As an illustrative example, we outline the application of this framework to the prediction of the reaction networks resulting from the Phase I metabolism of two compounds of important toxicological interest. The potential of this modeling technology is immense in providing a computer simulation platform for complex-chemical mixtures and complex-biological systems. It is possible that this technology will play an important role in formulating a 'Virtual Human'.

PBPK modeling of complex hydrocarbon mixtures: gasoline

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.

Arsenic and benzo[a]pyrene differentially alter the capacity for differentiation and growth properties of primary human epidermal keratinocytes

Normal human epidermal keratinocytes (NHEK) have been chosen as an in vitro model to test the hypothesis that chemicals which alter or interfere in cellular differentiation will concomitantly induce growth perturbations and are, thus, potential carcinogens. In these studies, we have focused on two known skin carcinogens, arsenic and benzo(a)pyrene (BaP). Our results demonstrated that BaP inhibits terminal differentiation in NHEK, as measured by cross-linked envelope (CLE) formation, up to 5.8-fold in control and 1.7-fold in calcium (Ca2+)-treated cells. In comparison, arsenic decreased CLE formation 20-fold in control cells and 5.5-fold in Ca2+-treated NHEK. To characterize the effects of these agents on the growth rate and cell cycle distributions of NHEK, flow cytometric analysis was used. BaP at 2 microM increased proliferation rates by 29%. Altered cell-cycle distribution in BaP-treated cells indicated a more rapid progression through the cell cycle, possibly by a shortened G2 phase. In contrast, arsenic at 5 microM inhibited proliferation by 25%; growth arrest (9%) was also observed in NHEK treated with 2 mM Ca2+. Our findings suggest that, although both BaP and arsenic inhibit CLE production in NHEK, different mechanisms may be involved. Studies in progress will attempt to identify molecular markers involved in the observed chemical effects. These markers will facilitate a mechanistic understanding of how an altered balance between growth and differentiation may play a role in the transformation process in NHEK.

Characterization of the pharmacokinetics of gasoline using PBPK modeling with a complex mixtures chemical lumping approach

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.

Gene expression patterns as potential molecular biomarkers for malignant transformation in human keratinocytes treated with MNNG, arsenic, or a metal mixture

In previous studies, treatment with 1-methyl-3-nitro-1-nitrosoguanidine (MNNG) enhanced malignant transformation of immortal human epidermal (RHEK-1) keratinocytes. In contrast, arsenic (As) alone or in a mixture of As, cadmium (Cd), chromium (Cr), and lead (Pb) inhibited this process. Microarray analysis showed unique gene expression patterns in RHEK-1 exposed to MNNG, As, or the metal mixture. From this analysis, we have selected 16 genes potentially involved in the enhancement or inhibition of transformation. These 16 genes, nine (IFN inducible protein 9-27, MAA A32, CCLB protein, integrin beta4, XRCC1, K8, K18, MT3, MAPKK6) of which were altered in a chemical-specific manner and seven (MIC1, bikunin, MTS1, BMP4, RAD23A, DOC2, vimentin) of which were commonly affected by the MNNG and As or mixture treatments, were examined for expression in detail by real-time RT-PCR. Qualitatively, both microarray and real-time RT-PCR analyses gave comparable results for 15 of 16 genes, i.e., genes were consistently induced or suppressed under the different treatment regimens when measured by either technique. Of the seven genes altered in their expression by multiple chemical treatments, five showed patterns consistent with a role in the transformation process, i.e., they were oppositely regulated in MNNG-transformed RHEK-1 cells (designated as OM3) as compared to the nonmalignant As- and mixture-exposed cells. Through time-course studies, we also identified markers whose expression correlates with acquisition of transformation-associated characteristics in OM3. Identification of a battery of genes altered during progressive transformation of RHEK-1 should aid in developing a mechanistic understanding of this process, as well as strengthening the utility of these genes as biomarkers.

Stochastic simulation of hepatic preneoplastic foci development for four chlorobenzene congeners in a medium-term bioassay

A combination of experimental and simulation approaches was used to analyze clonal growth of glutathione-S-transferase pi (GST-P) enzyme-altered foci during liver carcinogenesis in an initiation-promotion regimen for 1,4-dichlorobenzene (DCB), 1,2,4,5-tetrachlorobenzene (TECB), pentachlorobenzene (PECB), and hexachlorobenzene (HCB). Male Fisher 344 rats, eight weeks of age, were initiated with a single dose (200 mg/kg, ip) of diethylnitrosamine (DEN). Two weeks later, daily dosing of 0.1 mol/kg chlorobenzene was maintained for six weeks. Partial hepatectomy was performed three weeks after initiation. Liver weight, normal hepatocyte division rates, and the number and volume of GST-P positive foci were obtained at 23, 26, 28, 47, and 56 days after initiation. A clonal growth stochastic model separating the initiated cell population into two distinct subtypes (referred to as A and B cells) was successfully used to describe the foci development data for the four chlorobenzenes. The B cells are initiated cells that display a selective growth advantage under conditions that inhibit the growth of initiated A cells or normal hepatocytes. The simulation exercise for the four chlorobenzenes indicates a positive correlation between the estimated net growth rate of B cells during the 2-week regeneration period following partial hepatectomy and final foci volume at the end of the bioassay. This observation is consistent with the sensitivity analysis of model parameters. While TECB, PECB, and HCB all significantly increased foci volume, only HCB increased normal hepatocyte proliferation. Together, these results indicate that examining effects of chemicals on regenerative responses following partial hepatectomy may be a means for understanding the carcinogenicity potential of chlorobenzene compounds.

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.

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.

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.

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.

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.

Effects of developmental exposure to lead, magnesium and zinc mixtures on spatial learning and expression of NMDA receptor subunit mRNA in Fischer 344 rats

The N-methyl-D-aspartate receptor (NMDAR) is important for learning. Lead (Pb) exposure impairs learning ability and affects the NMDAR. This study tested whether developmental exposure to a combination of Pb, zinc (Zn), and magnesium (Mg) would result in effects different from those seen with individual metals. Fischer 344 (F344) rat pups of both genders were exposed from gestation day 5 to post-natal day (PND) 40, either to Pb, Mg, or Zn individually or to a (one-third or full concentration) mixture of the three metals. All Zn-treated pups died before PND7, but half of the litters given the full concentration mixture survived to PND40. Impaired learning in the Morris water maze was seen in the Mg and full concentration mixture groups. There were gender differences in NR2A subunit mRNA expression in the hippocampal CA3 region in the Mg and Pb groups, but combining the three metals in the full concentration showed no gender effect. Our results showed that exposure to all three metals affected mortality, learning ability and gender-dependent expression patterns of an NMDAR subunit in a different way from that seen with exposure to the individual metals.

Comparison of pharmacokinetic interactions and physiologically based pharmacokinetic modeling of PCB 153 and PCB 126 in nonpregnant mice, lactating mice, and suckling pups

Polychlorinated biphenyls (PCBs) are ubiquitous environmental contaminants that can induce neurological defects in infants and children via placental and lactational transfer. To investigate the lactational transfer of PCBs and compare pharmacokinetic interactions among nonpregnant, lactating mice and suckling pups, quantitative time-course measurements of PCB accumulation in tissues were performed. On postnatal day 1, nonpregnant and lactating C57BL/6 mice were exposed to PCB 153 (2,2',4,4',5,5'-hexachlorobiphenyl, 20 mg/kg) alone or a mixture of PCB 153 (20 mg/kg) and PCB 126 (3,3',4,4',5-pentachlorobiphenyl, 0.2 mg/kg) by oral gavage. At 1, 3, 6, and 13 days after treatment, PCB 153 and PCB 126 were determined in nonpregnant and maternal tissues as well as in neonatal tissues by gas chromatography (GC). Coadministration of PCB 153 and PCB 126 increased PCB 153 retention in the liver and decreased PCB 153 accumulation in the fat of nonpregnant mice. Lactational transfer was confirmed to be an efficient elimination mechanism for the lactating mice but a major source of exposure in the pups. However, little or no significant pharmacokinetic interactions were observed in lactating mice and suckling pups. To describe pharmacokinetic interactions between PCB 153 and PCB 126, a physiologically based pharmacokinetic model for PCB 153 disposition was developed. The effects of PCB 126 on the fat content in liver and a diffusion permeation constant in fat were incorporated into the physiologically based pharmacokinetic (PBPK) model. This model successfully describes PCB 153 disposition altered by PCB 126 in nonpregnant mice.

Identification of Hexachlorobenzene as a Contaminant in Laboratory Plastic Wash Bottles

Hexachlorobenzene (HCB) is identified as a contaminant in some Nalgene plastic wash bottles by gas-liquid chromatography-mass spectrometry. Of 34 new, unused Nalgene bottles tested, all 30 of the conventional polyethylene wash bottles contained significant quantities of HCB; the remaining 2 linear polyethylene and 2 polyvinyl chloride storage bottles contained little or no HCB.