Quantification of N-phenyl-2-naphthylamine by gas chromatography and isotope-dilution mass spectrometry and its percutaneous absorption ex vivo under workplace conditions.

N-Phenyl-2-naphthylamine (P2NA) is an antioxidant used to protect rubbers from flex-cracking. P2NA can be converted in vivo to 2NA, one of the most potent bladder carcinogens. Here, we report the specific and ultra-sensitive quantification of P2NA in the receptor fluid of Franz diffusion cells by gas chromatography and isotope-dilution tandem-mass spectroscopy (GC-MS/MS). The experimental conditions were optimized to minimize losses of P2NA due to surface absorption on glass, plastic, and rubber material, and subsequently validated. Static and dynamic diffusion cell conditions were used to study the percutaneous penetration of P2NA into freshly prepared porcine skin. The experimental settings closely resembled those of the printing industry in the 1960s/1970s in Germany where P2NA-containing solutions in dichloromethane have been used. P2NA penetrated the skin at very low levels (0.02 ± 0.01 µg/cm2/h) with a cumulative penetrated amount of 0.80 ± 0.26 µg/cm2, a lag time of 6.33 ± 2.21 h and under dynamic conditions. Compared to the receptor fluid, 10-40-fold higher concentrations were found in the skin, predominantly in the dermis and the stratum corneum. Dichloromethane acted as a penetration enhancer by increasing the cumulative penetrated amounts and the recovery of P2NA in both the receptor fluid and the skin, while shortening its lag time. However, the flux remained unaffected. Due to its accumulation in subcutaneous layers, we finally proved that P2NA is continuously released into the receptor fluid despite exposure cessation up to 160 h. Overall, the results show that close attention has to be paid to dermal absorption of P2NA in exposed workers.

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

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

Evaluation of three physiologically based pharmacokinetic (PBPK) modeling tools for emergency risk assessment after acute dichloromethane exposure.

INTRODUCTION: Physiologically based pharmacokinetic (PBPK) models may be useful in emergency risk assessment, after acute exposure to chemicals, such as dichloromethane (DCM). We evaluated the applicability of three PBPK models for human risk assessment following a single exposure to DCM: one model is specifically developed for DCM (Bos) and the two others are semi-generic ones (Mumtaz and Jongeneelen). MATERIALS AND METHODS: We assessed the accuracy of the models' predictions by simulating exposure data from a previous healthy volunteer study, in which six subjects had been exposed to DCM for 1h. The time-course of both the blood DCM concentration and percentage of carboxyhemoglobin (HbCO) were simulated. RESULTS: With all models, the shape of the simulated time course resembled the shape of the experimental data. For the end of the exposure, the predicted DCM blood concentration ranged between 1.52-4.19mg/L with the Bos model, 1.42-4.04mg/L with the Mumtaz model, and 1.81-4.31mg/L with the Jongeneelen model compared to 0.27-5.44mg/L in the experimental data. % HbCO could be predicted only with the Bos model. The maximum predicted % HbCO ranged between 3.1 and 4.2% compared to 0.4-2.3% in the experimental data. The % HbCO predictions were more in line with the experimental data after adjustment of the Bos model for the endogenous HbCO levels. CONCLUSIONS: The Bos Mumtaz and Jongeneelen PBPK models were able to simulate experimental DCM blood concentrations reasonably well. The Bos model appears to be useful for calculating HbCO concentrations in emergency risk assessment.

Susceptibility to the cytogenetic effects of dichloromethane is related to the glutathione S-transferase theta phenotype.

The carcinogenicity of dichloromethane (DCM) has been demonstrated by mutagenicity studies using bacteria and yeasts and using animal bioassays. Epidemiological studies indicate that exposure to DCM increases the incidences of liver and pancreas cancers. In the present study, we determine whether DCM generates DNA damage in human peripheral blood mononuclear cells (PBMCs) and whether that process depends on glutathione S-transferase theta (GSTT)-1 activity. GSTT1 is one of the enzymes that biotransforms DCM. To this end, PBMC cultures from healthy men were treated with DCM (15-500 ppm) for 72 h. Cell cultures were harvested and processed according to classical cytogenetic techniques. The frequency of sister chromatid exchanges (SCEs), the mitotic index (MI), the cell proliferation kinetic (CPK) value, and the level of GSTT1 activity were determined. DCM exposure decreased the MI in a dose-dependent manner in all individuals tested (20). The CPK value decreased from 125 ppm DCM, and the SCEs frequency increased from 60 ppm DCM. A significantly different response was observed when the group of individuals with low GSTT1 enzymatic activity (4 individuals), the group with medium GSTT1 activity (10 individuals), and the group of individuals with high GSTT1 enzymatic activity (6 individuals) were compared (0.077 ± 0.0124, 0.325 ± 0.0269, and 7.365 ± 1.3474 nmol HCOH/min/mg protein, respectively). These differences were reflected in the amount of change for all of the evaluated cytogenetic parameters (p<0.05, ANOVA) and indicated a clear susceptibility to DCM genotoxic effects related to GSTT1 activity because the cytogenetic effects were directly related to the GSTT1-specific activity. DCM was highly cytotoxic in PBMCs, even at doses within the safety range. Due to this toxicity, a review of the maximal limits for occupational exposure to DCM is advised.

Evaluation of two different metabolic hypotheses for dichloromethane toxicity using physiologically based pharmacokinetic modeling for in vivo inhalation gas uptake data exposure in female B6C3F1 mice.

Dichloromethane (DCM, methylene chloride) is a lipophilic volatile compound readily absorbed and then metabolized to several metabolites that may lead to chronic toxicity in different target organs. Physiologically based pharmacokinetic (PBPK) models are useful tools for calculation of internal and target organ doses of parent compound and metabolites. PBPK models, coupled with in vivo inhalation gas-uptake data, can be useful to estimate total metabolism. Previously, such an approach was used to make predictions regarding the metabolism and to make subsequent inferences of DCM's mode of action for toxicity. However, current evidence warrants re-examination of this approach. The goal of this work was to examine two different hypotheses for DCM metabolism in mice. One hypothesis describes two metabolic pathways: one involving cytochrome P450 2E1 (CYP2E1) and a second glutathione (GSH). The second metabolic hypothesis describes only one pathway mediated by CYP2E1 that includes multiple binding sites. The results of our analysis show that the in vivo gas-uptake data fit both hypotheses well and the traditional analysis of the chamber concentration data is not sufficient to distinguish between them. Gas-uptake data were re-analyzed by construction of a velocity plot as a function of increasing DCM initial concentration. The velocity (slope) analysis revealed that there are two substantially different phases in velocity, one rate for lower exposures and a different rate for higher exposures. The concept of a "metabolic switch," namely that due to conformational changes in the enzyme after one site is occupied - a different metabolic rate is seen - is also consistent with the experimental data. Our analyses raise questions concerning the importance of GSH metabolism for DCM. Recent research results also question the importance of this pathway in the toxicity of DCM. GSH-related DNA adducts were not formed after in vivo DCM exposure in mice and DCM-induced DNA damage has been detected in human lung cultures without GSH metabolism. In summary, a revised/updated metabolic hypothesis for DCM has been examined using in vivo inhalation data in mice combined with PBPK modeling that is consistent with up-to-date models of the active site for CYP2E1 and suggests that this pathway is the major metabolizing pathway for DCM metabolism.

Probabilistic dose-response modeling: case study using dichloromethane PBPK model results.

A revised assessment of dichloromethane (DCM) has recently been reported that examines the influence of human genetic polymorphisms on cancer risks using deterministic PBPK and dose-response modeling in the mouse combined with probabilistic PBPK modeling in humans. This assessment utilized Bayesian techniques to optimize kinetic variables in mice and humans with mean values from posterior distributions used in the deterministic modeling in the mouse. To supplement this research, a case study was undertaken to examine the potential impact of probabilistic rather than deterministic PBPK and dose-response modeling in mice on subsequent unit risk factor (URF) determinations. Four separate PBPK cases were examined based on the exposure regimen of the NTP DCM bioassay. These were (a) Same Mouse (single draw of all PBPK inputs for both treatment groups); (b) Correlated BW-Same Inputs (single draw of all PBPK inputs for both treatment groups except for bodyweights (BWs), which were entered as correlated variables); (c) Correlated BW-Different Inputs (separate draws of all PBPK inputs for both treatment groups except that BWs were entered as correlated variables); and (d) Different Mouse (separate draws of all PBPK inputs for both treatment groups). Monte Carlo PBPK inputs reflect posterior distributions from Bayesian calibration in the mouse that had been previously reported. A minimum of 12,500 PBPK iterations were undertaken, in which dose metrics, i.e., mg DCM metabolized by the GST pathway/L tissue/day for lung and liver were determined. For dose-response modeling, these metrics were combined with NTP tumor incidence data that were randomly selected from binomial distributions. Resultant potency factors (0.1/ED(10)) were coupled with probabilistic PBPK modeling in humans that incorporated genetic polymorphisms to derive URFs. Results show that there was relatively little difference, i.e., <10% in central tendency and upper percentile URFs, regardless of the case evaluated. Independent draws of PBPK inputs resulted in the slightly higher URFs. Results were also comparable to corresponding values from the previously reported deterministic mouse PBPK and dose-response modeling approach that used LED(10)s to derive potency factors. This finding indicated that the adjustment from ED(10) to LED(10) in the deterministic approach for DCM compensated for variability resulting from probabilistic PBPK and dose-response modeling in the mouse. Finally, results show a similar degree of variability in DCM risk estimates from a number of different sources including the current effort even though these estimates were developed using very different techniques. Given the variety of different approaches involved, 95th percentile-to-mean risk estimate ratios of 2.1-4.1 represent reasonable bounds on variability estimates regarding probabilistic assessments of DCM.

Stability and release of antiviral drugs from ethylene vinyl acetate (EVA) copolymer.

The use of polymer based drug delivery systems in dentistry is a relatively new area of research with the exception of the inhibition of secondary caries by the release of fluoride ions from polyalkenoate cements and their predecessors silicate cements. The present study was to test on orally biocompatible material, ethylene vinyl acetate copolymer (EVA), for release of antiviral drugs at oral therapeutic levels over extended periods of time. We also determined their stability during film casting and release. Materials studied include gancyclovir (GCY), acyclovir (ACY), dichloromethane (DCM), and ethylene vinyl acetate (EVA). The square films (3 x 3 x 0.1 cm) were prepared from the dry sheet obtained by solvent evaporation of polymer casting solutions. These solutions were made of EVA and the drug (40:1) in 70 ml of dichloromethane at 38 degrees C. Then drug release characteristics from the drug loaded films were examined at 37 degrees C for a minimum of 14 days in 10 ml medium (ddwater) replaced daily. Kinetics of drug release were followed by spectral measurements using previously determined lambda(max) values (GCY = 250 nm; ACY = 253 nm). A minimum of three samples was tested and reproducible results were obtained. Drug stability (ACY) during film casting and its release was determined using 1H NMR spectrometer (Bruker DRX-500 and 400). Rate of drug release was determined from the part of the curve (rate vs. time) after the onset of the "burst." Although GCY has a larger molecular weight (255) than ACY (225), GCY exhibited about three times higher rate of release than ACY. This difference in rate values may be explained due to its relatively greater solubility in EVA, facilitating faster diffusion of the molecules through the channels present in EVA. This is consistent with the observation that the rate at which drug molecules diffuse through the channels of the polymer, can be increased by decreasing the molecular weight. In the case of ACY, the molecules may be undergoing molecular associations, perhaps dimerization or trimerization in addition to its lower solubility in EVA. The diffusion of ACY tends to be slower under these circumstances compared to GCY resulting in lower rate value than in the case of GCY. Biological studies revealed that ACY exhibited a remarkable decrease in a number of viral organisms present in virus infected cell culture system using real-time polymerase chain reaction (RT-PCR). NMR analysis indicates that the chemical structure of the drug remains stable during film casting process and release.

Application of physiologically based pharmacokinetic modeling in setting acute exposure guideline levels for methylene chloride.

Acute exposure guideline levels (AEGLs) are derived to protect the human population from adverse health effects in case of single exposure due to an accidental release of chemicals into the atmosphere. AEGLs are set at three different levels of increasing toxicity for exposure durations ranging from 10 min to 8 h. In the AEGL setting for methylene chloride, specific additional topics had to be addressed. This included a change of relevant toxicity endpoint within the 10-min to 8-h exposure time range from central nervous system depression caused by the parent compound to formation of carboxyhemoglobin (COHb) via biotransformation to carbon monoxide. Additionally, the biotransformation of methylene chloride includes both a saturable step as well as genetic polymorphism of the glutathione transferase involved. Physiologically based pharmacokinetic modeling was considered to be the appropriate tool to address all these topics in an adequate way. Two available PBPK models were combined and extended with additional algorithms for the estimation of the maximum COHb levels. The model was validated and verified with data obtained from volunteer studies. It was concluded that all the mentioned topics could be adequately accounted for by the PBPK model. The AEGL values as calculated with the model were substantiated by experimental data with volunteers and are concluded to be practically applicable.

Revised assessment of cancer risk to dichloromethane II. Application of probabilistic methods to cancer risk determinations.

An updated PBPK model of methylene chloride (DCM, dichloromethane) carcinogenicity in mice was recently published using Bayesian statistical methods (Marino et al., 2006). In this work, this model was applied to humans, as recommended by Sweeney et al.(2004). Physiological parameters for input into the MCMC analysis were selected from multiple sources reflecting, in each case, the source that was considered to represent the most current scientific evidence for each parameter. Metabolic data for individual subjects from five human studies were combined into a single data set and population values derived using MCSim. These population values were used for calibration of the human model. The PBPK model using the calibrated metabolic parameters was used to perform a cancer risk assessment for DCM, using the same tumor incidence and exposure concentration data relied upon in the current IRIS entry. Unit risks, i.e., the risk of cancer from exposure to 1 microg/m3 over a lifetime, for DCM were estimated using the calibrated human model. The results indicate skewed distributions for liver and lung tumor risks, alone or in combination, with a mean unit risk (per microg/m3) of 1.05 x 10(-9), considering both liver and lung tumors. Adding the distribution of genetic polymorphisms for metabolism to the ultimate carcinogen, the unit risks range from 0 (which is expected given that approximately 20% of the US population is estimated to be nonconjugators) up to a unit risk of 2.70 x 10(-9) at the 95th percentile. The median, or 50th percentile, is 9.33 x 10(-10), which is approximately a factor of 500 lower than the current EPA unit risk of 4.7 x 10(-7) using a previous PBPK model. These values represent the best estimates to date for DCM cancer risk because all available human data sets were used, and a probabilistic methodology was followed.

Revised assessment of cancer risk to dichloromethane: part I Bayesian PBPK and dose-response modeling in mice.

The current USEPA cancer risk assessment for dichloromethane (DCM) is based on deterministic physiologically based pharmacokinetic (PBPK) modeling involving comparative metabolism of DCM by the GST pathway in the lung and liver of humans and mice. Recent advances in PBPK modeling include probabilistic methods and, in particular, Bayesian inference to quantitatively address variability and uncertainty separately. Although Bayesian analysis of human PBPK models has been published, no such efforts have been reported specifically addressing the mouse, apart from results included in the OSHA final rule on DCM. Certain aspects of the OSHA model, however, are not consistent with current approaches or with the USEPA's current DCM cancer risk assessment. Therefore, Bayesian analysis of the mouse PBPK model and dose-response modeling was undertaken to support development of an improved cancer risk assessment for DCM. A hierarchical population model was developed and prior parameter distributions were selected to reflect parameter values that were considered the most appropriate and best available. Bayesian modeling was conducted using MCSim, a publicly available software program for Markov Chain Monte Carlo analysis. Mean posterior values from the calibrated model were used to develop internal dose metrics, i.e., mg DCM metabolized by the GST pathway/L tissue/day in the lung and liver using exposure concentrations and results from the NTP mouse bioassay, consistent with the approach used by the USEPA for its current DCM cancer risk assessment. Internal dose metrics were 3- to 4-fold higher than those that support the current USEPA IRIS assessment. A decrease of similar magnitude was also noted in dose-response modeling results. These results show that the Bayesian PBPK model in the mouse provides an improved basis for a cancer risk assessment of DCM.

Simulation of the toxicokinetics of trichloroethylene, methylene chloride, styrene and n-hexane by a toxicokinetics/toxicodynamics model using experimental data.

The toxicokinetics/toxicodynamics (TKTD) model simulates the toxicokinetics of a chemical based on physiological data such as blood flow, tissue partition coefficients and metabolism. In this study, Andersen and Clewell's TKTD model was used with seven compartments and ten differential equations for calculating chemical balances in the compartments (Andersen and Clewell 1996, Workshop on physiologically-based pharmacokinetic/pharmacodynamic modeling and risk assessment, Aug. 5-16 at Colorado State University, U.S.A) . Using this model, the authors attempted to simulate the behavior of four chemicals: trichloroethylene, methylene chloride, styrene and n-hexane, and the results were evaluated. Simulations of the behavior of trichloroethylene taken in via inhalation and oral exposure routes were also done. The differences between simulations and measurements are due to the differences between the absorption rates of the exposure routes. By changing the absorption rates, the simulation showed agreement with the measured values. The simulations of the other three chemicals showed good results. Thus, this model is useful for simulating the behavior of chemicals for preliminary toxicity assessment.

Dose-dependent transitions in mechanisms of toxicity: case studies.

Experience with dose response and mechanisms of toxicity has shown that multiple mechanisms may exist for a single agent along the continuum of the full dose-response curve. It is highly likely that critical, limiting steps in any given mechanistic pathway may become overwhelmed with increasing exposures, signaling the emergence of new modalities of toxic tissue injury at these higher doses. Therefore, dose-dependent transitions in principal mechanisms of toxicity may occur, and could have significant impact on the interpretation of reference data sets for risk assessment. To illustrate the existence of dose-dependent transitions in mechanisms of toxicity, a group of academic, government, and industry scientists, formed under the leadership of the ILSI Health and Environmental Sciences Institute (HESI), developed a series of case studies. These case studies included acetaminophen, butadiene, ethylene glycol, formaldehyde, manganese, methylene chloride, peroxisome proliferator-activated receptor (PPAR), progesterone/hydroxyflutamide, propylene oxide, vinyl acetate, vinyl chloride, vinylidene chloride, and zinc. The case studies formed the basis for technical discourse at two scientific workshops in 2003.

Estimation of interindividual variation in oxidative metabolism of dichloromethane in human volunteers.

A modified version of the original physiologically based pharmacokinetic (PBPK) model by Andersen et al. (1987) has been developed and used in conjunction with previously published human kinetic data for dichloromethane (DCM) metabolism and to assess interindividual variability in the rate of oxidative metabolism. Time-course data for 13 volunteers (10 males, 3 females) exposed to one or more concentrations of DCM (50 ppm, 100 ppm, 150 ppm, or 200 ppm) for 7.5h were used to optimize the maximal rate of hepatic metabolism (V(maxC)) through the cytochrome P450 pathway for each individual. DCM breath and blood concentrations were used, along with carboxyhemoglobin concentrations in blood and carbon monoxide (CO) concentrations in exhaled breath, to estimate the model parameters. Significant improvements in model fit were achieved when extrahepatic oxidative metabolism of DCM was added to the model structure. The 13 individual V(maxC) values ranged from 7.1 to 23.6 mg/h/kg0.7 and appeared to be bimodally distributed. The distribution was not sex related and may be related to differential CYP2E1 induction. A comparison of the observed variation in V(maxC) values to other estimates of variability in the rate of oxidative metabolism and human CYP2E1 activity suggest a relatively narrow range in human hepatic activity toward DCM.

Microparticle formation and its mechanism in single and double emulsion solvent evaporation.

The emulsification is the first step of the emulsification solvent evaporation method and has been extensively investigated. On the contrary the second step, the solvent transport out from the emulsion droplets that determine the particle morphology and with great influence on the microparticles encapsulation and release behavior has been scarcely studied. This study investigates the mechanism of the solvent elimination from the emulsion droplets and its influence on the particle morphology, encapsulation and release behavior. Usually, the solvent is highly volatile that makes the solvent elimination process very fast thus difficult to observe. In order to observe in detail the microparticle formation, the initial emulsion was monitored by optical microscope under controlled solvent evaporation conditions. The results from the optical microscopic observations corroborated with laser diffractometry analysis showed that in single emulsion formulations, spherical microparticles are formed by accelerated solvent elimination due to the combined effects of high solvent volatility and polymer precipitation. The solvent expulsion accompanied by important shrinkage generates on the microparticle surface a thin layer of nanoparticles attested by scanning electron microscopy and laser diffractometry. During the intense solvent elimination, the encapsulated substance is drained, affecting the loading efficiency. Furthermore, it will concentrate towards the microparticle surface contributing to the initial burst release. In double emulsion formulations, microparticles with different morphologies are generated due to the presence of the aqueous-phase microdroplets inside the emulsion droplet. During the solvent elimination, these microdroplets generally coalesce under the pressure of the precipitating polymer. Depending mainly on the polymer concentration and emulsification energies, the final microparticles will be a mixture of honeycomb, capsule or plain structure. During the shrinkage due to the incompressibility of the inner microdroplets, the precipitating polymer wall around them may break forming holes through which the encapsulated substance is partly expulsed. Through these holes, the encapsulated substance is further partitioning with the external aqueous phase during solvent evaporation and contributes to the initial burst release during the application.

Transdermal drug delivery systems of a beta blocker: design, in vitro, and in vivo characterization.

The matrix type transdermal drug delivery systems (TDDS) of metoprolol were prepared by film casting technique using a fabricated stainless steel film casting apparatus and characterized in vitro by drug release, skin permeation, skin irritation, and in vivo pharmacodynamic and stability studies. Four formulations were prepared that differed in the ratio of matrix forming polymers. Formulations M-1, M-2, M-3, and M-4 were composed of Eudragit RL-100 and polyvinyl acetate with the following ratios: 2:8, 4:6, 6:4, and 8:2, respectively. All the four formulations carried 10% (w/w) of metoprolol tartrate, 5% (w/w) of dibutylphthalate, and 5% (w/w) of (+/-) menthol in dichloromethane:isopropyl alcohol (80:20 v/v). Cumulative amount of drug released in 48 hr from the four formulations was 79.16%, 81.17%, 85.98%, and 95.04%. The corresponding values for cumulative amount of drug permeated for the said formulations were 59.72%, 66.52%, 77.36%, and 90.38%. On the basis of in vitro drug release and skin permeation performance, formulation M-4 was found to be better than the other three formulations and it was selected as the optimized formulation. The formulation appeared to be stable when stored at 40 degrees C and 75% RH with negligible degradation of the drug. The TDDS was found to be free of any skin irritation as suggested by skin irritation score of 1.16 (<2.00) under Draize score test. Statistically significant reduction in mean blood pressure (p < .01) was achieved in methyl prednisolone-induced hypertensive rats on treatment with the TDDS.

Evaluation of the potential impact of age- and gender-specific pharmacokinetic differences on tissue dosimetry.

The physiological and biochemical processes that determine the tissue concentration time courses (pharmacokinetics) of xenobiotics vary, in some cases significantly, with age and gender. While it is known that age- and gender-specific differences have the potential to affect tissue concentrations and, hence, individual risk, the relative importance of the contributing processes and the quantitative impact of these differences for various life stages are not well characterized. The objective of this study was to identify age- and gender-specific differences in physiological and biochemical processes that affect tissue dosimetry and integrate them into a predictive physiologically based pharmacokinetic (PBPK) life-stage model. The life-stage model was exercised for several environmental chemicals with a variety of physicochemical, biochemical, and mode-of-action properties. In general, predictions of average pharmacokinetic dose metrics for a chemical across life stages were within a factor of two, although larger transient variations were predicted, particularly during the neonatal period. The most important age-dependent pharmacokinetic factor appears to be the potential for decreased clearance of a toxic chemical in the perinatal period due to the immaturity of many metabolic enzyme systems, although this same factor may also reduce the production of a reactive metabolite. Given the potential for age-dependent pharmacodynamic factors during early life, there may be chemicals and health outcomes for which decreased clearance over a relatively brief period could have a substantial impact on risk.

The Bayesian population approach to physiological toxicokinetic-toxicodynamic models--an example using the MCSim software.

The calibration of physiologically based toxicokinetic models against experimental data encompasses the merging of prior knowledge with information present in the data. This prior knowledge is manifested in the scientific literature and associated with various degrees of uncertainty. The most convenient way to combine these sources of information is via the use of Bayesian statistical methods. Furthermore, toxicokinetic models are subject to both inter- and intra-individual variability. This variability may be handled statistically by the use of a population model. The MCSim software, which is available for free download on the Internet, permits the use of a population model in combination with a Bayesian statistical approach. An example of the use of MCSim in a recent model-based risk assessment of dichloromethane (DCM) is given and discussed.

Genetic polymorphisms in assessing interindividual variability in delivered dose.

Increasing sophistication in methods used to account for human variability in susceptibility to toxicants has been one of the success stories in the continuing evolution of risk assessment science. Genetic polymorphisms have been suggested as an important contributor to overall human variability. Recently, data on polymorphisms in metabolic enzymes have been integrated with physiologically based pharmacokinetic (PBPK) modeling as an approach to determining the resulting overall variability. We present an analysis of the potential contribution of polymorphisms in enzymes modulating the disposition of four diverse compounds: methylene chloride, warfarin, parathion, and dichloroacetic acid. Through these case studies, we identify key uncertainties likely to be encountered in the use of polymorphism data and highlight potential simplifying assumptions that might be required to test the hypothesis that genetic factors are a substantive source of human variability in susceptibility to environmental toxicants. These uncertainties include (1) the relative contribution of multiple enzyme systems, (2) the extent of induction/inhibition through coexposure, (3) allelic frequencies of major ethnic groups, (4) the absence of chemical-specific data on the kinetic parameters for the different allelic forms of key enzymes, (5) large numbers of low-frequency alleles, and (6) uncertainty regarding differences between in vitro and in vivo kinetic data. Our effort sets the stage for the acquisition of critical data and further integration of polymorphism data with PBPK modeling as a means to quantitate population variability.

A PBPK modeling-based approach to account for interactions in the health risk assessment of chemical mixtures.

The objectives of the present study were: (1) to develop a risk assessment methodology for chemical mixtures that accounts for pharmacokinetic interactions among components, and (2) to apply this methodology to assess the health risk associated with occupational inhalation exposure to airborne mixtures of dichloromethane, benzene, toluene, ethylbenzene, and m-xylene. The basis of the proposed risk assessment methodology relates to the characterization of the change in tissue dose metrics (e.g., area under the concentration-time curve for parent chemical in tissues [AUCtissue], maximal concentration of parent chemical or metabolite [Cmax], quantity metabolized over a period of time) in humans, during mixed exposures using PBPK models. For systemic toxicants, an interaction-based hazard index was calculated using data on tissue dose of mixture constituents. Initially, the AUCtarget tissue (AUCtt) corresponding to guideline values (e.g., threshold limit value [TLV]) of individual chemicals were obtained. Then, the AUCtt for each chemical during mixed exposure was obtained using a mixture PBPK model that accounted for the binary and higher order interactions occurring within the mixture. An interaction-based hazard index was then calculated for each toxic effect by summing the ratio of AUCtt obtained during mixed exposure (predefined mixture) and single exposure (TLV). For the carcinogenic constituents of the mixture, an interaction-based response additivity approach was applied. This method consisted of adding the cancer risk for each constituent, calculated as the product of q*tissue dose and AUCtt. The AUCtt during mixture exposures was obtained using an interaction-based PBPK model. The approaches developed in the present study permit, for the first time, the consideration of the impact of multichemical pharmacokinetic interactions at a quantitative level in mixture risk assessments.