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 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.

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.

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.

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.

PBPK modeling/Monte Carlo simulation of methylene chloride kinetic changes in mice in relation to age and acute, subchronic, and chronic inhalation exposure.

During a 2-year chronic inhalation study on methylene chloride (2000 or 0 ppm; 6 hr/day, 5 days/week), gas-uptake pharmacokinetic studies and tissue partition coefficient determinations were conducted on female B6C3F1, mice after 1 day, 1 month, 1 year, and 2 years of exposure. Using physiologically based pharmacokinetic (PBPK) modeling coupled with Monte Carlo simulation and bootstrap resampling for data analyses, a significant induction in the mixed function oxidase (MFO) rate constant (Vmaxc) was observed at the 1-day and 1-month exposure points when compared to concurrent control mice while decreases in glutathione S-transferase (GST) rate constant (Kfc) were observed in the 1-day and 1-month exposed mice. Within exposure groups, the apparent Vmaxc maintained significant increases in the 1-month and 2-year control groups. Although the same initial increase exists in the exposed group, the 2-year Vmaxc is significantly smaller than the 1-month group (p < 0.001). Within group differences in median Kfc values show a significant decrease in both 1-month and 2-year groups among control and exposed mice (p < 0.001). Although no changes in methylene chloride solubility as a result of prior exposure were observed in blood, muscle, liver, or lung, a marginal decrease in the fat:air partition coefficient was found in the exposed mice at p = 0.053. Age related solubility differences were found in muscle:air, liver:air, lung:air, and fat:air partition coefficients at p < 0.001, while the solubility of methylene chloride in blood was not affected by age (p = 0.461). As a result of this study, we conclude that age and prior exposure to methylene chloride can produce notable changes in disposition and metabolism and may represent important factors in the interpretation for toxicologic data and its application to risk assessment.

Physiologically based pharmacokinetics and cancer risk assessment.

Physiologically based pharmacokinetic (PBPK) modeling involves mathematically describing the complex interplay of the critical physicochemical and biological determinants involved in the disposition of chemicals. In this approach, the body is divided into a number of biologically relevant tissue compartments, arranged in an anatomically accurate manner, and defined with appropriate physiological characteristics. The extrapolation of pharmacokinetic behavior of chemicals from high dose to low dose for various exposure routes and species is possible with this approach because these models are developed by integrating quantitative information on the critical determinants of chemical disposition under a biological modeling framework. The principal application of PBPK models is in the prediction of tissue dosimetry of toxic moiety (e.g., parent chemical, reactive metabolite, macromolecular adduct) of a chemical. Such an application has been demonstrated with dichloromethane, a liver and lung carcinogen in the B6C3F1 mouse. The PBPK model-based risk assessment approach estimated a cancer risk to people of 3.7 x 10(-8) for a lifetime inhalation exposure of 1 micrograms/m3, which is lower by more than two orders of magnitude than that calculated by the U.S. Environmental Protection Agency using the linearized multistage model (for low-dose extrapolation) and body surface correction factor (for interspecies scaling). The capability of predicting the target tissue exposure to toxic moiety in people with PBPK models should help reduce the uncertainty associated with the extrapolation procedures adopted in conventional dose-response assessment.

Analysis of PBPK models for risk characterization.

Adoption of a Bayesian framework for risk characterization permits the seamless integration of different kinds of information available in order to choose and parameterize risk models. It also becomes easy to disentangle uncertainty from variability, through hierarchical statistical modeling. Appropriate numerical techniques can be found, for example, in the recently developed arsenal of Markov chain, Monte Carlo simulations. The developments in this area can actually be viewed as extensions of the traditional or standard Monte Carlo methods for uncertainty analysis. Following a brief review of the techniques, examples of Bayesian analyses of physiologically-based pharmacokinetic models are presented for tetrachloroethylene and dichloromethane. The discussion touches on some open problems and perspectives for the proposed methods.

Physiologically based pharmacokinetic modeling of inhalation exposure of humans to dichloromethane during moderate to heavy exercise.

Dichloromethane (methylene chloride, DCM) is metabolized via two pathways in humans: mixed-function oxidases (MFO) and glutathione-S:-transferase (GST). Most likely, the carcinogenicity for DCM is related to metabolic activation of DCM via the GST pathway. However, as the two pathways are competing, the metabolic capacity for the MFO pathway in vivo is also of interest in risk assessment for DCM. Past estimates of MFO metabolism are based on the in vitro activity of tissue samples. The aim of the present study was to develop a population model for DCM in order to gain more knowledge on the variability of DCM inhalation toxicokinetics in humans, with main emphasis on the MFO metabolic pathway. This was done by merging published in vitro data on DCM metabolism and partitioning with inhalation toxicokinetic data (Astrand et al., 1975, Scand. J. Work.Environ. Health 1, 78-94) from five human volunteers, using the MCMC technique within a population PBPK model. Our results indicate that the metabolic capacity for the MFO pathway in humans is slightly larger than previously estimated from four human liver samples. Furthermore, the interindividual variability of the MFO pathway in vivo is smaller among our five subjects than indicated by the in vitro samples. We also derive a Bayesian estimate of the population distribution of the MFO metabolism (median maximum metabolic rate 28, 95% confidence interval 12-66 micromol/min) that is a compromise between the information from the in vitro data and the toxicokinetic information present in the experimental data.

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.

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.

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.

Incorporating biological information in quantitative risk assessment: an example with methylene chloride.

The interplay between chemical risk assessment and scientific research is discussed in the context of recent attempts to improve the scientific basis for estimates of the human carcinogenic risk from methylene chloride. A combination of basic biochemical research and risk assessment oriented research, both mechanistic and pharmacokinetic, provided the initial impetus for re-evaluating the use of the default risk estimation approach. Resulting efforts to incorporate the new scientific information into the risk assessment process in turn identified specific additional research needed to reduce uncertainty in the estimated risk. This healthy interchange between the two disciplines has served both to refine the human risk estimates for methylene chloride and to more clearly identify key scientific issues for chemical risk assessment in general.

Use of quantitative modelling in methylene chloride risk assessment.

The benefits of basing quantitative risk assessment on measures of 'internal dose', i.e. target organ exposures as estimated, for instance, by pharmacokinetic models, have been extensively discussed. Recasting risk assessment methods at the level of internal dose raises novel issues, however, some of which are explored by examining the 1987 revision by the US Environmental Protection Agency (EPA) of its cancer risk assessment for inhaled methylene chloride, which was based on the 1987 pharmacokinetic model results of Andersen and coworkers. The internal dose measure was the daily amount of methylene chloride metabolized by a glutathione-S-transferase pathway per 1 of target organ (liver and lung). Owing to high-dose saturation of a competing detoxification reaction, this metabolic activation is less-than-proportionally active at low exposure levels. For a given inhalation exposure, humans have relatively less metabolic activation than do mice, but this is shown to be a foreseeable consequence of their relatively lower breathing rate, a cross-species difference already accounted for in standard EPA methodology. Indeed, many species differences in the rates and tempos of physiological processes evince regular 'scaling' relationships across differently sized mammals. EPA's practice of scaling carcinogen doses by body surface area for cross-species extrapolation, often viewed as a correction for metabolic activation, is shown to be more reasonably regarded as an accommodation for the more general species variation in the pace of physiological processes underlying both pharmacokinetics and the carcinogenic response to internal doses. Under this view, the issue of cross-species dose scaling is not obviated by the use of pharmacokinetics.

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.

Spreadsheet programming--a new approach in physiologically based modeling of solvent toxicokinetics.

Physiological models are useful tools in the understanding of organic solvent toxicokinetics. An approach is presented where a physiological model is designed and solved by means of a spreadsheet macro instruction on a personal computer. The spreadsheet template is easy to use. Model parameters are entered and stored in tabular form, and any parameters and variables may be plotted. The model may be changed by editing the spreadsheet template, allowing compartments to be added, nonlinear metabolism to be introduced, etc. Accordingly, the kinetics of any substance and its metabolites, and any route of exposure may be modeled. The spreadsheet approach of physiologically based kinetic modeling is illustrated by simulating inhalation exposure to four organic solvents (acetone, 2-butoxyethanol, methylene chloride and styrene) in humans at various work loads. The results of the simulations are graphically compared with experimental data. By separating resting and working muscle tissue, the model successfully predicted the solvent concentrations not only in arterial but also in venous blood samples at various work loads (shown for acetone and methylene chloride).

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.

Incorporation of in vitro enzyme data into the physiologically-based pharmacokinetic (PB-PK) model for methylene chloride: implications for risk assessment.

Physiologically-based pharmacokinetic (PB-PK) models provide a mechanism for reducing the uncertainty inherent in extrapolating the results of animal toxicity tests to man. This paper discusses a technique for incorporating data from in vitro studies of xenobiotic metabolism into in vivo PB-PK models. Methylene chloride is used as an example, and carcinogenic risk estimates incorporating PB-PK principles are presented.