Use of a mathematical model of rodent in vitro benzene metabolism to predict human in vitro metabolism data

Mark R. Lovern1,2, M. Elizabeth Maris1,2 and Paul M. Schlosser1,3

1 Chemical Industry Institute of Toxicology, 6 Davis Drive, PO Box 12137, Research Triangle Park, NC 27709-2137 and
2 Biomathematics Program, North Carolina State University, Raleigh, NC, USA


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Benzene, a ubiquitous environmental pollutant, is known to cause leukemia and aplastic anemia in humans and hematotoxicity and myelotoxicity in rodents. Toxicity is thought to be exerted through oxidative metabolites formed in the liver, primarily via pathways mediated by cytochrome P450 2E1 (CYP2E1). Phenol, hydroquinone and trans-trans-muconaldehyde have all been hypothesized to be involved in benzene-induced toxicity. Recent reports indicate that benzene oxide is produced in vitro and in vivo and may be sufficiently stable to reach the bone marrow. Our goal was to improve existing mathematical models of microsomal benzene metabolism by including time course data for benzene oxide, by obtaining better parameter estimates and by determining if enzymes other than CYP2E1 are involved. Microsomes from male B6C3F1 mice and F344 rats were incubated with [14C]benzene (14 µM), [14C]phenol (303 µM) and [14C]hydroquinone (8 µM). Benzene and phenol were also incubated with mouse microsomes in the presence of trans-dichloroethylene, a CYP2E1 inhibitor, and benzene was incubated with trichloropropene oxide, an epoxide hydrolase inhibitor. These experiments did not indicate significant contributions of enzymes other than CYP2E1. Mathematical model parameters were fitted to rodent data and the model was validated by predicting human data. Model simulations predicted the qualitative behavior of three human time course data sets and explained up to 81% of the total variation in data from incubations of benzene for 16 min with microsomes from nine human individuals. While model predictions did deviate systematically from the data for benzene oxide and trihydroxybenzene, overall model performance in predicting the human data was good. The model should be useful in quantifying human risk due to benzene exposure and explicitly accounts for interindividual variation in CYP2E1 activity.

Abbreviations: AML, acute myelogenous leukemia; BHT, hydroxytoluene; CYP, cytochrome P450; DCE, trans-dichloroethylene; LSS, liquid scintillation spectrometry; TBAS, tetrabutylammonium hydrogen sulfate; TCPO, trichloropropene oxide; TWA-PEL, time-weighted average permissible exposure level


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
In the past, benzene was used extensively in the production of paints, resins, rubber, inks and dyes. Evidence that persons exposed to high levels of benzene for extended periods of time suffer an increased risk of aplastic anemia and acute myelogenous leukemia (AML) (1) prompted the adoption of regulations by the US government that limited occupational exposures to benzene (2). In the USA, benzene is still used as a feedstock for the synthesis of many organic chemicals and is found in gasoline at a relative abundance of 1% by volume (3). Common sources of environmental exposure include gasoline fumes, automobile exhaust and both mainstream and sidestream tobacco smoke (4). Recent reports of increased hematotoxicity and leukemia among benzene-exposed workers in China corroborate the existing evidence that benzene causes both aplastic anemia and AML (5,6). As in previous studies, AML in the Chinese cohort was associated with constant, high level exposures or high cumulative exposures.

In mice and rats, exposure to benzene has been shown to cause both hematotoxicity (e.g. aplastic anemia) and myelotoxicity (e.g. decreases in bone marrow cellularity) (7,8). Because hematotoxicity is frequently a precursor of AML in humans, these end-points are presumed to be predictive of AML risk for humans.

A number of studies have indicated that benzene toxicity is dependent on activation by oxidative hepatic metabolism (9). Sammett et al. (10) demonstrated that partial hepatectomy protected rats against benzene-induced hematotoxicity. Cytochrome P450 (CYP) monooxygenases have been shown to catalyze the oxidative metabolism of benzene (1113) and a positive correlation has been observed in humans between CYP2E1 activity and risk of benzene-induced hematotoxicity (14). Also, mice lacking the genes for CYP2E1 expression did not exhibit myelotoxicity when exposed to benzene (15), indicating that this particular isoform plays a key role in the bioactivation of benzene. However, some residual metabolism of benzene was observed in these animals, indicating that a second CYP isozyme also contributed to in vivo metabolism. This second isozyme is most likely CYP2B1. In vitro, CYP isozymes 2E1 and 2B1 both act as hydroxylases of benzene and phenol (11,16). For both isoforms, the chief products of benzene metabolism were phenol and hydroquinone. Phenol metabolism resulted mainly in the formation of hydroquinone, although some catechol was produced at sufficiently high phenol concentrations. Also, CYP2B1 exhibited a much lower affinity for benzene than CYP2E1 and functioned far less efficiently at low benzene concentrations (16). Thus the metabolites produced by CYP2B1 and its relatively low efficiency are consistent with the residual metabolism observed in CYP2E1 knockout mice.

The metabolite or combination of metabolites responsible for the toxic effects associated with benzene exposure is not known. At present, trans-trans-muconaldehyde, an open ring metabolite, as well as quinones and radicals formed from the oxidation of hydroquinone are among the suspect agents (17). Recently, it has been reported that GC/MS analysis of samples from microsomal incubations of 1 mM benzene revealed the presence of benzene oxide, the initial oxidative metabolite of benzene (18). HPLC analysis of an authentic standard for benzene oxide revealed that its elution time matched exactly that of an unknown metabolite reported by Seaton et al. (19). At 16 min, this metabolite accounted for up to 10% of the total metabolites produced in incubations of [14C]benzene with human microsomes (19). As an epoxide, benzene oxide may have significant potential for binding to macromolecules and its recent detection in the blood of rats after benzene exposure by gavage (20) suggests that it should now be considered as a potential agent of benzene toxicity. This metabolite is also important because it is intermediate to the production of all other benzene metabolites. While a number of products are formed indirectly via serial hydroxylations of phenol (i.e. hydroquinone, catechol and trihydroxybenzene), S-phenyl-N-acetylcysteine (phenylmercapturic acid), an important biomarker of benzene exposure (21), and the potentially toxic trans-trans-muconaldehyde are both believed to be formed directly from benzene oxide (22). A direct route from benzene oxide to catechol via epoxide hydrolase catalysis has also been reported (2325). Due to its potential for toxicity and its importance as a reaction intermediate, characterization of the kinetics of benzene oxide production would significantly enhance current quantitative models of benzene metabolism.

There is considerable uncertainty in what bone marrow dosage of putative benzene metabolites occurs in humans after benzene exposure. Target tissue dosages not only depend on the production of oxidative metabolites in the liver but also on their rate of removal via conjugation, primarily with sulfate and glucuronic acid. CYP2E1, phenol sulfotransferase and epoxide hydrolase activity are known to vary 50-, 10- and 500-fold, respectively, in human populations (26). Thus variability in enzyme expression may lead to individuals receiving vastly different bone marrow metabolite dosages from the same environmental exposure.

The incubation experiments reported here were performed to obtain better parameter estimates for a mathematical model of microsomal benzene metabolism and to determine whether or not enzymes other than CYP2E1 are important in oxidative metabolism of benzene by murine microsomes. More accurate values were sought for the affinities of benzene and phenol for CYP2E1. Hence, incubations were run with these substrates present at concentrations that were thought to be sufficiently high to exhibit partial enzyme saturation. Also, hydroquinone was incubated with mouse microsomes to measure its rate of conversion to trihydroxybenzene. Experiments with enzyme inhibitors were also performed to determine the roles of epoxide hydrolase and/or a second CYP in the in vitro metabolism of benzene by mouse microsomes.

A mathematical model of microsomal benzene metabolism could be used to simulate the oxidative component of in vivo benzene metabolism. In conjunction with strategic experiments, the model could also be used to assess whether a second CYP isozyme contributes to benzene metabolism and to determine the relative contribution of epoxide hydrolase to the metabolism of benzene oxide. Coupling this model with a model of in vitro conjugative metabolism would yield an overall model of benzene metabolism. Incorporating the final metabolism model into a physiologically based pharmacokinetic model would allow the simulation of the major processes associated with benzene disposition and metabolism in animals and humans. In theory, if the benzene exposure level and CYP2E1 activity of a human individual were provided as inputs along with the rates of removal by conjugation and renal clearance, such a model could be used to predict the bone marrow dosage of metabolites received by that individual. Schlosser et al. (27) developed a mathematical model that simulated the metabolism of benzene and phenol by rodent microsomes and Seaton et al. (19) extended the model to simulate the metabolism of benzene by human microsomes. The work reported here extends the mathematical model by reformulating the existing models to include data for the in vitro production of benzene oxide.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Animals
Male Fischer 344 rats [CDF (F-344)/CrlBR], 10–12 weeks old (200–250 g), and male B6C3F1 mice (B6C3F1/CrlBR), 7–9 weeks old (22–30 g), (Charles River Laboratories, Raleigh, NC) were used as tissue donors. Animals were housed in mass air displacement temperature and humidity controlled rooms (22 ± 1°C and 60 ± 15%, respectively), fed pelleted NIH-07 rodent chow (Zeigler Bros., Gardner, PA) and purified water ad libitum and maintained on a 12 h light/dark cycle. Animals were allowed to acclimate for at least 2 weeks prior to use. Sentinel animals were screened weekly for viral infection (Standard Rat Screen; Microbiological Associates, Bethesda, MD) and were negative throughout the study.

Chemicals
[U-14C]benzene (102.1 mCi/mmol, 98% purity), [U-14C]phenol (105.4 mCi/mmol, 97% purity) and [U-14C]hydroquinone (49.6 mCi/mmol, 98% purity) were purchased from Chemsyn Science Laboratories (Lenexa, KS). Benzene (99.9% purity), L-ascorbic acid (sodium salt), NADH and NADPH were purchased from Sigma Chemical Co. (St Louis, MO). Sodium phosphate was obtained from Fisher Scientific (Fair Lawn, NJ). Acetonitrile was purchased from J.T. Baker (Baker Analyzed® HPLC Reagent; Phillipsburg, NJ,) and KH2PO4 was purchased from Aldrich Chemical Co. (Milwaukee, WI). All other chemicals were of the highest quality commercially available.

For safe handling and storage, [U-14C]benzene primary stocks were kept in methanol at –80°C and diluted to working levels in 0.1 M NaH2PO4, pH 7.4, in sealed septum vials prior to incubation, so that 15–20 µl of working stock contained 106 d.p.m. radiolabeled compound, with the exact activity determined by liquid scintillation spectrometry (LSS). All LSS (including below) was performed with a Packard 1900CA TRI-CARB® liquid scintillation analyzer (Packard Instrument Co., Sterling, VA), with the automatic external standard method used for quench correction. The final volume of residual methanol from the primary stock in incubation mixtures was 0.15–0.36 µl/ml. [U-14C]phenol and [U-14C]hydroquinone primary stocks were stored in H2O at –80°C and diluted to working levels in buffer in sealed septum vials prior to incubation.

Microsome preparations
Rat and mouse microsomes used in these experiments were prepared from pooled liver samples by the method of Csanády et al. (28). Protein concentrations were determined by using either a modified micro-Lowry method (29) or a modification of the Biuret method (American Monitor Total Protein Kit; American Monitor Corp., Indianapolis, IN). Microsomes were stored at –80°C until time of use.

Assay of CYP2E1 activity
The CYP2E1 activity of the microsome preparations used in the hydroquinone and enzyme inhibition experiments (described below) was estimated spectrophotometrically by the method of Koop (30). Briefly, the reaction mixture consisted of microsomal protein (0.2 mg/ml), 0.1 M potassium phosphate, pH 6.8, and 1 mM p-nitrophenol. Samples were incubated at 37°C for 3 min prior to the addition of 1 mM NADPH to start the reaction. After 10 min, the reaction was stopped with 1.5 N perchloric acid. Formation of p-nitrocatechol was measured at 510 nm.

Incubation procedure
Incubations of either [U-14C]benzene or [U-14C]phenol with mouse or rat liver microsomes (1 mg/ml final concentration) were performed as described previously (18,19). Final concentrations for each substrate species combination were as follows (mean ± SD): 14.3 ± 2.2 µM (benzene, mouse), 13.8 ± 1.8 µM (benzene, rat), 304 ± 0.7 µM (phenol, mouse) and 303 ± 1 µM (phenol, rat). Incubations of [U-14C]hydroquinone [final concentration (mean ± SD) = 8.0 ± 1.6 µM] with a separate batch of mouse microsomes (0.4 mg/ml final protein concentration) were also performed. Each substrate was also incubated at least three times for 5 min with heat-inactivated microsomes to assess the extent of non-enzymatic transformation.

In benzene and hydroquinone incubations, reactions were terminated by extraction with 1 ml of ice-cold ethylacetate containing the following unlabeled metabolite standards and butylated hydroxytoluene (BHT): benzene (8.9 mg/ml), phenol (0.63 mg/ml), catechol (1.6 mg/ml), hydroquinone (2.9 mg/ml), trihydroxybenzene (1.3 mg/ml) and BHT (20 mg/ml). Samples were vortexed for 1 min and then centrifuged at 3000 r.p.m. for 5 min to separate the aqueous and organic fractions and to pellet the protein. The organic phase was transferred to a 1.5 ml amber vial and stored at –80°C.

Metabolism in phenol incubations was stopped by the addition of 125 µl of 40% trichloroacetic acid and rapid vortexing. After being left on ice for 5 min to allow protein precipitation, incubation vials were injected with 25 µl of metabolite standards in ethanol and immediately re-vortexed. The constituents of the internal standard mixture were trihydroxybenzene (80 mg/ml), hydroquinone (180 mg/ml), catechol (96 mg/ml) and phenol (39 mg/ml). Finally, the vials were centrifuged for 10 min at 2000 r.p.m. and the supernatant was transferred to 1.5 ml amber vials containing 20 µl of 6 N NaOH, which were stored at –80°C.

Enzyme inhibition experiments
In addition to the experiments described in the preceding section, two sets of experiments were also conducted to determine whether enzymes other than CYP2E1 contribute to the in vitro metabolism of benzene. To determine what fraction of the total catechol observed was formed directly from benzene oxide via a pathway mediated by epoxide hydrolase, 11 benzene incubations were performed. Of these incubations, five contained 100 µM trichloropropene oxide (TCPO). TCPO is an alternative substrate for epoxide hydrolase which is turned over slowly and hence acts as a competitive inhibitor when supplied in excess (31). In a previous study, this concentration inhibited ~90% of the epoxide hydrolase activity present in 1 mg of human microsomal protein (31). The other six incubations consisted of five benzene incubations without TCPO and one incubation with heat-inactivated microsomes. For these incubations, the initial benzene concentration was 2.2 ± 0.3 µM and the incubation time was 18 min.

The possibility that more than one CYP enzyme may be associated with benzene and phenol metabolism was investigated through experiments using trans-dichloroethylene (DCE), an inhibitor that is highly specific to CYP2E1 (32). Both benzene (8 ± 2 µM) and phenol (3.4 ± 0.2 µM) were incubated for 20 min in the presence and absence of DCE. For each substrate, active microsome incubations that contained the inhibitor, those that did not and incubations with heat-inactivated microsomes were each performed a total of three times. Benzene and phenol incubations were treated, respectively, with 100 and 200 ppm DCE.

The microsomes used in all these experiments were mouse microsomes from the same batch as those used in the hydroquinone incubations. The total protein concentration for all incubations was 0.3 mg/ml. In all other respects, these incubations were identical to the benzene incubations described in the previous section.

Chemical analysis
Analysis of ethylacetate extracts was by HPLC followed by LSS, as described previously (18,19). Aqueous samples were separated on a 4.6x250 mm C-18, 5-µm reverse phase HPLC column with a 0.5 µm inlet filter (Altech, Deerfield, IL). Prior to analysis, the following solvents were prepared. Solvent A was a mixture of 9 parts 50 mM tetrabutylammonium hydrogen sulfate (TBAS) in nanopure water with 1 part 35 mM TBAS in methanol. Solvent B was a 2:8 mixture of 50 mM TBAS (water) with 35 mM TBAS (methanol). The solvent program was adapted from the method of Sabourin et al. (34): a gradient from 100% solvent A at 0 min to 50% solvent A:50% solvent B at 30 min, followed by a gradient to 50% solvent A:50% methanol from 30 to 40 min and, finally, held at 50% solvent A:50% methanol until 50 min. The flow rate of the mobile phase was 1 ml/min.

Extraction efficiency
To estimate the concentration of metabolites present in incubation mixtures before extraction into ethylacetate, coefficients were determined that could be applied to concentration data from the organic phase samples. Extraction efficiencies were calculated by analyzing both the organic and aqueous phases as described above to determine the relative concentrations in the two phases and adjusting for the volume change that resulted from extraction. (After extraction with an equal volume of ethylacetate, the proportion of organic to aqueous phase is 0.88:1.12.) Extraction efficiencies were determined for several samples and a single extraction coefficient was determined for each metabolite as a weighted average of the set (based on 14C activity) for each metabolite.

Model development
The two-compartment model depicted in Figure 1Go was used to describe the distribution of benzene between the gas and liquid phases in the reaction vials as well as the biotransformation reactions that occurred in the liquid phase. The model used here is a modification of that described by Schlosser et al. (27). The values used for the first order rate constants associated with the transport of benzene between the gas and liquid phases, kG and kL, are the same as reported previously (27).



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Fig. 1. Schematic of the two-compartment model describing the transport and biotransformation processes hypothesized to occur in the incubation experiments modeled in this article. The clear area represents the vapor phase and the shaded area represents the liquid phase. Arrows represent the transport of benzene between the vapor and liquid phases and the biotransformation of benzene in the liquid phases; VL and VG are the volumes of the liquid and gas phases, respectively, and r1r8 represent the rate expressions governing the reactions, which are described in Materials and methods.

 
In addition to the data obtained from the experiments described here, the data reported by Schlosser et al. (27) and Seaton et al. (19) were used for fitting model parameters and evaluating model performance, respectively. The low concentration phenol incubations performed by Schlosser et al. (27) and heat-inactivated control incubations in the Seaton et al. (19) study all contained a metabolite that co-eluted with benzene oxide. Since benzene oxide is formed from benzene by an enzyme catalyzed reaction, it was unlikely to be present in either phenol or heat-inactivated incubations. An impurity that co-elutes with benzene oxide may have been present in the 14C stocks used for those particular experiments. 2,2'-Biphenol was previously observed to co-elute with benzene oxide and could potentially be the impurity. In the Schlosser et al. phenol incubation data (27), the compound was observed to decay more rapidly in mouse than in rat incubations. Because mouse microsomes have been observed to have higher CYP2E1 activities than rat microsomes, this suggests that it is a substrate for that enzyme. The amounts of the impurity in the Seaton et al. (19) benzene samples represented an appreciable fraction of the maximum amount of benzene oxide formed after the initiation of microsomal metabolism. Therefore, a variable was incorporated into the model to account for this impurity, and its degradation was assumed to be catalyzed by CYP2E1.

The initial oxidation of benzene to benzene oxide is presumed to be catalyzed by CYP2E1. Benzene oxide is then converted to catechol via an epoxide hydrolase catalyzed pathway or spontaneously degrades to form phenol. The kinetics for the degradation to phenol are assumed to be first order (non-enzymatic). While the kinetics for the formation of catechol from benzene oxide are ostensibly Michaelis–Menten, the observed concentrations of benzene oxide were so low that pseudo-first order kinetics were assumed to govern this reaction. The transformation of phenol to hydroquinone and catechol, the subsequent oxidation of these compounds to form trihydroxybenzene and the degradation of the impurity were all assumed to be catalyzed by CYP2E1. Based on this assumption, kinetic rate expressions for CYP2E1-mediated reactions incorporate a denominator term to account for the mutual inhibition that would arise from the various metabolites competing for enzyme active sites. For a more detailed description of the derivation see Schlosser et al. (27). The model equations and some details on how they were matched to experimental data are given in the Appendix.

Solutions to the set of differential equations (2a)–(2h) in the Appendix were obtained by numerical integration using the Advanced Continuous Simulation Language (ACSL) of Mitchell and Gauthier Associates (Concord, MA). The values of VG, VL, kG and kL were those determined by Schlosser et al. (27). Values of k1k8, ABZ, APH, AHQ, ACA and AUNK were fitted to data sets containing mouse and rat microsome time course data that were obtained both by Schlosser et al. (27) and by the experiments reported here. These parameters were fitted to three distinct data sets. One set contained only mouse data and the second set only rat data. For the third set, both of the mouse and rat data sets were merged. For the merged mouse–rat data set, a single set of kinetic parameters was fitted for both species, except that each species was accorded its own level of CYP2E1 activity. This is equivalent to assuming that the specific enzyme kinetics associated with benzene metabolism do not vary among species but that the levels of enzyme expression do. For all data sets, the Nelder–Mead search and Generalized Reduced Gradient optimization routines in the Simusolv' Modeling and Simulation Software were used to obtain parameter estimates.

Model performance was evaluated by comparing model predictions for the metabolite concentrations achieved at 16 min over a range of CYP2E1 activity levels with the measured concentrations produced at 16 min by microsomes of known CYP2E1 activities from mice, rats and 10 human individuals (19). Model predictions were also compared with time course data obtained from incubations with microsomes from three human individuals (19).


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Assay of CYP2E1 activity
The measured CYP2E1 activity for the microsomes used in the hydroquinone, TCPO and DCE experiments was 4.686 nmol/mg/min. The total protein used in these incubations was 0.4 mg/ml, so the actual CYP2E1 activity in these vials was 1.879 nmol/ml/min.

Incubation experiments
Benzene, benzene oxide, phenol, hydroquinone, catechol and trihydroxybenzene were detected in all incubations in which benzene was the initial substrate. No appreciable amounts of benzene or benzene oxide were detected in the phenol incubations reported here nor were any metabolites other than hydroquinone and trihydroxybenzene found to be present in the samples from hydroquinone incubations. The major metabolites formed in benzene incubations were phenol and hydroquinone, which together accounted for up to 95% of the metabolites formed in mouse incubations and up to 97% in rat incubations. In phenol incubations, hydroquinone represented 92 (mouse) and 89% (rat) of the metabolites formed.

Results from benzene incubations are shown in Figures 2 and 3GoGo, while phenol and hydroquinone incubation data are shown in Figures 4 and 5GoGo. To facilitate comparison with previously published data sets, data are reported here as fractions of the total 14C activity. The benzene and phenol incubation data published by Schlosser et al. (27) are superimposed on Figures 2–4GoGoGo. When plotted in this way, the two benzene data sets appear to be similar and most data points for all metabolites are within one standard deviation of previously reported values at the same time point. The data tend to indicate that a slight increase in overall benzene metabolism was associated with the higher substrate concentrations used here. This would suggest that the rate of metabolism is still proportional to substrate concentration at these concentrations and therefore that the benzene concentrations used were well below the enzyme saturation threshold.



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Fig. 2. Data and model predictions for metabolites formed in benzene incubations with mouse microsomes reported as fractions of total 14C activity. Error bars are ±1 standard deviation (SD). Circles represent data reported previously by Schlosser et al. (27). Diamonds are data obtained from experiments reported here. Model predictions are shown as continuous curves and are classified according to which data set (high concentration or low concentration) they are predicting. All predictions for benzene were virtually indistinguishable. Predictions were obtained by fitting the model to the entire mouse data set (benzene, phenol, and hydroquinone incubations).

 


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Fig. 3. Data and model predictions for metabolites formed in benzene incubations with rat microsomes reported as fractions of the total 14C activity. Error bars are ±1 standard deviation (SD). Circles represent data reported previously by Schlosser et al. (27). Diamonds are data obtained from experiments reported here. Model predictions are shown as continuous curves and are classified according to which data set (high concentration or low concentration) they are predicting. Predictions for benzene were virtually indistinguishable, the single exception being trihydroxybenzene. Predictions were obtained by fitting the model to the entire rat data set (benzene and phenol incubations).

 


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Fig. 4. Data and model predictions for metabolites formed in phenol incubations with mouse and rat microsomes reported as fractions of the total 14C activity. Error bars are ±1 standard deviation (SD). Circles represent data reported previously by Schlosser et al. (27). Diamonds are data obtained from experiments reported here. Model predictions are shown as continuous curves and are classified according to which data set (high concentration or low concentration) they are predicting. Predictions were obtained by fitting the model to either the entire mouse data set for the mouse plots or to the entire rat data set for rat plots.

 


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Fig. 5. Data and model predictions for metabolites formed in hydroquinone incubations with mouse microsomes reported as fractions of the total 14C activity. Error bars are ±1 SD. Predictions for these data were based on the mouse-only parameter set.

 
There are considerable differences between the high concentration and low concentration phenol data. As a fraction of the initial amount present, substantially less phenol was metabolized in high concentration incubations than was metabolized in the low concentration experiments performed by Schlosser et al. (27) and considerably less hydroquinone was formed. Also, the high concentration time courses tended to follow a linear path, while time courses in the earlier data set had a distinct curvature. Together, these facts suggest that enzyme active sites are saturated in the high concentration phenol incubations.

Examination of the data from hydroquinone incubations indicates a low rate of metabolism to trihydroxybenzene. At 80 min after the initiation of metabolism, trihydroxybenzene accounts for only 3% of the parent material.

Extraction efficiencies
Since benzene and benzene oxide were not detected in the aqueous phase, both were given an extraction efficiency/volume correction factor of 114% to account for the volume change after extraction. Correction factors for the other metabolites (including a volume change factor) were: phenol, 110%; hydroquinone, 100%; catechol, 106%; trihydroxybenzene, 54.6%.

Enzyme inhibition experiments
The fraction of benzene converted to oxidative metabolites was reduced in the presence of TCPO. The fraction of benzene that was converted to oxidative metabolites at 18 min in incubations with or without TCPO is shown in Table IGo. The amounts of all the metabolites were reduced in the presence of TCPO, with the total being reduced from 42 to 30%. This suggested that TCPO inhibited both epoxide hydrolase and CYP2E1. This hypothesis was tested using analysis of covariance. For this procedure, the micromolar concentration of oxidative metabolites was assumed to be dependent both on the presence or absence of TCPO (nominal variable) and on the initial concentration of benzene (µM) present in the incubation (continuous variable). When analyzed using this statistical model, the inhibitory effect of TCPO on CYP2E1 was significant (P = 0.0003).


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Table I. Metabolite production with and without 100 µM TCPOa,b
 
The importance of k3, the parameter governing flow through the epoxide hydrolase pathway, was assessed after the entire model parameter set had been optimized to fit the mouse time course data. Model predictions for benzene oxide and catechol concentrations in incubations with TCPO were then compared with their measured values. For these model simulations, the percentage inhibition of CYP2E1 was assumed to be equal to the percentage reduction in the production of oxidative metabolites. All other parameters were maintained at the values estimated from the non-inhibited mouse time course data. Addition of TCPO to incubation mixtures is expected to reduce the flux through the pathway catalyzed by epoxide hydrolase. In the model, this corresponds to reducing the value of k3. If a significant amount of catechol is produced via this pathway, then parameters derived from experiments without the inhibitor (k3 not reduced) would be expected to overpredict the amount of catechol and underpredict the amount of benzene oxide formed in the presence of TCPO. However, the model underpredicted catechol formation and overpredicted the formation of benzene oxide. The rate constant for the pathway from phenol to catechol that was estimated from phenol incubation data was sufficient to account for essentially all of the catechol formed in benzene incubations. This would imply that epoxide hydrolase did not contribute significantly to the metabolism of benzene oxide by mouse microsomes. Therefore, the epoxide hydrolase reaction was removed from the model and model parameters were then re-optimized to the time course data.

In incubations treated with DCE, benzene metabolism was reduced to 1% and phenol metabolism was reduced to 2% of that observed in untreated incubations. This would indicate that a second P450 isoform did not contribute significantly to metabolism for the substrate concentrations considered here. Therefore, CYP2E1 activity was assumed to be responsible for all oxidative metabolism.

Mathematical modeling
Parameter sets obtained by fitting to the mouse, rat and mouse–rat time course data are shown in Table IIGo. The values of several parameters are only listed as being less than or equal to respective orders of magnitude. In these cases, the model was insensitive to a choice of parameters below the indicated level and model fits were degraded by choices greater than that level. In the case of ABZ, for example, there was little or no improvement in model predictions when 0 = ABZ = O (10–4per µM) and the model fit was degraded when ABZ > O (10–4per µM). In these cases, the parameter was subsequently set to 0 and the other parameters were allowed to re-optimize.


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Table II. Parameter values used for the prediction of the human data (parameter sets are classified according to what rodent species data was used for parameter estimation)
 
The percentages of variation in the single species data sets explained by the individual model parameter sets as measured by the r2 statistic are shown in Table IIIGo. All three parameter sets performed well at predicting the mouse data. Rat-based predictions were the least effective of the three, but still explained ~98% of the variation. When predicting the rat data, in contrast, mouse-based predictions explained only 80% of the variation, while rat-based and combined mouse–rat-based predictions explained 96 and 90%, respectively.


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Table III. Percentage of the total variation in human and rodent data sets explained by the three rodent-based parameter sets
 
Human time course data reported by Seaton et al. (19) are shown in Figure 6Go. These data were obtained from incubations with microsomes from three human individuals. The model predictions given by the three animal-based parameter sets are superimposed on these plots. The best fit for the data from liver donor HL103 was achieved using the rat-based parameter set and explained 43% of the variation. If r2 is used as a measure of goodness of fit (see Table IIIGo), none of the parameter sets explained a significant fraction of the variation in the data from liver donors HL111 (data and curve fits not shown) and HL114. For these data sets, most model predictions are within a factor of 2 of the measured value and no model prediction is off by more than a factor of 4. Only one measurement was made at each time point for these data, so no information regarding the variability in measurements was available.



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Fig. 6. Data and model predictions for metabolites formed in incubations of benzene with microsomes from two human individuals. HL103 had relatively low CYP2E1 activity, whereas HL114 had relatively high CYP2E1 activity. Circles and diamonds represent the single measurement data previously published by Seaton et al. (19). Curves represent model predictions based on the three rodent-based parameter sets (thin lines are for HL103, heavy lines for HL114).

 
Seaton et al. also reported data for the metabolites formed in 16 min incubations of 4 µM benzene with microsomes from nine human individuals as well as mouse and rat pooled liver microsomes (19). These data are shown in Figure 7Go. Also shown on these plots are model predictions for metabolite concentrations at 16 min as a function of CYP2E1 activity. Model predictions explained >74% of the variation in the 16 min data (see Table IIIGo). Mouse-based predictions performed the best, explaining 81% of the total variation. The model systematically overpredicted human benzene oxide concentrations and underpredicted trihydroxybenzene for both mouse and human microsomes. Otherwise, the model performed well at predicting these data.



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Fig. 7. Metabolites produced at 16 min by incubations with microsomes from nine human individuals are plotted versus the measured CYP2E1 activity levels of the associated microsomes. Model predictions for the metabolites produced at 16 min across the range of CYP2E1 activities are superimposed on the data. Data were previously reported by Seaton et al. (19) and are plotted as fractions of the total 14C activity ±1 SD. Model prediction curves are classified according to which parameter set they were based upon.

 

    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
While the affinities for phenol in Table IGo indicate that the high concentration phenol experiments were at 65–80% enzyme saturation, the high concentration benzene data were apparently still in the linear range. [The concentration of 14 µM was chosen for benzene because, based upon an estimate of the affinity of benzene from the data of Schlosser et al. (27), this concentration was expected to achieve substantial saturation of CYP2E1.] However, the benzene concentrations used for these experiments are at the upper end of the levels that are likely to be of interest to human risk assessment. Currently, the Occupational Safety and Health Administration (OSHA) has set the time-weighted average permissible exposure level (TWA-PEL) at 1 ppm (35). Based on published values for blood:air and liver:air partition coefficients, an exposure concentration 10 times higher than the TWA-PEL would result in blood and liver benzene concentrations no greater than 9.4 and 2.9 µM, respectively. Both of these are lower than the maximum concentration used here. Further, these values would only occur in the total absence of CYP2E1 activity and true values may be much lower.

Results from the enzyme inhibition experiments indicated that neither epoxide hydrolase nor a second CYP isoform played a significant role in benzene and phenol metabolism by mouse microsomes. To minimize the number of parameters requiring estimation, these results were assumed to hold true for both rat and human microsomes. However, the model predicts mouse data better than rat data and is even less effective at predicting human data. This may be an indication that these assumptions are not valid, particularly with regard to humans.

As in previous studies using this system in our laboratory, no significant production of muconaldehyde or muconic acid was detected. In general the mass balance, based on 14C activity, among the identified metabolites and benzene was quite good. It is not clear why this pathway, which is significant in vivo, does not seem to be operative in these experiments, but it is likely to be an artifact of this semi-artificial system in our hands. This may be related to the fact that no significant changes were observed with inhibition of epoxide hydrolase. In particular, while hydrolysis of benzene oxide by epoxide hydrolase may be a key step in the formation of muconaldehyde and muconic acid in vivo, inhibition of epoxide hydrolase would not change the levels of other metabolites in our experiments by blocking their production, because we do not see those products even under control conditions. In short, the epoxide hydrolase inhibition experiments are inconclusive as to whether or not hydrolase plays a role in the production of muconic acid, since that product is not formed in our system anyway.

It should be noted that further oxidation of hydroquinone and catechol to quinones, which occurs in vivo, was suppressed in this system by addition of ascorbate. As mentioned above, with the ascorbate, >90% of the original benzene (14C activity) was accounted for among the oxidized metabolites measured. When the mass balance was thoroughly checked (Schlosser et al., 1993), several percent of the 14C activity were also found to be bound to microsomal protein, but no other significant peaks were found in the radiochromatograms of either the aqueous or organic fractions. While only 5–10% of the material was not identified, a relatively small fraction, the amount of trihydroxybenzene is even smaller (<1% in benzene and phenol incubations) and so this could contribute to the lack of fit for trihydroxybenzene. Given that this lack of fit is so small relative to the total metabolism, we did not feel it appropriate to further complicate the model, which would introduce additional adjustable parameters, in order to improve that fit.

It may be valid to assume that 2E1 was the only CYP isozyme responsible for metabolism for all the data sets modeled here. Although CYP2B1 is known to metabolize benzene in rats, this isozyme requires induction (9) and even when induced does not contribute significantly to metabolism at the substrate concentrations used in our experiments (17). The nearest human homolog is CYP2B6, which is 76% similar to CYP2B1 (36), and little is known about its substrate specificity. Furthermore, in 60 human individuals (30 Japanese and 30 Caucasians), CYP2B6 accounted for <1% of the total CYP assayed, suggesting that its activity will be low (37). Therefore, it seems unlikely that a second CYP enzyme played a significant role in producing the metabolites present in the human and rat data sets.

The conversion of benzene oxide to catechol by epoxide hydrolase could be more important in humans than in rodents. If this is so, then humans would be expected to eliminate benzene oxide more rapidly than rodents. This may explain why, in the 16 min data of Seaton et al., all three rodent-based parameter sets systematically overpredicted the benzene oxide formed in human microsomal incubations, but accurately predicted the benzene oxide formed in rodent incubations.

In addition to the assumption that humans do not significantly express any enzymes that are not expressed in mice, the prediction of the human data by the rodent-based model is also based upon the assumption that the kinetic behavior of the CYP2E1 isozyme (e.g. the affinity for phenol) does not differ greatly among species. While there is considerable homology (67%) between human and rat CYP2E1 (39) and both are known to metabolize many of the same substrates (36), there may be sufficient differences in the kinetic parameters of the two forms to explain model shortcomings in predicting the human data. Fitting a separate set of kinetic parameters directly to the human data does indeed improve model predictions, particularly with regard to the single time point data (results not shown). However, the vast majority of in vitro benzene metabolism data are derived from experiments with mouse and rat microsomes. Therefore, our primary interest was in determining how well a rodent-based model would predict human in vitro metabolism.

In general, when the simplifying assumptions are taken into consideration, we feel that the model performs well at predicting the human data and that the inflections and overall shapes of model prediction curves matched those found in the human metabolite time courses. While the set of pathways shown in Figure 1Go may give the impression that the model is overly complex, it should be noted that due to the results described above from the epoxide hydrolase inhibition experiments, the direct pathway from benzene oxide to catechol was eliminated in the final model. Further, in both the mouse-based and the combined mouse and rat-based parameter sets (Table IIGo), the rate constant for the conversion of catechol to trihydroxybenzene was found to be indistinguishable from 0, essentially eliminating that pathway from the model. The remaining pathways are all supported by clear experimental data. Only for the rat-based parameter set was the rate constant for catechol to trihydroxybenzene significantly greater than zero. This may be due in part to the fact that the conversion of hydroquinone to trihydroxybenzene was not investigated with rat microsomes, so there is no a priori reason to retain the path from hydroquinone to trihydroxybenzene over the path from catechol. Beyond that, we believe that the final model is as parsimonious as possible, given the data available.

The model did even better at predicting the human single time point data of Seaton et al. (19). For most of the metabolites, model predictions tended to fall within one standard deviation of the data and errors in prediction did not seem to be systematic. Where systematic errors exist, they may be informative. For instance, while the 16 min rodent data of Seaton et al. were not included in the data set to which kinetic parameters were fitted, the model succeeded in predicting benzene oxide concentrations formed in these incubations while simultaneously overpredicting human benzene oxide concentrations. This phenomenon may be an indication of significant species differences in the contribution of epoxide hydrolase activity to the removal of benzene oxide. In contrast, the underprediction of trihydroxybenzene in Figure 7Go probably does not reflect species differences in metabolism, because it was underpredicted for both mice and humans in those experiments. (Note that the mouse data point for trihydroxybenzene in Figure 7Go is also well above the prediction.) The human-derived parameters of Seaton et al. similarly failed to predict the 16 min trihydroxybenzene data for these species. This suggests that a reaction which contributed to the formation of this metabolite in the 16 min samples was omitted from both models.

With regard to particular metabolites, model predictions of the human data for benzene, phenol, hydroquinone and catechol suggest that there is considerable homology between rodent and human microsomal metabolism with regard to these compounds. Phenol and hydroquinone are compounds of particular interest because they have both been detected in the bone marrow of mice and rats following benzene exposure (40,41). When co-administered to mice, phenol and hydroquinone caused adverse effects on bone marrow cellularity (42). Hydroquinone is known to be metabolized by the bone marrow enzyme myeloperoxidase to form 1,4-benzoquinone (43,44), a compound that binds extensively to proteins and DNA (45) and is believed to contribute significantly to benzene-induced myelotoxicity (43). The formation of benzoquinone by this mechanism is enhanced in the presence of phenol (44). Therefore, rodent-based predictions of the formation of these metabolites from human in vitro metabolism may offer some indication of how rodent risk of myelotoxicity might relate to the human risk associated with similar exposures. Likewise, catechol can be converted in bone marrow to a DNA-reactive quinone. However, the concentration of catechol produced is so much lower than hydroquinone that this is not as likely to be a significant risk factor.

While the characterization of the major oxidative metabolism pathways for benzene in humans is an important step toward quantifying the risks associated with benzene exposure, conjugative pathways also play a significant role in determining the severity of toxicity. Phase II reactions hasten the in vivo excretion of phenol and hydroquinone (46) and are generally regarded as detoxification mechanisms. In cynomologous monkey, phenol and hydroquinone conjugates account for 61 and 27% of the urinary metabolites, respectively (46). The balance between Phase I and Phase II reactions is therefore likely to determine toxicity and any model designed to predict in vivo hepatic metabolite concentrations must include these reactions. In vitro work by Seaton et al. (47) resulted in estimates of the kinetic parameters associated with phenol sulfation and hydroquinone glucuronidation, two of the major pathways for the urinary excretion of benzene. Our goal is to develop an overall model of hepatic benzene metabolism based on the data reported here, the oxidative metabolism data reported previously (27) and the Phase II metabolism data reported by Seaton et al. (47). The ultimate extension of this work will be the incorporation of the metabolism model into a physiologically based pharmacokinetic model, resulting in a model that describes all the important processes associated with the absorption, metabolism and excretion of benzene in vivo.

Appendix
Rate expressions for all model reactions in the liquid phase are as follows:
















where


The model variables are the concentrations (µM) of the metabolites found in the liquid phase. Model parameters are as follows. Total protein concentration in mg/ml is represented by [p]. V2E1 is the CYP2E1 activity present in a 1 ml incubation expressed in units of nmol/mg/min. The relative efficiencies of CYP2E1 at metabolizing the individual substrates are estimated by k1 and k4k8, which function as proportionality constants, in ml/nmol. The remaining model parameters are k2 and k3, which are first order rate constants with units of per min, and ABZ, APH, AHQ, ACA and AIMP, which are the affinities of benzene, phenol, hydroquinone, catechol and the impurity for CYP2E1 in per µM. The denominator D accounts for both the saturability of enzyme active sites and competition for active sites between chemical species.

The model variable associated with the concentration of benzene in the gas phase is [benzeneG]. Given a head space volume, VG, and assuming that the transfer of benzene between the gaseous and aqueous states is governed by first order kinetics, the time derivative for this variable is:


The concentration of benzene in the liquid phase is dependent on both its equilibration with the vapor phase and its oxidation to phenol. If the liquid volume in an incubation is defined as VL, then the time derivative for aqueous benzene is:


The concentrations of the other metabolites are dependent solely on aqueous phase reactions and therefore their derivatives can be written in terms of rate expressions r1r8:












In our experiments, benzene from both the liquid and vapor phases is trapped by extraction into ethylacetate. After accounting for the extraction efficiency, the measured benzene concentration in the ethylacetate extract is the sum of the amounts extracted from the vapor and liquid phases, which is:


Likewise, the HPLC method used here did not resolve benzene oxide from the impurity. Therefore, any radioactivity present in these fractions actually represents the sum of the amounts of both compounds present ([benzene oxide]+[impurity]). Model predictions for [benzene]TOTAL and [benzene oxide]+[impurity] were compared with the measured values for these quantities.


    Acknowledgments
 
We thank Drs James Bond, Michele Medinsky, and Gregory Kedderis (CIIT) and Dr Thomas Kepler (NCSU) for helpful discussions and suggestions. We are also grateful to Dr Patrick Lilly for his help in implementing the trans-DCE experiments. Valuable assistance in the laboratory was provided by Mr Horace Parkinson. Editorial assistance was kindly provided by Dr Barbara Kuyper (CIIT). Dr Mark Lovern was supported via an unrestricted grant from CIIT to North Carolina State University for his graduate studies.


    Notes
 
3 To whom correspondence should be addressed Email: schlosser{at}ciit.org Back


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Received January 27, 1998; revised March 12, 1999; accepted April 12, 1999.