Genetic and dietary factors affecting human metabolism of 1,3-butadiene

Chemico-Biological Interactions 135– 136 (2001) 407– 428

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Genetic and dietary factors affecting human metabolism of 1,3-butadiene Thomas J. Smith a,*, Yu-Sheng Lin a, Maura Mezzetti a, Fre´de´ric Y. Bois b, Karl Kelsey a, Joseph Ibrahim a a

Har6ard School of Public Health, 665 Huntington A6e, Boston, MA 02115, USA b Institut National de l’En6ironnement Industriel et des Risques, Paris, France

Abstract The objective of this project was to determine the factors associated with differences in butadiene (BD) inhalation uptake and the rate of metabolism for BD to epoxy butene by monitoring exhaled breath during and after a brief exposure to BD in human volunteers. A total of 133 subjects (equal males and females; four racial groups) provided final data. Volunteers gave informed consent and completed a questionnaire including diet and alcohol use. A venous blood sample was collected for genotyping CYP2E1. Subjects received a 20 min exposure to 2.0 ppm of BD, followed by a 40 min washout period. The total administered dose was 0.6 ppm*h, which is in the range of everyday exposures. Ten, 1 or 2 min exhaled breath samples (five during and five after exposure) were collected using an optimized strategy. BD was determined by GC-FID analysis. Breathing activity (minute ventilation, breath frequency and tidal volume) was measured to estimate alveolar ventilation. After the washout period, 250 mg of chlorzoxazone were administered and urine samples collected for 6 h to measure 2E1 phenotype. The total BD uptake during exposure (inhaled BD minus exhaled) was estimated. A three-compartment PBPK model was fitted to each subject’s breath measurements to estimate personal and population model parameters, including in-vivo BD metabolic rate. A hierarchical Bayesian PBPK model was fit by Monte Carlo simulations to estimate model parameters. Regression and ANOVA analyses were performed. Earlier data analysis showed wide ranges for both total uptake BD and metabolic rate. Both varied significantly by sex and age, and showed suggestive differences by race, with Asians having the highest rates. The analyses reported here found no correlation between total BD uptake and metabolic rate. No significant differences were found for oxidation rates by 2E1 genotype or phenotype, but the rates showed trends consistent with

* Corresponding author. Tel.: + 1-617-4323315/4113; fax: +1-617-432-4122. E-mail address: [email protected] (T.J. Smith). 0009-2797/01/$ - see front matter © 2001 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 0 0 9 - 2 7 9 7 ( 0 1 ) 0 0 1 8 0 - 6

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reported differences by genotype and phenotype for chlorzoxazone metabolism. No effects on metabolic rate were observed for long-term alcohol consumption, or consumption in the past 24 h. Overall, neither dietary factors nor genetic differences explained much of the wide variability in metabolic rates. Population characteristics, age, sex, and race, were the most important explanatory variables, but a large fraction of the total variability in metabolism remains to be explained. © 2001 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Butadiene; CYP2E1; Diet; Metabolism; PBPK modeling

1. Introduction Human disease risk for an environmental contaminant is a function of exposure intensity. However, if the substance is metabolically activated and deactivated, which includes many chemical carcinogens, the risk does not depend on exposure intensity alone, but also depends on metabolism. 1,3-Butadiene (BD) is such a chemical. BD metabolism has a complex series of oxidation and reduction steps that has been reviewed in detail by Himmelstein and coworkers [1]. In summary, BD is oxidized (activated) by P450 enzymes, including CYP2E1 (2E1) and CYP2A6 (2A6), to a series of toxic mono- and di-epoxide compounds, which are deactivated by epoxide hydrolase (EH) and glutathione-S-transferase enzymes (GSTm, and GSTu). Large differences have been observed in the rates of these activation and deactivation processes by in-vitro studies of rat, mouse and human tissues. Additionally, rats and mice differ substantially in their risk of cancer from inhalation of BD, which correlates with the differences in their activation/deactivation (A/D) ratios [2]. Measurement of human in-vitro A/D ratios showed they are closer to rat ratios than those of mice, which, if true in vivo, have important implications for risk projections. Thus, it is critical to assess human in-vivo metabolic rates and A/D ratios to assess risk. Individuals who have a high A/D ratio (rapid activation and slow deactivation), and who also have a high exposure will have highest risk. Since it has not been possible to assess metabolic activity of individuals in epidemiologic studies, the variation in A/D ratios among the subjects may lead to considerable misclassification when only exposure is considered when determining risk. As a consequence of this misclassification, sensitivity will be reduced for detecting cancer risk in exposed populations, and the findings across small populations will be variable, which is consistent with the epidemiologic findings. BD is classified as a suspected human carcinogen by IARC, but the cancer risk is uncertain and controversial [1,3]. Genetic polymorphisms have been observed to affect metabolic rates for both activation and deactivation enzymes important for BD epoxides [1]. The P450 CYP2E1 and CYP2A6 enzymes have been identified as the major phase I oxidation enzymes for butadiene [4], and both are polymorphic. Alterations in the DraI at intron 6 for CYP2E1 showed changes in protein expression and catalytic activity [5,6]. The CYP2A6 also has several polymorphisms that affect its activity [7]. The most common variant is CYP2A6*2, which has an exon 3 mutation that results in

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an inactive protein. Epoxide hydrolase has two variants at exons 3 (Tyr/His 113) and exon 4 (His/Arg 139), which can significantly affect the metabolic activity of the enzyme: down 60% or up 125%, respectively [8]. Similarly, individuals who are homozygous for a gene deletion in the glutathione-S-transferase isozymes, GSTM1 and GSTT1, will have no metabolic activity for these protective isozymes [9]. Thus, genotyping of subjects should permit some estimation of the A/D ratios. However, possible sharp differences will be blurred by differences in enzyme induction and/or inhibition for 2E1 and EH enzymes, and possibly other enzymes [6,8]. Because of these limitations and uncertainties about differences in activity of 2E1 based on genotype definition, the activity of 2E1 can be phenotyped with chlorzoxazone [6]. 2E1 catalyzes conversion of chlorzoxazone to 6-hydroxy chlorzoxazone, which is excreted and readily measured in the urine [6,10]. While early studies of chlorzoxazone appeared to give inconsistent findings, possibly because they tested small populations with different genetic backgrounds, further research has shown it to be a useful probe of 2E1 activity [6]. The rate of the first oxidation step for BD to form the butene epoxide (EB) is a critical limit for the formation of all of the epoxide metabolites of BD. Formation rates of other downstream epoxide metabolites, such as di-epoxy butane and diol-epoxy butane, are limited by the rate of the first step. Thus, determination of individual oxidation rates for a range of subjects will provide insight into the variations in potential risk, and a determination of population factors associated with differences in the oxidation rate can indicate who is at risk. Direct assessment of metabolic rates can be done either by in-vitro studies of liver tissues or microsomal samples, or by analysis of timed serial blood samples during and after a test exposure to the agent. Inhalation exposure to BD has been used repeatedly to study metabolic rates for a number of test animals, mice, rats, hamsters, and monkeys [1]. Until the present study, there have been no laboratory exposures of humans to BD. It is not feasible to directly assess human metabolism in field studies of environmentally exposed subjects because the exposures are highly variable across time and difficult to measure, and it is not feasible to collected timed blood or breath samples during and after exposures. Human metabolic rates are best measured under controlled conditions in the laboratory. A computer-controlled system was developed by Y-S. Lin and others in our laboratory that permits brief, low-intensity, highly controlled inhalation exposures with collection of precisely timed exhaled breath samples [11]. This approach is non-invasive, well tolerated by the subjects, and uses an exposure concentration that is within the range of every day environmental exposures. A recently developed Bayesian statistical methodology by Gelman and Bois can use the timed exhalation data to fit metabolic rate parameters in physiologically-based pharmacokinetic (PBPK) models [12,13]. The analysis and findings reported in the present paper are an extension of the analysis of a data set of 144 human subjects tested, and 133 subjects with complete data who were exposed to BD by T.J. Smith and coworkers. The first round of results was reported in papers by Lin [14] and Mezzetti [15]. The findings from these studies are briefly summarized in Section 4. The background follows Section

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2 because the methods were the same for all of the papers. The present paper reports new findings on the uptake and metabolic differences associated with the genotype and phenotype of CYP2E1, which is thought to be the primary enzyme responsible for oxidation of BD at low-level exposures [1]. Additionally, analyses were conducted to assess if there were differences in either uptake or Kmet associated with detoxification enzymes, alcohol and caffeine intake, over-the-counter drug use, vitamin supplements, and other dietary factors.

2. Methods

2.1. Human subjects BD is a suspect human carcinogen, so subject recruiting was conducted in a way to fully inform the subjects about the potential health risks. BD is also a common environmental air contaminant, and all potential subjects have had daily exposures because of contact with cigarette smoke, auto exhaust, and urban air pollution [1]. In this context, the added potential risk to the subjects is small and of the same magnitude as daily exposures. It was estimated that the experimental exposure, 2 ppm for 20 min, would result in less than a one per million increase in lifetime risk of leukemia, using the California EPA, Air Resources Board risk assessment, which was based on the mouse as the most sensitive species [16]. The butadiene exposure is approximately equal to ever having smoked one cigarette because cigarette smoke contains about 2 ppm BD, and it takes approximately 20 min to smoke one. Nearly all of the volunteers reported smoking or attending smokey clubs at some time in their lives, so this was not considered an unacceptable risk. Before accepting a volunteer, it was clearly stated verbally and in writing in the consent form that this exposure could increase their lifetime risk of leukemia. All subjects read and signed a consent form before being allowed to participate in the study. Volunteers were paid $100 to compensate them for parking, food, and for the 4 h needed for the tests (the fee was equivalent to 4 h at the lab technician hourly rate of pay). Subjects were recruited to obtain equal numbers of males and females, a range of ages, and equal representation from four racial groups: Caucasians, Asians, American Blacks, and American Hispanics. Before being tested volunteers were asked about their current health status and personal health history. Those with a visible illness, cold or flu, or who reported a chronic illness such as diabetes that might modify their metabolism, or who were planning a pregnancy in the next 6 months, were excluded from the testing. At the time of the testing, each subject was weighed and his/her height was measured in stocking feet. Subjects completed a detailed questionnaire about their racial background, health history and current medication use, smoking, diet including consumption of alcoholic and caffeinated beverages, hobbies, and work history. Ten subjects reported racial backgrounds that did not clearly belong to any of the four racial groups, such as Asian Indian (6), and multiple parental backgrounds. The Asian Indians were all assigned to the Asian group. The subjects with multiple backgrounds were assigned to Hispanic (2) or

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Caucasian (2). The dietary section, emphasized current diet, was adapted from the Willett dietary frequency questionnaire [17]. The exposure apparatus and its evaluation have been reported in detail in by Lin and coworkers [14]. They will be briefly summarized here for the convenience of the reader. The testing apparatus is diagrammed in Fig. 1. Bags of 2.0 ppm BD test gas were prepared from a primary standard, 99.9% pure BD (Aldrich Inc.), by injecting 100 ml of the gas into a Tedlar bag containing 50 l of air that was passed through charcoal and molecular sieve beds (Cole-Palmer Instrument Company, Vernon Hills, IL). The concentration in each bag was checked by gas chromatography, and the coefficient of variation for these measurements was 10%. Depending on the subject’s minute ventilation, one to four bags of test gas were used during an exposure session.

2.2. Exposure protocol Each subject was seated in a comfortable chair and outfitted with the chest and abdominal elastic bands for the RespiTrace cardiorespiratory diagnostic device with RespiEvents data processing software (NIMS, Inc., Miami Beach, FL). The RespiTrace unit measures changes in lung volume with time and uses a series of internal algorithms to estimate tidal volume, breath frequency, and minute volume. The subject was fitted with a face mask (choosing one of three sizes), and the face seal was checked. Before the exposure, the subject was asked to relax and breath normally for about 5 min. Then, a timed series of six to 10 breaths were captured in a recording spirometer (Warren Collins Inc., Braintree, MA) put in place of a sampling bag. This provided a calibration of the respiration monitor, and a direct

Fig. 1. Exposure apparatus and control unit.

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measurement of the subject’s breath frequency and tidal volume, which was used to calibrate the RespiTrace. As a result of operating problems, not every subject was monitored with the RespiTrace, 20% were not monitored and data from spirometer calibrations (number/min and tidal volume) and exhaled volume in samples (minute ventilation) were used. Based on Bois’ optimization, five breath samples were collected during exposure beginning at 2, 5, 10, 15, and 19 min after starting, and five samples were collected after exposure stopped, beginning at 21, 22, 28, 38, and 58 min after the experiment began [18]. The first two samples post-exposure were 1 min, and the last three were 2 min samples to increase the analytical sensitivity during the period when BD levels were rapidly declining. The timing of these samples was controlled to within 0.5 s by computer control of the valves. Given that the minute ventilation of the subjects had a range of 3.2– 13 l/min, the breath sample volumes ranged from 3.2 l (1 min sample) to 25 l (2 min sample).

2.3. BD in breath-sampling and analysis BD in mixed alveolar breath was measured by collecting exhaled air in Tedlar bags, sampling for 1.0 or 2.0 min. Immediately after the testing, the BD was removed from the breath by drawing it through (100 ml/min) standard air sampling media: glass tubes containing 150 mg of charcoal treated with a small amount of tert-butyl catechol to prevent self polymerization (No. 226-73, SKC, Inc., Eighty Four, PA). The volume of breath drawn out of the sampling bag was recorded to calculate the BD in breath concentration and to estimate the minute ventilation during the sample. BD collected in the charcoal was desorbed with methylene chloride and quantified by gas chromatography using a modified form of NIOSH method 1024 [11,19]. The limit of quantification was 0.006 ppm in a 5 l sample with a coefficient of variation of 10.1%.

2.4. Determination of blood– air partition coefficients and genotypes Venous blood samples were collected by venipuncture. A portion of the blood was used to measure the blood–air partition coefficient using the head-space technique of Fiserova-Bergerova [20]. The blood was equilibrated with a known amount of BD at 37°C for 120 min. Then, the quantity of BD remaining in the headspace of the vial was determined by GC analysis. Another portion of the blood was used to determine the genotypic polymorphism for the RsaI site for CYP2E1 using the method reported by Kim and coworkers [21]. The genotype analyses were done with primer sequences and PCR amplification in Dr. Kelsey’s laboratory.

2.5. Phenotyping CYP2E1 and analysis of metabolites in urine At the end of the exposure experiment, each subject was given 250 mg of chlorzoxazone and asked to collect all of their urine for the next 6 h. After collection, each subject’s urine samples were blended into a composite sample, and

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five 150 ml aliquots were prepared and frozen. A portion of the urine was analyzed for 6 hydroxy-chlorzoxazone by HPLC using the method of O’Shea et al. [10]. Based on very small urine volumes, several subjects showed evidence of incomplete urine collection. Data from those individuals were not included in some data analyses.

3. Data processing

3.1. Al6eolar 6entilation rate This was determined by subtracting the dead-space ventilation from the total pulmonary ventilation. Dead-space ventilation is the dead-space volume times the number of breaths per minute, where volume is the sum of the anatomical airways dead space and the exposure mask dead space. None of the subjects reported any respiratory disease that would affect the functional magnitude of the lung dead space. Anatomic dead space was estimated using an algorithm developed by Harris and coworkers [22], which has an R 2 of 0.89 for predicting both male and female deadspace as a function of age, height (Ht, cm), breath frequency (freq, per min), and tidal volume (VT, ml per breath). The equation is given below. VDS = 0.834 × Age + 1.26 ×Ht(cm)+ 0.296× VT −879/freq − 174.

(1)

The mask dead space, VM, when worn on a subject’s face was 100910 ml (n= 5), which was measured by the volume of water remaining in the mask after a subject’s face displaced the excess until the mask fit. Therefore, the alveolar ventilation rate was calculated as shown below. Falv =(VT − VDS − VM) ×freq.

(2)

3.2. Uptake of BD during exposure Uptake of BD during exposure was estimated by the difference between total quantity BD inhaled (Qinh) during exposure minus total quantity (Qexh) exhaled during exposure. Uptake = Qinh −Qexh.

(3)

The inhalation rate, mg/min (the top line in Fig. 2), is given by the alveolar ventilation rate (Falv) times the concentration inhaled (Cinh). The total quantity inhaled (Qinh) was determined by the inhalation rate times the duration of exposure (T). Qinh =Falv ×Cin ×T.

(4)

Similarly, the total quantity exhaled is the sum of the averaged rate that BD is exhaled during each time interval between a breath sample; there are five intervals between 0 and 20 min. The alveolar concentration (Calv) times the alveolar

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Fig. 2. An example of the calculation for estimating total uptake during exposure. The inhalation and exhalation rates were directly estimated from measured quantities, and the area between the two curves is the total uptake.

ventilation rate (Falv) defines the exhalation rate (the lower line in Fig. 2). Since the alveolar concentration exhaled is not constant, the total quantity exhaled is the sum over all of the time intervals, which was estimated by the equation below. The quantity exhaled during an interval, i, is the trapezoidal area between the sampling points at i and i+ 1 (see Fig. 2). Qex =SUM {Falv ×(Calv[i ]+ Calv[i+ 1])/2 × (t[i+ 1]− t[i ])} for all i.

(5)

This relationship gives the area under the lowest line in Fig. 2. By pharmacologic convention, this estimate of uptake does not include the material exhaled after exposure stops [23–26]. The BD concentration exhaled at a point in time (t, or ith interval) is a function of the alveolar concentration, the concentration inhaled (Cinh), and the ratio of dead space (VDS+ VM) to tidal volume (VT), which is shown below. Cexh[i ]= (1 −(VDS + VM)/VT) × Calv[i ] +(VDS + VM)/VT × Cinh[i ].

(6)

This expression can be rearranged to calculate the alveolar concentration from the inhaled concentration, the subject’s observed ventilation parameters, and the measured values of the exhaled concentration. Calv[i ]= {Cexh[i ]− (VDS + VM)/VT) ×Cinh[i ]}/(1 − (VDS + VM)/VT).

(7)

Finally, the values from Eq. (7) can be inserted into Eq. (5) to calculate the total quantity exhaled during exposure. The total uptake during exposure is not a direct measurement of the total quantity metabolized. There are other processes operating during exposure that also

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contribute to uptake: loss of BD into the fat, uptake by the poorly perfused tissues and redistribution among the tissues. All of these processes will vary across individuals, as will their relative contributions to total uptake. As a result, there is no simple relationship between uptake and metabolic rate. However, it is a useful parameter widely used in pharmacology to examine the pharmacokinetic behavior of a substance [23–26]. Uptake has the advantages of being easy to calculate, independent of assumptions about internal processes, good for substances with low blood solubility, and relatively unaffected by uneven alveolar ventilation rate across the period of exposure because inhalation and exhalation are affected equally. Since all subjects had the same exposure concentration, 2.0 ppm BD, and the same duration of exposure, 20 min, the differences between their total uptake values will be a strong function of their personal physiology and metabolic rates. To address the possible concurrent effects of differences in body size, fat content, blood flows, and other factors, multivariate analyses were conducted to adjust for the concurrent effects of these factors.

3.3. Estimation of Kmet Markov Chain – Monte Carlo (MCMC) simulations were used to estimate parameters of a three compartment physiologically based pharmacokinetic (PBPK) model (shown in Fig. 3). This approach is based on a Bayesian hierarchical model and uses a numerical integration technique to solve the differential equations that define the pharmacokinetics. This method was developed by Gelman, Bois and Jiang [12] and made available in the MCSim software developed by Bois and Maszle [13]. The parameters of the model investigated are given in Table 1. Six of the model parameters were fitted for each subject’s data: volume of well-perfused tissues (liver, kidneys, brain, other organs), fraction of blood flow to the poorly perfused tissues (muscles and skin) and fat, tissue partition coefficients for poorly perfused and fat, and Kmet. Two additional parameters that had been measured were also estimated from the data as a check on the fitting process: pulmonary ventilation, and blood – air partition coefficient. Some parameters were estimated using physiologic algorithms developed and validated by other investigators (fraction of total body weight as fat and anatomic pulmonary dead space). The smallest possible number of fitted parameters was chosen for fitting because there were only 10 data points per subject. Using measurements or calculated values as constants to estimate parameters, instead of fitting them, introduces some error into the fitting process because the model’s overall fit does not include the effects of individual variation and measurement errors in those parameters. A simplified description of the Bayesian hierarchical fitting process using MCMC simulation is provided here for the convenience of the reader, and a fuller description of the methods used is given by Mezzetti and coworkers and the papers by Gelman and Bois [12,13,15]. Initially, a set of prior distributions for each of the model parameters is specified, including the mean, variance, type of distribution (normal, lognormal, gamma, etc.), and upper and lower truncation boundaries

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(parameter interval). These prior distributions are defined using all available existing data and hypotheses, including in-vitro tissue measurements, physiological scaling, and extrapolation from animal tests, as shown in the footnotes to Table 1. The MCSim program then draws a random set of parameter values from the prior distributions for each model parameter to be fitted, calculates the resulting PBPK model for the time points measured, and determines the likelihood score (a measure of fit) of the data given the model at all time points. Then, using a set of acceptance and rejection rules, the parameter values are updated, and the process is repeated. If the model can fit the data, then after a large number of replications (10 000 in our case), the parameter values drawn at each step represent samples for the posterior parameter distributions. Replicate fittings will converge on the same parameter distributions within a few per cent.

Fig. 3. The diagram of the three-compartment PBPK model showing the tissue compartments connected in parallel by the circulatory system, with a mass balance input and output via inhaltion via a mask. The parameters are: Flow for flow rates for air or blood; V for tissue volumes; PC for partition coefficients; C for concentrations in the air or tissues; and Kmet for the apparent first-order rate constant for metabolism. Average fitted relative flow rates, compartment volumes, and fitted tissue group partition coefficients are also shown.

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Table 1 Parameters for the PBPK model General parameters

Source

Three-compartment model parameter

Body weight (kg)

Measured

BDW

Fractional Tissue 6olumes (1 kg/l density) Well perfused Poorly perfused Fat Alveolar ventilationc Flowalv = Flowpul−FlowDS

Fitted Estimated by differencea Calculatedb

F – BDWwp F – BDWpp F – BDWfat

Calculated from measured Flowpul and estimated dead space

Flowalv

Total blood flow Flowtot = Flowalv/1.14

Calculated from Flowalv

Flowtot

Fractional blood flows Well perfused Poorly perfused Fat

Estimated by differenced Fitted Fitted

F – Flowwp F – Flowpp F – Flowfat

Partition coefficients Blood to air Well perfused tissue to blood Poorly perfused tissue to blood Fat tissue to blood

Measured 0.8 assignede Fitted Fitted

PCba PCwp PCpp PCfat

Metabolic rate constant Kmet

Fitted

Kmet

a

The fraction of body mass in the poorly perfused compartment, which is the largest, was calculated as the difference of 0.9 minus the other compartments. F – BDWpp =0.9−F – BDWwp−F – BDWfat. b The fraction of body mass in the fat compartment was calculated using the algorithm of Deusenberg [29], which was calibrated against a variety of physiologic measurements of body fat. F – BDWfat = (1.2×(BDW/Ht‚ 2)−10.8×Sex+0.23×Age−5.4)/100, where Ht is in meters, and Sex is 1 if male and 0 if female. c The alveolar ventilation is the total pulmonary ventilation (minute volume) minus the dead space ventilation (non-exhanging airways and face mask volumes). Dead-space ventilation (FlowDS) is the volume of dead space times the number of breaths per minute, where the dead space (DS) is the sum of the anatomical airway dead space plus the dead space in the face mask. The anatomical dead space was estimated by the equation developed by Harris and coworkers [22], and the mask dead space was measured (see Section 3.2 for details). d The fraction of blood flow in the well-perfused compartment, which is the largest, was calculated as the difference of 1.0 minus the other compartments. F – Flowwp =1.0−F – Flow pp−F – Flow fat. e The partition coefficient for blood to well-perfused tissues was taken from in-vitro tissue measurements [30].

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4. Background This section briefly presents the findings of the earlier, separate analyses of the uptake and PBPK modeling for this population. This review will set the stage for the new analyses presented here, which integrate the two types of earlier analysis and provide additional data on the relationships with genetic and dietary data.

4.1. Test subjects A total of 144 test subjects were recruited. Of these, 133 provided complete data sets. For some of the analyses, smaller data sets were analyzed because some subjects had missing data; where that occurred, it is indicated. The 11 subjects were excluded for a variety of reasons: difficulty with fitting the face mask, equipment failure, one subject refusing to continue because of discomfort breathing through the mask, and sample losses during analysis. The characteristics of the final set of subjects are shown in Table 2. As expected from the recruiting process, there were nearly equal numbers of males and females, and both sexes had similar age and race distributions. Recruiting problems resulted in fewer American Black and Hispanic subjects. Males were significantly taller, heavier, and had higher blood– air partition coefficients and alveolar ventilation rates than females.

4.2. Findings from uptake study [14] As a result of their significantly larger alveolar ventilation rates and blood– air partition coefficients, males inhaled significantly (PB 0.05) more total BD during Table 2 Study population tested

Number of subjects (total 133)

Age (years) Height (m) Weight (kg) Blood–air partition coefficient (PCb/a) Alveolar ventilation (l/min) Total uptake (mg) Uptake (mg/kg)a Kmet (min−1)a

Male

Female

71 (53%)

62 (47%)

Mean9S.D.

Mean 9S.D.

30.39 8.1 1.749 0.08* 77.7916* 1.62 90.35*

29.0 9 8.9 1.61 9 0.07 61.3 9 16 1.46 90.34

3.5 90.9* 135 950* 1.79 90.74 Mean (95% interval) 0.311 9 0.075

3.2 90.8 121 955 2.01 90.89 Mean (95% interval) 0.333 9 0.075

a Uptake and Kmet were measured by Lin et al. [14] and Mezzetti et al. [15] respectively. *Males and females were statistically different, PB0.05.

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the 2.0 ppm for 20 min exposure than females, and males had more total uptake, which was marginally significantly larger (Wilcoxon rank sum test, P = 0.058). However, the average uptake per kilogram of body weight was somewhat higher for females than males, possibly because of the generally higher body fat content of females. Males and females had nearly the same mean uptake fractions, 45.69 13.9 vs. 43.4912.9%, where the uptake fraction is defined as the ratio of the uptake to the total inhaled expressed as a percentage. The overall uptake per kilogram was approximately log normal, skewed to higher values, and there was a wide range across the population: 0.6– 4.9 mg/kg. In multiple regression analyses, log10(uptake per kilogram) was a strong function of alveolar ventilation and blood–air partition coefficient for butadiene. Lin and coworkers [14] also observed that population variables, sex, age, and race, were also associated with statistically significant differences in uptake. When each population parameter was considered alone in univariate regression analyses with log10(uptake per kilogram), the only significant relationship was seen for cigarette smoking status (yes= 1, no = 0). However, when multivariate analyses were conducted, sex (females had 24% more uptake than males) and age (10% loss per decade of life) were both significant, in addition to cigarette smoking. Smoking in the multivariate analysis was somewhat less important, accounting for 20% instead of 26% of the variability. In the racial analysis, Asians (predominantly Chinese) showed a statically significant 22% increased uptake relative to Caucasians, and a non-significant increases relative to American Blacks and Hispanic Americans. Overall, the multiple regression was statistically significant with an adjusted R 2 of 0.35. Thus, there are significant differences across the subjects in the amount of BD retained in the body during the 20 min exposure, which will affect the quantity available for metabolism. Although a portion of the uptake observed in these experiments was attributable to metabolism, other major parts will be associated with uptake by the body fat, poorly perfused tissues, and redistribution.

4.3. Findings from PBPK modeling Mezzetti and coworkers [15] fitted PBPK models to each individual’s exhaled breath data and fitted individual and population parameters simultaneously. The population distribution of data is shown in Fig. 4. They found that the exhaled breath data could be well fit by both two- and three-compartment models with metabolism in the central or well-perfused compartment, respectively. However, a four-compartment model with metabolism in only the liver compartment could not be fit to data for all of the subjects. Some subjects had more metabolism than could be accounted for in the liver alone. In fitting the three-compartment model for these subjects, their clearance rate for the well perfused compartment generally exceeded a reasonable total blood flow through the liver compartment; the median extraction fraction was 0.68 (range 0.32– 0.86) when metabolism was assumed to occur in the well perfused tissues. When three-compartment models were fit to male and female subpopulations separately, differences were observed for all of the fitted parameters except percent-

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Fig. 4. Population distribution of timed breath samples for 133 subjects tested (the mean and standard deviation are shown for each time point).

age blood flow to fat and poorly perfused tissues. The fitted values for Kmet by sex are shown in Table 2. Female poorly perfused tissues had a higher fitted tissue– blood partition coefficient for BD than similar male tissues, with means of 0.87 and 0.69, respectively. This clearly indicates that the differences between males and females are not just in metabolism, but also a function of tissue composition differences. When a population model was fitted, only allowing separate values for Kmet by sex, there is a non-significant difference between males and females (mean of 0.327 min − 1 for males, and 0.353 min − 1 for females compared to those in Table 2), but the difference is not as large as with separate models fitted to each sub-population. The multivariate analysis of Kmet values against population characteristics showed that there were significant differences by sex, age, and race, consistent with the analysis of BD uptake conducted by Lin and coworkers [14] using the same data set.

5. Results

5.1. Relationship between BD uptake and Kmet A comparison was made between the uptake values and the Kmet estimates obtained from the three-compartment model. Multiplying a subject’s Kmet by the volume of the well-perfused tissue gives the apparent clearance, CLwp, in l min − 1.

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There was no correlation between uptake per kilogram, and Kmet, r= − 0.004 (P= 0.99). Fig. 5 shows a scatter plot of Kmet and CLwp versus each subject’s uptake. A multiple regressions with uptake per kilogram as the independent variable and alveolar ventilation, BD blood–air partition coefficient, age, sex, race, and cigarette smoking as the dependent variables (all significant covariates) was statistically significant, as noted above. This regression showed no improvement when Kmet was also included as a dependent variable.

5.2. Genotypic findings Genotype and phenotype distributions for 2E1 are shown in Table 3. These genotype distributions were consistent with those reported in the literature for racial subpopulations in our test population [5]. There were no significant differences in either BD uptake per kilogram or Kmet associated with particular genotypes. Additionally, the distribution of 2E1 phenotypes as measured by chlorzoxazone metabolism showed no significant correlation with either the 2E1 genotypes, BD uptake, or Kmet among the subjects. The two multiple regressions that described the variation in BD uptake and Kmet with population variables

Fig. 5. Scatter plot of the fitted metabolic parameter expressed as both fraction metabolized per minute, Kmet, and as clearance from the well-perfused compartment, CLwp (l/min).

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Table 3 Relations of the genetic factors to uptake and metabolic rate Covariates

n

Uptake (mg/kg)

Kmet (per min)

Genotype for CYP2E1 Homozygous Normal Heterozygous Homozygous variant

101 (76%) 28 (21%) 4

1.86 9 0.78 2.05 9 0.94 1.79 9 0.99

0.319 90.079 0.326 90.061 0.344 90.091

64 (50%) 64

1.86 90.74 1.96 90.91

0.316 90.081 0.330 9 0.068

Phenotype for CYP2E1 a 6-hyroxy-CHZ urinary recovery\median 6-hyroxy-CHZ urinary recoveryBmedian

a The phenotype for CYP2E1 was measured by determining the 6 h excretion fraction for 6-hyroxychlorzoxazone (6-hydroxy-CHZ). The 6 h median urinary recovery for 6-hyroxy-CHZ was 44.5%. Five subjects did not take chlorzoxazone or provide a urine sample.

showed no significant change from introducing either the genotypes or phenotype variables shown in Table 3. None of the differences shown in Table 3 were significant, and the differences associated with genotype and phenotype for 2E1 did not go in the expected directions for Kmet, but not uptake. Kmet activity for 2E1 was expected to decline going from the wild-type to the homozygous variant genotype. However, the homozygous variant group was too small to test the difference, only four individuals. The homozygous variant group had a Kmet activity that was 15% lower than the wild-type group, and the average Kmet for the heterozygous group was intermediate between the two homozygous groups. Similarly, the 2E1 phenotype group with below median recovery of 6-hydroxy-chlorzoxazone was expected to be associated with a lower level of Kmet activity, but the activity was slightly higher.

5.3. Questionnaire findings These are summarized in Table 4. Few subjects reported any significant illness: one reported a past cancer treatment, four reported high blood pressure, and one reported Crohn’s disease. The overall rate of smoking was low, only 17% were current smokers, and 16% were past smokers, and more males reported smoking than females. Only 19% reported alcohol use in the 24 h prior to the test, but most, 70%, reported moderate regular alcohol consumption during the past year. Most of the subjects, 84%, reported regular consumption of caffeinated drinks, such as soft drinks, coffee, and tea. However, the consumption rates were highly skewed, only a modest number had a high consumption, two or more cups/cans per day of soft drinks, coffee, and tea: 17, 21, and 10% by beverage type, respectively (data not shown in Table 4). Only four individuals were concurrent heavy consumers of more than one type of caffeinated drink. A small number of subjects reported allergies and were taking antihistamines. Similarly, a few reported regular use of over-thecounter (OTC) drugs: antacids and Tagamet (cimetidine), and analgesics, especially Tylenol, (acetaminophen). Both cimetidine and acetaminophen are reported to

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affect 2E1 activity [1,6], but the numbers of subjects using them was small. Some subjects reported using vitamin supplements, including large doses vitamins C, 18%, and E, 16%. Most of the subjects reported eating breakfast before the test, somewhat fewer females. There was no broad prevalence of types of food whose ingestion before the tests might affect metabolism, such as broccoli, which could be tested for effects on BD metabolism. Multiple regressions (not shown) were conducted to consider the possibility that there might be joint effects and subtle interactions of ingestion of alcohol, smoking, OTC mediations containing cimetidine and/or acetaminiphen, and ingestion of caffeinated drinks on BD uptake and Kmet. Only slight differences were seen for individuals with differences in use of medications and caffeine exposure, which were consistent with the differences seen in the simple comparisons shown in Table 5.

Table 4 Distribution of life style and medicationa by sex Covariate Smoking Current, % (packs/day) Past, % Never, % Alcohol consumption Past 24 h, % Past yearb, % (drinks/week) Caffeinated drink consumption Coffee, % (cups/day)c Tea, % (cups/day)c Soft drinks, % (cans/day) Antihistamines/allergy drugs in past % (tablets) Acetaminophen in past 5 days % (tablets) Tagamet (cimetidine) in past 5 days % (tablets) Daily 6itamin intake C, % (supplements)d E, % (supplements)d Consumption of breakfast

All (n =133)

Male (n = 71)

Female (n = 62)

17% (0.7 90.6) 16% 67%

23%* (0.8 9 0.6) 19% 58%

8% (0.6 90.6) 15% 77%

19% 70% (3.6 9 4.7)

18% 72% (3.9 94.4)

19% 68% (3.2 9 5.2)

76% (1.0 9 1.3) 61% (0.7 91.1) 84% (1.6 93.3) 5 days 6% (4.2 92.3)

75% (1.0 91.3) 52%* (0.9 91.8) 83% (2.2 94.1)

77% (1.0 91.2) 71% (0.8 91.5) 85% (1.0 91.7)

4% (5.5 91.8)

8% (3.4 92.3)

12% (3.6 9 2.4)

10% (3.7 91.6)

15% (3.6 9 3.0)

4% (4.6 93.5)

6% (4.0 91.9)

18% 16% 82%

17% 15% 87%

2% (10)e 19% 16% 76%

Quantity is expressed as: mean 9 S.D. At least one regular drink per week. A drink is defined as 12 oz of beer, 5 oz of wine, or 1.5 oz of liquor. c One cup: 8 oz. d Including multiple vitamins. e One subject only. *Statistically significant male vs. female difference,  2 at PB0.05. a

b

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Table 5 Relations of life style and medications to BD uptake and Kmet Covariates

n

Uptake (mg/kg)

Kmet (per min)

23 (17%) 110

1.68 90.75 1.88 90.80*

0.308 90.077 0.324 90.075

Alcohol consumption In past 24 h Yes No In past year At least one drinka/wk No regular drinking

25 (19%) 108 93 (70%) 40

1.68 90.75 1.94 90.83 1.88 90.80 1.93 90.86

0.315 90.074 0.348 90.078 0.327 90.076 0.308 90.074

Caffeinated drink consumption Coffee Yes No Tea Yes No Soft drinks Yes No

101 (76%) 32 81 (61%) 52 112 (84%) 21

1.86 9 0.80 1.99 90.89 1.96 9 0.92 1.79 9 0.62 1.93 90.83 1.73 90.73

0.320 90.073 0.324 90.083 0.322 90.073 0.319 9 0.080 0.319 9 0.078 0.331 9 0.063

Analgesics/acetaminophen Past 5 days Yes Past 5 days No

16 (12%) 117

1.74 90.81 1.92 9 0.82

0.284 90.066 0.326 9 0.076

Antihistamines/allergy drugs Past 5 days Yes Past 5 days No

8 (6%) 125

1.74 90.79 1.91 90.82

0.329 9 0.046 0.320 9 0.077

Tagamet/cimitidine Past 5 days Yes Past 5 days No

5 (4%) 128

1.73 90.45 1.90 90.83

0.360 9 0.022 0.319 9 0.077

Daily 6itamin intake Vitamin C Yes No Vitamin E Yes No

24 (18%) 109 21 (16%) 112

1.94 90.94 1.89 90.80 2.11 90.99 1.86 90.78

0.320 90.076 0.321 90.076 0.346 90.081 0.316 9 0.074

Consumption of breakfast Yes No

109 (82%) 24

1.89 90.85 1.93 90.69

0.325 9 0.077 0.325 90.077

Current smoking Yes No (never+past)

a A drink was defined as 12 oz of beer, 5 oz of wine, or 1.5 oz of liquor. *Statistically significant at PB0.05.

6. Discussion and conclusions This report presents the findings from further analyses of a large data set of human exposures to 1,3-butadiene (BD) conducted in our laboratory. The overall goal of this project was to investigate the relationship between BD exposure and internal dose for humans exposed to under controlled conditions. Our premise was that administered dose, exposure intensity times duration, which is widely used in

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environmental epidemiology and risk assessment, is only weakly related to internal dose of the active agent, especially where a toxic chemical is metabolically activated and/or deactivated. BD was studied as a model toxic air contaminant. All subjects were exposed to the same administered dose, 2.0 ppm BD by inhalation for 20 min, or 0.66 BD ppm*h. Thus, any differences in internal dose were due to differences among the individuals. Internal doses may vary for two primary reasons: physiologic differences and metabolic differences. Analyses of these data by Lin and Mezzetti have shown that both uptake and PBPK modeling estimates of Kmet have a wide variation across the population that we tested [14,15]. While BD uptake and the Kmet rate of BD oxidation may be related, they were not correlated in this data set. This finding is consistent with the differences between the two: uptake is a cumulative measure of all sources of BD retention and loss during exposure, while Kmet is only related to metabolism in the well-perfused tissue compartment. These two would be expected to be more closely related after an exposed individual had reached a steady-state balance between the inhaled input of BD and its removal from the body by exhalation and metabolism. After many hours of exposure, an approximate steady state may be reached, and then the difference between the quantity inhaled and that exhaled could be directly attributed to the rate of metabolic loss. During the relatively short 20 min experimental exposure used in this study, the steady state was far from being established. Consequently, the observed uptake is not a direct estimate of the total metabolized during the experiment. In the early phase of this inhalation exposure, BD is distributed to the tissues, where it accumulates according the relative tissue capacity and blood flow rate per tissue volume. Nearly all BD reaching the fat is retained, and during the first 10 min, much of the BD going to the poorly perfused tissues is also retained. The relative fraction of total uptake contributed by each tissue group varies for each individual, depending on their height, weight, sex, age, and race. This is nicely illustrated by the algorithm used to calculate body fat (Table 1), which depends on age, sex, height, and weight. As a result of these variations in body characteristics, uptake was found to vary significantly with cigarette smoking, age, sex, and race, and sex also affected the blood–air partition coefficient [14]. Metabolism of BD was modeled as taking place within the well-perfused tissues. Significant human metabolic activity for BD has been found in the liver, kidney and lungs [1]. Studies of small numbers of human tissue samples have identified P450 enzymes CYP2E1 and CYP2A6 as both active for metabolizing BD, but 2E1 showed a much higher activity than 2A6 [4]. CYP3A1 and CYP1A1 have also been observed to be active in in-vitro studies, but more recent in-vivo tests suggest that their activity is negligible [27]. However, it is unlikely that 2E1 is the only enzyme important for oxidization of BD for all people. Our study did not reveal a statistically significant relationship between either genotype or phenotype for 2E1 and uptake or Kmet. The trends were not consistent with variation seen by Le Marchand for both 2E1 genotypes and phenotypes [6]. Larger numbers of subjects with the homozygous variant genotype may be needed to demonstrate a statistically significant relationship; there were only four in our study population. This finding suggests that 2E1 may not be the only important enzyme for BD metabolism at

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low-level exposures. Also, because the exposure was fixed at 2.0 ppm for all subjects, the upper end of the range of internal concentrations was low and may not have reached sufficiently high levels to clearly show the effects of 2E1. Perhaps at these low levels, another unidentified enzyme has the primary role. 2E1 activity is known to be affected by alcoholism, obesity, prolonged fasting, liver dysfunction, and a number of prescription and over-the-counter drugs [6,10,21,27]. Heavy alcohol ingestion induces 2E1 activity, which declines with a half-time of 2.5 days after cessation of drinking [28]. Overall, the alcohol ingestion by our test population was modest, 70% reported regular consumption with an average of 3.6 drinks per week, but the distribution was highly skewed, only 16% reported drinking more than the equivalent of two beers per week. None of the subjects reported ingesting alcohol within 12 h of the testing, so direct inhibition by ingested alcohol was not present. No significant differences were observed for Kmet with regular alcohol use. The potential effects of cimetidine and acetaminophen, and vitamin C and E supplements on 2E1 activity could not be evaluated because the numbers of subjects using them were too few. Our test population represented a broad cross-section of the people in the Harvard Medical Area in Boston. Equal numbers of males and females, and racial representation were sought. Because of limitations in the recruiting effort, American Blacks and Hispanics were under-represented in the test population. Although this group does not represent a statistical sample of the local population, there is a good representation of healthy adults with a broad age range. A few had chronic diseases, but most had no significant health problems. Thus, the population tested is a reasonable representation of the components of the healthy adult population.

6.1. Application to risk assessment The present study has three important implications: (1) large variations in metabolism will reduce the ability of epidemiologic studies to detect the risks of exposure to BD. Only a small fraction of the overall variability in uptake and metabolism was associated with population parameters of age, sex, and race, which are usually constant within a study population but vary among epidemiologic study sites. These differences may explain the variation in cancer risk observed among the small petrochemical populations that have been studied. (2) Genotypic differences associated with CYP2E1 did not account for the wide range in metabolic rate. Therefore, determination of an exposed subject’s 2E1 genotype will be a weak predictor of risk. (3) Although there are a number of external factors that show in-vitro effects on 2E1 metabolism, such as alcohol, diet, prescription drugs, and over-the-counter mediations, there were no evident effects on metabolic rate. Thus, the causes of the observed wide variability remain to be discovered. These factors must be determined and considered in a risk assessment to obtain a meaningful representation of the exposed population risks.

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Acknowledgements This report covers material taken from a study of 1,3-butadiene metabolism, funded by the National Institute of Environmental Health Sciences (NIEHS) grant: ES 07586. It was also partially funded by the NIEHS Environmental Center grant ES 00002. Dr G.R. Wilkinson provided the standard for the 6-OH-chloroxazone analysis.

References [1] M.W. Himmelstein, J.F. Acquavella, L. Recio, M.A. Medinsky, J.A. Bond, Toxicology and epidemiology of 1,3-butadiene, Crit. Rev. Toxicol. 27 (1997) 1 – 108. [2] J.A. Bond, M.W. Himmelstein, M. Seaton, P. Poogaard, M.A. Medinsky, Metabolism of butadiene by mice, rates and humans: a comparison of physiologically based pharmacokinetic model predictions and experimental data, Toxicology 113 (1996) 48 – 54. [3] International Agency for Research on Cancer (IARC), 1,3-Butadiene, in: Re-evaluation of Some Organic Chemicals, Hydrazine, and Hydrogen Peroxide, IARC Monographs on the Evaluation of Carcinogenic Risk to Humans, vol. 71, IARC, Lyon, 1999, p. 109. [4] R.J. Duescher, A.A. Elfarra, Human liver microsomes are efficient catalysts of 1,3-butdiene oxidation — evidence for major roles by cytochromes P450 2A6 and 2E1, Arch. Biochem. Biophys. 311 (1994) 342 –349. [5] Y. Hu, M. Oscarson, I. Johansson, Q.-Y. Yue, M.-L. Dahl, M. Tabone, S. Arinco, E. Albano, M. Ingelman-Sundberg, Genetic polymorphism of human CYP2E1: characterization of two variant alleles, Mol. Pharmacol. 51 (1997) 370 – 376. [6] L. Le Marchaud, G.R. Wilkinson, L.R. Wilkens, Genetic and dietary predictors of CYP2E1 activity: a phenotyping study in Hawaii Japanese using chlorzoxazone, Cancer Epidem. Biomark. Prev. 8 (1999) 495 –500. [7] M. Bourian, H. Bullsten, W. Legrum, Genetic polymorphism of CYP2A6 in the German population, Toxicology 144 (2000) 129 –137. [8] C. Hassett, L. Aicher, J.S. Sidhu, C.J. Omiecinski, Human microsomal epoxide hydrolase: genetic polymorphism and functional expression in vitro of amino acid variants, Hum. Mol. Gen. 3 (1994) 421–428. [9] D.-X. Lin, Y.-M. Tang, Q. Peng, S.-X. Lu, C.B. Ambrosone, F.F. Kadlubar, Susceptibility to esophageal cancer and genetic polymorphisms in glutathione-S-transferase T1, P1, and M1, and cytochrome P450 2E1, Cancer Epid. Biomark. Prev. 7 (1998) 1013 – 1018. [10] D. O’Shea, S.N. Davis, R.B. Kim, G.R. Wilkinson, Effect of fasting on obesity in humans on the 6-hydroxylation of chlorzoxazone: a putative probe of CYP2E1 activity, Clin. Pharmacol. Ther. 56 (1994) 359 –367. [11] Y-S. Lin, T.J. Smith, P-Y Wang, An automated human inhalation exposure system, submitted for publication. [12] A. Gelman, F.Y. Bois, J.M. Jiang, Physiological pharmacokinetic analysis using population modeling and informative prior distributions, J. Am. Stat. Assoc. 91 (1996) 1400 – 1412. [13] F.Y. Bois, D. Maszle, MCSim: a simulation program, J. Stat. Software 2 (9) (1997) program and manual may be accessed via: http://www.jstatsoft.org/v02/i09/mcsim/doc/mcsim.html. [14] Y-S Lin, T.J. Smith, K.T. Kelsey, D. Wypij, Human physiological factors affecting respiratory uptake of 1,3-butadiene, Environ. Health Persp., (in press). [15] M. Mezzetti, J.G. Ibrahim, F.Y. Bois, T.J. Smith, L. Ryan, Bayesian compartmental model for evaluation of 1,3-butadiene metabolism, submitted for publication. [16] California Environmental Protection Agency, Air Resources Board, Proposed Identification of 1,3 Butadiene as a Toxic Air Contaminant, Calif, EPA, Sacramento, CA, 1992.

428

T.J. Smith et al. / Chemico-Biological Interactions 135–136 (2001) 407–428

[17] W.C. Willett, L. Sampson, M.J. Stampfer, B. Rosner, C. Bain, J. Witschi, C.H. Hennekens, F.E. Speizer, Reproducibility and validity of a semiquantitative food frequency questionnaire, Am. J. Epidemiol. 122 (1985) 51 –65. [18] F.Y. Bois, T.J. Smith, A. Gelman, H.-Y. Chang, A.E. Smith, Optimal design for a study of butadiene toxicokinetics in humans, Toxicol. Sci 49 (1999) 213 – 224. [19] NIOSH, Method 1024, Airborne 1,3-Butadiene, NIOSH Manual of Analytical Methods. NIOSH Publication no. 94-113, fourth ed., National Institute for Occupational Safety and Health, Cincinnati, OH, 1994. [20] V. Fiserova-Bergerova, M. Tichy, F.J. Di Carlo, Effects of biosolubility on pulmonary uptake and disposition of gases and vapors of lipophilic chemicals, Drug Metab. Rev. 15 (1984) 1033 – 1070. [21] R.B. Kim, D. O’Shea, G.R. Wilkinson, Relationship in healthy subjects between CYP2E1 genetic polymorphisms and the 6-hydroxylation of chlorzoxazone: a putative measure of CYP2E1 activity, Pharmacogenetics 4 (1994) 162 –165. [22] E.A. Harris, E.R. Seelye, R.M. Whitlock, Revised standards for normal resting dead-space volume and venous admixture in men and women, Clin. Sci. Mol. Med. 55 (1978) 125 – 128. [23] E.S. Munson, E.I.D. Eger, M.K. Tham, W.J. Embro, Increase in anesthetic uptake, excretion, and blood solubility in man after eating, Anesth. Analg. 57 (1978) 224 – 231. [24] V. Fiserova-Bergerova, Introduction to Mathematical Model, in: V. Fiserova-Bergerova (Ed.), Modeling of Inhalation Exposure to Vapors: Uptake, Distribution, and Elimination, vol. 1, CRC Press, Boca Raton, FL, 1983, pp. 51 – 70. [25] M. Wallen, P.H. Naslund, M.B. Nordqvist, The effects of ethanol on the kinetics of toluene in man, Toxicol. Appl. Pharmacol. 76 (1984) 414 – 419. [26] D. Groeseneken, H. Veulemans, R. Masschelein, Respiratory uptake and elimination of ethylene glycol monoethyl ether after experimental human exposure, Br. J. Ind. Med. 43 (1986) 544 – 549. [27] D. Lucas, R. Ferrara, E. Gonzalez, P. Bodenez, A. Albores, M. Manno, B. Berthou, Chlorzoxazone a selective probe for phenotyping CYP2E1 in humans, Pharmacogenetics 9 (1999) 377 – 388. [28] D. Lucas, C. Menez, C. Girre, P. Bodenez, E. Hispard, J.-F. Menez, Decrease in cytochrome P4502E1as assessed by the rate of chlorzoxazone hydroxylation in alcoholics during the withdrawal phase, Alcohol. Clin. Exp. Res. 19 (1995) 362 – 366. [29] P. Deurenberg, J.A. Weststrate, J.C. Seidell, Body mass index as a measure of body fatness: ageand sex-specific prediction formulas, Br. J. Nutr. 65 (1991) 105 – 141. [30] J.G. Filser, G. Johanson, W. Kessler, P.E. Kreuzer, P. Stei, C. Baur, G.A. Csanady, A pharmacokinetic model to describe toxicokinetic interactions between 1,3-butadiene and styrene in rats: predictions for human exposure, in: M. Sorsa, K. Pletonen, H. Vainio, K. Hemminki (Eds.), Butadiene and Styrene: Assessment of Health Hazards, IARC Scientific Publication No. 127, IARC, Lyon, 1993.

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