* Department of Environmental Sciences and Engineering and Department of Biostatistics, School of Public Health, University of North Carolina, Chapel Hill, North Carolina 275997431
Received August 17, 2004; accepted November 8, 2004
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ABSTRACT |
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Key Words: volatile organic compounds; benzene; perchloroethylene; acrylonitrile; variation of exposure.
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INTRODUCTION |
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As a worker breathes a contaminant at a given air level (mg/l), he or she takes up some portion of the chemical in the lungs. The uptake is related to the breathing rate (l/h) and the retention, a dimensionless quantity that depends upon the physical and chemical properties of the contaminant (gaseous or particulate, solubility, particle size, etc.). Thus, during a brief period of time, uptake = (air level) x (breathing rate) x retention, with units of mg/h. Once the contaminant is cleared from the lungs, it can be distributed to tissues and eliminated by a host of excretory and metabolic processes. The difference between input and output gives rise to an internal mass, or burden (mg), of the substance at a particular time. From mass-balance considerations, the rate at which the burden changes during a brief period is
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The connection between exposure and AUC is complicated by the intermittent nature of the occupational regimen, where workers tend to be exposed for 8 h per day, and by the profound variability in air levels occurring from one workday to another. Occupational exposures typically vary 15-fold from day to day within workers (median value), and variation greater than 70-fold is observed in about a fourth of occupational groups (Kromhout et al., 1993). Given such great variability, it is reasonable to ponder whether day-to-day fluctuations in air levels might alter the relationship between exposure and internal dose. If air levels vary greatly from day to day (about some mean value), would the AUC differ from that observed when the air level is the same (mean) value each day? This subject has received only limited attention (Kumagai and Matsunaga, 1995
; Rappaport, 1985
, 1991
; Roach, 1966
, 1977
; Smith, 1987
; Smith, 1992
).
Logically, exposure variability can affect the relationship between exposure and internal dose only if two conditions are met (Rappaport, 1991). First, the contaminant must be eliminated from the body sufficiently rapidly so as not to accumulate from week to week. This is because substances that accumulate (notably insoluble dusts, heavy metals, and lipophilic organic compounds) achieve burdens much greater than the mass taken up in a single day and thereby are reasonably invariant to daily fluctuations in air levels. For such contaminants, cumulative exposure (CE), i.e., the product of the mean exposure and time (with units of mg·h/l), should be a valid predictor of the long-term internal dose (AUC). Second, the contaminant must be either taken up by, or eliminated from, the body by a nonlinear process over the relevant range of exposure. This condition is necessary because linear kinetics would maintain a strict proportionality between AUC and CE even when the contaminant is rapidly absorbed and eliminated (a restatement of Haber's Law) (Cox, 1995
; Olson and Cumming, 1981
; Rappaport, 1991
).
Volatile organic compounds (VOCs) are rapidly eliminated via nonlinear (saturable) metabolism. Since many VOCs have been associated with chronic health effects, the purpose of this investigation is to explore the influence of exposure variability upon the internal doses of these compounds and their metabolites. Points will be illustrated with three chemicals that are known or suspected human carcinogens, namely, benzene (Hayes et al., 1997; Savitz and Andrews, 1996
, 1997
; Snyder, 2002
), perchloroethylene (Lash and Parker, 2001
), and acrylonitrile (Collins and Strother, 1999
; Kirman et al., 2000
). These substances were chosen because they are biotransformed in the liver by phase-I metabolism and possess an important toxicokinetic parameter (
, to be defined) that ranges in value from low (perchloroethylene), to moderate (benzene), to high (acrylonitrile). The carcinogenicity of all three compounds is likely due to the action of one or more reactive metabolites. The sites of tumor formation include the hematopoietic system (benzene), liver and kidney (perchloroethylene), and the brain (acrylonitrile).
In what follows, we will couple a random time series of simulated air levels, representing the variability in occupational exposure over many years, with a physiologically based toxicokinetic model, representing the disposition of VOCs and metabolite production in the body. Such toxicokinetic models provide the means to relate external exposure to internal levels and, thus, are well suited for evaluating the doses of VOCs and their metabolites following prolonged periods of occupational exposure.
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MATERIALS AND METHODS |
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Model parameters and simulation. The flow rates (l/h) and tissue volumes (l) were scaled to a 70-kg human working at 50 W of exercise according to Tardif et al. (2002), i.e., QAlv = 1323, QCar = 603, QL = 96.4, QF = 36.2, QRPT = 163, QSPT = 307, VL = 1.82, VF = 13.3, VRPT = 3.50, and VSPT = 40.6. The chemical-dependent partition coefficients and biochemical constants, shown in Table 1, were compiled from (Dobrev et al., 2001
; Sweeney et al., 2003
; Travis et al., 1990
) after scaling Vmax for a 70-kg human as Vmax=Vmaxc · 700.75, where Vmaxc is the scaling coefficient given by the authors.
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The toxicokinetic model employed mass-balance equations to describe the rates of change of the chemical concentration in each compartment, based largely upon relationships given by Ramsey and Andersen (1984) (see Supplementary Data). These equations were solved numerically by Euler's method, using a program written with SAS software (SAS Institute, Cary, NC) and employing a time step
t of either 0.01 h (benzene and perchloroethylene) or 0.002 h (acrylonitrile), as required to maintain continuity. Following 8 h of simulated exposure at air concentration Xi, the inhaled air concentration was reduced to zero, and liver concentrations were monitored until t = 336 h to ensure complete clearance of the chemical from all tissues. The time series {Xi} produced the corresponding time series of internal doses, represented by the AUC of the parent chemical in the liver after each exposure {AUCLi} (mg·h/l), where
and CLij is the liver concentration (mg/l), after the jth time step for the ith exposure. Metabolism was monitored in terms of the time series of the amount of metabolite produced from the ith exposure, referred to as the area under the metabolic rate-time curve (Andersen, 1981b
), i.e.,
(mg), and RMij is the rate of metabolism (mg/h) after the jth time step for the ith exposure. The long-term liver dose of parent chemical after 10,000 exposures was calculated as
(mg·h/l), and the corresponding long-term dose of metabolite as
(mg). Even though both AUCL and AURC are long-term dose metrics, note that they are dimensionally different.
We focused our models upon the liver and metabolite doses (AUCL and AURC) even though the liver is not necessarily the target of toxicity for the VOCs investigated. Because our model only permits metabolism to take place in the liver, the liver is more sensitive to perturbations in levels of the parent compound than are the blood and other tissue groups. Thus, effects of exposure variability upon AUCL are at least as great as upon the analogous AUC values for other tissues. Likewise, AURC represents a global measure of metabolite production that should be relevant to all tissues where metabolites are ultimately distributed from the liver by the systemic circulation.
Sensitivity analysis. Additional simulations were conducted to determine the sensitivity of the two dose metrics AUCL and AURC to each of the parameters in the model. Each parameter was increased by 1% and the full simulation was repeated for 4 exposure distributions, representing a wide range of mean values (µX = 0.0003 and 0.3 mg/l) and variability (CVX = 0.23 and 2.18). While testing sensitivity to blood flow rates, mass balance was maintained by reducing QSPT, as necessary, to compensate for the 1% increase in QRPT, QL, or QF. Normalized sensitivity coefficients were estimated as the percentage change observed in AUCL or AURC divided by the 1% change in the parameter of interest (e.g., a 2% increase in AUCL or AURC would correspond to a normalized sensitivity coefficient of 2).
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RESULTS |
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The effects of exposure variability on the slopes AUCL/CE and AURC/CE (after 10,000 8-h workdays) are summarized in Figures 5A 5F for benzene, perchloroethylene, and acrylonitrile when 0.0003 µX
1.0 mg/l and when CVX = 0.23, 0.62, and 2.18. For each chemical, AUCL/CE and AURC/CE are hardly affected by exposure variability when µX
0.01 mg/l, even when CVX = 2.18. However, as µx increases above 0.01 mg/l, upwards divergence of AUCL/CE was observed (Figs. 5A, 5C, and 5E) along with downwards divergence of AURC/CE (Figs. 5B, 5D, and 5F), consistent with increasing saturation of VOC metabolism. These changes occur first for CVX = 2.18 and then for CVX = 0.62 and 0.23, respectively.
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The picture regarding the metabolite dose was different, given maximum deviations in AURC/CE of about 30% for all three VOCs (Fig. 6B). This is because the large baseline value of AURC/CE for acrylonitrile tended to offset the abrupt reduction in daily metabolite dose (AURCi) occurring during the high-exposure days. Since benzene and perchloroethylene do not exhibit flip-flop kinetics, their reductions in AUCLi or AURCi were more modest during the high-exposure days; but these changes were offset by their smaller baseline values of AURC/CE, yielding essentially the same percent deviations as for acrylonitrile.
Sensitivity Analysis
Results of the sensitivity analyses are summarized in Figure 7 for AUCL and in Figure 8 for AURC, based upon a 1% increase in each of the toxicokinetic parameters. The sensitivities of the two dose metrics were greatly influenced by the variability of exposure (CVX) at a given mean exposure (µX). That is, long-term doses were much more sensitive to changes in model parameters when CVX = 2.18 than when CVX = 0.23. Indeed, it was common to observe normalized sensitivity coefficients greater than ±5 when CVX = 2.18, whereas coefficients rarely exceeded ±2 when CVX = 0.23. This suggests that AUCL and AURC would vary considerably across a population exposed at a given µX when exposure was highly variable under either linear (µX = 0.0003 mg/l) or saturated (µX = 0.3 mg/l) kinetics. The normalized sensitivity coefficients shown in Figures 7 and 8 indicate general sensitivity to most parameters. This probably reflects the structure of the model, where all parameters, except the partition coefficients and KM, were functions of body weight and, therefore, were highly correlated. In comparing among VOCs, the most notable difference concerns sensitivity of AUCL in the high-exposure, high-variability scenario (Fig. 7D), where deviations were negative for acrylonitrile but were positive for benzene and perchloroethylene. This probably points to the lipophobic nature of acrylonitrile (whereas the other compounds are lipophilic), because days of saturating exposure would not lead to a buildup of acrylonitrile in the fat (with subsequent release to the circulation and the liver) but rather to increased passive clearance in the exhaled air.
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DISCUSSION |
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The purpose of this paper has been to evaluate the premise that CE is a sufficient predictor of the internal doses of benzene, perchloroethylene, and acrylonitrile, three carcinogenic VOCs that are cleared in part by saturable metabolism over a wide range of toxicokinetic behaviors (as reflected by differing values of ). We observed in all cases that both the long-term liver dose (AUCL) and the long-term metabolite dose (AURC) were essentially linear functions of CE over about 40 simulated years of occupational exposure, even when daily-dose increments (AUCLi and AURCi) were saturated (e.g., see Fig. 4). Thus, despite the enormous range of daily exposures that can be observed in the workplace, the straight-line slope representing AUCL/CE or AURC/CE after several years is essentially fixed for an individual worker at a given mean exposure (µX).
Despite the linear relationship between AUCL or AURC and CE for an individual worker, the corresponding relationship across a population could well be nonlinear if some workers have mean air levels in the saturable range. For example, in a population of workers heavily exposed to the three VOCs investigated here, we would anticipate a concave-downwards shape in the exposure-biomarker relationship across the population for any biomarker located downstream from the initial metabolic step (see Fig. 2B). Indeed, such shapes have been reported for protein adducts and urinary metabolites of benzene (both downstream biomarkers) (Rappaport et al., 2002a,b
; Waidyanatha et al., 2004
), as well as for the mortality-CE curve for lymphohematopoietic cancers among benzene-exposed workers (Hayes et al., 1996
).
Our results indicate that individual workers who experience the same CE could nonetheless have different long-term internal doses (AUCL or AURC) if their individual levels of exposure variability (values of CVX) differed greatly. The magnitude of such deviations would depend upon the particular dose metric. Since most VOCs are metabolized to toxic products, the more important effect of exposure variability concerns its relation to the internal metabolite dose (AURC). Here, our results indicate that differences in AURC/CE (arising from different values of CVX across the population) should be small for VOCs, with maximum deviations in the range of about 30% as observed for benzene, perchloroethylene, and acrylonitrile (Fig. 6B). We conclude that assignment of metabolite doses to VOCs, based solely on CE, should not compromise estimation of exposure-response relationships. This conclusion is at odds with the observation of Collins et al. that the number of peak exposures to benzene (greater than 100 ppm) was a better predictor of lymphohematopoietic cancers than was CE, and could point to the large uncertainties in estimation of individual CEs mentioned by the authors (Collins et al., 2003). Another recent study found no evidence that the risk of lymphohematopoietic cancers was influenced by peak exposures (Glass et al., 2003
).
Turning now to the liver dose of a VOC per se, our results indicate that AUCL/CE can differ by several hundred percent between high- and low-variability scenarios, but only when µX and CVX are large and when . If not all three of these conditions are met, then deviations of AUCL/CE should only be a few percent (see Fig. 6A for acrylonitrile when µX
0.003 mg/l or CVX
0.62 and for benzene and perchloroethylene at all values of µX). Yet there could well be situations where such a nexus of three independent factors could occur. For example, if a worker was exposed to acrylonitrile in intermittent outdoor operations, giving rise to a large
(Kromhout et al., 1993
), at a mean exposure level µX = 0.008 mg/l, we would anticipate a deviation in AUCL/CE of about 100% from that of a coworker having the same µX but low-to-moderate exposure variability (see Fig. 6A). This mean air concentration (µX = 0.008 mg/l) is about twice the 2004 Threshold Limit Value (2 ppm =4.3 mg/m3) (ACGIH, 2004
) and, thus, would be unacceptable by current norms. However, exposures of this magnitude could easily escape detection in the developed world, where workplace air monitoring is sporadic at best, and could be commonplace in much of the developing world. Thus, we recommend that VOCs be screened to identify chemicals with
, where exposure variability might lead to significant deviations in the long-term liver dose of parent chemical at a given CE. Of 16 VOCs reviewed in our perusal of the recent toxicokinetics literature, 5 chemicals had estimated values of
.
Sensitivity analyses indicated that AUCL and AURC were both sensitive to small changes in the toxicokinetic parameters when CVX was large (see Figs. 7 and 8). This suggests that populations of workers exposed to a given mean exposure could have quite different long-term liver and metabolite doses of VOCs when exposure variability is great, due to differences in toxicokinetic parameters among individuals. Since physiologically-based toxicokinetic models rarely consider exposure variability, which is often quite large for VOCs in occupational and environmental settings (Rappaport and Kupper, 2004), our results indicate that such simulations probably underestimate the true sensitivity of model predictions to variability in model parameters across a population.
Finally, it is worth reiterating the sentiments of Clewell et al. that physiologically based toxicokinetic models offer powerful tools for investigating complex exposure-dose-response relationships in living organisms (Clewell et al., 2002). While most such applications have focused upon interspecies extrapolations and modes of toxic action, our analyses suggest that such models also offer logical avenues for elucidating the particular effects of exposure variability upon these complex relationships.
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APPENDIX |
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![]() | (A1) |
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![]() | (A3) |
For the gth nonmetabolizing tissue group (richly-perfused, slowly-perfused, or fat), the rate of change (mg h1) in the amount Ag (= VgCg) of chemical is
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Finally, the metabolism of the chemical is assumed to take place exclusively in the liver according to Michaelis-Menten kinetics. The mass-balance equation determining the rate of change in chemical concentration in the liver is
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ACKNOWLEDGMENTS |
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NOTES |
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1 To whom correspondence should be addressed at CB# 7431, University of North Carolina, Chapel Hill, NC 275997431. Fax: (919) 966-0521. E-mail: smr{at}unc.edu
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