Environmental Toxicology and Chemistry, Vol. 22, No. 1, pp. 26–34, 2003 q 2003 SETAC Printed in the USA 0730-7268/03 $12.00 1 .00
DEVELOPMENT AND APPLICATION OF A GENERALIZED PHYSIOLOGICALLY BASED PHARMACOKINETIC MODEL FOR MULTIPLE ENVIRONMENTAL CONTAMINANTS THOMAS M. CAHILL, IAN COUSINS, and DONALD MACKAY* Canadian Environmental Modelling Centre, Trent University, 1600 West Bank Drive, Peterborough, Ontario K9J 7B8, Canada ( Received 18 February 2002; Accepted 18 June 2002) Abstract—The pharmacological disposition of four environmental contaminants resulting from acute and chronic exposure regimes is simulated using a general physiologically based pharmacological (PBPK) model. The model, which is detailed in supporting materials, is mechanistic in structure and relies on available physical-chemical partitioning and reactivity data, but experimental partitioning and absorption efficiency data can be used to refine the parameters. It is designed to complement environmental fate models, thus linking chemical emission rates with environmental and physiological behavior as part of the larger environmental risk assessment process. The model is illustratively applied to inhaled styrene and trichloroethene as well as ingested dibutyl phthalate and di(2-ethylhexyl) phthalate. The phthalate simulations include the corresponding monoester and conjugated monoester as metabolites. Tissue concentrations for each of the chemicals and metabolites are simulated for acute, occupational, and environmental exposure regimes. The same model is used for all chemicals and exposure regimes with only the physical-chemical properties, reaction rates, and exposure estimates being changed. Keywords—Physiologically based pharmacokinetic model
Phthalate
Trichloroethene
Styrene
be as accurate as the chemical-specific PBPK models. In addition, the inclusion of all processes that may be important for a range of chemicals may lead to excessively complex models, which increases their data demands and computer running times. Despite these limitations, mechanistic models can serve several purposes. First, they are designed to give an approximation of chemical behavior when detailed pharmacokinetic data are not available. Second, they provide a testable theory for chemical behavior in organisms. If they are able to replicate chemical disposition without calibration, then it gives confidence that the important processes controlling chemical fate are understood and quantified. If the model is unable to simulate chemical behavior, then at least one important process exists that is not adequately treated by the model, and a chemical-specific process may be important in determining chemical fate. Third, a general model can be consistently applied to many chemicals in situations where standardization may be more important than accuracy, such as screening a large number of chemicals in terms of chemical persistence in the organism. Finally, an incentive exists to use a single model for a wide range of chemicals, thus providing an evenhanded treatment during risk assessment. The objective of this study is to demonstrate how a general, mechanistic, human PBPK model can be used to contribute to environmental risk assessment by estimating tissue concentrations for a variety of environmental contaminants under different exposure regimes. The model, which is described in detail in the supplemental materials, is designed to minimize chemical-specific calibration, although it can incorporate available empirical data to improve the accuracy of the simulations. The model is illustratively applied to four chemicals, namely, styrene, trichloroethene (TCE), dibutylphthalate (DBP), and di(2-ethylhexyl)phthalate (DEHP), for acute, occupational, and
INTRODUCTION
A major task of environmental toxicology is to assess the impacts of chemicals discharged into the environment, which implies the assessment of both the environmental fate of the chemical and the risk of toxic effects on organisms. The first component of estimating environmental fate can be accomplished using a variety of multimedia models [1–4]. These models predict the environmental distribution and degradation of chemicals and give chemical concentrations in various media, such as air, water, and biota, from which exposures can be estimated. The assessment of potential toxic effects resulting from chemical exposure often involves comparing predicted environmental media concentrations with those known to cause toxic effects. Toxic effects are, however, the result of internal tissue concentrations rather than external media concentrations [5]. Therefore, an incentive exists to relate external environmental exposures to specific tissue concentrations. This task of estimating concentrations in specific tissues resulting from chemical exposure is addressed by physiologically based pharmacokinetic models [6–9]. These models have proven very effective for predicting chemical disposition in organisms, but they are frequently chemical specific because of empirical calibration. However, the literature is growing on mechanistic physiologically based pharmocokinetic (PBPK) models that describe chemical partitioning [e.g., 10,11] and organism functions mechanistically using physical descriptions of the processes (e.g., describing alveolar absorption with chemical diffusivities and air–water partition coefficients) rather than empirical rate constants that are measured directly for each chemical and organism. The primary disadvantage of the mechanistic approach is that the models may not account for chemical-specific properties, so they are not expected to * To whom correspondence may be addressed (
[email protected]). 26
Application of a general PBPK model to four chemicals
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environmental exposure regimes. In the case of DBP and DEHP, two metabolites are also modeled in order to account for the formation and loss of the primary metabolite, which is believed to be the toxic species. The same model is utilized for all simulations with only the chemical-specific partition coefficients, reaction rates, and exposure estimates being changed MODEL PROCESSES AND PARAMETERS
Physiological processes The current model shares the same basic structure as standard PBPK models (Fig. 1), so only the distinctive features of the model are mentioned here. A complete model description, including model equations and default parameterization, is presented in the Environmental Toxicology and Chemistry web site (SETAC Supplemental Data Archive, Item ETC 22-01001; http://etc.allenpress.com). Notable physiological features of the current model include mechanistic descriptions for respiratory exchange, renal filtration, intestinal absorption, and an empirical intra-adipose tissue transport description. Chemical gain and loss by respiration is modeled as three transport processes in series, namely, airflow carrying the chemical from the external air to the alveolar sacs, diffusion through the epithelial cells to the lung capillaries, and transport in the blood flow to the general circulation. Since these transport processes occur in series, the overall rate of chemical transport is limited by the slowest process. Renal excretion is also modeled as a series of processes. The chemical in the bloodstream is fractionated into the aqueous fraction and the lipid fraction that is associated with cells and proteins. The glomerular filtration rate, expressed as a fraction of the renal blood flow excreted into the proximal tubule, is applied only to the aqueous blood fraction and the chemicals therein because cells and larger proteins are not excreted by the glomerulus. Once the chemical is filtered into the proximal tubule, it can be reabsorbed. For an ionic species that lacks an active transport uptake pathway, reabsorption is expected to be negligible. Tubular secretion is not currently considered in the model, although it may be an important loss process for the elimination of anionic metabolites. Chemical absorption from food is assumed to occur primarily in the small intestine, which is divided into four segments where the intestinal contents pass sequentially through each segment. The volume and lipid fraction of the intestinal contents change as they pass through the four intestinal segments. Transport of the chemical from the intestinal contents to the intestinal wall is described by parallel aqueous and micelle-mediated diffusion across the aqueous unstirred boundary layer followed by molecular diffusion through the cell membrane as described by Dulfer et al. [12]. The micellemediated transport of the chemical is considered to be unidirectional from the lumen contents to the intestinal wall, while the aqueous diffusion occurs in both directions. Once the chemical crosses the cell membrane, it is considered absorbed, and its mass is added to the intestinal wall compartment. The adipose tissue compartment is often slow to equilibrate with the blood stream because of slow chemical diffusion within the adipose tissue [13,14]. This is represented by dividing the adipose tissue into perfused and deep compartments with a resistance to chemical transport between [14].
Fig. 1. Schematic diagram of model transport and reaction processes. The arrows marked D represent partitioning and transport processes that distribute a chemical in the system. All the transport parameters are chemical species specific, but the species superscripts are omitted for clarity. The transport term between the intestinal lumen segment and the intestinal wall is a function of three diffusion processes, but it is portrayed as a single term on the figure. The first subscript denotes the chemical’s origin and the second the destination. The subscripts are as follows: A 5 air; W 5 intestinal wall; U 5 intestinal lumen, with the number indicating the particular lumen segment; B 5 blood; F 5 adipose tissue (with F1 being the perfused adipose tissue and F2 being the deep adipose tissue); L 5 liver; M 5 muscle; O 5 bone; N 5 brain; T 5 other tissues; R 5 urine; K 5 kidney; and S 5 specific organ defined by user. Greek letters represent chemical species as follows: a 5 parent chemical; b 5 primary metabolite; and g 5 secondary metabolite. Note that reactions can occur in both the liver and the intestinal lumen segments.
Chemical partitioning and reactions The distribution of the chemical in the organism is dependent largely on tissue:blood partition coefficients (KTB), which are estimated using the algorithm and lipid composition data presented by Poulin and Krishnan [11,15]. The algorithm uses the neutral lipid, phospholipid, and water fractions of tissues
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to estimate KTB as a function of the octanol–water partition coefficient (KOW). Measured values for KTB, when available, can be used to override the default estimation algorithm and thus generate a quasi-empirical simulation as described later. The model simultaneously evaluates the distribution, conversion, and losses of three chemical species, namely, the parent chemical (a), the primary metabolite (b), and the secondary metabolite (g). Chemical reactions can occur in the liver (a → b and b → g) and the intestinal lumen (a → b and g → b). If a reaction pathway is not desired, then it is shut off by setting the reaction rate constant to zero (e.g., the intestinal reactions are not used in the simulations of styrene and TCE). A fourth chemical class is included that represents other chemical species formed by nonunity product yields of the reactions between the three primary species. The model does not track these chemicals, so they are lost from the model on formation. The chemical conversions considered in the model are therefore a → b S g, which accounts for the formation and loss of a potentially toxic primary metabolite. Conventional Michaelis–Menten kinetic parameters are used to determine the reaction rates. Reactions follow firstorder kinetics at low concentrations, and a maximum reaction rate is approached at high concentrations. If only a first-order rate constant is available, the saturation parameter is set to an arbitrarily high value so that enzyme saturation does not occur.
Assumptions and calibrations The model makes the following significant simplifying assumptions: All chemical transport is based on passive and facilitated transport mechanisms; dermal uptake is insignificant and is not included; the chemical is lost only by reaction, urination, exhalation, and fecal excretion; the modeled human is an adult and is not growing; and the physiological processes and parameters do not change as a result of prolonged chemical exposure. Three physiological parameters influencing intestinal absorption, namely, the width of aqueous unstirred boundary layer, effective surface area of the small intestine, and micelle fraction in the intestine, are poorly known or the estimates are too imprecise to be useful. Therefore, these three parameters were calibrated using empirical uptake efficiencies. To make the calibration as generally applicable as possible, a series of PCBs with different log KOW values were run through the model, and the three physiological parameters were adjusted in order to give the best fit to the observed absorption efficiencies [16] over a range of log KOW values. It is important to note that this calibration involves setting physiological parameters that should be independent of the chemical simulated. It is also acknowledged that a different combination of these three parameters may also result in satisfactory intestinal absorption characteristics, so the parameters chosen are not necessarily the only combination that may yield good agreement with empirical data. The adipose tissue compartments required minor calibration to determine the relative fractions and exchange rates between the perfused and deep adipose compartments. The model was calibrated using the stable inhaled anesthetic isoflurane (CF3CHClOCHF2) to give agreement with the kinetic data of Carpenter et al. [13], namely, a deep adipose fraction of 50% and a chemical exchange rate of 25% of the exchange rate between the perfused adipose tissue and the bloodstream. Again, these two physiological parameters are independent of the chemical being simulated.
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The calibration of the intestinal absorption and the adipose tissue transport parameters was deliberately conducted using chemicals that are not simulated in the current study. Therefore, the calibration used a separate training set of chemicals that are independent of the chemical being simulated. This was done to avoid circular reasoning where the model is calibrated and subsequently validated on the same data set.
Modes of operation The model has three modes of operation depending on the availability of data. The first mode is mechanistic, where the model estimates tissue concentrations utilizing only physicalchemical partition coefficients (KOW and KAW), an exposure estimate, and chemical reaction rate data, although the reaction rate estimates are reliant on either in vitro or in vivo experimental data and are therefore empirical. The model can provide a screening-level assessment when no experimental pharmacokinetic data are available. The second mode is quasi-empirical in which measured blood:tissue partition coefficients are used to override the KOW-estimated blood:tissue partition coefficients. This is needed if the chemical binds to specific tissues [17]. Finally, several parameters can be systematically adjusted to force the model to give results that agree with results from acute exposure experiments, which results in a completely empirical simulation. These latter simulations, by virtue of calibration, give the most accurate estimations of tissue concentrations.
Model simulations Chemical concentrations for each of the four chemicals were estimated for acute, occupational, and environmental exposure regimes. In the acute simulations, the model predicted the fate of a chemical from a single exposure, although the TCE simulation considered five exposure events spread over 5 d. These results were then compared to existing pharmacokinetic studies. The occupational simulations estimated tissue concentrations resulting from 8 h of inhalation exposure each day for a year at the Occupational Safety and Health Administration (OSHA) permissible exposure limit for each chemical, which is 50 ppm (v/v) for styrene and TCE, while it is 5 mg/m3 for DBP and DEHP. These simulations represent an overestimate of expected tissue concentrations since they assume that the occupational exposure is always at the permissible exposure limit and that the exposed person works every day. Finally, the environmental simulations estimated tissue concentrations for a person resulting from either ambient indoor air concentrations (TCE and styrene) or chemical contaminants in food (DBP and DEHP). These yearlong simulations were designed to represent a nonoccupationally exposed person in the general public. In all cases, the styrene and TCE simulations only considered the parent chemical, while the DBP and DEHP simulations also considered both the primary metabolites, which are formed from liver and intestinal reactions, and secondary metabolites, which result only from liver reactions. The model simulations varied in their degree of empiricism depending on the availability of data. Styrene, TCE, and DBP were modeled mechanistically using the physical-chemical properties listed in Table 1. For comparison, styrene and TCE were also modeled in a quasi-empirical fashion in the acute simulations since blood:tissue partition coefficients were available for these chemicals. However, the occupational and environmental exposure conditions used only the mechanistic
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Application of a general PBPK model to four chemicals
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Table 1. The principal physical-chemical properties used in the simulations. A complete list of all chemical properties, including the phthalate ester metabolites, is presented in the Supplemental Materials along with the measured blood : tissue partition coefficients used for the quasi-empirical simulations of styrene and trichloroethene Trichloroethene
Dibutyl phthalate
Parameter
Styrene
Molar mass Log KOW Log KAW Reaction rates Liver a to b conversion Rate constant (k, per hour) Vmax (mg/h·(L of liver)) Intestinal a to b conversion Rate constant (k, per hour)
104 3.05a 20.91a
132 2.53a 20.38a
104d 102d
29e 155e
1.5f NAh
NA
NA
2.4i
278 4.27b 24.27b
Di(2-ethylhexyl) phthalate 390 (9.0)c 22.8b 6.2g 1,340g 0.08j
a
Mackay et al. [36]. Cousins et al. [18]. c Empirically fitted value because estimated value, by Leo’s fragment method [22], gave better results than measured values. d Calculated from data in Ramsey and Anderson [6] assuming that all metabolism occurs in the liver. e Fisher [30]. f Data from Tanaka et al. [37] after adjusting for the liver homogenate concentration in their test system. g Reaction data was obtained from Keys et al. [33]. It was assumed that all reactions outside the intestine occurred in the liver, so Vmax and Km were converted into liver-normalized values. This liver Vmax value is a little higher than a measured value of 800 (mg/h·L of liver) [38]. h NA 5 not applicable. i Data from Rowland et al. [39] using the lowest concentration tested in order to avoid saturation effects. j Average from data presented in Rowland et al. [39] and Rowland [40]. b
mode of operation to illustrate the model’s general capabilities with minimal data. Di(2-ethylhexyl)phthalate was modeled empirically because the mechanistic intestinal absorption expression failed to predict chemical uptake accurately using the measured log KOW values for DEHP. Measured values of log KOW, around 7.75 [18], result in predictions of highly efficient absorption of intact DEHP based on the Moser and McLachlan [16] regression. However, animal dosing experiments have shown that intact DEHP is poorly absorbed for the intestine [19–21]. If log KOW of DEHP is estimated using Leo’s fragment method [22], a considerably higher log KOW value of about 9.0 is obtained if dimethylphthalate is used as the base structure. This higher value was utilized since it gives an absorption efficiency that is in better agreement with the animal dosing experiments when the Moser and McLachlan [16] regression is used. Another limitation in the case of DEHP was the lack of conjugation reaction rate constants for the primary metabolite. The ratio of the conjugated and nonconjugated metabolites in the urine has been measured [23], so the conjugation reaction rates were varied until the observed percent of conjugated metabolites appeared in the urine. Since some of the model parameters were uncertain in the DBP and DEHP simulations, an uncertainty analysis was conducted to identify which parameters had the greatest impact on the model results. A subjective uncertainty interval was assigned to each model parameter. One thousand Monte Carlo simulations were then conducted using Crystal Bally version 4.0g (Decisioneering, Denver, CO, USA) with randomly determined input parameters to estimate the 5th- and 95th-percentile values for tissue concentrations that would be expected to occur on the basis of the uncertainty and variability of the input parameters.
RESULTS
Acute simulations The aim of the acute simulations is to reproduce experimental data from dosing experiments. Good agreement between the model and experimental results gives greater confidence in extrapolating the model to longer time scales as is done in the occupational and environmental exposure regimes. The model estimated the blood concentrations of styrene as a function of time resulting from a single 6-h inhalation exposure to 80 ppm as was conducted by Ramsey and Young [24]. Figure 2 shows a comparison of model predictions and experimental results. Both simulations gave good agreement with the experimental results with the largest discrepancy between model and experimental results being approximately a factor of two. The mechanistic simulation predicted styrene uptake well, but it underpredicted the rate of styrene loss once exposure had ceased, which resulted in higher concentrations in the depuration phase than were observed in the experiment. The quasi-empirical simulation slightly overpredicted styrene uptake and predicted a more rapid decrease in initial styrene concentrations once exposure had ceased. In addition to predicting blood concentrations, the model was able estimate the fate of inhaled and absorbed styrene. In this example, the primary loss mechanisms were metabolic degradation (90%) and exhalation (10%). It should be noted that the depuration of styrene did not follow first-order kinetics, so its clearance cannot be accurately described by a single half-life as is typical in many environmental applications. Next, the model was applied to estimate blood concentrations of TCE resulting from multiple inhaled exposures as studied by Monster et al. [25]. The results, presented in Figure 3, showed that both simulations gave reasonable approximations of TCE concentration with a maximum discrepancy of
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Fig. 2. Comparison of model predictions to experimental results of Ramsey and Young [24]. Two model simulations were conducted with the first being purely mechanistic, while the second one used measured tissue partition coefficients and was quasi-empirical.
twofold. Unlike the styrene example, where both simulation modes were of comparable accuracy, the TCE example clearly showed that using measured blood:tissue partition coefficients improved the model predictions. The quasi-empirical simulation consistently gave higher TCE concentrations in the tissues, and TCE was retained longer than in the mechanistic simulation.
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The ultimate fate of inhaled TCE was similar to that of styrene with metabolic degradation (62%) and loss by exhalation (38%) being the only two substantial loss processes. Since TCE is a more stable chemical, it is not surprising that less TCE was lost by degradation and more was lost by exhalation. While blood TCE concentrations at the end of each exposure period were the same, the minimum concentrations between exposure increased slightly during the consecutive exposures. This indicates that TCE was retained between exposures and resulted in higher daily average TCE concentrations. Dibutyl phthalate was modeled mechanistically as an ingested chemical. Since the conversion rate of the primary metabolite to the secondary metabolite was unknown, it was assumed to be the same as the conversion rate of the parent chemical to the primary metabolite. The results of the acute DBP simulation (Table 2) indicate that about 2.4% of the initial DBP dose was retained as intact DBP in the organism after 24 h. Dibutyl phthalate rapidly reacts in the intestine (21.8% of dose) and is efficiently absorbed (78.0% of dose); hence, very little of the initial mass (;0.2%) was excreted in the feces. The absorbed chemical was converted to monobutyl phthalate (MBP) or a conjugated form of monobutyl phthalate (conj-MBP), both of which were readily excreted in the urine since they are ionized predominantly under physiological conditions. Since the conjugation reaction rate was not known, the relative amounts of MBP and conj-MBP are uncertain, and estimates should be regarded as only tentative. Overall, the model results are in good agreement with rat dosing experiments. The results of the empirical DEHP simulation are presented in Table 3. In contrast to DBP, only 19% of DEHP was absorbed intact, while 42% was converted to the monoester in the intestine and the remaining 39% was excreted in the feces. Only about 2% of the DEHP remained in the organism as intact DEHP after 96 h with the majority residing in adipose tissue. The remaining DEHP was metabolized to the monoester and the conjugated monoester, which were excreted in the urine. In all cases, the model is capable of reproducing the observed dynamic disposition of the chemical resulting from acute exposure. Such data, which are obtained under controlled laboratory conditions, are regarded as providing the most reliable information on pharmacological disposition, but the results cannot be easily or directly extrapolated to chronic or environmental conditions.
Occupational simulations
Fig. 3. Comparison of model predictions to empirical results for multiple inhaled exposures of trichloroethene (TCE). The exposure conditions were 70 ppm TCE for 4 h/d for 5 d. The lines represent the two model predictions, mechanistic and quasi-empirical, while the dots with error bars represent the data presented in Monster et al. [25].
The model was then applied to estimate tissue concentrations in an occupationally exposed person who is subject to 8 h of inhalation exposure at the Occupational Safety and Health Administration’s permissible exposure limit each day for a year. The model predicted time-weighted styrene tissue concentrations (wet-wt basis) for an occupationally exposed person to be approximately 0.22 ng/g for blood, 40 ng/g for adipose tissue, 0.12 ng/g for liver, and 1.7 ng/g for muscle. The liver concentrations were lower than those in the blood because of the very rapid degradation of styrene in the liver. The rate of styrene metabolism in the liver may be partly limited by chemical delivery by the blood flow. Tissue concentrations from the TCE simulation were comparable to the styrene concentrations, although they were somewhat lower. In the TCE simulation, the time-weighted tissue concentrations were ap-
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Table 2. Predicted fate of a single low (100 mg/kg) dose of dibutyl phthalate after 24 h. The point estimate represents the simulation results using the best parameter estimates. The range represents the 5th and 95th percentile values resulting from 1,000 Monte Carlo simulations Model predictions Point estimate
5–95% range
2.4 0.8 0.8
0.7–7.1 0.1–4.9 0.1–5.1
0.2 96 0.0 4.0
0.1–0.2 86–99 0.0–0.0 1.3–14
Mass retained (% of dose) Dibutyl phthalate (DBP) Monobutyl phthalate (MBP) Conjugated monobutyl phthalate Total mass loss by route (%) Total fecal excretion Total urinary excretionb Total respiratory excretion Total mass retained
Rat studies
2.2a; 1.0–8.2a 85c; 85–98a; 63–78d ,0.1 (as CO2)c 7.2 (0.1)c,e 6.8 (1.1)a,e
a
Data from Tanaka et al. [37]. The other metabolites of MBP are assumed to be excreted in the urine. c Data from Williams and Blanchfield [41] using the lower dosing (270 mg/kg body wt) data. d Data from Foster et al. [42]. This experiment used a high dose (2 g/kg body wt), so both the reaction rate and absorption efficiency were low because of saturation effects. e The first mass retained value represents the amount of additional mass recovered in urine and feces in the subsequent 3 d from the same organisms [37] or the difference in mass excretion between a set of dosed rats after 24 and 48 h [41]. The value in parentheses represents direct radioactivity measurement in the tissues after 24 h excluding radioactivity in the intestine. b
proximately 0.17 ng/g for blood, 17 ng/g for adipose tissue, 0.26 ng/g for liver, and 0.81 ng/g for muscle. In contrast to styrene, the liver TCE concentrations were higher than the bloodstream, indicating that the slower reaction rate was less effective in clearing the chemical from the body as compared to styrene. The occupational DBP and DEHP simulations used an inhalation route of exposure, which is different than the oral route of exposure used in the acute and environmental simulations. The DBP simulation indicated that the highest concentrations (time-weighted) of the parent chemical were found in the adipose tissue (4,500 ng/g) followed by muscle tissue (190 ng/g) and the liver (180 ng/g), while the blood had the lowest concentrations (21 ng/g). The concentrations of the monoester metabolite were universally low in all tissues with concentrations ranging from 6.4 ng/g (adipose) to 35 ng/g (muscle). Finally, the concentrations of the conjugated sec-
ondary metabolite were lower still with concentrations ranging from 2.3 to 12 ng/g in all tissues. The fate of absorbed DBP was controlled by metabolic conversion and subsequent urinary loss of the primary (82%) or secondary (17%) metabolites. Negligible quantities were lost by the fecal route or by exhalation. The occupational simulation of DEHP gave comparable results to the DBP simulation, which was unexpected since the physical-chemical properties are very different between the two chemicals. The adipose tissue had the highest concentrations (2,100 ng/g) again followed by muscle (86 ng/g), liver (45 ng/g), and blood (9.5 ng/g). The concentrations of the monoester were much lower than the parent chemical with values ranging from 6.2 ng/g in blood to 37 ng/g in adipose tissue. Unlike the parent and primary metabolite, the conjugated secondary metabolite showed the highest concentrations in the liver (36 ng/g) and the lowest concentrations in the
Table 3. Predicted fate of a single low (100 mg/kg) dose of di(2-ethylhexyl) phthalate after 96 h. The point estimate represents the simulation results using the best parameter estimates. The range represents the 5th and 95th percentile values resulting from 1,000 Monte Carlo simulations Model predictions
Mass retained (% of dose) Di(2-ethylhexyl) phthalate (DEHP) Mono(2-ethylhexyl) phthalate (MEHP) Conjugated mono(2-ethylhexyl) phthalate Total mass loss by route (%) Total fecal excretion Total urinary excretionc Total respiratory excretion Total mass retained a
Point estimate
5–95% range
2.2 0.1 0.1
0.8–9.9 0.0–0.4 0.0–0.3
39 59.0 0.0 2.3
14–57 40–81 0.0–0.0 0.9–10
Monkey studiesa
Rat studiesb
39–49 20–55
43–57 33–51 1.9d
Astill [19]. [19–21]. c The other metabolites of MEHP are assumed to be excreted in the urine. d Data from Tanaka et al. [43]. The retained mass was calculated as the fraction of 14C label in all of the tissues except the intestine 24 h after a single oral dose of 500 mg/kg 14C-labeled di(2-ethylhexyl) phthalate. b
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adipose tissue (7.3 ng/g). The secondary metabolite is highly water soluble; hence, it was not expected to concentrate in adipose tissues. The fate of inhaled DEHP was metabolic conversion and subsequent excretion in the urine as either mono(2ethylhexyl)phthalate (MEHP) (21%) or conjugated MEHP (79%). It should be noted that the percentage of conjugated chemical in the urine, reported as 80% in Huber et al. [23], was used to calibrate the unknown conjugation rate; hence, these model predictions are the result of empirical calibration rather than mechanistic prediction.
Simulations of chronic environmental exposure The last set of simulations was designed to predict tissue concentrations of the four chemicals in people as the result of environmental exposure. The styrene and TCE simulations used typical indoor air concentrations of the chemicals, which are approximately 2.5 mg/m3 for both chemicals [26], and a 24-h inhalation exposure regime. For styrene, the highest concentrations were found in the adipose tissue (1,400 pg/g) and muscle (60 pg/g), while the blood (8.0 pg/g) and liver (3.5 pg/g) had the lowest concentrations. The relative styrene concentrations in the different tissues were very similar to those in the occupational simulation, but the absolute concentrations were approximately 30-fold lower. These results suggest that environmental exposure to styrene is relatively minor compared to potential occupational exposure. The environmental simulation of TCE gave similar results. The TCE concentrations ranged from a maximum of 450 pg/g in the adipose tissue to a minimum of 4.3 pg/g in the blood. These concentrations were approximately 40-fold lower than the occupational exposure scenario, which indicates that environmental TCE exposure is probably minor. Trichloroethene disposition and relative concentrations in the different tissues were similar to the occupational exposure simulation. The environmental simulations of DBP and DEHP used two exposure estimates for each chemical that represent lower and upper dietary exposure estimates. These simulations are shown on Table 4 along with a 95% confidence interval based on 1,000 Monte Carlo simulations. In each case, the concentrations of a particular chemical species were generally within the same order of magnitude in the different tissue types. The uncertainty interval around each tissue estimate was about an order of magnitude despite some input parameters being poorly known. The DBP and DEHP concentrations resulting from the upper estimate of environmental exposure were approximately 100-fold lower than an occupationally exposed person. It is stressed that relative concentrations of MBP and conj-MBP are speculative since the conversion rate between these two species was unknown. In contrast, the DEHP simulations were calibrated with acute experimental data, so the unknown reaction rate was estimated, and the tissue concentrations are probably more accurate. The estimated tissue concentrations can be compared to monitoring data, such as the National Human Adipose Tissue Survey (NHATS) [27]. An analysis of the NHATS data reveals that the median DBP and DEHP concentrations in human adipose tissue are 31 and 77 ng/g, respectively, although the mean value was much higher because of a few high statistical outliers. Unfortunately, some of the blank samples from the NHATS study showed phthalate esters, so these values may be artificially elevated. Model predictions for adipose tissue concentrations range from 3.0 to 30 ng/g for DBP and 0.55 to 27 ng/g for DEHP depending on the exposure estimate. This
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indicates that the model predictions are consistent with observed data from a human population, especially if the NHATS data represent an overestimate of tissue concentrations. An alternative method of comparison is to utilize the concentrations of the chemical or its metabolites in the urine. Depending on which exposure estimate is utilized, the model estimates that 100 to 1,000 mg/d of DBP metabolites (sum of MBP and conj-MBP) and 17 to 850 mg/d of DEHP metabolites (sum of MEHP and conjugated MEHP) were lost in the urine. These estimates can be compared to monitoring data such as data presented in Blount et al. [28]. The daily urinary loss rates presented in Blount et al. [28] correspond to a loss rate of 164 mg/d of DBP metabolites and 13 mg/d of DEHP metabolites assuming a maximum urinary creatinine clearance rate of 20 mg/kg/d and a 70-kg person. Unfortunately, the lower exposure estimates by David [29] were back-calculated from the Blount et al. [28] data set; hence, they do not represent independent validation of the model. The use of urinary metabolites provides an attractive alternative to tissue sampling since sample collection is considerably easier. DISCUSSION
The purpose of this research was to demonstrate that a general, mechanistic PBPK could be developed and applied to different classes of chemicals and different exposure regimes. The simulations presented here show that it is feasible to apply a general PBPK model to multiple chemicals, including metabolites, and different exposure regimes. It is acknowledged that chemical-specific PBPK models will provide more accurate predictions because of their extensive calibration to a particular chemical, but a general model has the advantage of being applicable to a wide variety of chemicals with minimal input data. This general applicability of the model is useful for screening studies where detailed pharmacokinetic data are not available. When the mechanistic simulations prove inaccurate (e.g., DEHP), available pharmacokinetic data can be incorporated to improve the accuracy of the predictions. An advantage of the current model is that it can be consistently applied to different chemicals. All four simulated chemicals already have existing human or rat PBPK models in the literature (e.g., styrene [6], TCE [30,31], DBP [32], and DEHP [33]), but the models vary in structure and availability since they were developed by different research groups. The creation of a general model allows multiple chemicals to be compared under standardized conditions, which could contribute to improved interchemical pharmacokinetic comparisons. The last major reason for developing a general PBPK model that uses physical-chemical parameters to predict chemical fate in an organism is that it can then be incorporated into or linked with environmental fate and transport models. Chemical concentrations in human tissues (or excreta) can then be estimated on the basis of chemical emissions to the environment. The environmental fate and PBPK modeling fields are both well developed, but they are rarely linked to give a holistic assessment of the impacts of a chemical release to the environment. The current model was programmed in the fugacity system, which is frequently used for environmental modeling [1], to facilitate its future linkage to environmental fate models. A PBPK model designed for environmental purposes has several advantages. Since the model uses the same equations and parameters for both acute and chronic exposure regimes, it can exploit available acute dosing results for parameter cal-
Environ. Toxicol. Chem. 22, 2003
Application of a general PBPK model to four chemicals
33
Table 4. Predicted tissue concentrations (daily average) resulting from daily dietary exposure to dibutyl phthalate (DBP) and di(2-ethylhexyl) phthalate (DEHP). The point estimate and the 5th and 95th percentile range from 1,000 Monte Carlo simulations are given. Two exposure regimes were used that correspond to a lower and an upper exposure estimate. All concentrations are in pg/g wet weight DBP exposure 1.6 mg/(kg/d)a Parent chemical Blood Adipose Liver Muscle Whole body Primary metabolite Blood Adipose Liver Muscle Whole body Secondary metabolite Blood Adipose Liver Muscle Whole body
14 3,000 150 130 740 290 62 270 340 210 74 15 73 46 54
(2.4–31) (540–6,800) (23–350) (23–290) (130–1,700) (160–500) (35–100) (160–460) (200–580) (120–370) (35–330) (7.2–66) (36–310) (22–200) (25–230)
DEHP exposure
16 mg/(kg/d)b
0.6 mg/(kg/d)a
140 30,000 1,500 1,300 7,400
(25–340) (5,700–74,000) (240–3,800) (240–3,100) (1,400–18,000)
2.5 550 29 23 140
2,900 620 2,700 3,400 2,100
(1,600–5,000) (350–1,000) (1,500–4,600) (2,000–5,700) 91,200–3,600)
14 86 18 22 30
(9.3–23) (50–160) (13–29) (15–35) (20–51)
680 4,200 890 1,100 1,500
(450–1,100) (2,300–7,900) (610–1,400) (720–1,800) (900–2,600)
(310–3,700) (62–740) (320–3,500) (190–2,300) (220–2,600)
82 17 82 63 59
(41–200) (8.2–40) (43–190) (31–150) (29–140)
4,000 810 4,000 3,100 2,900
(2,000–9,900) (400–2,000) (2100–9,500) (1,500–7,600) (1,400–7,100)
740 150 730 460 540
(1.1–10) (240–2,300) (13–120) (10–96) (60–560)
30 mg/(kg/d)c 120 27,000 1,400 1,100 6,700
(52–490) (11,000–110,000) (590–5,700) (470–4,500) (2,700–26,000)
a
David [29]. Clark et al. [44]. c Huber et al. [23]. b
ibration and then apply the same equations to chronic exposure situations as are typical of environmental exposure. A model that can treat multiple chemical species can address situations in which the toxic species is a metabolite rather than the parent chemical. The model can be used to elucidate the relative importance of respiratory and dietary intake routes and can reveal the primary routes of elimination. It can also provide a link between exposure and measured tissue concentrations, thus enabling concentrations to be deduced from exposures and vice versa. Indeed, the difficulties inherent in measuring human exposure make the alternative option of using tissue or excreta concentrations as a basis for back-calculating exposure attractive. Models can also estimate the persistence of a chemical in the organism, which is an invaluable descriptor of the potential for bioaccumulation. The model described here, like all models, has limitations. It is designed primarily for neutral and predominantly ionic organic compounds, so inorganic, strongly surface-active chemicals and chemicals that bind to specific tissues cannot be mechanistically modeled because of their unusual partitioning properties, although these chemicals can be modeled quasi-empirically. Chemicals that are partially ionized under physiological conditions must receive special treatment since it may be necessary to consider each ionization state as a separate chemical species. In the current simulations, the phthalate monoesters (MBP and MEHP) are assumed to be completely ionized, but a more accurate description of these chemicals may have to treat the ionic and nonionic forms separately. A need exists to include and parameterize enzyme induction processes as was done by Leung et al. [34], especially when considering chronic exposure regimes. The model was programmed with a tentative induction routine, but it was disabled because of a lack of data necessary to parameterize the processes for the four chemicals studied. Currently, it is assumed that typical environmental exposures are too low to cause enzyme induction, which may result in a conservative overestimate of tissue concentration by underestimating deg-
radation rates. Finally, the current model assumes that the physiological parameters of the person do not change over time, but multiyear simulations of persistent chemicals should incorporate changes in the person as a function of time as was done in van der Molen et al. [35]. Another limitation of the current model is its complexity. The model includes detailed descriptions for many processes that are important only for certain classes of chemicals, but the inclusion of these detailed process descriptions increases the data demands and computer running times for all model applications. For example, the evaluation of TCE and styrene does not require a detailed description of intestinal absorption processes, so in these cases the model is more complex than is necessary. A complex model may also be excessive for assessing the relative importance of the different chemical loss processes (urinary, fecal, respiratory, and metabolic). A limitation of creating increasingly general or accurate mechanistic PBPK models is that the model complexity will also increase, which results in greater data demands and computer time even when the increase in complexity is not warranted. In summary, we have demonstrated that it is both desirable and feasible to develop mechanistic PBPK models that are flexible in their degrees of empiricism and accuracy. Such models can exploit the results of environmental monitoring and modeling programs to deduce the routes of uptake, tissue concentrations, and body burdens. These mechanistic PBPK models can, we hope, contribute to an improved assessment of the potential effects of environmental contaminants. The model presented here is available free of charge from the authors as a Visual Basicy program contained in a Microsoft Excely (Microsoft, Redmond, WA, USA) spreadsheet. Acknowledgement—We would like to thank the Natural Sciences and Engineering Research Council of Canada, the Canadian Network of Centres of Toxicology, the Canadian Chemical Producers Association, and the consortium of companies, and especially the Environmental Research Task Group of the Phthalate Ester Panel of the American
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