* The K.S. Crump Group, ICF Consulting, P.O. Box 14348, Research Triangle Park, North Carolina 27709;
Toxicology, Health and Environmental Sciences, Dow Corning Corporation, Midland, Michigan 48686; and
Department of Environmental Health, CETT/Foothills Campus, Colorado State University, Ft. Collins, Colorado 80523
Received August 17, 2001; accepted January 4, 2002
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ABSTRACT |
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Key Words: siloxane; pharmacodynamic; enzyme induction; inhalation; modeling.
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INTRODUCTION |
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Tissue responses to compounds, such as D4, depend on the interplay of pharmacokinetic (distribution, metabolism, clearance) and pharmacodynamic (receptor binding, protein induction, hypertrophy, and hyperplasia) processes. Of interest for a risk assessment for D4 is whether the resulting dose-response curves for these receptor-mediated responses are linear or nonlinear in the low dose region. Pharmacokinetic and pharmacodynamic models of biological responses constructed based on a clearly articulated hypothesis relating to a receptor-mediated mode of action for D4 could be utilized to develop biologically based dose-response (BBDR) models for predicting low dose behavior. The development of BBDR models is consistent with risk assessment guidance noted in the proposed U.S. EPA guidelines for risk assessment with carcinogens (EPA, 1996).
Studies were conducted measuring D4 tissue concentrations and hepatic induction of CYP2B1/2 protein and activity in female Fisher F344 rats following repeated inhalation exposure to D4 (McKim, 1998). Andersen et al. (2001) developed a physiologically based pharmacokinetic (PBPK) model for the disposition of D4 in rats after single and multiple inhalation exposure. In this present work, we coupled this PBPK model with a pharmacodynamic (PD) model for hepatic enzyme induction by D4. The objectives of this study were threefold: (1) to use a 1-compartment liver model to analyze and compare both the induction of CYP2B1/2 protein in the liver and increases in liver weight, (2) to compare the 1-compartment liver model to a 5-compartment model that accounted for regional variations in protein induction patterns in the liver, and (3) to describe and compare the low dose behavior predicted by these two models.
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MATERIALS AND METHODS |
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Model development.
The dose-response analysis of hepatic induction requires knowledge of the concentrations and time course of D4 in liver. The high lipid solubility and low blood:air partition coefficient of D4 result in atypical distribution in the body following inhalation exposure, compared to most inhaled compounds. To describe the time course behaviors with inhaled D4, Andersen et al. (2001) developed a PBPK model with standard compartments such as fat, lung, liver, and richly perfused and poorly perfused tissues, which also included deep tissue compartments in lung and liver, multiple fat compartments, and a mobile lipoprotein pool in the blood. This PBPK model structure was adopted here to study protein induction in the liver following multiple inhalation exposure of D4 in F344 rats. A brief description of this model (Fig. 2) is provided here.
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In this PBPK description, two different measures of liver tissue D4 concentration were calculated: total D4 and free D4. The total D4 concentration is the volume-averaged composite of D4 in shallow liver and deep liver compartments. Thus, total D4 concentration includes liver D4 that is not partitioned into tissue (free D4), nonspecifically bound D4 in the shallow liver (equivalent to tissue partitioning), and D4 in the deep tissue pool. Free D4 in the shallow liver compartment equilibrates with the venous blood exiting the compartment. Free venous D4 is available for gas exchange in the lung. The shallow and deep pool blood:tissue partition coefficients for liver are 16 and 100, respectively (Andersen et al., 2001). The free and total D4 concentration in liver varies by about a factor of 20 or more.
Modeling CYP2B induction using the 1-compartment liver model.
Pharmacodynamic approaches to modeling hepatic protein induction have been developed previously (Andersen et al., 1997a,b
; Kohn et al., 1993
; Wang et al., 1997
). In these reports, induction of proteins by 2,3,7,8-tetrachlorodibenzo-p-dioxin in liver was described as a receptor-mediated process resulting in transcriptional activation of genes coding for the proteins. Binding of ligand to receptor was described with a Hill equation. In the absence of knowledge of binding constants for the D4 receptor complex or concentrations of specific binding proteins, presumably the CAR receptor (Waxman, 1999
) or some associated proteins, the fractional occupancy (FO) of the DNA promoter elements by the D4 receptor complex was related to either the free or total tissue concentration of the parent compound (D4). The FO was calculated from liver D4 concentration by a Hill equation, with an apparent dissociation constant (Kd) and a Hill coefficient (N).
![]() | ((1)) |
Here, D4:R:DNA is the concentration of DNA ligand binding sites bound with the putative D4 receptor complex and DNAtotal is the total concentration of binding sites for this complex. The rate of change of total amount of CYP2B1/2 protein in the liver was related to basal production rate (K0), the induced rate (proportional to the fractional occupancy of the DNA binding site), and first order degradation rate constant (kelim) of CYP2B1/2 protein, as follows:
![]() | ((2)) |
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![]() | ((3)) |
In the competitive inhibition model, the only extra parameter required is the inhibition dissociation constant of D4 for binding the enzyme and inhibiting pentoxyresorufin metabolism (Kinh, in µM). The proportionality constant (ß), relating the protein activity units to the amount of protein, is computed based on the basal level of CYP2B1/2 protein and PROD activity. Models with noncompetitive or uncompetitive inhibition could also describe the data since we have no constraints on values of binding constants or exact concentrations of D4 in the microsomal preparations. Equations for suicide inhibition (Appendix) required an unusual depiction of the dose dependence of inactivation of the enzyme by D4. Usually suicide inhibition is described in relation to the rate of metabolism of substrate by the protein that is lost during the inhibition (Lilly et al., 1998). In the present case, only descriptions based on free D4 concentrations were successful in reproducing the dose response for diminished activity. This point is discussed further below.
Multicompartment liver model.
A 5-compartment liver model was used to account for regional induction of the CYP2B1/2 proteins. In the 5-compartment model, the compartment numbered 1 surrounded the portal triad and the compartment numbered 5 surrounded the central vein. The intermediate compartments 2, 3, and 4 were interposed between compartments 1 and 5. The geometric analysis with portal triad regions draining to two central veins gave three star-shaped concentric regions around the central vein and one triangular shaped region around the portal triad. Compartment 2 became distributed throughout the entire 2-dimensional liver. Based on computing surface areas for these two-dimensional compartments and scaling them to three-dimensional cylinders, the volume for the 5 compartments was estimated as 13.5, 25.5, 33.9, 20.3, and 6.8% of total liver, respectively, going in order from the periportal to the centrilobular regions. The 5-compartment liver model used in this study was adopted directly from an earlier description for TCDD (Andersen et al., 1997b).
The mathematical formulation of the pharmacokinetics of the multicompartment liver closely parallels that of the single-compartment liver model. The multicompartment liver model partitions the liver into individual regions; each contains a shallow and deep tissue compartment (Fig. 2). The shallow liver compartment is perfused by the arterial blood and transports D4 to the deep compartment by diffusional transfer. In the multicompartment liver model the five compartments are perfused by blood sequentially, with compartment 1 (i.e., periportal region) receiving the arterial blood. Venous blood exits the liver from compartment 5 (i.e., centrilobular region). The rate of change of D4 in each of the shallow liver compartments is determined by the amount gained due to blood flow, the amount lost due to metabolism, the amount transferred to the deep compartment, and the amount lost by elimination into the mobile lipid pool in the blood perfusing the shallow compartment.
![]() | ((4)) |
Here vli is the volume of the ith liver compartment in ml, Cvli and Cvli-1 are the venous concentration (µg/ml) of D4 exiting the ith and the (i1)th compartment, respectively, Cdli is the concentration (µg/ml) of D4 in the ith deep compartment, kld is the transfer coefficient (h-1) between the shallow and deep compartments, klb is the first order elimination (h-1) of D4 from the liver to the blood via lipid pool transfer, and Vmi and Km are the kinetic parameters for D4 metabolism in the ith compartment.
Andersen et al. (2001) found it necessary to include induction of D4 metabolism to improve fits to liver tissue and plasma concentrations following multiday inhalation exposures of male and female rats to D4. In order to fit these tissue concentrations following 700 ppm exposures, the Vmax had to be increased by 2.0-fold for the female F344 rats in the inhalation model (Andersen et al., 2001). For the present study, the increase in D4 metabolism due to enzyme induction in the liver was expressed empirically by the equation:
![]() | ((5)) |
Model parameterization.
The parameters used in the PBPK model were from Andersen et al. (2001). Parameters for the pharmacodynamic submodel were either obtained from literature (below) or were estimated by fitting the PD model to the induction dose-response data. The pharmacodynamic parameters for induction were the basal CYP2B1/2 protein production rate, the degradation rate constant for the protein, the delay time from transcriptional activation to appearance of functional protein, and the maximum zero-order production rate due to induction. The increase in production rate due to changes in FO is described in Equation 1 with the Hill coefficient (N) and the apparent dissociation constant (Kd). Similarly, quantitative estimation of PROD activity requires parameter estimates for the competitive inhibition constant, Kinh.
Radiolabeling studies have been used to determine the degradation rate of cytochromes P450, including CYP2B1/2 protein. Shiraki and coworkers administered NaH14CO3 to male Sprague Dawley rats after a 5-day treatment with PB (80 mg/kg) or vehicle. Radiolabeled CYP2B1/2 was immunoprecipitated from isolated hepatic micorosomes at various times after administration of the radiolabel and plotted as a function of time to determine the half-life. The measured half-lives for CYP2B1 and CYP2B2 were between 37 to 50 h (Parkinson et al., 1983; Shiraki and Guengerich, 1984
). The optimal fit to the half-life from the present studies, 22 h, was fairly close to this range.
The basal production rate of CYP2B1/2 protein and the proportionality constant relating CYP2B1/2 protein and PROD activity were derived using basal CYP2B1/2 and PROD data collected by McKim (1998). The amount of basal CYP2B1/2 protein in the liver of female F344 rats was determined by laser scanning densitometry, and reported as15.1 area-under-curve (AUC) units/µg protein (AUC = pixels x signal strength product; McKim, 1998). The basal CYP2B1/2 protein production rate is uniquely determined by the product of the protein degradation rate and the basal CYP2B1/2 level, giving an estimate of 0.45 AUC/h/µg protein for K0. The basal PROD activity in the uninduced liver was 2.45 pmoles/h/µg protein in female F344 rats (McKim, 1998
). The proportionality constant is uniquely determined by the ratio of the basal PROD activity to the basal level of CYP2B1/2 protein, resulting in a value of 0.16 pmoles/h/AUC for ß.
Other values, the maximum CYP2B1/2 production rate, the Hill coefficient, and Kd, were not available from the literature. These three parameters were estimated simultaneously using the dose-response data for CYP2B1/2 protein induction (McKim, 1998). For the inhibition models, neither the inhibitory dissociation constant for D4 nor the second order rate constant for inactivation of CYP2B1/2 protein were available from any other studies. These constants were estimated by optimizing the model output against measured PROD activity for a wide range of D4 doses. The initial values for all the above parameters were estimated by visually fitting model predicted CYP2B1/2 induction and PROD activity to measured data. These initial values were used as seed values for parameter estimations conducted using the Nelder-Mead algorithm in ACSL-Optimize (Advanced Continuous Simulation Language, Aegis, Inc., Huntsville, AL). The optimized parameters were within10% of the initial estimates obtained using the manual visual fits. The only additional parameters to be estimated for the 5-compartment liver model, compared to the 1-compartment model, are the individual Kd for the various regional compartments in the liver. The process adopted to estimate these values in the 5-compartment model is similar to that in the 1-compartment model and are elaborated in the Results section. The parameters for the PD submodel are listed in Table 2
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RESULTS |
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Protein Induction
CYP2B1/2 protein increased to a maximum following exposure to D4 of 500 ppm and greater concentrations (Fig. 5). PROD activity increased monotonically with increasing inhaled concentration up to 500 ppm and declined at higher concentrations (Fig. 6
). The liver induction model was applied to estimate the dose dependence of total CYP2B1/2 protein and PROD activity using two different measures of dose, either total D4 concentration or free D4 concentration in the liver. Free liver concentration gave a better fit to the dose-response data (Figs. 5 and 6
). Based on free D4 concentration in the liver, optimized estimates for the Kd and N were 0.67 µM and 1.9, respectively. The estimated inhibitory dissociation constant for D4 in the PROD assay was 1.69 nM free D4. Blood and tissue concentrations were not changed significantly when D4 inhibition was included in the model. Even though there is nearly a 35-fold increase in PROD activity, the estimated increase in D4 metabolism is only about twofold. The percent inhibition in PROD is actually quite small, about 20%. These small changes in metabolic clearance of a poorly soluble volatile will have minimal effects on blood concentrations and kinetics of the parent compound (Andersen, 1981
).
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Low Dose Responses
Low-dose behavior of the 1- and 5-compartment liver models were compared by determining the inhaled concentration required to provide a 10, 1, or 0.1% of maximum increase in CYP2B1/2 induction in rats (i.e., ED10, ED01, or ED001). The ED10, ED01, and ED001 estimated using the 1-compartment PBPK/PD models were 24.1, 6.9, and 2.1 ppm, respectively, and those estimated using the 5-compartment model were 25.1, 9.3, and 5.1 ppm, respectively (Table 3). The effective dose for a given percent increase in response differs between the 1- and the 5-compartment models more significantly at low dose than at high dose. Induction dose-response data was also fit to a Hill equation (U.S. EPA BMDS v1.2) to derive empirical estimates of effective dose, resulting in ED10, ED01, and ED001 values of 28.5, 7, and 1.8 ppm, respectively. These equate to concentrations that give a 10, 1, or 0.1% increase in induction and compare directly with the ED values calculated using the model.
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DISCUSSION |
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Comparative Induction Characteristics of D4 and PB
Dose-dependent induction of CYC2B1/2 by PB has been well characterized. Two published reports on PB induction of CYP2B1/2 were used to estimate Kd and Hill coefficients for PB induction. Nims and coworkers treated F344 rats with PB at feed concentrations of between 6.17 and 1500 ppm in the diet for 2 weeks (Nims et al., 1993). CYP2B1 protein was determined in microsomal protein preparations by Western blot using a mouse monoclonal CYP2B1 antibody. The experiment was also conducted with hepatocytes from F344 rats at nominal PB concentrations between 1 and 1000 µM in the media. The Kd for induction was 14.5 µM in vitro and the Kd for induction based on serum concentration was 10 µM in vivo. Kocarek and coworkers exposed primary hepatocytes from male Sprague-Dawley rats to nominal PB concentrations ranging from 1500 µM in the media and measured CYP2B1 mRNA by Northern blot (Kocarek et al., 1990
). The Kd for PB induction of CYP2B1 in this study was approximately 15 µM.
The CYP2B1 induction data from these two reports can also be used to determine Hill coefficients. The values of N determined for the in vivo and in vitro protein induction experiments (Nims et al., 1993) were 1.5 and 2.0, respectively. These values are similar to the N value (1.7) determined using the in vitro CYP2B1 mRNA induction data (Kocarek et al., 1990
). Thus, Hill equations also adequately accounted for the induction of hepatic CYP2B1/2 induction by PB with an N value close to 2.0. The Kd for CYP2B1 induction by PB, expressed as plasma PB concentration, is in the range of 10 to 15 µM.
Using the free D4 concentration in liver to fit the dose response, the optimized values for Kd and N were 0.67 µM free D4 and 1.9, respectively. In addition to fitting the PBPK model to the data, empirical fits of D4 CYP2B1/2 induction data to a Hill equation were made using ACSL Optimize. These induction data were fit to several exposure metrics: exposure concentration (in ppm), free liver D4, total liver D4, free plasma D4, and total plasma D4 concentrations. With the exception of total plasma D4, the resulting optimized values of the Hill coefficient for D4 were between 1.55 and 1.67 (average = 1.6, SD = 0.06). The similarity in the Hill coefficient for PB and D4 implies that a similar ligand-receptor process could be involved in induction with both compounds. The Kd for PB in relation to plasma concentration is approximately 15 times higher than is the Kd for D4 expressed in relation to free concentrations of D4 in the liver.
McKim et al. (1998) compared maximum CYP2B1/2 induction caused by D4 and PB at the end of 3 days following either repeated daily inhalation exposures to 700 ppm D4 for 6 hrday or a repeated ip administration at 80 mg/kg/day for 3 days. The maximal increase with PB was three to four times greater than from D4 in both male and female F344 rats. It would be easy to conclude that the maximal induction with D4 was lower than with PB. However, this comparison is misleading since PB concentrations in plasma following ip administration for 3 days saturate the receptor over most of the 24 h. In contrast, the 6-h exposure regimen with D4 produces maximally inducing concentrations for little more than the 6-h period due to rapid exhalation of D4 at the cessation of exposure leading to a duration effect on maximal induction for D4 (Fig. 7). Currently, our D4 modeling results indicate that D4 is a somewhat more potent inducer of CYP2B family proteins than is PB and that the two ligands cause similar levels of maximal induction when the pharmacokinetics of inhalation are taken into consideration. Interestingly, the Hill coefficient of close to 2 differs from that for hepatic induction with TCDD where the Hill coefficient was closer to 1.0 (Kohn et al., 1993
). This observation may indicate that a dimeric form of liganded receptor is involved in transcriptional regulation of CYP2B proteins by these inducers.
D4 Inhibition of Induced PROD Activity
High dose D4 inhibition of CYP2B1/2 activity was modeled to evaluate mechanisms of inhibition and the potential impact of inhibition on D4 pharmacokinetics. Blood and tissue concentrations were not affected by including D4 inhibition of CYP2B1/2. It is difficult to establish a mechanism for the inhibition of PROD by D4 at high concentrations. The carryover of D4 to a microsomal preparation is likely given the poor water solubility of D4 (Andersen et al., 2001) and the lipophilic nature of the microsomal preparation required for the PROD analysis. D4 concentrations in these microsomes have been measured, but not compared directly to concentrations in the livers before sacrifice of the animals. The present estimate of the inhibitory dissociation constant would need further study to determine the D4 concentrations in microsomes derived after different inhalation exposures. Suicide inhibition or competitive inhibition by D4 was explored as possible modes of inhibition. Either competitive or suicide inhibition model formulation could successfully describe the dose dependence (Fig. 9
). However, we believe that the competitive inhibition is more likely than suicide inhibition for two reasons, namely, the nature of intermediate metabolites for D4 and the dose dependence of inhibition. Oxidative metabolism of D4 is believed to hydroxylate a methyl group, leading to rearrangement to a methoxy substituent, and hydrolysis of the ring. These intermediates should not be sufficiently reactive to irreversibly inhibit the enzyme by covalent modification. The dose response for suicide inhibition should also reflect the rate of production of the metabolite rather than D4 concentration as required to fit the inhibition dose response.
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Regional Induction
Accounting for regional induction leads to predictions of markedly nonlinear dose response behavior in the low dose region. TCDD induction of CYP1A1 had an N value of close to 1.0 when modeled as a homogenous liver (Tritscher et al., 1992). However, intercompartmental N values of 4 are necessary to simulate regional induction in a 5-compartment liver (Andersen et al., 1997a
). Our analysis of the PB induction data in vitro and in vivo also demonstrates that PB-type induction is nonlinear, whether it is modeled as a homogenous or regional process. For D4, the Hill coefficient of 1.9 for the 1-compartment model leads to considerable nonlinearity at low doses even for the homogenous liver. The 5-compartment liver model uses a large Hill coefficient (N = 4.0) to achieve a demarcation of responses among the compartments. The interpretation of the Hill coefficient in these two formulations differs subtly. For the 1-compartment model, the Hill coefficient of close to 2.0 indicates a level of cooperativity between binding proteins or other components in the promotional machinery for the CYP2B1/2 genes. In the 5-compartment description, Andersen and coworkers have interpreted the high N-value as representing a switching process initiated by interaction of the ligand with the receptor protein that sets off a cascade of processes in the cell (Andersen et al., 1997a
).
With the modeling of tetrachlorodibenzo-p-dioxin, the 5-compartment liver model provided a better fit to the regional induction and to the low dose induction of the CYP 1A1 message (Andersen et al., 1993). As parameterized for D4, the 5-compartment model underestimates the amount of CYP2B1/2 at the lowest dose while the 1-compartment model provides a good fit (Fig. 10
). One explanation is that the fraction of the liver induced in the 5-compartment model at the lowest dose is lower than the observed amount, a reflection of the current distribution of compartment sizes and Kds associated with them. While the 5-compartment model could be optimized to fit the data, such optimization requires quantitative data on compartment level regional induction (Fig. 1
) that is not available.
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Comparative Dose Response
Expressing doses normalized to a given response level facilitates comparison of dose-response relationships in a selected portion of a dose-response curve. The effective dose that results in a 10% (ED10) or 1% (ED01) response can be used to compare unit potency within and below the range of experimental observation, respectively. Benchmark dose methods currently employed for risk assessment use empirical fitting to estimate effective doses, relying on extrapolations to estimate effective doses below the range of the observed data (EPA, 1996). Data in the experimental range (ED10 and higher) are used to guide extrapolation below the ED10, but the approach is statistical in nature rather than being informed by explicit knowledge of the underlying biological processes that determine the shape of the curve in the low-dose region. An alternative approach for calculating ED01 values is to use PBPK/PD models. The basal level of CYP2B1/2 in the liver in control rats is approximately 15 AUC/µg protein and the maximum measured CYP2B1/2 in rats exposed to high concentrations of D4 is approximately 600 AUC/µg protein. A 1% increase in induction is then equal to the basal protein level plus 1% of dynamic range (maximum protein level basal protein level). Hence, ED01 is the D4 exposure that will result in a CYP2B1/2 of 15 + 6 or 21 AUC/µg protein.
The effective dose causing a specified increase in response differs for the 1- and the 5-compartment liver models (Table 3). The 5-compartment liver model predicts a larger nonlinearity in the response at low dose compared to the 1-compartment liver model, resulting in a larger effective dose for a given response. Empirical benchmark analysis using a Hill-type model fit to the induction data resulted in a lower ED as compared to those estimated using the 5-compartment liver PBPK/PD model. The model-based approach for calculating effective dose is an improvement over empirical models because it captures nonlinearities in the underlying biological processes to predict low-dose behavior. Our best representation of the fundamental processes, such as receptor occupancy and basal and maximal induction rates, rather than the statistical approach, determine the shape of the dose response in the low-dose region.
Uncertainties in Pharmacodynamic Representations of Biological Responses
These physiologically based PD models for enzyme induction by D4 attempt to convert qualitative hypotheses regarding modes of action into quantitative structures that can be evaluated, compared to available data and used to design targeted research. These models have several layers of uncertainty: the most important of which is the question of whether the model structure itself is actually correct. The present work includes specific components to evaluate fat concentrations, to assess mechanisms of inhibition of enzyme activity, and to assess mechanisms of total and regional induction in the liver. In each area alternate model forms were evaluated.
For fat concentrations, the original PBPK model that assumed constant fat compartment volume over time, while liver compartment volumes changed, slightly underestimated fat concentrations. The parameters that influence the achieved D4 concentration in fat are fat compartment volume and fat:blood partition coefficient. The alteration in volume in concert with liver volume changes is a more tenable idea than alterations in fat:blood partitioning in an otherwise time-invariant compartment. The basic assumption of contrasting changes in fat and liver compartment might be more readily examined in studies with inducers that cause larger increases in liver weight, such as peroxisomal proliferators (Van Rafelghem et al., 1987), or by carefully evaluating lipid content of the body minus liver in rats after PB-type induction. While the overall improvement in fit to fat data is relatively small, the ability to draw inferences with tissue concentration data with such small standard deviations makes these exercises especially valuable with a highly lipophilic compound such as D4.
The modeling of PROD inhibition represents implementation of several hypotheses regarding modes of inhibition coupled with considerations of biological plausibility. The favored mode of action relates to potential inhibition by D4 that is carried over in preparation of microsomal samples. This hypothesis can be tested by adding D4 to microsomes derived from PB-treated rats before conducting PROD determinations. The requirement in the PBPK model to include deep tissue stores for D4 in the liver is consistent with sequestration of D4 in cell membranes and potential for carryover to microsomal preparations.
Undoubtedly, the most significant decisions in our PD modeling effort with D4 was implementation of the presumed receptor-based mode of action of D4 and the comparison of a 1- and 5-compartment description of the liver acinus. The similarities with the induction pattern with PB, the degree of liver enlargement, and the similarity in the Hill term for overall induction by D4 and PB are all consistent with a PB-like response working through the same CAR receptor that regulates PB responses. While full induction requires many steps, our receptor model captures the binding interactions as the primary determinant of the dose response. More detailed studies, especially with in vitro preparations, should add more molecular detail to this macroscopic formulation of induction. The present PD model did clearly show that the maximal induction for PB and D4 were similar, although initial empirical estimates would have presumed D4 to have a lower maximal induction.
The receptor-based gene induction model was then embedded into a larger model to evaluate responses of the entire liver. Which macroscopic liver model is preferred? The most obvious visual characteristic of induction is the nonuniform response with increasing dose. This behavior cannot be reproduced in any description that regards the liver as a single, uniformly responding organ. The multicompartment model (Andersen et al., 1997b) was developed because of the regional induction noted with Ah receptor regulated induction. This model was motivated by a desire to have a description that provided a smooth increase in induced area as dose increased with a minimum number of parameters. Five compartments provided the ability to describe relatively smooth induction (at three compartments the induction looks more stair stepped) with only two parameters, the binding affinity in compartment 3 and the difference in affinity between adjacent compartments.
The restriction that the relative affinities between compartments are fixed limits the freedom of the model to fit the low dose data point even though this model worked better than a 1-compartment model for dioxin. Despite the difficulty with the single data point, a multicompartment model is clearly essential to provide descriptions that recapitulate regional induction (Fig. 1). Future studies of regional induction that utilize a variety of in vivo methods to visualize and quantify induction in individual cells and in specific regions in vivo will be important for extending this multicompartment model and to make it more flexible. At this point, manipulation of compartment parameters to fit the single point does not seem warranted until more molecular information on induction is accrued by in vitro and in vivo methods.
The largest mechanistic uncertainty with this regional induction model is in the decision to alter Kd throughout the five regions to achieve variable sensitivity. Other changes could be made to achieve the same end, such as variability in receptor concentrations or CAR receptor protein interaction with other transcriptional actors. However, each has the same mechanistic uncertainty. Our belief is that resolution of these uncertainties will require new methods to examine responses of individual cells undergoing induction (French et al., 2002). The real issue with the single and multicompartment liver induction models is in the process of induction. The former regards induction as a smooth dose-response curve where individual cells have all possible levels of induction. The latter is based on the premise that induction occurs as an all-or-none switch, moving the cell to a new phenotypic state. The regional induction data argues persuasively for the latter type model. The details now need to be examined by appropriate molecular studies.
Model development includes trial and error; the final model is the one most consistent with both the available experimental data and current mechanistic information on the biological processes under investigation. Our model reflects a working hypothesis regarding induction in the intact rat. Modeling the observed regional pattern of hepatic CYP2B1/2 induction clearly requires a multicompartment liver model. However, the hypothesis that Kds are different in the various compartments is a possibility that needs confirmation. Despite some uncertainties at the molecular level of detail, the model presented here is completely consistent with the experimental data on induction on whole liver and regional basis and reflects our best understanding of the biochemical processes controlling induction.
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APPENDIX |
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![]() | ((A-1)) |
![]() | ((A-2)) |
Here ksui is a second-order rate constant for suicide inhibition, and ß is proportionality constant relating the protein activity units to the amount of protein. Since [CYP]inactive is zero in control animals (i.e., when D4 exposure concentration is zero), ß is the ratio of the baseline PROD activity to the baseline CYP2B1/2.
In the 5-compartment liver model, induction of CYP2B1/2 and the PROD activity in each of these compartments is directly related to the free D4 concentration in the respective compartment, similar to those described for the single compartment liver. Hence the rate of change of CYP2B1/2 protein and PROD activity with suicide inhibition in any given liver compartment is given by the following equations:
![]() | ((A-3)) |
![]() | ((A-4)) |
![]() | ((A-5)) |
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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Andersen, M. E., Birnbaum, L. S., Barton, H. A., and Eklund, C.R. (1997a). Regional hepatic CYP1A1 and CYP1A2 induction with 2,3,7,8-tetrachlorodibenzo-p-dioxin evaluated with a multi compartment geometric model of hepatic zonation. Toxicol. Appl. Pharmacol. 144, 145155.[ISI][Medline]
Andersen, M. E., Eklund, C. R., Mills, J. J., Barton, H. A., and Birnbaum, L. S. (1997b). A multi compartment geometric model of the liver in relation to regional induction of cytochrome P450s. Toxicol. Appl. Pharmacol. 144, 135144.[ISI][Medline]
Andersen, M. E., Mills, J. J., Gargas, M. L., Kedderis, L., Birnbaum, L. S., Neubert, D., and Greenlee, W. F. (1993). Modeling receptor-mediated processes with dioxin: Implications for pharmacokinetics and risk assessment. J. Risk Anal. 13, 2536.
Andersen, M., Sarangapani, R., Reitz, R. H., Gallavan, R. H., Debrev, I. D., and Plotzke, K. (2001). Physiological modeling reveals novel pharmacokinetic behavior for inhaled octamethylcyclotetrasiloxane in rats. Toxicol. Sci. 60, 214231.
Angelo, M. J., and Pritchard, A. B. (1984). Simulations of methylene chloride pharmacokinetics using a physiologically based model. Regul. Toxicol. Pharmacol. 4, 329339.[Medline]
Bars, R. G., Bell, D. R., Elcombe, C. R., Oinonen, T., Jalava, T., and Lindros, K. O. (1992). Zone-specific inducibility of cytochrome P450 2B1/2 is retained in isolated perivenous hepatocytes. Biochem. J. 282, 635638.[ISI][Medline]
Carthew, P., Edwards, R. E., and Nolan, B. M. (1998). The quantitative distinction of hyperplasia from hypertrophy in hepatomegaly induced in the rat liver by phenobarbital. Toxicol. Sci. 44, 4651.[Abstract]
De-Oliveira, A. C., Ribeiro-Pinto, L. F., and Paumgartten, J. R. (1997). In vitro inhibition of CYP2B1 monooxygenase by ß-myrcene and other monoterpenoid compounds. Toxicol. Lett. 92, 3946.[ISI][Medline]
EPA (1996). Proposed Guidelines for Carcinogen Risk Assessment. Office of Research and Development, Washington, DC.
French, C. T., Chubb, L. S., Billings, R., and Andersen, M. E. (2002). Developing in vitro model systems to evaluate switch-like behaviors of hepatocytes in response to various enzyme inducers. Toxicologist.
Kocarek, T. A., Schuetz, E. G., and Guzelian, P. S. (1990). Differentiated induction of cytochrome P450b/e and P450p mRNAs by dose of phenobarbital in primary cultures of adult rat hepatocytes. Mol. Pharmacol. 38, 440444.[Abstract]
Kohn, M. C., Lucier, G. W., Clark, G. C., Sewall, C., Tritscher, A.M., and Portier, C. J. (1993). A mechanistic model of the effects of dioxin on gene expression in the rat liver. Toxicol. Appl. Pharmacol. 120, 138154.[ISI][Medline]
Lilly, P. D., Thornton-Manning, J. R., Gargas, M. L., Clewell, H. J., and Andersen, M. E. (1998). Kinetic characterization of CYP2E1 inhibition in vivo and in vitro by the chloroethylenes. Arch. Toxicol. 72, 609621.[ISI][Medline]
McKim, J. M., Jr. (1998). Effects of Repeated Whole Body Inhalation Exposure to Octamethylcyclotetrasiloxane (D4) Vapors on Hepatic Microsomal CYP2B1/2 Induction in Female Fischer 344 Rats: A Dose-Response Study. Internal report, available on request from Dow Corning Corporation, Midland, MI.
McKim, J. M., Jr., Kolesar, G. B., Jean, P. A., Meeker, L. S., Wilga, P. C., Schoonhoven, R., Swenberg, J. A., Goodman, J. I., Gallavan, R. H., and Meeks, R. G. (2001). Repeated inhalation exposure to octamethylcyclotetrasiloxane produces hepatomegaly, transient hepatic hyperplasia, and sustained hypertrophy in female Fischer 344 rats in a manner similar to phenobarbital. Toxicol. Appl. Pharmacol. 172, 8392.[ISI][Medline]
Nims, R., Sinclair, P., Sinclair, J. F., Thomas, P. E., Jones, C. R., Mellini, D. W., Syi, J. L., and Lubet, R. A. (1993). Pharmacodynamics of cytochrome P450 2B induction by phenobarbital, 5-ethyl-5-phenylhydantoin, and 5-ethyl-5-phenyloxazolidinedione in the male rat liver or in cultured rat hepatocytes. Chem. Res. Toxicol. 6, 188196.[ISI][Medline]
Parkinson, A., Thomas, P. E., Ryan, D. E., Reik, L. M., Safe, S. H., Robertson, L. W., and Levin, W. (1983). Differential time course of induction of rat liver microsomal cytochrome P-450 isozymes and epoxide hydrolase by Aroclor 1254. Arch. Biochem. Biophys. 225, 203215.[ISI][Medline]
Peng, J., Singh, A., Ireland, W. P., and Chu, I. (1997). Polychlorinated biphenyl congener 153-induced ultrastructural alterations in rat liver: A quantitative study. Toxicology 120, 171183.[ISI][Medline]
Shiraki, H., and Guengerich, F. (1984). Turnover of membrane proteins: Kinetics of induction and degradation of seven forms of rat liver microsomal cytochrom P-450, NADPH-reductase and epoxide hydrolase. Arch. Biochem. Biophys. 235, 8696.[ISI][Medline]
Stark, F., Falender, J., and Wright, A. (1982). Silicones. In Comprehensive Organometallic Chemistry: The Synthesis, Reactions and Structures of Organometallic Compounds (G. Wilkinson, F. Gordon, A. Stone, and E. Abel, Eds.), pp. 306360. Pergamon Press, New York.
Sweeney, G. D., Jones, K. G., and Krestynski, F. (1978). Effects of phenobarbital and 3-methylcholanthrene pretreatment on size, sedimentation velocity, and mixed function oxygenase activity of rat hepatocytes. J. Lab. Clin. Med. 91, 444454.[ISI][Medline]
Tritscher, A. M., Goldstein, J. A., Portier, C. J., McCoy, Z., Clark, G. C., and Lucier, G. W. (1992). Dose-response relationships for chronic exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin in a rat tumor promotion model: Quantification and immunolocalization of CYP1A1 and CYP1A2 in the liver. Cancer Res. 52, 34363442.[Abstract]
Vanden Heuvel, J. P., Clark, G. C., Kohn, M. C., Tritscher, A. M., Greenlee, W. F., Lucier, G. W., and Bell, D. A. (1994). Dioxin-responsive genes: Examination of dose-response relationships using quantitative reverse transcriptase-polymerase chain reaction. Cancer Res. 54, 6268.[Abstract]
Van Rafelghem, M. J., Mattie, D. R., Bruner, R. H., and Andersen, M. E. (1987). Pathological and hepatic ultrastructural effects of a single dose of perfluoro-n-decanoic acid in the rat, hamster, mouse, and guinea pig. Fundam. Appl. Toxicol. 9, 522540.[ISI][Medline]
Wang, X., Santostefano, M. J., Evans, M. V., Richardson, V. M., Diliberto, J. J., and Birnbaum, L. S. (1997). Determination of parameters responsible for pharmacokinetic behavior of TCDD in female Sprague-Dawley rats. Toxicol. Appl. Pharmacol. 147, 151168.[ISI][Medline]
Waxman, D. J. (1999). P450 gene induction by structurally diverse xenochemicals: Central role of nuclear receptors CAR, PXR, and PPAR. Arch. Biochem. Biophys. 369, 1123.[ISI][Medline]