Application of a Physiologically Based Pharmacokinetic Model to Estimate the Bioavailability of Ethanol in Male Rats: Distinction between Gastric and Hepatic Pathways of Metabolic Clearance

Gina M. Pastino*,1 and Rory B. Conolly{dagger}

* Schering Plough Research Institute, P.O. Box 32, Lafayette, New Jersey 07848; and {dagger} Chemical Industry Institute of Toxicology, Research Triangle Park, North Carolina 27709

Received September 29, 1999; accepted January 24, 2000


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
A portion of ingested ethanol does not reach the systemic circulation in both rats and humans as indicated by higher blood ethanol concentrations following an intravenous administration compared to an equivalent oral administration. The mechanism for this decrease in the oral bioavailability is not yet completely understood. Metabolism by gastric or hepatic alcohol dehydrogenase (ADH), or both, has been implicated. However, the extent to which each pathway of elimination contributes to the first-pass clearance is not known. The purpose of this study was to utilize a physiologically based pharmacokinetic (PBPK) model for ethanol to estimate the relative contributions of hepatic and gastric metabolic clearance to the oral bioavailability of ethanol in male rats. In the current model, calculations of hepatic-first pass metabolic clearance accounted for the competition for metabolism between incoming ethanol from the GI tract and recirculating ethanol. This differs from previous methods that quantified the effect of ethanol entering the liver from the GI tract on the overall rate of metabolism of ethanol by the liver. These models did not specifically describe the effect of recirculating ethanol on the first-pass metabolism of ethanol, and vice versa. The dependence of bioavailability on dose and absorption rate was also investigated. The use of a PBPK model for ethanol in rats allows a more detailed examination of physiological and biochemical factors affecting the bioavailability of ethanol than has previously been possible. The analysis indicates that both gastric and hepatic first-pass metabolism of ethanol contribute to ethanol bioavailability in male rats.

Key Words: blood ethanol concentrations; gastric and hepatic alcohol dehydrogenase; ethanol metabolism; PBPK modeling.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Despite the fact that much is known regarding the pharmacokinetics of ethanol, central issues are still not completely understood. Of particular interest is the extent of first-pass metabolic clearance of orally administered ethanol because interindividual variability in this clearance potentially contributes to susceptibility to the toxic effects of ethanol. For example, women are more susceptible to many of the adverse effects of chronic ethanol exposure. The cumulative dose of ethanol required to elicit cirrhosis in women is less than the dose required to elicit the same response in men (Mezey et al., 1988Go). Moreover, the time period for consumption of ethanol resulting in chronic pancreatitis is shorter for women than men (Mezey et al., 1988Go). A possible mechanism for the increase in susceptibility among women includes greater bioavailability due to less first-pass metabolic clearance (Frezza et al., 1990Go). Women have lower levels of gastric alcohol dehydrogenase (ADH) and metabolize less ethanol during the first pass through the gut (Frezza et al., 1990Go). Thus, a greater dose reaches the systemic circulation in women.

In addition, several factors have been shown to affect the bioavailability of ethanol that may influence toxicity. For example, administration of ethanol during fasting increases the bioavailability as compared to administration during the fed state (DiPadova et al., 1987Go; Gentry et al., 1992Go). That is, blood ethanol concentrations (BEC) are higher in the fasted state than when ethanol is given with food. The concentration of ethanol administered can also alter the bioavailability. Humans who ingested 4% ethanol had higher BEC than those who ingested 10% ethanol (Sharma et al., 1993Go). Similar effects of concentration have been found in rats (Roine et al., 1991Go). The effect of the prandial state and concentration on bioavailability is, in part, mediated by altered absorption rates. Fasting and alcohol dilution are associated with faster gastric emptying (i.e., increased absorption rate), which in turn affects first-pass metabolic clearance (Gentry et al., 1994Go).

Blood ethanol concentrations (BEC) in rats following a dose of 1.0 g/kg (Lim et al., 1993Go), and in humans following a dose of 0.15 g/kg (Julkunen et al., 1985Go) illustrate the first-pass metabolism (FPM) of ethanol associated with oral ingestion. BEC are much higher following an intravenous (iv) administration when compared to an equivalent oral administration. Although FPM typically refers to loss due to hepatic metabolism, gastric metabolism has also been implicated in the decrease in bioavailability of ethanol. The terms gastric FPM (FPMG) and hepatic FPM (FPMH) have been introduced by several authors to provide the distinction (Lim et al., 1993Go; Roine et al. 1991Go). The relative contribution of each pathway in either rats or humans is not known with certainty and is the main focus of this paper.

Lim et al. (1993) proposed that the FPM of orally administered ethanol is due primarily to gastric ADH. Gastric ADH activity has been measured in rats (Lamboeuf et al., 1981Go; Caballeria et al., 1987Go), mice (Algar et al., 1983Go), and humans (Hempel and Peitruszko, 1975). Histamine2-blockers, such as cimetidine, inhibit gastric ADH activity in vitro (Palmer, 1987Go) and also increase BEC, suggesting that metabolism by gastric ADH is at least partly responsible for the first-pass clearance (Caballeria et al., 1989Go).

Despite the evidence regarding gastric FPM, other reports have implicated the liver as the primary site of FPM and also suggest dependency of bioavailability on the rate of ethanol absorption (Smith et al., 1992Go). Levitt and Levitt (1994) used a 2-compartment model developed for human males to illustrate that the FPM of ethanol was a result of hepatic metabolism and that gastric ADH did not contribute significantly to FPM. Their model also illustrated the dependency of first-pass clearance on the absorption rate of ethanol. The results from a pharmacokinetic model developed by Derr (1993) agree with those of Levitt and Levitt (1994).

FPM has been quantified by comparisons of the ratio of the blood area under the curve (AUC) following oral versus intravenous (iv) or intraperitoneal (ip) administration of ethanol (DiPadova et al., 1987Go). Another approach has been to quantify the total amount (mg) of ethanol absorbed following an oral versus an iv route of administration, with the difference being equal to the amount of FPM (Lim et al., 1993Go; Roine et al., 1991Go). Lim and coworkers (1993) administered ethanol by routes that bypassed the stomach (i.e., intraduodenal and intraportal), and found BEC equivalent to those obtained following an iv administration, thereby implicating the gastric mucosa as the primary site of FPM.

The reasons for the inconsistent results of the studies described above are not entirely clear. The use of invasive techniques may have been a factor. Animals were administered ethanol by the intraduodenal or intraportal route (Lim et al., 1993Go) or via isolated liver perfusions (Matsumoto et al., 1994Go). These techniques require the use of anesthetics other than ethanol that can potentially influence results. In addition, the use of blood AUC may not accurately estimate bioavailability for chemicals whose metabolism can be saturated. When the rate of metabolism is first order, the bioavailability of a chemical is typically measured by blood AUCOral:AUVIV. However, when metabolism is pseudo-zero order, an increase in dose does not result in a proportional increase in blood AUC. That is, if the dose is doubled, the blood AUC is not necessarily doubled. Comparisons of AUC are appropriate only when BEC are low and the rate of metabolism is proportional to BEC. Ethanol metabolism is saturated at pharmacologically relevant doses (i.e., doses typically consumed by social drinkers and alcohol abusers). Therefore, blood AUCOral:AUCIV may not provide an accurate estimate of bioavailability under exposure scenarios of clinical relevance.

Physiologically based pharmacokinetic (PBPK) modeling (Pastino et al., 1997Go) has recently been used to characterize the disposition of ethanol. In contrast to classical pharmacokinetic methods, PBPK models take into consideration anatomical and physiological processes (tissue volumes and blood flows) as well as biochemical (metabolic rate constants) and physiochemical (partition coefficients) properties of the specific chemical (Clewell and Andersen, 1985Go; Himmelstein and Lutz, 1979Go). These characteristics allow for a more biologically based approach to quantifying FPM and bioavailability of ethanol than classical methods. A PBPK model can also be used to characterize the dosimetry of ethanol under a variety of conditions that may alter the bioavailability. The purpose of this study was to utilize a physiologically based pharmacokinetic (PBPK) model for ethanol to estimate the relative contributions of hepatic and gastric metabolic clearance to the oral bioavailability of ethanol in male rats.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
PBPK Model Theoretical Development.
The blood flow limited PBPK model for ethanol previously developed for mice (Pastino et al., 1996bGo) and rats (Pastino et al., 1997Go) was extended to include oral administration in rats. Compartments for the present model include liver, stomach, brain, fat, rapidly perfused tissue, slowly perfused tissue, and blood (Fig. 1Go). Mass-balance equations were written describing the rate of change in ethanol concentration for each compartment, as described by Pastino et al. (1996b, 1997).



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FIG. 1. Schematic diagram of the PBPK model for ethanol in the male rat.

 
Mass-balance hepatic metabolism of ethanol was quantified by a Michaelis-Menten expression for hepatic ADH included in the equation describing the rate of change of ethanol in the liver (dAL/dt):

(1)
where QL is the blood flow to the liver (l/h), CA is the concentration of ethanol in the arterial blood entering the liver (mg/l), CVL is the concentration of ethanol in the venous outflow from the liver (mg/l), VmaxH (mg/h) and KMH (mg/l) are the Michaelis-Menten constants for metabolism by hepatic ADH, KaS is the first order oral absorption rate constant (hr–1), and AS is the amount of ethanol in the stomach (mg). Under the experimental conditions of the studies used in the development of this model, it is appropriate to describe the absorption of ethanol as first order (Holford, 1987Go).

The rate of change of ethanol in the stomach is a function of the rate of absorption and gastric metabolism, as given by:

(2)
where VmaxG and KMG are the Michaelis-Menten constants for gastric ADH metabolism and CGMuc is the concentration of ethanol in the gastric mucosa. The equation used to estimate CGMuc was:

(3)
where AS is the amount of ethanol in the stomach (mg), VS is the stomach volume (l), and PML is the fraction of ethanol in the gastric mucosa relative to the amount of ethanol in the lumen of the stomach.

The typical tissue:blood partition coefficient, which normally describes the ratio of the chemical in the tissue relative to that in the blood, is not appropriate, because the partitioning of ethanol is dependent on the volume of the stomach and changes with time. Previous research estimated that the concentration of ethanol at the active site of gastric ADH is 4% of the concentration of ethanol in the stomach lumen (Smith et al., 1992Go). However, Pastino et al. (1996a) reported that the concentration of ethanol in the gastric mucosa exceeded 4% and changed over time relative to the amount in the stomach lumen. In the study by Pastino et al. (1996a), rats were administered 1.0 g/kg (16% w/v) ethanol orally and the ratio of ethanol in the gastric mucosa to the stomach lumen (i.e., stomach contents) was measured at several time points following the administration. This ratio is equivalent to PML (Equation 3Go). A nonlinear regression analysis of these data presented by Pastino et al. (1996a) provided the following equation:

(4)
where T is time (h). The upper bound on PML was set equal to 0.8. That is, at time zero, PML was set to 0.8, the maximum value measured by Pastino et al. (1996b). However, the ratio ranged from 0.8 to 0.04 (Pastino et al., 1996bGo). Equation 4Go was used in the model to calculate the concentration of ethanol at the active site of gastric ADH at any given time after a bolus oral dose (CGMuc, Equation 3Go).

The experimental data used in this model development were BEC obtained from rat tail blood. Previous research demonstrated that during periods of rising and declining ethanol levels, the concentration in the tail lagged behind the arterial, jugular or femoral vein blood (Levitt et al., 1994). Concentrations in the tail blood are not equivalent to the pooled venous blood concentration at early time points after bolus dosing. The equation must take into account the rate of transfer into the tail blood. Thus, the concentration of ethanol in the tail blood (CVTail) was described by:

(5)
where KTail is a first-order constant (hr–1), CVPooled is the pooled venous blood concentration.

The blood flows and tissue volumes for each compartment (Table 1Go) were obtained from the report prepared by the International Life Sciences Institute, Risk Science Institute, the United States Environmental Protection Agency on "Physiological Parameter Values for PBPK Models" (International Life Science Institute, 1994). The ethanol partition coefficients for rats were determined by Kaneko et al. (1994). The kidney:blood partition coefficient was used for the rapidly perfused compartment, and the skeletal muscle:blood partition coefficient was used for the slowly perfused compartment. The absorption and metabolic rate constants were determined as outlined below.


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TABLE 1 Ethanol-specific Parameters Used in the PBPK Model for Ethanol in the Male Sprague-Dawley Rat
 
The mass-balance equations were solved simultaneously using a Dell Latitude computer with an Intel Pentium processor (Dell Computers, Round Rock, TX) and the software package ACSL Tox for Windows (Pharsight Inc., Mountain View, CA). ACSL Tox is designed for modeling continuous systems that can be described by time-dependent, nonlinear, differential equations and includes optimization capabilities. The average time to run a simulation using this system was less than 1 min. For parameter estimation, the Nelder-Mead algorithm was used to maximize the likelihood function defined in ACSL Tox.

Rate Constants for Metabolism by Alcohol Dehydrogenase in Male Rats.
Mammalian ADH exists in multiple molecular forms (Agarwal and Goedde, 1990Go; Bosron and Li, 1986Go; Kedishvili et al., 1995Go). There are several isozymes of rat ADH, specifically ADH1, ADH2, and ADH3. ADH3 is primarily responsible for ethanol metabolism in the liver whereas ADH1 is responsible for ethanol metabolism in the stomach (Julia et al., 1987Go). While each enzyme is comprised of two active subunits and require zinc and NAD+ for metabolic activity, the kinetic properties of each isozyme differ. For example, the isoelectric points are 5.1 and 8.25–8.4 for ADH1 and ADH3, respectively (Julia et al., 1987Go). In addition, the KM for ethanol oxidation differs significantly. The potential for each isozyme to contribute to the in vivo elimination of ethanol is therefore different. In the interests of model parsimony, only a single Michaelis-Menten pathway was described in the gastric and hepatic compartments. Attempting to describe multiple pathways in each compartment would not be a useful exercise given the extent of the data available for parameter estimation. The metabolic pathways described in the current model can thus be thought of as representing the average metabolic behavior of the enzymes capable of metabolizing ethanol.

The hepatic and gastric ADH KMs utilized in the PBPK model were experimentally determined by Caballeria et al. (1989; 23 mg/L and 18,400 mg/L, respectively). The remaining rate constants were optimized against experimental BEC and included KTail, VmaxH, KaS and VmaxG (Table 2Go). KTail and VmaxH were optimized against BEC following an iv administration of 250 mg/kg [Caballeria et al., 1989; numerical data was kindly provided by Joan Caballeria (personal communication)] and 1.0 g/kg (Lim et al., 1993Go). The model was coded to account for the multi-step iv infusion rate used by Lim et al. (1993). In this study, 50% of the dose (1.0 g/kg) was administered in the first 15 min, 25% given over the next 15 min, 12.5% given over the following 90 min, and the final 12.5% over the last 240 min. The total infusion time was 6 h.


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TABLE 2 Summary of Data Used to Optimize for Kinetic Parameters
 
Once KTail and VmaxH were obtained, the PBPK model was used to optimize for KaS and VmaxG. The data used for the determination of KaS and VmaxG were BEC following an oral administration of 1.0 g/kg (Lim et al., 1993Go; Roine et al., 1991Go) and 250 mg/kg (Caballeria et al., 1989Go). The Caballeria et al. (1989) data were obtained from rats pretreated with cimetidine, an inhibitor of gastric ADH (Palmer, 1987Go), and in control animals receiving ethanol only. For simulations using the data from animals pretreated with cimetidine, VmaxG was set to zero.

Calculation of Gastric First Pass Metabolism of Ethanol.
The first-pass clearance of orally administered ethanol results from metabolism by gastric ADH, which occurs prior to absorption from the gastrointestinal (GI) tract into the liver, and metabolism by the liver through the first pass prior to absorption into the systemic circulation. The amount of gastric ethanol metabolism (AMG; mg) was calculated using the PBPK model, through integration of the Michaelis-Menten expression, for metabolism by gastric ADH (Equation 2Go). Gastric FPM, represented as a percentage of the administered dose, was then calculated by:

(6)

Calculation of Hepatic First-Pass Metabolism of Ethanol.
In order to calculate FPMH, a distinction between the two sources of hepatic ethanol metabolism was made. The assumptions of the PBPK model are that the liver is well mixed and delivery of ethanol to the liver is blood-flow limited. Ethanol enters the liver from the GI tract and as recirculating ethanol. Regardless of how ethanol reaches the liver, it is metabolized by a saturable system having Michaelis-Menten kinetics characterized by Vmax and KM. The total rate of hepatic ADH metabolism (RAMT; mg/h) is described by:

(7)
where RAMR – L is the rate of metabolism of recirculating ethanol (mg/h) and RAMGI – L is the rate of metabolism of ethanol entering the liver from the GI tract (mg/h). RAMGI – L is the rate of first pass hepatic metabolic clearance.

Given that the liver is assumed to be well mixed, ethanol entering the liver by one route competes for metabolism of ethanol entering by another route. The rate of metabolism by each pathway (i.e., RAMR – L and RAMGI – L) can therefore be described by the equation for competitive inhibition (York, 1997Go). In describing the rate of metabolism of ethanol entering the liver from the GI tract, the substrate concentration is the concentration of ethanol in the liver resulting from newly absorbed ethanol from the GI tract, and the concentration of the inhibitor is the concentration of recirculating ethanol. The inhibitor affinity constant is the hepatic ADH KMH for ethanol oxidation. The rate of metabolism of ethanol entering the liver from the GI tract is estimated by:

(8)
where CVGI-L is the liver venous blood concentration of ethanol that results from ethanol received from the GI tract (mg/l), VmaxH (mg/h) and KMH (mg/l) are the Michaelis-Menten constants for metabolism by hepatic ADH, and CVR – L (mg/l) is the liver venous blood concentration resulting from recirculating ethanol. The total liver venous BEC is:

(9)
Therefore, Equation 8Go reduces to:

(10)
Integration of Equation 10Go provides the amount of ethanol metabolized by hepatic ADH during the first pass through the liver (AMH; mg), and was used to calculate the hepatic FPM of ethanol:

(11)

Calculation of the Oral Bioavailability of Ethanol.
The bioavailability, represented as a percentage of the total dose administered (BIO), was calculated as:

(12)
The bioavailability of ethanol was represented as a percentage of the administered dose since the bioavailability is the amount of administered ethanol that reaches the systemic circulation and is not specific to a particular organ (e.g., liver or gut). When estimating the contribution of an organ to the overall metabolic clearance, FPM is calculated relative to the amount presented to the organ. In the case of hepatic FPM, the amount presented to the liver is the amount of dose absorbed, not the dose administered. For this reason, the decrease in oral bioavailability is not equivalent to the sum of the gastric and hepatic FPM.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The ethanol pharmacokinetic parameters used in the model were either obtained from the literature or were optimized against previously published experimental data (Table 1Go). The KMH (23 mg/L) and KMG (18,400 mg/L) were experimentally determined by Caballeria et al. (1989). Table 2Go provides an outline of the data utilized in optimizing for the remaining parameters, which included the VmaxH, VmaxG, KaS and KTail. Experimental tail BEC following an iv administration of 250 mg/kg (Figure 2Go, top panel) and 1000 mg/kg (Figure 3Go, top panel) were used to optimize for KTail and VmaxH. Experimental tail BEC following an oral administration of 250 mg/kg (Figure 2Go, bottom panel) and 1000 mg/kg (Figure 3Go, bottom) were used to optimize for KaS and VmaxG with the values for KTail and VmaxH that were estimated from the iv data.



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FIG. 2. Experimental versus simulated blood ethanol concentrations in male rats following an iv (top) and oral (bottom) administration of 250 mg/kg. The experimental data are from Caballeria et al. (1989) and are the mean ±SEM from 6 male Sprague-Dawley rats. In this study, animals were administered either 50 mg/kg cimetidine or saline followed by administration of ethanol either orally or intravenously. The circles represent the data obtained in animals pretreated with cimetidine prior to ethanol exposure and the squares represent the data obtained in animals receiving ethanol only. Simulations of the data obtained from cimetidine treated animals were obtained in the absence of gastric ADH activity (i.e., VmaxG = 0) because cimetidine inhibits gastric ADH activity. The simulation of the iv data assumed an infusion time of 5 min.

 


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FIG. 3. Experimental versus simulated blood ethanol concentrations following an iv (top) and oral (bottom) administration of 1.0 gm/kg. The triangles are the experimental data from Roine et al. (1991), and the circles are the experimental data obtained from Lim et al. (1993). The experimental data from Roine et al. (1991) are the mean ±SEM from 7 rats, and the experimental data from Lim et al. (1993) are the means from 6 rats. The iv administration used in Lim et al. (1993) was a 4-step infusion rate over a 6-h time period. Fifty percent of the dose was administered in the first 15 min, 25% given over the next 15 min, 12.5% given over the following 90 min, and the final 12.5% over the last 240 min. The solid lines are the PBPK model simulations obtained using the body weights reported for each study: 0.315 kg for the Lim et al. (1993) study and 0.351 kg for the Roine et al. (1991) study. In each study, male Sprague-Dawley rats were administered 1000 mg/kg ethanol as a 16% (w/v) in saline by intragastric intubation.

 
The model predicted BEC obtained from animals pretreated with cimetidine, an inhibitor of gastric ADH (Fig. 2Go, bottom and top panels, squares). These simulations were obtained in the absence of gastric metabolism (i.e., the VmaxG was set to zero) while keeping all other parameters constant. That is, VmaxH, KMH and KaS were not changed from the simulations of data from animals receiving ethanol only (Fig. 2Go, top and bottom panels, circles), thereby strengthening the validity of the model.

The optimizations provided accurate simulations of the experimental data, with the possible exception of the BEC, following oral administration of 1000 mg/kg reported in Lim et al. (1993; Fig. 3Go, bottom panel, circles). It is unclear why the PBPK model overpredicted the Lim et al. (1993) data but not the Roine et al. (1991) data. The only differences between the experimental conditions in these studies was the body weight of the animals. The dose, concentration, and strain of rats were the same in both studies. The PBPK model accounted for the body weights when simulating each respective data set, as illustrated by the differences in the PBPK model simulations (Fig. 3Go, bottom panel). Although the specific ages of the animals were not specified in each study, the animals used in the Roine et al. (1991) study were purchased as adults whereas the animals used in the Lim et al. (1993) study were purchased as weanlings. It is possible that the age difference may have affected the rates of hepatic metabolism and therefore the BEC (Seitz et al., 1992Go). The simulations in Figures 2 and 3GoGo were obtained using a single hepatic ADH Vmax and did not account for possible age-dependent differences in hepatic metabolism.

The experimental data used for the optimization procedures were tail BEC obtained following both oral and iv administration. Previous research found that, during periods of rising and declining ethanol levels, tail BEC lag behind concentrations in the arterial, jugular, and femoral vein blood in rats (Levitt et al., 1994). This is due to the low blood perfusion to tissue water ratio in the tail. The simulations in Figure 4Go illustrate the discrepancy between pooled venous BEC and tail BEC following a bolus oral administration. At earlier time points, tail BEC are lower than pooled venous BEC. However, these concentrations eventually equilibrate.



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FIG. 4. Simulated blood ethanol concentrations following an oral administration. The solid line represents pooled venous blood and the dashed line represents the tail blood. Tail BEC were calculated using Equation 5Go.

 
The model was then applied to provide quantitative estimates of first pass metabolism and bioavailability of ethanol. As the simulated dose increased, gastric and hepatic FPM decreased as a fraction of dose (Fig. 5Go). In addition, PBPK model predictions of gastric FPM were higher than the model predictions of hepatic FPM except at the lowest simulated dose (100 mg/kg). At a simulated dose of 500 mg/kg, gastric FPM was predicted to be 26% of the administered dose, whereas hepatic FPM was predicted to be 12% of the administered dose. Simulation of an intermediate dose (1000 mg/kg) provided estimates of 22% and 5% for gastric and hepatic FPM, respectively. At the highest simulated dose (3000 mg/kg) gastric and hepatic FPM were predicted to be 15% and 2% of the administered dose, respectively.



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FIG. 5. Simulated dose-dependent effects on the first-pass metabolism of ethanol in male rats. Gastric FPM (triangles) was calculated using Equation 6Go and hepatic FPM (circles) was obtained using Equation 11Go.

 
The predicted dose- and absorption rate-dependent changes in gastric and hepatic FPM were reflected in predictions of bioavailability. In the simulated dose range (200 mg/kg to 1200 mg/kg), the predicted bioavailability increased as the dose and absorption rate increased (Fig. 6Go). The bioavailability did not reach 100% in the simulated dose range and was greatest at the highest dose with the fastest absorption rate.



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FIG. 6. Simulated effect of dose and absorption rates on the oral bioavailability of ethanol in male rats. The bioavailability was calculated using Equation 12Go.

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Previous research illustrated that a portion of ingested ethanol does not reach the systemic circulation, due to metabolism by gastric or hepatic ADH (Roine et al., 1991Go; Caballeria et al., 1987Go). The significance of this first pass clearance of ethanol is that it may play a protective role against the toxic effects of ethanol since it provides for presystemic elimination, thereby decreasing the amount of ethanol to which sensitive tissues are exposed. Gastric metabolism of ethanol reduces the dose of ethanol to which the liver is exposed. Hepatic metabolism further decreases the dose of ethanol to the remaining tissues, such as the brain. Consequently, first pass clearance is a mitigating factor in susceptibility to the toxicity of ethanol. To completely understand the possible protective effect of FPM, its site and the mechanism need to be fully elucidated. The purpose of this study was to refine a previously developed PBPK model for ethanol in male rats to quantify the relative contribution of gastric and hepatic FPM to the oral bioavailability of ethanol.

In calculating FPMH, the PBPK model provided a distinction between metabolism of incoming ethanol from the GI tract and metabolism of recirculating ethanol. Under the assumptions of a well-stirred liver, Michaelis-Menten elimination kinetics, and flow-limited delivery of ethanol, the ethanol entering the liver from the GI tract necessarily competes for metabolism with ethanol that has entered the liver through recirculation. When the duration of absorption from the GI tract is long with respect to the time it takes the blood to recirculate, the calculation of FPMH must take into account the competition with recirculating ethanol. Accordingly, at least some of the ethanol will be metabolized during the first pass through the liver, even when concentrations in the blood are high. Thus, the bioavailability should never reach 100%, as was illustrated by the PBPK model.

In addition, because hepatic and gastric metabolism were described as saturable processes, the predicted bioavailability should be dose-dependent. As the amount of ethanol administered increases, the capacity of the liver to metabolize it, relative to the amount presented to the liver, decreases, and more of the ethanol escapes metabolism during the first pass. An increase in bioavailability is therefore expected with an increase in the dose, based on the kinetic characteristics of gastric and hepatic ADH.

The calculated FPMH of ethanol is higher than previously found (Levitt and Levitt, 1994Go) and is probably due to the respective definition and the method for calculating FPM. The traditional definition of FPM is the removal of chemicals before entrance into the systemic circulation, typically by metabolism in the gut or liver (Rozman and Klaassen, 1996Go). However, Levitt and Levitt (1994) defined hepatic FPM as:

(13)
where VMAX and KM are the Michaelis-Menten constants for metabolism by hepatic ADH, CL is the total concentration of ethanol in the liver, and CLR is the concentration of ethanol in the liver resulting from recirculating ethanol. Thus, the first term is the total rate of liver metabolism and the second is the rate of metabolism if the only source of ethanol to the liver was from recirculating ethanol with no direct supply of newly absorbed ethanol from the gastrointestinal tract (Levitt and Levitt, 1994Go).MFPM as defined by Levitt and Levitt (1994; Equation 13Go) quantifies the effect of ethanol entering the liver from the GI tract on the overall rate of metabolism and therefore makes no provision for the competition between the ethanol entering the liver from the GI tract and recirculating ethanol. Accordingly, if the recirculating concentration of ethanol is high enough and metabolism is saturated, this equation specifies that there will be no additional metabolism of ethanol because it is already occurring at the maximum velocity. That is, at pharmacologically relevant doses (i.e., higher doses) the first and second terms approach equivalency, there is very little effect on the overall rate of metabolism of ethanol, and MFPM becomes negligible. In the current model, Equation 13Go was modified to account for the continual absorption of ethanol from the GI tract. When this is done, the first-pass clearance increases and bioavailability decreases. It is the portion of ingested ethanol undergoing first-pass clearance, due to gastric and hepatic metabolism, that is the relevant factor in understanding the relationship between ethanol consumption, exposure to target tissues, and biological effects.Levitt and Levitt (1994) have also suggested that in rats and humans the first-pass clearance of ethanol attributed to first-pass gastric metabolism might result from differences in the rate of ethanol absorption and its influence on hepatic metabolism. That is, based on the kinetic characteristics of hepatic ADH, the amount of ethanol cleared by the liver is extremely sensitive to changes in the absorption rate of ethanol. Furthermore, it is the increase in this absorption rate, as the dose increases, that results in a proportionally lower amount of ethanol metabolism in the liver. The PBPK model simulations agree with the suggestion that the differences in the absorption rate might explain, at least in part, the discrepancy between the estimates of the oral bioavailability by Levitt and Levitt (1994) and those of Roine et al. (1991). However, the model showed that even at a given absorption rate other factors, such as the dose and gastric metabolism, affect measurements of first-pass clearance and bioavailability.While some of the literature reports support a lack of effect of gastric metabolism on the oral bioavailability of ethanol (Derr, 1993Go; Levitt and Levitt, 1994Go), other reports indicate that gastric metabolism does play a role in bioavailability (Lim et al., 1993Go; Roine et al., 1991Go). These latter studies reported that FPMG is approximately 25 to 30% of the administered dose in the male rat (1000 mg/kg; Lim et al., 1993Go; Roine et al., 1991Go). At a simulated dose of 1000 mg/kg, the PBPK model predicted FPMG to be 22%, which is in agreement with Roine et al. (1991) and Lim et al. (1993). The model also predicted a dose-dependent effect on FPMG; as the simulated dose increased predictions of FPMG decreased.The model predicted considerable first-pass metabolic clearance attributable to both gastric and hepatic metabolism at all simulated doses. At a dose roughly equivalent to the consumption of 1.5 standard alcoholic beverages by an average, healthy adult male (500 mg/kg), FPMG and FPMH were predicted to be 26 and 15% of the administered dose, respectively. Thus, these metabolic barriers are protective against toxicity because they reduce exposure to target organs such as the liver and brain. Differences in capacity of gastric or metabolic clearance therefore can influence susceptibility to the toxic effects of ethanol.Moreover, the PBPK model accurately simulated experimental BEC from animals pretreated with cimetidine, a gastric ADH inhibitor (Palmer, 1987Go). These simulations were obtained in the absence of gastric ADH activity while keeping all other parameters the same. Specifically, the absorption rate and hepatic metabolic constants were not changed from those used to simulate data from animals receiving ethanol only. The validity of the model was strengthened by its ability to predict these data and further illustrate the influence of gastric ethanol metabolism on oral bioavailability. If gastric ADH metabolism of ethanol was not a factor in the bioavailability, it is likely that the model would not have provided accurate predictions of experimental BEC from animals pretreated with cimetidine.Use of Equation 5Go to describe a difference between the mixed venous and tail vein blood concentrations of ethanol is consistent with experimental observations (Levitt et al., 1994). However, Equation 5Go provides an empirically, rather than a physiologically based description. The latter would be more complex, and would require a specific description of tail vein blood flow and perhaps of diffusional exchange of ethanol between arterial blood flowing towards the distal regions of the tail and venous blood returning from the distal regions. This being the case, the parameter KTail cannot be assigned a rigorous physiological definition. This means that the value identified by optimization against the iv data might not be optimal for other experimental conditions, such as oral dosing. The estimates of VmaxG and KA and the related calculations of FPM are thus uncertain to the extent that the value of KTail may be experiment-specific. Although a rigorous examination of this issue was not conducted in the present study, a preliminary study indicated that optimization of KTail against oral data did not significantly affect the estimated value of VmaxG, rather, some covariance of KTail and KA was seen (results not shown). Thus, the overall conclusions of this work with respect to FPM are not likely to be sensitive to the strategy used to identify KTail.The inhibition of gastric ADH activity by drugs, such as cimetidine, may have implications for people being treated for gastritis. The inhibition of gastric ADH activity by the concomitant use of certain H2-receptor antagonists (e.g., cimetidine) has been demonstrated in humans (Gupta et al., 1995Go). In this study, healthy male volunteers were administered 600-mg/kg ethanol postprandially, before and after cimetidine treatment. Peak BEC following exposure to cimetidine were significantly increased. Although the mean increase in peak BEC in this study was small (approximately 3 mM), 3 subjects exceeded the legal limit for driving while impaired, following pretreatment with cimetidine (Gupta et al., 1995Go). Thus, the use of these drugs may result in higher BEC than would otherwise be expected and may actually potentiate ethanol toxicity. For example, in a recent study, an oral administration of ethanol to male rats resulted in a less pronounced decrease in hepatic glutathione levels, as well as a quicker recovery compared to an ip administration (Battiston et al., 1996Go). However, this difference was eliminated when the rats were pretreated with cimetidine.As previously discussed, difference in gastric FPM may play a role in the observed sex-dependent differences in the adverse health effects of ethanol consumption. Frezza et al. (1990) found that women have lower levels of gastric ADH and metabolize less ethanol during the first pass through the liver. In fact, the opposite is true in rats. There are no sex-dependent differences in hepatic metabolism of ethanol, but female rats have higher gastric ADH activity and enzyme protein levels (Mezey et al., 1992Go). Although the current model was developed for male rats, it would be expected to predict lower blood ethanol levels and reduced bioavailability in female rats as compared to male rats.In summary, the PBPK model developed in male rats illustrated how the observed first-pass clearance of ethanol following an oral administration can be attributed to metabolism by both gastric and hepatic ADH, and it demonstrated the dependence of this clearance on the dose and the absorption rate. The PBPK model was used to provide quantitative estimates of gastric and hepatic FPM and bioavailability, and provided insight regarding different approaches to calculate the FPM of ethanol. The ability of the model to accurately simulate ethanol pharmacokinetic data from several experiments, including data from animals pretreated with cimetidine, which inhibits gastric ADH, is consistent with roles of both gastric and hepatic metabolism as determinants of the oral bioavailability of ethanol.


    ACKNOWLEDGMENTS
 
We would like to acknowledge the assistance of Drs. Lester Sultatos, Edward J Flynn, and Charles Lieber for their discussions pertaining to this work. The authors also thank Drs. Michael Gargas and Susan Borghoff for their review of this paper. During her fellowship at the Alcohol Research and Treatment Center (Mount Sinai School of Medicine, Bronx, N.Y.), G.M.P. was supported by NIH Research Training Fellowship AA07275.


    NOTES
 
1 To whom correspondence should be addressed. E-mail: gina.pastino{at}spcorp.com. Back


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