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Department of Pharmaceutics, Victorian College of Pharmacy—Monash University, Parkville, Victoria, Australia
(Received February 6, 2003; accepted May 28, 2003)
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Abstract |
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 Top Abstract Materials and MethodsResultsDiscussionAppendixReferences |
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;
Benet and Cummins, 2001).
Subsequent findings have led to the further hypothesis that P-gp and CYP3A
metabolism are functionally linked and may act in concert to limit the passage
of intact xenobiotics (including drugs) across the enterocyte. Evidence to
support this hypothesis includes, first, the fact that P-gp and CYP3A share a
close cellular localization within the enterocyte, and that this spatial
relationship may benefit the detoxifying properties of the two proteins
(Kolars et al., 1992;
Watkins, 1997;
Cummins et al., 2001). Second,
there is an overwhelming overlap in substrate specificity for P-gp and CYP3A,
with the two proteins sharing common inhibitors, inducers, and regulators
(Kivistö et al., 1995;
Wacher et al., 1995;
Schuetz et al., 1996a).
Finally, the extent of P-gp activity or expression has a significant influence
over CYP3A expression levels (Schuetz et
al., 1996b; Schuetz et al.,
2000), which is supported by the recent discovery that P-gp and
CYP3A expression may be coordinately regulated by ligand binding to nuclear
receptors such as the steroid and xenobiotic receptor, SXR
(Synold et al., 2001).
Therefore, enterocyte-based P-gp and CYP3A appear to form a coordinately
regulated alliance to reduce the intestinal permeation and, therefore, the
oral absorption of a number of xenobiotics and drugs.
Enterocyte-based CYP3A metabolism has been shown to reduce the intestinal
permeability of a number of drugs, including verapamil, tacrolimus, and
cyclosporin (Kolars et al.,
1991; Fromm et al.,
1996; Hashimoto et al.,
1998). These compounds are all P-gp substrates, and, therefore,
permeability across the enterocyte may also be limited by P-gp-mediated efflux
or antitransport. In addition to the individual effects of CYP3A and P-gp on
drug permeability, it has further been suggested that P-gp-mediated efflux
increases drug exposure to CYP3A within the enterocyte through repeated cycles
of efflux and subsequent reabsorption, resulting in increased intestinal
residence time and extraction (Benet and
Cummins, 2001; Johnson et al.,
2001; Cummins et al.,
2002).
In an attempt to rationalize the interaction between P-gp and CYP3A within
the enterocyte, a diffusion-based simulation model was recently proposed
(Ito et al., 1999). In these
studies, whereas the fraction of drug absorbed from the lumen into the portal
blood was shown to be both P-gp- and CYP3A-dependent, the model did not
predict a change to the intestinal extraction ratio in response to a change in
P-gp efflux; instead, P-gp efflux reduced only the rate of drug entering the
enterocyte. This reduced rate of drug entry subsequently reduced the overall
extent of drug absorption due to incomplete gastrointestinal absorption and
fecal elimination. In contrast to this theoretical model approach, studies in
Caco-2 cells have demonstrated that P-gp can modulate the extent of drug
metabolism across the enterocyte (Gan et
al., 1996; Hochman et al.,
2000; Cummins et al.,
2002); however, little work has been performed utilizing either
animal or human intestinal tissues, where both metabolic enzymes and efflux
transporters are expected to be expressed at physiological levels (for the
particular species under examination).
Therefore, to further probe the impact of P-gp on intestinal extraction, the current studies have characterized the bidirectional uptake, transport, and metabolism of the P-gp and CYP3A substrate verapamil, in an in vitro model of intestinal permeability utilizing excised rat jejunal tissue. The relative impact of P-gp and/or CYP3A on verapamil permeability was isolated by the use of selective and nonselective inhibitors of P-gp efflux and CYP3A metabolism. The development of a four-compartment kinetic model describing verapamil transport and metabolism across the intestinal mucosa also facilitated further simulation-based examination of the complex interplay between P-gp and CYP3A. These simulations suggest that although the intestinal extraction ratio of verapamil can be reduced by P-gp inhibition, for this to occur, the increase in drug uptake resulting from P-gp inhibition must result in disproportionate increases in the extent of drug transport relative to the extent of drug metabolism.
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Materials and Methods |
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TopAbstractMaterials and MethodsResultsDiscussionAppendixReferences |
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Tissue Preparation. All animal studies were performed in accordance
with the guidelines of the Australian and New Zealand Council for the Care of
Animals in Research and Teaching, and the study protocol was approved by the
institutional animal experimentation ethics committee. The experimental
procedure was based on methods described previously
(Johnson et al., 2002).
Briefly, male Sprague-Dawley rats were anesthetized, and a segment of jejunum
was removed, rapidly stripped of its serosal muscle layers, and mounted in
side-by-side diffusion chambers (Navicyte Inc., Sparks, NV). Modified Krebs'
bicarbonate-Ringer's (KBR) buffer (7 ml) was immediately added to both the
mucosal and serosal chambers (Ungell et
al., 1992). The modified KBR contained 147.2 mM Na+,
5.1 mM K+, 1.25 mM Ca2+, 1.2 mM Mg2+, 115.2
mM Cl-, 15 mM HCO3-, 0.4 mM
H2PO4-, 1.8 mM HPO42-,
1.2 mM SO42-, 11.5 mM D-glucose, 4.9 mM
L-glutamate-, 4.9 mM pyruvate-, and 5.4 mM
fumarate2-. Tissues were allowed to equilibrate for 30 min before
the commencement of permeability experiments, when verapamil was spiked into
the donor chamber to give a final concentration of 20 µM. During inhibition
studies with the P-gp inhibitor PSC833 (20 µM), the CYP3A inhibitor
midazolam (20 µM), or the P-gp/CYP3A inhibitor ketoconazole (50 µM), the
inhibitors were added to both the mucosal and serosal chambers 15 min before
the addition of verapamil. Potent inhibition of the P-gp-mediated transport of
digoxin by PSC833 and ketoconazole has previously been demonstrated in rat
jejunum in vitro (Johnson et al.,
2002).
Study Protocol. Verapamil (20 µM) transport studies were
conducted in both the mucosal to serosal (m to s) and the serosal to mucosal
(s to m) directions in the presence and absence of the inhibitors described
above. This concentration of verapamil is below the Km of
both P-gp-mediated transport and CYP3A-mediated metabolism of verapamil. It
was therefore assumed that neither process was saturated during these studies
(Makhey et al., 1998;
Tracy et al., 1999). Although
verapamil and verapamil analogs have been shown to inhibit certain organic
cation transporters and the multidrug resistance-associated protein 1,
respectively, the current literature suggests that the impact of these
transporters on the intestinal permeability of verapamil is of less
significance than that of P-gp and is therefore unlikely to have a significant
impact on the interpretation of data collected in the current studies
(Saitoh and Aungst 1995;
Takano et al., 1998;
Loe et al., 2000;
Dresser et al., 2001). After an
initial (t = 0) 100-µl donor chamber sample, additional donor (100
µl) and receptor (200 µl) chamber samples were taken every 30 min for
180 min. Receptor chamber samples were replaced with an equal volume of fresh
buffer (including inhibitor if appropriate). Donor chamber samples were not
replaced and were diluted with 100 µl of fresh buffer to bring the
verapamil concentration into the linear range of the assay. All samples were
acidified with 50 µl of 90% methanol in 0.05 M H2SO4
to prevent precipitation or adsorption of un-ionized verapamil or its
metabolites during analysis. At the conclusion of transport studies (180 min),
the tissue segment was removed from the diffusion chamber, cut into small
pieces, and frozen for later analysis. Additional studies using 200 µM
verapamil in the donor chamber were also conducted, and samples were
appropriately diluted for use in the following assay.
Verapamil and Metabolite Assays. Diffusion chamber samples were
analyzed for verapamil, and metabolites (norverapamil, D-617,
D-620, and D-703) were analyzed by gradient HPLC based
on a previously described method (von
Richter et al., 2000). The HPLC consisted of a Waters 712 WISP
autosampler, 610 Fluid Unit and Valve Station, and 600 Controller (Waters,
Milford, MA) and a PerkinElmer LC 240 fluorescence detector (PerkinElmer,
Beaconsfield, UK) with excitation and emission wavelengths set to 280 and 310
nm, respectively; and data were collected on a Shimadzu C-R5A integrator
(Shimadzu, Kyoto, Japan). A 100-µl portion of acidified sample was injected
onto an Xterra MS C18 5 µM 3.9 x 150 mm column (Waters),
and mobile phase was delivered at 1 ml · min-1. Mobile phase
A consisted of 90% 5 mM ammonium acetate, pH 4.2, and 10% acetonitrile, and
mobile phase B was 100% acetonitrile. The initial proportion of mobile phase B
was 13%, which was increased to 30% over 12 min, held at 30% for 3 min,
increased to 32% over an additional 2 min, returned to 13% over 1 min, and
finally held at 13% to facilitate an 8-min re-equilibration period. The
retention times of verapamil, norverapamil, D-617,
D-620, and D-703 were 19.7, 19.0, 6.6, 6.0, and 10.0
min, respectively. The assay was validated using quadruplicate quality control
(QC) standards prepared in KBR on four separate occasions, at high (10 µM),
medium (1 µM), and low (0.1 µM) concentrations for verapamil, and medium
(1 µM) and low (0.1 µM) concentrations for the metabolites. Accuracy (of
all analytes) was within 3% of target at high and medium concentrations and
within 10% of target at low concentrations. Precision, expressed as the %CV,
was less than 5% for the high and medium QC standards, and less than 10% for
the low QC standards. Interday accuracy and precision varied by less than 10%.
The LOQ, defined as the minimum quantifiable concentration with appropriate
accuracy and precision (within 15% of target and CV <15%) was 0.01 µM
for verapamil and norverapamil, and 0.05 µM for D-617,
D-620, and D-703.
Tissue samples were analyzed for verapamil and metabolites using a
modification of a previously described extraction method
(Hendrikse et al., 1998).
Frozen samples were rapidly thawed, and 1 ml of 0.05 M NaOH and 50 µl of
internal standard solution (20 µM D-519) were added and briefly
vortexed. Tissues were subsequently homogenized for 2 min (Polytron PT 1200;
Kinematica, Basel, Switzerland) before the addition of 1 ml of methyl
t-butyl ether (EM Scientific, Gibbstown, NJ). The samples were
vortexed for 1 min and then centrifuged at 2500g for 10 min. The
supernatant was transferred using a glass pipette to a clean 1.5-ml tube
containing 150 µl of 0.5 M H2SO4. After vortexing and
centrifugation (5000g for 10 s), 100 µl of the aqueous portion was
removed and diluted with an equal volume of Milli-Q water, prior to injection
(100 µl) onto the HPLC. Extracted tissue samples were assayed using the
chromatographic conditions described above against a standard curve
constructed using blank tissue homogenate spiked with known amounts of
verapamil and metabolites, and 50 µl of internal standard solution. The
retention time of the internal standard (D-519) was 7.9 min. The
tissue homogenization assay was validated utilizing QC standards spiked into
blank tissue homogenate at high, medium, and low levels for verapamil (200,
100, and 25 nmol, respectively) and metabolites (4, 2, and 0.5 nmol,
respectively). Accuracy (of all analytes) was within 10% of target for the
high- and medium-level QC standards, and within 20% of target for the
low-level QC. Precision (expressed as %CV) was no more than 15% for all
analytes at each QC level. The efficiency of the extraction procedure for each
compound was similar over all concentration ranges, and the mean extraction
efficiency (recovery) of verapamil, norverapamil, D-617,
D-620, and D-703 was 88, 91, 76, 69, and 88%,
respectively. The LOQ, defined as the minimum quantifiable amount with
appropriate accuracy and precision (within 20% of target and CV <20%) was
0.05 nmol per tissue segment for verapamil and norverapamil, and 0.20 nmol per
tissue segment for D-617, D-620, and
D-703.
No significant adsorption of verapamil to the diffusion chamber was observed over the 3-h period of the permeability experiments. Mass balance of verapamil and its metabolites across all experiments was 94 ± 6% (mean ± S.D., n = 52). Verapamil was stable in the diffusion chambers at 37°C for >3 h when spiked into fresh KBR or into KBR collected after incubation with jejunal segments for 2 h, indicating that no significant degradation or metabolism of verapamil occurred in the buffer chambers.
Data Analysis. The only metabolites of verapamil detected in these
studies were norverapamil and D-617. Both metabolites were formed
in almost identical quantities and responded equally to CYP3A inhibition by
midazolam and ketoconazole (data not shown). Although norverapamil and
D-617 were quantified individually, for ease of data presentation
and to reduce the number of parameters that required estimation during
compartmental modeling, metabolite levels have been pooled. Data were
corrected for buffer evaporation over the course of the experiment (7.38
± 1.62% over 3 h, mean ± S.D. n = 36). The amount of
verapamil and metabolite detected in intestinal tissue segments were corrected
for verapamil and metabolite present in the adhered buffer volume, via data
determined in studies conducted with [3H]polyethylene glycol 4000
(adhered buffer volume was 58.1 ± 9.5 µl · cm-2,
mean ± S.D., n = 9). Apparent permeability coefficients were
calculated from both verapamil appearance in the receptor chamber and
disappearance from the donor chamber using eqs. 1 and 2, respectively
(Martin, 1993).
 | (1) |
 | (2) |
), where
the subscripts d, r, and t represent the total
amount (nmol) of metabolite or parent compound in the donor,
receptor, and tissue compartments, respectively.
 | (3) |
Compartmental Modeling. Compartmental kinetic modeling of the diffusion chamber data from control experiments was performed using nonlinear ordinary least-squares regression (ModelMaker 4.0, ModelKinetix; AP Benson, Reading, Berkshire, UK). To adequately fit a closed model to the diffusion chamber data, correction for any discrepancy in mass balance had to be made; therefore, data in each compartment (donor chamber, receptor chamber, or tissue segment) were adjusted proportionally to achieve a mass balance of 100% (requiring 6 ± 6% adjustment, mean ± S.D., n = 52). Regression and parameter estimation was achieved using the Levenberg-Marquardt fitting algorithm with fourth-order Runge-Kutta integration, and models were simultaneously fitted to data from all individual m to s and s to m control experiments only. Model schemes are shown in Fig. 1, where Model I represents the simplest model to describe transport events across the tissue segments, and compartments 1, 2, 3, and 0 represent the mucosal chamber, tissue segment, serosal chamber, and metabolite pool, respectively, and were all assumed to be well stirred. The metabolite pool represents the total amount of metabolite formed at the conclusion of the experiment (mucosal, tissue, and serosal compartments combined). In the interest of parsimony and identifiability, all rate constants in Model I were assumed to be first order in nature, despite k21 possibly being better represented by parallel first-order (passive diffusion) and Michaelis-Menten (capacity-limited P-gp efflux) functions and k20 by a second Michaelis-Menten function (capacity-limited metabolism). Model IIa includes a second tissue compartment, essentially separating the enterocyte (where metabolism occurs) from the submucosal layers of the tissue, and the sum of verapamil in these two compartments was therefore fitted to the tissue data and represented total tissue uptake. To simplify Model IIa, it was assumed that partitioning between the enterocyte (compartment 2) and submucosal layers (compartment 3) was first order in nature and equal in both directions (i.e., k23 = k32); as with Model I, all other rate constants in Model IIa were assumed to be first order. Model IIb is identical to Model IIa, with the exception of k20 being replaced by the Michaelis-Menten function: Vmax/(Km + X2), where Vmax, Km, and X2 represent the maximum rate of metabolism (% · min-1), the Michaelis-Menten constant (%), and the amount of verapamil in compartment 2 (%), respectively. As a means of testing the validity of the Michaelis-Menten metabolism parameters, a simulation of the ER of 200 µM verapamil was compared with experimental data collected using 200 µM verapamil in the donor chamber. This simulation does not conclusively justify the use of the more complex Model IIb; however, it does provide some evidence that the derived metabolism parameters are reasonable estimates. More complicated models, such as those describing the individual disposition of each metabolite in the tissue, mucosal, and serosal compartments, could not be supported by the current data set and resulted in large CVs of these additional parameters (data not shown). The aims of the modeling procedures described above were not compromised by this simplification and still allowed the discrepancy between disappearance and appearance permeability coefficients to be reconciled and examination of the ER under certain theoretical conditions to be examined. The compartmental model analysis was not extended to the PSC833, midazolam, and ketoconazole data sets, because the parameter estimates obtained were complicated by difficulties in accurately defining the individual degree of inhibition of P-gp and/or CYP3A by these inhibitors.
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Simulations based on Models IIa and IIb were conducted to further examine the impact of P-gp on CYP3A metabolism by calculating the model-simulated extraction ratio under various conditions (ER, calculated as per eq. 3 from simulated data at 180 min). These simulations were only performed in the m to s direction. The model parameter k21 was assumed to encompass P-gp-mediated efflux that removed drug from compartment 2 to compartment 1. The rate constant k21 was varied from 20 to 180% of its estimated parameter value to simulate the impact of inhibiting and stimulating P-gp efflux on the ER. Simultaneous to changes in k21, k20 in Model IIa was varied to simulate changes to intestinal intrinsic clearance, and the Km of metabolism in Model IIb was varied to simulate pseudo-first-order and pseudo-zero-order metabolism conditions. The percentage change in ER observed with each of these conditions with respect to the control ER (model predicted) was then calculated.
Statistical Analysis. Comparisons within experimental data sets
(e.g., permeability coefficients, tissue uptake, extraction ratios) collected
with or without the inhibitors midazolam, ketoconazole, and PSC833 were
compared by one-way analysis of variance using either parametric (general
linear model) or nonparametric (Kruskal-Wallis analysis of variance on ranks)
methods. Post hoc multiple comparisons between groups were made using Tukey
(parametric) or Dunn's (nonparametric) tests, where p < 0.05 was
considered significant. Statistical comparisons between alternative
pharmacokinetic models were made by comparison of the residual sums of squares
(RSS), the %CV of the parameter estimates, bias or systematic deviation of
standardized residuals, and the F ratio test and Akaike Information
Criterion (AIC) as tests for parsimony
(Boxenbaum et al., 1974;
Landaw and DiStefano,
1984).
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Results |
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TopAbstractMaterials and MethodsResultsDiscussionAppendixReferences |
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Permeability coefficients measured from verapamil disappearance from the donor chamber were not significantly different between m to s and s to m data sets, or significantly affected by any of the inhibitors (Table 1). Interestingly, permeability coefficients calculated on verapamil disappearance from the donor chamber were approximately 10-fold higher than those calculated on verapamil appearance and, apparently, completely insensitive to the effects of the metabolic and/or P-gp inhibition. These data suggest substantial tissue uptake of verapamil during the course of the experiment, which is consistent with the significant accumulation of verapamil and metabolite within the tissue segment observed at the conclusion of transport experiments (Table 2). The amount of verapamil in the intestinal tissue segment at the conclusion of these transport studies was not significantly altered by the direction of the transport study or the presence of PSC833, midazolam, or ketoconazole.
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Impact of Midazolam, Ketoconazole, and PSC833 on Metabolite
Formation. As reported previously (Johnson et al.,
2001,
2002), D-617 and
norverapamil are the two major verapamil metabolites formed in the rat
intestine, and in the current studies, they were formed in almost identical
quantities (data not shown). For ease of data presentation, the metabolite
data described in Fig. 3
represent the sum of both metabolites. In control m to s experiments,
approximately twice as much metabolite was detected in the mucosal chamber
compared with intact verapamil detected in the receptor chamber, reinforcing
the likelihood that P-gp is partly responsible for the large amount of
metabolite seen in this donor chamber. The appearance of metabolite in this
chamber was nonlinear with respect to time, with metabolite formation
significantly reduced by midazolam, totally abolished by ketoconazole, and
generally unchanged in the presence of PSC833. The specificity of midazolam to
reduce metabolism but not efflux was evident from the significant difference
in m to s and s to m appearance permeability coefficients for verapamil in the
presence of midazolam (i.e., evidence of a lack of P-gp inhibitory activity,
Table 1) at the same time as a
significant reduction in metabolite formation
(Fig. 3). The amount of
metabolite recovered from the tissue segment after 180 min was significantly
decreased by midazolam and ketoconazole, but unaffected by PSC833
(Table 2).
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Determination of Verapamil Extraction Ratio. Utilizing eq. 3, the ER of verapamil across rat jejunal tissue was determined at 180 min (Table 3). The ER in the m to s direction was 0.31 ± 0.04 and was halved when experiments were performed in the s to m direction. Inhibition of CYP3A by midazolam, as expected, reduced the ER of verapamil by 80%, and no extraction of verapamil was observed in the presence of ketoconazole (i.e., 100% reduction). The ER was not significantly altered by the P-gp inhibitor PSC833.
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Compartmental Modeling. Predicted data from Models I, IIa, and IIb are shown in Fig. 4, and a summary of the parameter estimates and model selection criteria is presented in Table 4. Model I does not adequately predict the difference in the total amount of metabolite formed between m to s and s to m experiments, creating bias in the tissue and metabolite residuals, and therefore predicts an insignificant difference in the m to s and s to m ER that is inconsistent with the experimental data (Table 4). The inclusion of a second tissue compartment (Model IIa) did not significantly increase the %CV of parameter estimates and provided a superior fit to the data over Model I, as evidenced by a decrease in the RSS, less bias in the tissue and metabolite residuals (see Fig. 4), a lower AIC, and a significant F ratio test between Models IIa and I (p < 0.025). The inclusion of the second tissue compartment predicts that the amount of verapamil in the enterocyte compartment is approximately 2-fold higher in m to s experiments when compared with s to m. This difference, in addition to the potential impact of P-gp efflux, was adequate to explain the differences in m to s and s to m metabolism observed in these studies and predicts an ER that is in excellent agreement with the observed data (Tables 3 and 4). Model IIb is structurally identical to Model IIa, with the exception of the replacement of k20 (the first-order metabolism parameter) with a Michaelis-Menten function. This increase in model order lowered the RSS but was not considered a statistically better model than Model IIa, due to a higher AIC, a nonsignificant p value for the F ratio test (between Models IIb and IIa), and large CVs obtained for the additional metabolism parameters (Table 4). However, notwithstanding the high CVs of the metabolism parameters in Model IIb, the predicted ER for verapamil in m to s and s to m experiments were still consistent with the experimental data (Table 4). Furthermore, using Model IIa, the m to s ER of verapamil at 200 µM donor chamber concentrations was (as expected for an entirely linear model) the same as that predicted with 20 µM donor chamber concentrations (i.e., 0.31). In contrast, Model IIb predicted that the ER at 200 µM verapamil donor chamber concentrations would drop to 0.063, which is in excellent agreement with experimental data collected from 200 µM m to s verapamil permeability studies where the ER was found to be 0.071 ± 0.018 (mean ± S.D., n = 9).
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Simulations on the Impact of P-gp on Intestinal Extraction Ratio. To probe the interaction of P-gp and CYP3A in a theoretical setting, the influence of P-gp on the modulation of verapamil metabolism (as described by the ER) was first investigated by varying k21 in Model IIa to simulate P-gp inhibition and stimulation. However, in an entirely linear model (such as Model IIa), changes in k21 cannot result in changes to the m to s ER, and the ER only varies significantly with changes to intrinsic clearance or k20 (Fig. 5A). Therefore, Model IIb was developed to introduce the potential for nonlinear verapamil metabolism, where k20 was replaced by a Michaelis-Menten function (described above). Simulations of the impact of varying k21 (varying efflux) were then performed using Model IIb as a mechanism to illustrate the potential impact of P-gp on the extent of metabolism in a nonlinear system.
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The degree of "linearity" of metabolism within the model can be varied by adjustment of the Km in Model IIb, where the higher the Km, the more "linear" the metabolism becomes (i.e., the more difficult to saturate, and the model tends toward Model IIa: entirely linear). Simulations performed using Model IIb and a high Km show a reduced "P-gp dependence" on the ER, consistent with simulations utilizing Model IIa, where no P-gp dependence on the ER was observed. Simulations performed using a low Km (essentially creating pseudo-zero-order or saturated metabolism conditions) resulted in an increased dependence of the ER on P-gp efflux (k21). The above findings are illustrated in Fig. 5B, where the initial model-predicted ER of verapamil is plotted centrally, and changes in P-gp activity (via k21) and linearity (via Km) are indicated on their respective axes as the percentage change in the original parameter value from Table 4. As shown in Fig. 5B, increasing P-gp efflux activity (k21) leads to an increase in ER, and for those compounds with a lower Km, the ER is most sensitive to a change in efflux, i.e., changes to P-gp efflux are most likely to have the greatest impact on the ER of compounds where intestinal metabolism is most readily saturated.
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Discussion |
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TopAbstractMaterials and MethodsResultsDiscussionAppendixReferences |
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). In the presence of midazolam, a CYP3A inhibitor, the
cellular metabolism of verapamil was reduced, and this increased verapamil
flux regardless of the direction of transport, i.e., m to s, or s to m (Tables
1 and
3; Figs.
2 and
3). Verapamil transport under
conditions of metabolic inhibition (i.e., plus midazolam, inhibition of
metabolism alone; and plus ketoconazole, inhibition of metabolism and P-gp)
showed that P-gp inhibition could significantly reduce the efflux of verapamil
(Fig. 2B). Surprisingly,
however, the P-gp inhibitor PSC833 had little impact on verapamil transport,
despite it being an effective and selective inhibitor of verapamil efflux in
situ (Johnson et al., 2003)
and of digoxin efflux in the rat intestine in vitro
(Johnson et al., 2002). A
possible reason for this discrepancy is that PSC833 cannot adequately compete
for the P-gp efflux pump in the presence of the large amounts of verapamil
retained in the tissue in the present in vitro studies [6.4% ·
cm-2 compared with 0.3% · cm-2 in the previous in
situ studies (Johnson et al.,
2003)]. This higher retention of verapamil in the tissue of the in
vitro model is presumably a result of reduced partition into the aqueous
transport buffers. This suggestion is consistent with previous studies in
which an increase in verapamil transport across Caco-2 monolayers was observed
when human plasma was employed in the receptor chamber
(Chung et al., 2001).
Unfortunately, the employment of lower concentrations of verapamil and higher
concentrations of PSC833 were impractical in the current studies due to
limitations of analytical sensitivity and solubility, respectively. Although ketoconazole significantly increased the m to s permeability of verapamil through inhibition of both P-gp and CYP3A, the s to m permeability of verapamil was unchanged in the presence of ketoconazole when compared with control. This most likely reflects the fact that the effects of P-gp and CYP3A inhibition on the s to m transport of verapamil run counter to each other; i.e., P-gp and CYP3A inhibition should reduce and increase the s to m flux of verapamil, respectively; and, therefore, inhibition of both of these processes may result in no net change in s to m transport (Fig. 2D).
In contrast to the verapamil appearance data, the disappearance of
verapamil from the donor chamber in m to s or s to m experiments was more
rapid and was not significantly altered by the addition of the P-gp and/or
CYP3A inhibitors. The insignificant changes in disappearance permeability are
most likely due to the small attenuation of verapamil uptake resulting from
P-gp efflux, relative to the significant tissue accumulation of verapamil
observed. It is evident, therefore, that permeability assessment made only
using drug disappearance data may show less sensitivity to the potential
impact of P-gp and/or CYP3A inhibition on drug absorption. This is consistent
with data obtained in human jejunal perfusion studies where the presence or
absence of ketoconazole in the jejunal perfusate had no impact on the
disappearance permeability coefficient of verapamil, but led to a significant
increase in the absorptive flux and bioavailability when compared with control
(Sandström et al., 1999).
The disappearance permeability coefficient of verapamil in this in vitro model
was also significantly higher (approximately 10-fold) than that calculated on
verapamil appearance in the receptor chamber, another observation consistent
with the substantial tissue uptake of verapamil and the affinity of lipophilic
compounds for the tissue segment in such in vitro experiments. Such an
observation may therefore cast doubt on the validity and usefulness of in
vitro permeability experiments in which permeability has been assessed on drug
appearance data alone, and may also mask the impact of P-gp (or P-gp
inhibitors) on drug permeability.
In an effort to examine the impact of P-gp on the intestinal metabolism of
verapamil more thoroughly (Johnson et al.,
2001), a mechanistic assessment of the interaction between P-gp
efflux and CYP3A metabolism was undertaken utilizing a compartmental kinetic
model. Model I describes the most parsimonious system with which to describe
the in vitro permeability data. However, this simple three-compartment model
led to under- and overestimation of the extent of verapamil metabolism in m to
s and s to m transport experiments, respectively
(Fig. 4A), and in the
model-predicted ER (Table 4). A
four-compartment model was therefore proposed to allow inclusion of a
submucosal tissue compartment (Model IIa), and application of this model
resulted in a superior fit to the data. Model IIa predicts an approximately
2-fold higher verapamil concentration in compartment 2 (the putative
metabolizing compartment) in m to s studies compared with s to m, which, in
addition to the potential impact of P-gp, may explain the higher ER observed
in m to s experiments when compared with s to m. Permeability data analysis
using compartmental kinetics is not commonly performed; however, the current
analysis has demonstrated that the interaction between uptake, efflux,
metabolism, and transport processes occurring within the intestine may be best
examined using such techniques. This type of analysis allowed the discrepancy
between disappearance and appearance permeability coefficients, and the
attainment of steady-state tissue levels of verapamil to be reconciled in a
valid and mechanistic manner.
Using Model IIa as an initial framework, the impact of P-gp on the extent
of metabolism was further probed by running a number of theoretical
simulations. Assuming that P-gp only influences passage of verapamil across
the apical membrane and, therefore, that P-gp inhibition results only in a
lowered value of k21, simulation studies performed with
Model IIa (an entirely linear model) demonstrate that lowering or raising the
value of k21 cannot result in a significant change in the
m to s ER (Fig. 5A). Rather,
alterations to k21 lead to proportional changes in tissue
uptake, transport, and metabolism, and therefore, the ER (a ratio of these
proportionally varying components) is relatively unchanged. However,
experimental studies similar to those described here, performed with the
P-gp/CYP3A substrates K11777 and indinavir across Caco-2 cells expressing
CYP3A4, are inconsistent with these simulations
(Fig. 5A) and demonstrate that
P-gp inhibition can lead to a reduction in the apical to basolateral ER
(Hochman et al., 2000;
Cummins et al., 2002). The
changes in ER evident in these previous Caco-2 studies can be most simply
explained by the inclusion of a nonlinear function to describe drug metabolism
within the enterocyte (Model IIb). This inclusion uncouples the directly
proportional relationship between the extent of metabolism and the extent of
verapamil transport inherent in Model IIa, consistent with conclusions made by
Hochman et al. (2000). From
the simulations in Fig. 5B, it
can be seen that the greater the metabolizing capacity (higher
Km of metabolism), the smaller the impact
k21 (or P-gp) has on the ER and, conversely, that under
pseudo-zero-order metabolism (low Km), the largest P-gp
effect is observed.
Given the complex nature of drug disposition in the current in vitro
system, Fig. 6A may depict a
more physiologically representative model. This model allows for P-gp and
CYP3A to act on different drug pools within the enterocyte, consistent with
the suggestion that P-gp extrudes drug from the apical membrane, whereas CYP3A
draws on its substrates from the cytoplasm
(Raviv et al., 1990;
Stein, 1997). Fitting the
model described in Fig. 6 to
the current data set is beyond the realm of identifiability; however,
simulations run using the rate constants obtained in Model IIa as estimates
clearly show that substantial changes in ER would be expected if compartment 4
(the metabolizing compartment) is capacity limited, even if all rate constants
(including k40) are first-order. Such a situation might
occur, for example, on saturation of a cytoplasmic binding protein, a
suggestion that is consistent with observations made in previous in situ
studies (Johnson et al.,
2003).
|
In summary, the m to s appearance permeability of verapamil was significantly increased by CYP3A and CYP3A/P-gp inhibition, indicating the significant influence of these processes on verapamil transport in vitro. A substantial discrepancy between permeability coefficients calculated on verapamil disappearance from the donor chamber and appearance in the receptor chamber was well captured by a compartmental model, and allowed a comprehensive description of the data gathered in the current in vitro studies. From the compartmental modeling described, it is evident that P-gp-mediated changes in the intestinal ER may be stimulated via changes to the intracellular drug concentration and, therefore, saturation (or desaturation) of the metabolizing enzyme. Alternatively, P-gp may influence the ER via a currently unidentified mechanism whereby the accumulation and transport of verapamil across the enterocyte are disproportionately related to the extent of metabolism. In either case, under appropriate kinetic conditions, P-gp efflux inhibition may result in disproportionate changes to the extent of drug metabolism relative to drug transport and directly impact on the intestinal extraction ratio of P-gp/CYP3A substrates.
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Appendix |
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TopAbstractMaterials and MethodsResultsDiscussionAppendixReferences |
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Model I:
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Model IIa:
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Model IIb:
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Acknowledgments |
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Footnotes |
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1 Abbreviations used are: P-gp, P-glycoprotein; AIC, Akaike Information
Criterion; CV, coefficient of variation; D-519,
5-[(3,4-dimethoxyphenethyl)methylamino]-2-(3,4-dimethoxyphenyl)-valeronitrile;
D-617, 2-[3,4-dimethoxyphenyl]-5-methylamino-2-isopropylvaleronitrile; D-620,
5-amino-2-[3,4-dimethoxyphenyl]-2-isopropylvaleronitrile; D-703,
5-[(3,4-dimethoxyphenethyl)methylamino]-2-(4-hydroxy-3-methoxyphenyl)-2-isopropylvaleronitrile;
ER, extraction ratio; HPLC, high performance liquid chromatography; KBR,
Krebs' bicarbonate-Ringer's buffer; LOQ, limit of quantitation; m to s,
mucosal to serosal; norverapamil,
5-[(3,4-dimethoxyphenethyl)amino]-2-(3,4-dimethoxyphenyl)-2-isopropylvaleronitrile;
PSC833, valspodar; QC, quality control; RSS, residual sums of squares; s to m,
serosal to mucosal; verapamil,
5-[(3,4-dimethoxyphenethyl)methylamino]-2-(3,4-dimethoxyphenyl)-2-isopropylvaleronitrile. 
Address correspondence to: Dr. Christopher J. H. Porter, Dept. of Pharmaceutics, Victorian College of Pharmacy, Monash University, 381 Royal Pde., Parkville, 3052, Victoria, Australia. E-mail: Chris.Porter{at}vcp.monash.edu.au
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, 
) and presence of 20 µM midazolam
(
, 
), 50 µM ketoconazole (
,
), or 20 µM PSC833 (
, 
).
