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School of Pharmaceutical Sciences, Kitasato University, Shirokane, Minato-ku, Tokyo, Japan
(Received September 3, 2002; Accepted March 28, 2003)
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Abstract |
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 Top Abstract Materials and MethodsResultsDiscussionReferences |
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- and
4-Hydroxylation of midazolam by human liver microsomes were evaluated as
CYP3A-mediated metabolic reactions, and the effect of preincubation with
macrolides was examined. The hydroxylation of midazolam was inhibited in a
time- and concentration-dependent manner following preincubation with
macrolides in the presence of NADPH, whereas almost no inhibition was observed
without preincubation. The kinetic parameters for enzyme inactivation
(K'app and kinact) involved in
midazolam -hydroxylation were 12.6 µM and 0.0240
min–1, respectively, for erythromycin, 41.4 µM
and 0.0423 min–1, respectively, for
clarithromycin, and 623 µM and 0.0158 min–1,
respectively, for azithromycin. Similar results were obtained for the
4-hydroxylation pathway. These parameters and the reported pharmacokinetic
parameters of midazolam and macrolides were then used to simulate in vivo
interactions based on a physiological flow model. The area under the
concentration-time curve (AUC) of midazolam after oral administration was
predicted to increase 2.9- or 3.0-fold following pretreatment with
erythromycin (500 mg t.i.d. for 5 or 6 days, respectively) and 2.1- or
2.5-fold by clarithromycin (250 mg b.i.d. for 5 days or 500 mg b.i.d. for 7
days, respectively), whereas azithromycin (500 mg o.d. for 3 days) was
predicted to have little effect on midazolam AUC. These results agreed well
with the reported in vivo observations.
). Honig et al.
(1994) have reported that the
plasma concentrations of terfenadine, which is undetectable when given alone
(60 mg b.i.d. for 7 days), increased to detectable levels following
coadministration of erythromycin (500 mg t.i.d. for 7 days). This interaction
has been shown to be caused by the inhibitory effect of erythromycin on CYP3A,
a cytochrome P450 isoform responsible for terfenadine metabolism
(Jurima-Romet et al.,
1994).
The degree of inhibition of CYP3A varies among the macrolides: the area
under the concentration-time curve (AUC) of orally administered midazolam, a
substrate of CYP3A, was reported to increase by a factor of 3.8 or 3.6 after
pretreatment with erythromycin (500 mg t.i.d. for 5 days)
(Zimmermann et al., 1996) or
clarithromycin (250 mg b.i.d. for 5 days)
(Yeates et al., 1996),
respectively. On the other hand, a relatively small increase, by a factor of
1.5 and 1.2, has been reported in the case of pretreatment with roxithromycin
(300 mg o.d. for 6 days) (Backman et al.,
1994) and azithromycin (500 mg o.d. for 3 days)
(Yeates et al., 1996),
respectively.
These interactions are based on a "mechanism-based inhibition"
(Silverman, 1988), which
differs from competitive or noncompetitive inhibition. CYP3A demethylates the
macrolide to a nitrosoalkane which then forms a stable, inactive complex with
P450 (Periti et al., 1992). In
such a case, the inhibitory effect remains after elimination of the inhibitor
from plasma or tissues, which may lead to more serious toxicity compared with
the case of reversible inhibition.
We have already succeeded in making quantitative predictions of in vivo
5-fluorouracil/sorivudine and triazolam/erythromycin interactions, both
involving mechanism-based inhibition of metabolic enzymes, based on a
physiologically based pharmacokinetic model and in vitro data (Kanamitsu et
al.,
2000a,b;
Ito et al., 1998). In the
present study, using a similar methodology, an attempt was made to predict the
degree of in vivo drug interactions in humans involving macrolides with
different inhibitory potencies based on in vitro metabolic inhibition
studies.
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Materials and Methods |
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TopAbstractMaterials and MethodsResultsDiscussionReferences |
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-hydroxy (OH) midazolam,
and 4-OH midazolam were generously donated by Nippon Roche K.K. (Tokyo,
Japan), etizolam by Welfide Corp. (Osaka, Japan), clarithromycin by Taisho
Pharmaceutical Co., Ltd. (Tokyo, Japan), and azithromycin by Pfizer, Inc.
(Groton, CT). Erythromycin and EDTA-2Na were purchased from Wako Pure Chemical
Industries, Ltd. (Osaka, Japan). NADP, glucose 6-phosphate and
glucose-6-phosphate dehydrogenase were obtained from Roche Diagnostics
(Mannheim, Germany). Acetonitrile, methanol, and other reagents of analytical
grade were purchased from Kanto Chemical Co. (Tokyo, Japan). Pooled human
liver microsomes (H161) were a gift from BD Gentest (Woburn, MA). Midazolam Metabolism by Pooled Human Liver Microsomes. After 5 min of preincubation, at 37°C, of a reaction mixture (0.72 ml) consisting of 0.1 mg/ml human liver microsomes and an NADPH-generating system (0.33 mM NADP, 8 mM glucose 6-phosphate, 0.1 U/ml glucose-6-phosphate dehydrogenase, 6 mM MgCl2) in 100 mM potassium phosphate buffer (pH 7.4) containing 0.1 mM EDTA, enzyme reactions were initiated by adding 80 µl of midazolam in 20% acetone solution. After incubation at 37°C in a shaking water bath for 3 min, the reaction was terminated by transferring the 600-µl aliquot to another tube containing 800 µl of ice-cold 100 mM Na2CO3 and 4 ml of ethyl acetate followed by vortex mixing for extraction as described below. The final midazolam concentration ranged from 2.5 to 320 µM, and the linearity of metabolism had been confirmed under the above conditions in terms of both protein concentration and incubation time. Data are presented as the means ± S.D. of triplicate experiments.
Inhibition of Midazolam Metabolism by Macrolides. After 5 min of preincubation at 37°C of a reaction mixture (0.64 ml) consisting of human liver microsomes and an NADPH-generating system in potassium phosphate buffer containing EDTA as described above, 80 µl of macrolide solution (10% acetone solution for erythromycin and clarithromycin; 20% acetone solution for azithromycin) was added and then preincubated further at 37°C for 0, 5, 10, or 20 min. Then, 80 µl of midazolam in 20% acetone solution was added and incubated at 37°C for another 3 min. The enzyme reaction was terminated as described above. The final concentration of midazolam was set at 200 µM, whereas that of erythromycin and clarithromycin ranged from 5 to 100 µM, and that of azithromycin ranged from 50 to 1000 µM. Data are presented as means ± S.E. of three determinations.
Quantification of Midazolam Metabolites by HPLC. - and 4-OH
Midazolam in the incubation mixture were determined by an HPLC-UV detection
method. One hundred microliters of 2 µg/ml etizolam (methanol solution) was
added to the extraction mixture as an internal standard and centrifuged at
1500g for 10 min after vortex mixing. Three milliliters of
supernatant was evaporated to dryness under a gentle stream of nitrogen. The
residues were reconstituted with 300 µl of HPLC mobile phase as described
below, and 50 µl was injected into the HPLC column. The HPLC system
consisted of a model LC-10AD pump (Shimadzu Ltd., Kyoto, Japan), a model
SIL-10A sample injector (Shimadzu), a model SPD-10A UV absorbance detector
(Shimadzu) set at 220 nm, and a Mightysil RP-18 reversed-phase column (150
x 4.6 mm inner diameter, Kanto Chemical Co., Tokyo, Japan). The mobile
phase consisted of a 12/5/7 (v/v) mixture of 10 mM potassium phosphate buffer
(pH 7.4), methanol, and acetonitrile delivered at 1.0 ml/min. All
chromatograms were recorded using a model C-R4 Chromato-Integrator (Shimadzu),
and - and 4-OH midazolam were quantified based on their peak areas.
Kinetic Analysis of Midazolam Metabolism by Pooled Human Liver
Microsomes. The kinetic parameters (Vmax and
Km) for midazolam - and 4-hydroxylation by human
liver microsomes were determined by the nonlinear least-squares regression
program MULTI (Yamaoka et al.,
1981) according to the following equation:
 | (1) |
Kinetic Analysis of Enzyme Inactivation by Macrolides. Kinetic
parameters for enzyme inactivation were obtained as reported elsewhere
(Ito et al., 1998). The
logarithm of the remaining enzymatic activity (formation rate of - or
4-OH midazolam) was plotted against the preincubation time, and the apparent
inactivation rate constant (kobs) was determined from the
slope of the initial linear phase. Then, the value of kobs
was plotted against the macrolide concentration ([I]), and the parameters
(kinact, K'app, and
kd) were obtained by the nonlinear least-squares
regression method (MULTI) according to the following equation
(Waley, 1985;
Silverman, 1988):
 | (2) |
Quantitative Prediction of the in Vivo Midazolam/Macrolide
Interaction. The differential equations for active and inactive CYP3A in
the liver (Eact and Einact, respectively) can be
described as follows:
 | (3) |
 | (4) |
The differential equations for midazolam (S) and macrolides (I) can be expressed as follows according to the perfusion model (Fig. 1):
|
For midazolam:
 | (5) |
 | (6) |
 | (7) |
 | (8) |
 | (9) |
 | (10) |
 | (11) |
For macrolides:
 | (12) |
 | (13) |
 | (14) |
 | (15) |
 | (16) |
- and 4-hydroxylation of midazolam,
respectively; Vmax represents the maximum rate of
metabolism; Vmax,1 and Vmax,2
represent the Vmax for - and 4-hydroxylation of
midazolam, respectively; Eact,1 and Eact,2 represent the
Eact for - and 4-hydroxylation of midazolam, respectively;
Vabs represents the absorption velocity;
ka represents the first-order absorption rate constant;
Fa represents the fraction absorbed from the gastrointestinal
tract; and Fg represents the intestinal availability. In the case
of intravenous administration of midazolam, the absorption term in eq. 9
(Vabs) was deleted, and the dose (nmol) was used as the
initial value of Vsys · Ssys. The following assumptions were made in the above mass-balance equations.
The pharmacokinetic parameters of midazolam and macrolides were determined
from data in the literature (Tables
1 and
2). Using the program STELLA II
(High Performance Systems, Inc., Hanover, NH), and kinetic parameters for
CYP3A inactivation obtained in in vitro studies, the above differential
equations were numerically solved to simulate the time courses of the
macrolide concentration in blood, the active CYP3A content in the liver
(Eact), and midazolam concentration in blood. According to clinical
reports, the dosing schedules were assumed as follows: in the case of
intravenous administration of midazolam, erythromycin (500 mg = 681 µmol,
t.i.d. for 6 days) followed by midazolam (9.67 µmol)
(Olkkola et al., 1993) or
clarithromycin (500 mg = 668 µmol, b.i.d. for 7 days), followed by
midazolam (9.82 µmol) (Gorski et al.,
1998). In the case of oral administration of midazolam,
erythromycin (500 mg = 681 µmol, t.i.d. for 5 or 6 days) followed by
midazolam (15 mg = 46 µmol) (Olkkola et
al., 1993; Zimmermann et al.,
1996), clarithromycin (250 mg = 334 µmol, b.i.d. for 5 days)
followed by midazolam (15 mg = 46 µmol)
(Yeates et al., 1996),
clarithromycin (500 mg = 668 µmol, b.i.d. for 7 days) followed by midazolam
(4 mg = 12 µmol) (Gorski et al.,
1998), or azithromycin (500 mg = 637 µmol, o.d. for 3 days)
followed by midazolam (15 mg = 46 µmol)
(Yeates et al., 1996;
Zimmermann et al., 1996). The
AUCs from time 0 to infinity of the simulated midazolam concentration profiles
were compared with the reported values.
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Results |
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TopAbstractMaterials and MethodsResultsDiscussionReferences |
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-hydroxylation pathway, whereas the
Vmax was greater for -hydroxylation. Consequently,
the CLint (Vmax/Km ratio)
for the - and 4-hydroxylation pathways was 84.8% and 15.2%,
respectively, of the total CLint, indicating that
-hydroxylation is the major metabolic pathway of midazolam. This result
is consistent with the previous finding by Gorski et al.
(1994).
Inhibition of Midazolam Metabolism by Macrolide Antibiotics.
Figure 2 shows the effect of
macrolide concentration and preincubation time on midazolam metabolism by
human liver microsomes. Midazolam metabolism was not inhibited without
preincubation, even if the macrolide concentration was increased. The degree
of inhibition depended on the preincubation time and the macrolide
concentration. -Hydroxylation of midazolam by human liver microsomes
was reduced to 43.1%, 39.4%, and 67.5% of the control value following a 20-min
preincubation in the presence of 100 µM erythromycin, 100 µM
clarithromycin, and 1000 µM azithromycin, respectively. Similar results
were obtained for the 4-hydroxylation pathway.
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The calculated kinetic parameters for CYP3A inactivation are summarized in Table 4. The data points of the 0- to 10-min preincubation were considered to reflect the initial inactivation rate and were used to estimate the values of kobs. For each macrolide, the obtained values of both K'app and kinact were almost identical for both hydroxylation pathways of midazolam.
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Quantitative Prediction of the Midazolam/Macrolide Interaction.
Concentration profiles of midazolam and macrolides simulated by the kinetic
parameters in Tables 1 and
2 were compared with the
reported profiles (Birkett et al.,
1990; Foulds et al.,
1990; Chu et al.,
1992; Olkkola et al.,
1993). Figure 3 shows the concentration profiles of midazolam in blood after a single
intravenous or oral administration and those of macrolides after a single oral
administration. In all cases, the simulated and reported profiles were
comparable. Furthermore, the simulated profiles were constant after each dose
following repeated administration (Figs.
4 and
5), which was also consistent
with the previous findings that no accumulation was observed for each
macrolide following repeated administration
(Smith et al., 1953;
Suwa et al., 1988;
Foulds et al., 1990). These
findings indicate the validity of the pharmacokinetic parameters used in the
present simulation.
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Figures 4 and 5 also show the simulated effects of erythromycin and clarithromycin, respectively, on the active CYP3A content in the liver and midazolam concentrations in blood. The results are also summarized in Table 5. Following administration of erythromycin (500 mg t.i.d. for 6 days), the active CYP3A was predicted to fall gradually, and a maximum 45% inactivation of CYP3A involved in both hydroxylation pathways of midazolam was predicted after 3 days of administration of erythromycin, with no further inactivation thereafter [Fig. 4A, (2)]. The concentration of midazolam in blood was predicted to increase following administration of erythromycin, and the predicted AUC increase compared with control group was 1.7- and 3.0-fold after intravenous and oral administration of midazolam, respectively [Fig. 4A, (3) and (4)]. In another case of erythromycin administration (500 mg t.i.d. for 5 days), a 2.9-fold increase was predicted in the AUC of midazolam after oral administration [Fig. 4B, (3)].
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Following administration of clarithromycin (500 mg b.i.d. for 7 days), a
maximum of 34 and 38% inactivation of the CYP3A concerned with the -
and 4-hydroxylation pathway, respectively, of midazolam was predicted after 3
days of administration, with no further inactivation thereafter
[Fig. 5A, (2)]. The predicted
increase in midazolam AUC was 2.0- and 2.5-fold after intravenous and oral
administration, respectively [Fig.
5A, (3) and (4)]. In another case of clarithromycin administration
(250 mg b.i.d. for 5 days), a maximum of 20 and 23% inactivation of the CYP3A
concerned with - and 4-hydroxylation pathway, respectively, of
midazolam was predicted after 3 days of administration, with no further
inactivation thereafter [Fig.
5B, (2)]. A 2.1-fold increase was predicted in the AUC of
midazolam after oral administration [Fig.
5B, (3)].
In the case of azithromycin administration (500 mg o.d. for 3 days), only 1% of the CYP3A concerned with both hydroxylation pathways was predicted to be inactivated after 2 days of administration, with no further inactivation thereafter (data not shown). Almost no change was predicted in the AUC of midazolam after oral administration (Table 5).
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Discussion |
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TopAbstractMaterials and MethodsResultsDiscussionReferences |
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). They
are also reported to cause many in vivo interactions with drugs that are
substrates of CYP3A, and the severity of such interactions varies from
macrolide to macrolide (Periti et al.,
1992; Westphal,
2000). Whereas erythromycin and clarithromycin markedly affect the
AUC of midazolam, a CYP3A substrate, in humans, the effect of azithromycin is
reported to be small (Backman et al.,
1994; Yeates et al.,
1996; Zimmermann et al.,
1996). Periti et al.
(1992) have classified
troleandomycin and erythromycin, 14-carbon member ring macrolides, as
belonging to the first group with a high drug interaction potential;
clarithromycin and roxithromycin (14-carbon), midecamycin and josamycin
(16-carbon) belong to the second group, with a low interaction potential; and
azithromycin (15-carbon), rokitamycin, and spiramycin (16-carbon) represent
the third group with no interaction potential.
The inhibitory effects of macrolides on CYP3A have been analyzed based on
competitive or noncompetitive inhibition
(Echizen et al., 1993;
Wrighton and Ring, 1994;
von Moltke et al., 1996;
Thummel and Wilkinson, 1998).
On the other hand, complex formation with P450 is also reported to be involved
in the inhibition by macrolides (Murray,
1987; Periti et al.,
1992). Tinel et al.
(1989) evaluated the complex
formation potential of macrolides using the liver microsomes from
dexamethasone-treated rats and reported that the rate of complex formation was
highest for troleandomycin, followed by erythromycin, and that the rates for
clarithromycin and roxithromycin were lower than those for erythromycin. Using
human liver microsomes, Yamazaki and Shimada
(1998) have reported similar
results, showing that the complex formation potential is greatest for
troleandomycin, followed by erythromycin, with roxithromycin having the lowest
potential.
In the prediction of in vivo drug interactions involving this type of
enzyme inhibition from in vitro studies, the exposure time of the enzyme to
the inhibitor should be taken into account as well as the turnover rate of the
enzyme. We have already succeeded in quantitatively predicting the
triazolam/erythromycin interaction in humans based on a physiologically based
pharmacokinetic model taking the type of inhibition into consideration
(Kanamitsu et al., 2000b).
Yamano et al. (2001) have also
presented a successful prediction of interaction between midazolam and
erythromycin using a similar model. In the present study, a similar model was
applied to the interaction between midazolam and macrolides with different
inhibitory potentials.
At first, we tried to predict the increase in midazolam AUC assuming
competitive inhibition of CYP3A by erythromycin and clarithromycin
(Table 6). Gascon and Dayer
(1991) have reported the
inhibition constant (Ki) of erythromycin on midazolam
-hydroxylation by human liver microsomes, assuming competitive
inhibition. The maximum unbound concentration of erythromycin at the inlet to
the liver (Iin,u) was estimated by Iin,u =
(Imax + ka Dose Fa/Q) x
fb, where Imax is the maximum concentration in the
systemic circulation (Ito et al.,
1998). Since midazolam is eliminated from human body predominantly
via CYP3A-mediated metabolism (Smith et
al., 1981), the AUC increase by erythromycin was predicted by 1 +
Iin,u/Ki
(Ito et al., 1998). The
increase in the midazolam AUC produced by clarithromycin was also predicted in
the same way, except that the IC50 value reported by Gascon and
Dayer (1991) was used instead
of the Ki, which was not available for clarithromycin. As
shown in Table 6, almost no
increase was predicted in the AUC of midazolam, indicating that the reported
3.6- to 7.0-fold increase in vivo (Olkkola
et al., 1993; Yeates et al.,
1996; Zimmermann et al.,
1996; Gorski et al.,
1998) cannot be explained by competitive inhibition of the
enzyme.
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In the present in vitro studies using human liver microsomes, midazolam
metabolism was not inhibited without preincubation, even if the concentration
of the macrolides was increased, and the degree of inhibition depended on the
preincubation time and the macrolide concentration
(Fig. 2). These findings
indicate that the inhibitory effect of macrolides on midazolam metabolism is
predominantly caused by mechanism-based inhibition of CYP3A, with little
contribution from competitive inhibition. The estimated values of
K'app and kinact for each
macrolide were almost the same for both midazolam hydroxylation pathways
(Table 4), and the values for
erythromycin were comparable to those for triazolam metabolism reported by
Kanamitsu et al. (2000b).
As shown in Table 5, using
the kdeg of 0.0005 min–1, the
increase in midazolam AUC was slightly underestimated. The ratio of the
predicted and reported increase in midazolam AUC was between 0.6 and 0.8
except for the case of oral administration of midazolam after clarithromycin
treatment (500 mg b.i.d. for 7 days)
(Gorski et al., 1998), in
which case the in vivo interaction was significantly underestimated. One of
the reasons for this may be incorrect estimation of the observed AUC due to
the lack of midazolam concentration data in the elimination phase.
The average turnover rate constant (kdeg) of rat P450
(0.0005 min–1)
(Shiraki and Guengerich, 1984)
was used in the present simulation because the corresponding value for human
CYP3A has not been reported. When the minimum reported value of
kdeg (0.00033 min–1) was used,
almost the same results were obtained for clarithromycin and azithromycin,
whereas the predicted degree of in vivo interaction was increased in the case
of erythromycin, compared with using the kdeg of 0.0005
min–1 (Table
5). Once an enzyme is inactivated in vivo by a mechanism-based
inhibitor such as macrolides, the recovery of the metabolic activity depends
solely on the synthesis of the enzyme. Thus, the turnover rate of the enzyme
is one of the most important parameters in the prediction of interactions
involving mechanism-based inhibition, and in the present study, this was found
to affect the results in some cases. In cases where the turnover rate of the
human enzyme is unavailable, it seems to be important to alter this parameter
to some extent in the simulation, referring to animal data, to predict the
range of the interaction.
In addition, the liver-to-blood concentration ratio (Kp) of midazolam and macrolides was assumed to be 1 in the present prediction because this cannot be measured in humans. To examine the effect of Kp on the prediction, the Kp of midazolam was changed to 0.1 and 10, whereas that of the macrolides was fixed at 1, and the same simulation was conducted using the values of Vd and CLint of midazolam redetermined to fit its concentration profile in blood. In another case, the Kp of macrolides was changed to 10, with that of midazolam being fixed at 1 and the simulation was conducted in a similar manner. As a result, the predicted increase in midazolam AUC was 2.8- to 3.0-fold for erythromycin (500 mg t.i.d. for 6 days), 2.8- to 2.9-fold for erythromycin (500 mg t.i.d. for 5 days), 2.3- to 2.5-fold for clarithromycin (500 mg b.i.d. for 7 days), 2.1-fold for clarithromycin (250 mg b.i.d. for 5 days), and 1.0- to 1.1-fold for azithromycin (500 mg o.d. for 3 days), suggesting that the value of Kp has little impact on the prediction.
Although only the interaction involving the hepatic enzyme has been
evaluated in the present in vitro study, it has been reported that CYP3A is
also present in the small intestine (Paine
et al., 1997) and that midazolam is metabolized by human
intestinal microsomes in vitro (Thummel et
al., 1996). In vivo human studies have revealed that the hepatic
and intestinal availability of midazolam is 0.74 and 0.42, respectively
(Gorski et al., 1998),
demonstrating that there is also significant intestinal metabolism of
midazolam in vivo. Furthermore, the intestinal availability (Fg) of
midazolam is reported to increase approximately 2-fold by pretreatment with
either erythromycin or clarithromycin
(Olkkola et al., 1993;
Gorski et al., 1998),
indicating an interaction involving intestinal first-pass metabolism of
midazolam. In the present study, the reported values of midazolam
Fg, with and without pretreatment with macrolides, were used in the
prediction. However, in the case of interaction with azithromycin, the control
Fg value was used as a constant value assuming the absence of an
interaction involving intestinal metabolism, because of lack of any reported
value of Fg after pretreatment with azithromycin. The effects of 10
min of preincubation with erythromycin (100 µM), clarithromycin (100
µM), and azithromycin (1000 µM) on midazolam metabolism were similar
between liver and jejunal microsomes from the same three donors, the degree of
inhibition by azithromycin being much smaller than that by erythromycin or
clarithromycin (unpublished observation). This finding indicates the validity
of the above assumption that azithromycin does not cause an interaction in the
small intestine. However, because the Fg values used were in vivo
data ignoring the time course of intestinal metabolism, efforts are being made
by our group to predict the interaction involving the small intestine from in
vitro data.
Recently, Mayhew et al.
(2000) proposed a more simple
method of predicting in vivo drug interactions involving metabolic
intermediate complex formation. Based on the inhibitor-induced change in the
steady-state enzyme level ([E]ss), the degree of increase in the AUC after
oral administration can be estimated by the following equation:
 | (17) |
-hydroxylation of midazolam obtained in the present study
(Table 7). The maximum
concentration in blood (Imax), maximum unbound concentration in
blood (Imax,u), or maximum unbound concentration at the inlet to
the liver (Iin,u) was used as [I]. As shown in
Table 7, the predicted increase
in the midazolam AUC was comparable to the reported value when
Iin,u was used. Applying this methodology to other interactions
involving mechanism-based inhibitors should provide information for
determining what concentration of inhibitor (Imax, Iin,u
etc.) is appropriate for [I] in eq. 17.
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The degree of interaction with CYP3A substrates varies among the macrolides. In the present study, some of these interactions have been successfully predicted from in vitro data. If an interaction involving mechanism-based inhibition of the enzyme is analyzed assuming a competitive inhibition, the in vivo interaction should be greatly underestimated. Quantitative predictions from in vitro data taking the inhibition type into consideration are essential for avoiding toxic interactions in clinical practice.
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Acknowledgments |
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Footnotes |
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1 Abbreviations used are: AUC, area under the concentration-time curve; o.d.,
once daily; P450, cytochrome P450; HPLC, high-performance liquid
chromatography. 
Address correspondence to: Dr. Tomoo Itoh, School of Pharmaceutical Sciences, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo 108-8641, Japan. E-mail: itoht{at}pharm.kitasato-u.ac.jp
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A. Seithel, S. Eberl, K. Singer, D. Auge, G. Heinkele, N. B. Wolf, F. Dorje, M. F. Fromm, and J. Konig The Influence of Macrolide Antibiotics on the Uptake of Organic Anions and Drugs Mediated by OATP1B1 and OATP1B3 Drug Metab. Dispos., May 1, 2007; 35(5): 779 - 786. [Abstract] [Full Text] [PDF]
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R. S. Obach, R. L. Walsky, and K. Venkatakrishnan Mechanism-Based Inactivation of Human Cytochrome P450 Enzymes and the Prediction of Drug-Drug Interactions Drug Metab. Dispos., February 1, 2007; 35(2): 246 - 255. [Abstract] [Full Text] [PDF]
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T. Prueksaritanont, Y. Kuo, C. Tang, C. Li, Y. Qiu, B. Lu, K. Strong-Basalyga, K. Richards, B. Carr, and J. H. Lin In Vitro and in Vivo CYP3A64 Induction and Inhibition Studies in Rhesus Monkeys: A Preclinical Approach for CYP3A-Mediated Drug Interaction Studies Drug Metab. Dispos., September 1, 2006; 34(9): 1546 - 1555. [Abstract] [Full Text] [PDF]
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D. F. McGinnity, A. J. Berry, J. R. Kenny, K. Grime, and R. J. Riley EVALUATION OF TIME-DEPENDENT CYTOCHROME P450 INHIBITION USING CULTURED HUMAN HEPATOCYTES Drug Metab. Dispos., August 1, 2006; 34(8): 1291 - 1300. [Abstract] [Full Text] [PDF]
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A. Galetin, H. Burt, L. Gibbons, and J. B. Houston PREDICTION OF TIME-DEPENDENT CYP3A4 DRUG-DRUG INTERACTIONS: IMPACT OF ENZYME DEGRADATION, PARALLEL ELIMINATION PATHWAYS, AND INTESTINAL INHIBITION Drug Metab. Dispos., January 1, 2006; 34(1): 166 - 175. [Abstract] [Full Text] [PDF]
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 R. S. Obach, R. L. Walsky, K. Venkatakrishnan, E. A. Gaman, J. B. Houston, and L. M. Tremaine The Utility of in Vitro Cytochrome P450 Inhibition Data in the Prediction of Drug-Drug Interactions J. Pharmacol. Exp. Ther., January 1, 2006; 316(1): 336 - 348. [Abstract] [Full Text] [PDF]
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P. Zhao, K. L. Kunze, and C. A. Lee EVALUATION OF TIME-DEPENDENT INACTIVATION OF CYP3A IN CRYOPRESERVED HUMAN HEPATOCYTES Drug Metab. Dispos., June 1, 2005; 33(6): 853 - 861. [Abstract] [Full Text] [PDF]
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T. M. Polasek, D. J. Elliot, B. C. Lewis, and J. O. Miners Mechanism-Based Inactivation of Human Cytochrome P4502C8 by Drugs in Vitro J. Pharmacol. Exp. Ther., December 1, 2004; 311(3): 996 - 1007. [Abstract] [Full Text] [PDF]
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