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Vol. 50, Issue 3, 387-412, September 1998
Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
I. Introduction
II. Drug-Drug Interactions Other Than Involving Metabolism
A. Drug-drug interactions involving plasma protein binding
B. Drug-drug interactions at the transport carrier
III. Drug-Drug Interactions Involving Metabolism in the Liver
A. Examples of In Vivo Drug-Drug Interactions Involving P450 Metabolism
B. Inhibition Mechanism of Drug Metabolism by P450
C. Inhibition Patterns of Drug Metabolism
1. Competitive Inhibition.
2. Noncompetitive Inhibition.
3. Uncompetitive Inhibition.
D. Prediction of In Vivo Drug-Drug Interactions Based on In Vitro Data
1. General Equations.
2. The evaluation of the unbound concentration of the inhibitor in vivo.
E. Examples of the Prediction of Drug-Drug Interactions Based on Literature Data
1. Successful Cases of In Vitro/In Vivo Prediction.
2. Interactions Predictable for the Objective Metabolic Pathway but not Predictable for the Overall Data.
3. Interactions Not Predictable by In Vitro/In Vivo Scaling.
F. Procedure for Predicting Inhibitory Effects of Coadministered Drugs on the Hepatic Metabolism of Other Drugs
G. Mechanism-Based Inhibition
1. Characteristics of Mechanism-Based Inhibition.
2. Kinetic Analysis of Mechanism-Based Inhibition: Analysis of In Vitro Data.
3. Prediction of In Vivo Interactions from In Vitro Data in the Case of Mechanism-Based Inhibition.
H. Problems To Be Solved for the More Precise Prediction of Drug-Drug Interactions
1. Estimation of the Tissue Unbound Concentration of the Inhibitor That Is Actively Transported into Hepatocytes.
2. Evaluation of Drug-Drug Interactions Involving Drug Metabolism in the Gut.
References
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I. Introduction |
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Serious side-effects caused
by drug interactions have attracted a great deal of attention and have
become a social problem since the coadministration of ketoconazole and
terfenadine was reported to cause potentially life-threatening
ventricular arrhythmias (Monahan et al., 1990
), and an
interaction between sorivudine and fluorouracil resulted in fatal
toxicity in Japan (Watabe, 1996; Okuda et al., 1997). The
possible sites of drug-drug interaction which can change
pharmacokinetic profiles include: (1) gastrointestinal absorption, (2)
plasma and/or tissue protein binding, (3) carrier-mediated transport
across plasma membranes (including hepatic or renal uptake and biliary
or urinary secretion), and (4) metabolism. Pharmacodynamic interactions
such as antagonism at the receptor may also increase or decrease the
effects of a drug.
In this review, after brief comments on (2) and (3), we intend to focus on (4) and to discuss the possibility of the quantitative prediction of drug-drug interactions in vivo based on the analyses of data from literature obtained by in vitro experiments using human liver samples. Furthermore, strategic proposals for avoiding toxic interactions will be given from a pharmacokinetic point of view.
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II. Drug-Drug Interactions Other Than Involving Metabolism |
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A. Drug-drug interactions involving plasma protein binding
Although interactions involving plasma protein binding are well
known, they rarely cause clinically serious problems (Rowland and
Tozer, 1995; Rolan, 1994). The reasons are summarized below.
The unbound fraction (fu)b of a
drug in plasma is increased when it is displaced by other drugs at the
plasma protein binding sites. Subsequent alterations in plasma
concentration profiles can be caused by changes in both clearance (CL)
and volume of distribution (Vd) of the drug. The effect on
the steady-state concentration (Css) and the area under
concentration-time curve (AUC) can be predicted from the change in CL.
It should be noted that the effect of protein binding replacement
depends on the magnitude of CL and the route of administration. As
shown in table 1, an analysis based on
the well-stirred model has revealed that the protein binding
replacement has little effect on the Css and AUC for
unbound drugs (Cu,ss and AUCu) after oral
administration, which are parameters directly related to the
pharmacological and adverse effects, irrespective of the magnitude of
CL. In the case of low clearance drugs, Cu,ss
and AUCu after intravenous administration also are affected little by protein binding replacement. The only situation for a possible interaction is after the intravenous administration of a high clearance drug and there are few examples of
this in clinical practice (Rolan, 1994).
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The alteration of Vd caused by protein binding replacement
also has an effect on the blood drug concentration (Rowland and Tozer,
1995). In the case of drugs with a relatively large Vd, Vd increases in parallel with fu. Although this
leads to a transient reduction in total blood concentration caused by
the redistribution of the drug into tissues, the unbound concentration
is not affected. However, in the case of drugs with a small
Vd, which depend on fu to a lesser extent, the
total blood concentration is not affected so much by the change in
fu, but the unbound concentration is greatly altered.
Figure 1 shows the simulation of the effects of protein binding replacement on the blood concentration profile during a constant intravenous infusion, where the protein binding and the tissue distribution of the drug are assumed to reach equilibrium rapidly, i.e., the concentration changes rapidly in response to a change in fu. In this simulation, changes in both CL and Vd associated with the change in fu were considered. As just described above, the steady-state unbound concentration is altered with the change in fu only for a high clearance drug. It is also clear from figure 1 that, in the case of drugs with a small Vd, a transient increase in the unbound concentration is observed even for a low clearance drug, and caution for the possible occurrence of side effects is needed.
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B. Drug-drug interactions at the transport carrier
Very few studies have focused on drug-drug interactions involving
carrier-mediated transport across membranes, including the interactions
involving renal secretion and reabsorption and those where
p-glycoprotein (p-gp) plays a role (Tsuruo et al., 1981; Slater et al., 1986; Kusuhara et al., in press).
Along with metabolism, renal excretion is one of the most important
processes affecting the total body clearance of a drug. Alterations in
this process caused by drug-drug interactions should, therefore, be
carefully considered. Secretion of drugs at the renal tubule is an
active transport process, where organic anion transporters, organic
cation transporters, and p-gp are known transport carriers (Hori
et al., 1982; Takano et al., 1984; Tanigawara et al., 1992). The renal clearance of a drug is reduced by
inhibition of these transport processes. It is known that both organic
anion transporters and organic cation transporters exist on both the basolateral membrane (BLM) and the brush border membrane (BBM) and that
they are different from each other, whereas p-gp is only present on the
BBM. The inhibitors of these transporters interact with other drugs;
for example, inhibition of the renal excretion of penicillin and other
related drugs by probenecid (Hunter, 1951), methotrexate excretion by
nonsteroidal anti-inflammatory drugs (Statkevich et al.,
1993), and digoxin excretion by quinidine (Tanigawara et
al., 1992) all involve this kind of interaction.
Most studies of pharmacokinetic drug-drug interactions reported so far
have been limited to the analysis of hepatic metabolism. However, the
hepatic clearance of many drugs has been found to be determined mainly
by hepatic uptake (Yamazaki et al., 1995, 1996). The overall
intrinsic clearance (CLint,all) can be expressed using the
intrinsic clearance for metabolism (CLint) and that for
membrane permeation (PS) as follows:
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(1) |
It is clear from equation (1) that CLint,all equals
CLint in the case of drugs with large (PS 
CLint) and symmetrical (PSinf = PSeff) membrane permeability. Otherwise, hepatic clearance
is affected by the membrane permeability of the drug. In such cases, it
is important to evaluate drug-drug interactions involving not only
metabolism but also membrane permeation. In our laboratory, several
cases of drug-drug interactions were found in rats at the level of
transporters involved in hepatobiliary transport as shown below. In the
future, similar interactions at the transporter level possibly may be
found in the clinical situation. The interactions found in rats
include: inhibition of biliary excretion of glucuronides and sulfates
of liquiritigenin, a flavonoid, by organic anions such as
dibromosulfophthalein (DBSP) and glycyrrhizin, which has a glucuronide
moiety (Shimamura et al., 1994); inhibition of biliary excretion of glycyrrhizin by DBSP (Shimamura et al., 1996);
inhibition of biliary excretion of leukotriene C4, which
has a glutathione moiety, by DBSP (Sathirakul et al., 1994);
and reduction of plasma clearance, based on hepatic uptake and biliary
excretion, of octreotide, a small octapeptide, by DBSP and taurocholate
(Yamada et al., 1997). In vivo drug-drug interactions
involving membrane transport remain to be predicted based on in vitro
studies of the membrane permeability of drugs.
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III. Drug-Drug Interactions Involving Metabolism in the Liver |
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As a pharmacokinetic parameter directly related to the
pharmacological and/or adverse effects of drugs, it is very important to predict the hepatic clearance. Because the use of animal scale-up is
limited in the case of hepatic metabolic clearance due to large inherent interspecies differences, we have developed an alternative methodology to predict in vivo metabolic clearance in the liver; it is
based on in vitro studies using mainly rat liver microsomes and
isolated rat hepatocytes (Sugiyama and Ooie, 1993; Iwatsubo et
al., 1996). Recently, with the greater availability of human liver
samples, the method of in vitro/in vivo scaling can now be applied to
human studies. We have already demonstrated that the method can be
applied to P450 metabolism in humans based on in vitro and in vivo data
obtained from the literature (Iwatsubo et al., 1997).
However, the prediction of intrinsic clearance was not successful for
some drugs, possibly because of the contribution of active transport
into the liver and/or first-pass metabolism in the gut.
In order to prevent toxic drug-drug interactions, it is important to
quantitatively predict pharmacokinetic changes caused by
coadministration of drugs that are known to inhibit the hepatic metabolism of the drug under study (Sugiyama and Iwatsubo, 1996; Sugiyama et al., 1996). In this review, we have focused on
the drug-drug interactions via inhibitory mechanisms and have tried to
predict in vivo interactions from in vitro data on drug metabolism obtained from the literature.
A. Examples of In Vivo Drug-Drug Interactions Involving P450 Metabolism
Drug-drug interactions involving metabolism are one of the
principal problems in clinical practice to evaluate the pharmacological and adverse effects of drugs. Parkinson (1996) summarized examples of
substrates, inhibitors, and inducers of the major human liver microsomal P450 enzymes involved in drug metabolism. In the case of
drugs that undergo metabolism by CYP3A4 and 2D6, particular attention
should be paid to the interactions resulting in alterations in blood
concentrations possibly accompanied by a change in its effects, because
a number of drugs are metabolized by these enzymes (Bertz and
Granneman, 1997). For example, blood concentrations of imipramine and
desipramine, substrates for CYP2D6, are elevated several-fold by
coadministration of fluoxetine, another substrate for CYP2D6 (Bergstrom
et al., 1992). Similarly, concentrations of terfenadine,
which is metabolized by CYP3A4, are increased in patients taking
erythromycin, which is also a substrate for CYP3A4 (Honig et
al., 1992). Quinidine is metabolized mainly by CYP3A4 but inhibits
the metabolism of substrates for CYP2D6, such as sparteine, rather than
those for CYP3A4 (Schellens et al., 1991). Furthermore, in
the case of drugs whose metabolism is mediated by multiple isozymes
(e.g., diazepam), drug-drug interaction may be complicated because of
possible dose-dependent changes in the contribution of each isozyme to
the overall metabolism (Iwatsubo et al., 1997).
B. Inhibition Mechanism of Drug Metabolism by P450
Drug metabolism by P450 can be inhibited by any of the following three mechanisms.
The first is mutual competitive inhibition caused by coadministration
of drugs metabolized by the same P450 isozyme, such as the
above-mentioned (see Sec. A.) combinations of imipramine or desipramine
and fluoxetine (CYP2D6). In this case, as reported for metoprolol and
propafenone (CYP2D6) (Wagner et al., 1987), blood
concentrations of both drugs may be increased.
The second is the inactivation of P450 by the drug metabolite forming a
complex with P450. This type of inhibition is designated as
"mechanism-based inhibition" (Silverman, 1988). Inhibition by
macrolide antibiotics, such as erythromycin, is a typical example of
this type of interaction. As shown in figure
2, P450 demethylates and oxidizes the
macrolide antibiotic into a nitrosoalkane that forms a stable, inactive
complex with P450 (Periti et al., 1992).
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The third is inhibition by the binding of imidazole or a hydrazine
group to the haem portion of P450. In the case of cimetidine, the
nitrogen in the imidazole ring binds to the haem portion of P450
causing nonselective inhibition of many P450 isozymes (Somogyi and
Muirhead, 1987).
C. Inhibition Patterns of Drug Metabolism
The effects of inhibition of drug metabolism on in vivo
pharmacokinetics are highly variable and depend on the properties of
the drug, the route of administration, etc. (Rowland and Martin, 1973;
Tucker, 1992). Except for the case of mechanism-based inhibition, inhibition of drug metabolism can be classified into the following three categories, and the equations corresponding to each inhibition type have been derived (Todhunter, 1979).
1. Competitive Inhibition. Competitive inhibition is a pattern of the inhibition where the inhibitor competes with the drug for the same binding site within an enzyme protein:
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(2) |
2. Noncompetitive Inhibition. Noncompetitive inhibition is a pattern of inhibition where the inhibitor binds to the same enzyme as the drug but the binding site is different, resulting in a conformation change, etc., of the protein:
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(3) |
3. Uncompetitive Inhibition. Uncompetitive inhibition is a pattern of inhibition where the inhibitor binds only to the enzyme forming a complex with the drug:
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(4) |
S),
the degree of inhibition (R) is expressed by the following equation
(5), independent of the inhibition pattern, except in the case of the
uncompetitive inhibition (Tucker, 1992):
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(5) |
D. Prediction of In Vivo Drug-Drug Interactions Based on In Vitro
Data
1. General Equations.
The following factors determine the
degree of change in Css and AUC caused by the drug-drug
interaction in vivo:
| 1) | The route of administration (intravenous or oral, i.e., whether the drug first passes through the liver or not). |
| 2) | Fraction (fh) of hepatic clearance (CLh) in total clearance (CLtot). |
| 3) | Fraction (fm) of the metabolic process subject to inhibition in CLh. |
| 4) | Unbound concentration of the inhibitor (Iu) around the enzyme. |
| 5) | Inhibition constant (Ki). |
| 6) | Plasma unbound concentration (Cu,ss) of the drug subject to inhibition. |
| 7) | Michaelis constant (Km) for the drug subject to inhibition. |
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(6) |
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(7) |
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(8) |
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(9) |
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(10) |
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i. High clearance drug.
Because
fb · CLint is much larger than the hepatic
blood flow rate (Qh) (Qh 
fb · CLint), CLh is
rate-limited by the flow rate (CLh = Qh). When
the altered CLh is still rate-limited by the flow rate
(CLh' = Qh), i.e., Qh fb. CLint', then CLh' equals CLh. Thus, Rc can be calculated to be unity by
equation (10), indicating no change in AUCiv or
Css. This is not the case when the inhibition is so
extensive that CLh is not limited by the flow rate any
more.
ii. Low clearance drug. In the case of a low clearance drug, CLh = fb · CLint and CLh' = fb. CLint'. If the protein binding is not altered by the inhibitor, the ratio (y) of CLh and CLh' can be calculated as follows:
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(11) |
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(12) |
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(13) |
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(14) |
Cu,ss.
b. ORAL ADMINISTRATION.
The change in AUCpo
after a single oral administration and that in Css,av after
repeated oral administration can be expressed by the following equation
(15), if the dose and administration interval is constant:
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(15) |
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/Css,av = CLh/Fh = fb · CLint
can be derived based on the well-stirred model, irrespective of the
value of CLh relative to Qh, where D is dose.
In this model, therefore, either AUCpo or
Css,av is affected directly by the reduction in CLint without a contribution from the hepatic blood flow
rate. For this reason, the well-stirred model can detect the maximum effect of an inhibitor. Thus, the well-stirred model was used in the
following discussion of the prediction of drug-drug interactions after
oral administration.
i. High clearance drug.
Because
fb · CLint is much larger than the hepatic
blood flow rate (Qh fb · CLint), CLh is
rate-limited by the flow rate (CLh = Qh). When
the altered CLh is still rate-limited by the flow rate
(CLh' = Qh), i.e., Qh fb · CLint', then CLh' equals CLh. On the other hand, Fh = Qh/(fb · CLint) and
Fh' = Qh/(fb · CLint'). Therefore,
the following equation (16) can be derived from equation (15):
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(16) |
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(17) |
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(18) |
ii. Low clearance drug. Since the first-pass hepatic availability is close to unity for low clearance drugs, the final equation (14) should be the same for intravenous and oral administration.
The effect of the inhibitor on the Cmax after oral administration also depends on the clearance of the drug. Assuming that the drug absorption from the gastrointestinal tract is sufficiently rapid, Cmax is proportional to Fh. Based on the well-stirred model, Fh can be expressed as follows:
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(19) |
fb · CLint: Fh = 1), but is inversely proportional to CLint
in the case of a high clearance drug (Qh fb · CLint: Fh = Qh/fb · CLint), in which case
Cmax also changes in inverse proportion to
CLint. In summary, it is important to know the values of Ki,
Iu, fh, and fm in order to predict
in vivo drug-drug interactions. Approximated fh and
fm values can be estimated from the urinary recovery of the
parent compound and each metabolite. Ki values can be
evaluated by kinetic analysis of in vitro data using human liver
microsomes and recombinant systems and this has already been done for
many compounds. The key, therefore, is the evaluation of
Iu.
2. The evaluation of the unbound concentration of the inhibitor in vivo. Although Iu is the unbound concentration of the inhibitor around the metabolic enzyme in the liver, it is impossible to directly measure this in vivo. However, many drugs are transported into the liver by passive diffusion, allowing for the assumption that the unbound concentration in the liver equals that in the liver capillary at steady-state. This means that estimating the unbound concentration of the inhibitor in the liver capillary may be enough for some drugs. This assumption is not valid, however, in the case of drugs which are actively transported into or out of the liver; the unbound concentration in the liver may be higher in the former case or lower in the latter than in the liver capillary (fig. 3). In these cases, another experiment using human hepatocytes, human liver slices, etc., is required to estimate the kinetic parameters for the active transport. Furthermore, the unbound concentration in the liver capillary is always changing and a concentration gradient is formed from the entrance (portal vein) to the exit (hepatic vein). Which of these concentrations should be considered as Iu? An underestimation of Iu may lead to a "false negative" prediction of actually occurring in vivo drug interaction from in vitro data. In order to avoid a false negative prediction caused by underestimation of Iu, the plasma unbound concentration at the entrance to the liver, where the blood flow from the hepatic artery and portal vein meet, was considered the maximum value of Iu and was used in the prediction (Iin,u; fig. 4).
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).
According to the model in figure 4, influx into the liver consists of
contributions from the hepatic artery and portal vein (after
gastrointestinal absorption). When the drug is absorbed from the
gastrointestinal tract with a first-order rate constant (ka), the maximum influx rate into the liver
(vin,max) can be expressed as follows:
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(20) |
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(21) |
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(22) |
1 is reported for gastric emptying in rats
and humans (Oberle et al., 1990). On the other hand, the
absorption rate constant in humans can be calculated from the time to
reach the maximum concentration (Tmax) and the elimination
constant (kel) as follows:
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(23) |
1, though it should never exceed that,
theoretically, for gastric emptying. Therefore, the theoretically
maximum value of 0.1 min1 was used for
ka when it was calculated to be larger than 0.1 min1. Moreover, in order to avoid a false
negative prediction, the maximum ka of 0.1 min1 was used to obtain the largest inhibitor
concentration if ka was unknown.
E. Examples of the Prediction of Drug-Drug Interactions Based on Literature Data
The methodology described above (see Section III.D.) has been applied to the prediction of in vivo drug-drug interactions from in vitro data gathered from the literature.
1. Successful Cases of In Vitro/In Vivo Prediction.
a.
TOLBUTAMIDE-SULFAPHENAZOLE. Interactions between
tolbutamide and sulfa-agents cause serious side effects such as
hypoglycemic shock in patients (Christensen et al., 1963)
and exhibit the marked interspecies differences in animals. Veronese
et al. (1990) reported about a five-fold increase in both
AUCpo and t1/2 of tolbutamide in humans
following coadministration of 500 mg sulfaphenazole (table
2, fig. 5).
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). On the contrary, the
CLtot of tolbutamide is increased 15 to 30% in rabbits
with little change in the t1/2 (fig.
6) (Sugita et al., 1984).
Because tolbutamide is a low clearance drug with a small urinary
excretion, the CLtot after intravenous administration is
expressed by the following equation (24):
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(24) |
tried to reconstruct the
CLtot in vivo based on the values of unbound fraction in
blood (fb) and CLint estimated separately by in
vitro binding and metabolic studies. Sulfaphenazole inhibits both
plasma protein binding and hepatic metabolism of tolbutamide, causing
the increase in fb and the reduction in CLint,
in both species. Although the CLint falls to about
one-fourth and the fb increases about two-fold in rats, resulting in about a half-fold reduction in the CLtot, the
CLint falls to about one-half and the fb
increases about two-fold in rabbits resulting in little change in the
CLtot (fig. 7). The effects
on the CLint and fb of tolbutamide are not
constant among sulfa-agents; sulfadimethoxine also reduces the
CLint by about one-half in rabbits but increases the
fb more than two-fold, resulting in an increase in the
CLtot and a reduction in the AUC (Sugita et al.,
1984).
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; Nelson and
O'Reilly, 1961). The Ki of sulfaphenazole, a specific
inhibitor of CYP2C9, for tolbutamide methyl hydroxylation in human
liver microsomes in vitro is 0.1-0.2 µM (Miners et al.,
1988; Back et al., 1988). The Imax of
sulfaphenazole after a 500 mg dose was 70 µM in humans, and the
absorption term [the second term in equation (22)] was calculated to
be 8.0 µM using ka = 0.0095
min1, D = 500 mg,
Qh = 1610 ml/min, and Fa = 0.85. Iin,max was, therefore, calculated to be 78 µM, indicating that the contribution of systemic circulation is
greater than that of absorption. Taking the fu value (0.32)
of sulfaphenazole into consideration, Iin,u/Ki
was calculated to be 125-250 (table 2). The plasma protein binding of
tolbutamide is also inhibited by sulfaphenazole in humans, resulting in
about a three-fold increase in fb (Christensen et al., 1963). However, the inhibition is considered almost complete in terms of the product of fb and CLint because
the extent of inhibition of metabolism is much greater than that of its
plasma protein binding. The contribution of the CYP2C9-related
metabolic pathway of tolbutamide is about 80% of the total
(fh · fm = CLh,m/CLtot = 0.8) (table 2).
Therefore, complete inhibition of this metabolic pathway leads to an
80% reduction in CLint, and the AUCpo is
predicted to be five times larger than the control value, which is
consistent with the observed increase (table 2).
b. TRIAZOLAM-KETOCONAZOLE.
Von Moltke et
al. (1996) reported that plasma triazolam concentration after oral
administration of 0.125 mg was greatly elevated by oral ketoconazole
(200 mg), producing a nearly nine-fold reduction in the apparent oral
clearance. They predicted this interaction based on in vitro studies
using human liver microsomes (table 3).
Triazolam is eliminated in humans mainly by CYP3A-mediated metabolism
to 
-hydroxy (OH)- and 4-OH-triazolam. Ketoconazole is a powerful
inhibitor of both these metabolic pathways, with a mean Ki
value of 0.006 and 0.023 µM, respectively (Von Moltke et
al., 1996). In order to estimate ketoconazole concentrations in
the liver, they conducted an in vitro study using mouse liver homogenates in human plasma spiked with ketoconazole; a liver/plasma partition ratio of 1.12 was obtained. On the other hand, the partition ratio was calculated to be 2.03 in the in vivo mouse study where the
ketoconazole concentrations in plasma and liver were measured. The
concentration of ketoconazole in the liver was estimated by multiplying
this partition ratio by the total ketoconazole concentration in plasma
in the clinical study (0.04-9.32 µM). Using the in vitro Ki values, ketoconazole concentration in the liver, and the
contribution of both metabolic pathways (52.5% and 47.5% for - and
4-OH-pathway, respectively), the predicted degree of reduction (>95%)
in triazolam clearance in vivo was consistent with the 88% reduction
actually observed in vivo (table 3). However, it should be noted that in this report, the total concentration of the inhibitor was used instead of unbound concentration in the liver. The unbound
concentration needs to be estimated because the Ki values
obtained in the in vitro studies are based on the concentration in the
medium.
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),
and the absorption term [the second term in equation (22)] was
calculated to be 1.4-2.5 µM using ka = 0.0099-0.018 min1, D = 200
mg, Qh=1610 ml/min, and
Fa = 0.59. The ka was
calculated from equation (23) using the values of
Tmax and t1/2
(=0.693/kel) (Daneshmend et al.,
1984). The Iin,max is, therefore, calculated to be
8.0-9.1 µM. Since the fu of ketoconazole is 0.01, the Iin,max,u is calculated to be 0.080-0.091 µM and the
obtained Iin,max,u/Ki value is 13-15 and
3.5-4.0 for the -OH and 4-OH pathways, respectively, using a
Ki value of 0.006 and 0.023 µM, respectively. Therefore, the reduction in the clearance can be estimated as follows, considering the contribution of each pathway to the total metabolism:
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2. Interactions Predictable for the Objective Metabolic Pathway but
not Predictable for the Overall Data.
a.
SPARTEINE-QUINIDINE. Schellens et al.
(1991) reported that the CLoral of sparteine (dose: 50 mg)
fell from 979 to 341 ml/min (35% of the control value) after
coadministration of 200 mg quinidine (table
4). The main metabolic pathway of
sparteine is CYP2D6-mediated dehydration. Because quinidine is a
specific inhibitor of CYP2D6, it is reasonable that metabolic
inhibition is involved in this quinidine-induced reduction in the
CLoral of sparteine. The Ki of quinidine for
the CYP2D6-mediated metabolism in human liver microsomes in vitro is
reported to be 0.06 µM. The Imax of quinidine after a
dose of 200 mg was 4.1 µM, and the absorption term [the second term
in equation (22)] was calculated to be 0.86-22 µM using
ka = 0.0027-0.069
min1, D = 200 mg,
Qh = 1610 ml/min, and
Fa = 0.83. Iin,max is, therefore, calculated to be 5-26 µM. Because the fu of quinidine is
0.15, the Iin,u and Iin,u/Ki are
calculated to be 0.75-3.9 µM and 13-65, respectively (table 4).
Thus, it was predicted that the dehydration pathway of sparteine would
be almost completely inhibited by quinidine. The contribution of the
dehydration pathway of sparteine to the total elimination is about 25%
(fh · fm = CLh,m/CLtot = 0.25) (table 4). Therefore, the complete inhibition of this
dehydration pathway will reduce the CLoral to 75% of the
control value, which is about two-fold larger than the observed
reduction to 35%. The reasons for this discrepancy may include the
possibility that metabolic pathways other than dehydration may also be
inhibited by quinidine.
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reported that blood concentrations of terfenadine
(dose: 60 mg), detectable only in one of six subjects when administered
alone (Imax = 7 ng/ml), became detectable in all
subjects following coadministration of 200 mg ketoconazole, with the
highest Imax in the same subject elevated to 81 ng/ml (table 5). The main elimination pathway
of terfenadine is CYP3A4-mediated N-dealkylation and hydroxylation
yielding a carboxylate, which may undergo sequential N-dealkylation
(Garteiz et al., 1982). Ki values of
ketoconazole for terfenadine metabolism in vitro have been reported by
two groups. Jurima-Romet et al. (1994) reported the
Ki values of 3 and 10 µM in human liver microsomes and
<1 and 3 µM in human hepatocytes (Jurima-Romet et al.,
1996; Li et al., 1997) for the N-dealkylation and
hydroxylation pathways, respectively. Based on the Ki
values in human liver microsomes reported by Von Moltke et
al. (1994), Iin,u/Ki value was calculated to be 3.3-3.8 and 0.33-0.38 for the N-dealkylation and hydroxylation pathways, respectively (table 5) [see our previous review (Ito et al., 1998) for the details]. As the estimated
contribution (fh · fm = CLh,m/CLtot) of these two pathways to the
total metabolism was about 0.13 and 0.45, respectively, the increase in
the availability and Cmax caused by the metabolic
inhibition was predicted to be about 1.3-fold according to equation
(14). However, the Cmax was actually increased more than
10-fold. The possible reasons to explain this great discrepancy
include: inaccurate measurements of clinical concentrations of
terfenadine being around the detection limit of the assay, and the
contribution of the other 50% of the metabolism of terfenadine
neglected in the analysis. As shown later (see Section III.H.2.),
interactions involving metabolism and/or efflux process in the gut may
have some contributions in the in vitro/in vivo discrepancy. If the
value of Iin,u/Ki comparable to that for
N-dealkylation (3.8) can be applied to other metabolic pathways, the
AUC is predicted to increase more than five-fold, which is close to the
results in vivo.
|
3. Interactions Not Predictable by In Vitro/In Vivo Scaling.
a. IMIPRAMINE-FLUOXETINE. Coadministration of
tricyclic antidepressants such as imipramine and desipramine with
fluoxetine may induce severe side effects including delirium and grand
mal seizure (Preskorn et al., 1990). The AUCpo
of imipramine (dose: 50 mg) is reported to increase about 1.9-fold
after coadministration of 60 mg fluoxetine (table
6) (Bergstrom et al., 1992).
The main elimination pathways of imipramine are 2-hydroxylation and
N-demethylation yielding desipramine, which undergoes further
2-hydroxylation. The 2-hydroxylation pathway is mainly catalyzed by
CYP2D6. On the other hand, fluoxetine is a specific inhibitor of CYP2D6
with a Ki of 0.92 µM for the 2-hydroxylation of
imipramine in human liver microsomes (Skjelbo and Brosen, 1992). The
Imax of fluoxetine after administration of 60 mg is 0.2 µM (Aronoff et al., 1984), and the absorption term [the
second term in equation (22)] was calculated to be 0.83 µM using
ka = 0.012 min1,
D = 60 mg, Qh = 1610 ml/min, and
Fa = 0.80. The ka was
calculated from equation (23) using the values of Tmax and
t1/2(=0.693/kel). Therefore, the
Iin,max is 1.02 µM, indicating that absorption makes a
major contribution. Because the fu of fluoxetine is 0.06, the Iin,u and Iin,u/Ki are
calculated to be 0.061 µM and 0.066, respectively (table 6).
Furthermore, the contribution
(fh · fm = CLh,m/CLtot) of this metabolic pathway (2-hydroxylation) is about 18% of the total.
According to equation (14), therefore, the metabolic inhibition in vivo
was not predicted to have a significant effect on the AUC in spite of
the approximately 2-fold increase actually observed (table 6). Possible
reasons for this discrepancy include the estimation error of
Ki and the possibility that other metabolic pathways and
pharmacokinetic processes also may be altered by fluoxetine.
|
reported that the AUCpo of caffeine (dose:
220 to 230 mg) increased about 1.6-fold after coadministration of 250 mg ciprofloxacin (table 7). The main
metabolic pathways of caffeine are 1-demethylation,
3-demethylation, 7demethylation, and 8-hydroxylation, mediated by
CYP1A2. Because antimicrobial agents such as ciprofloxacin are specific
inhibitors of CYP1A2, metabolic inhibition should be involved in the
ciprofloxacininduced increase in the AUCpo of caffeine.
The Ki of ciprofloxacin for caffeine 3-demethylation in
human liver microsomes in vitro is around 150 µM (Kalow and Tang,
1991). The Imax of ciprofloxacin after administration of
250 mg is 4.3-6.0 µM (Guay et al., 1987), and the
absorption term [the second term in equation (22)] was calculated to
be 9.3-22 µM using ka = 0.02-0.04
min1, D = 250 mg,
Qh = 1610 ml/min, and Fa = 0.92. Therefore, the Iin,max was calculated to be
14-28 µM, indicating that the contribution of absorption is greater
than that of the systemic circulation. Because the fu of
ciprofloxacin is 0.8, the Iin,u and
Iin,u/Ki are calculated to be 11-22 µM and
0.07-0.15, respectively (table 7). The contribution
(fh · fm = CLh,m/CLtot) of this pathway to the total
elimination is about 79% (table 7). Therefore, if the maximum value of
Iin,u/Ki (0.15) is used in the evaluation of
the inhibition of this pathway, the ciprofloxacin-induced increase in
the AUCpo of caffeine can be predicted as follows:
|
|
).
|
reported that the AUCpo of
cyclosporin (dose: 6 mg) increased about 1.6-fold after
coadministration of 1.1 g erythromycin (table
8). Cyclosporin has many metabolic pathways, among which the metabolism to M1, M17, and M21 are mediated by CYP3A4 (Lensmeyer et al., 1988). These metabolites are
sequentially metabolized to form carboxylates in vivo (Pichard et
al., 1990). Because erythromycin is a specific inhibitor of
CYP3A4, metabolic inhibition should be involved in this increase in the
AUCpo of cyclosporin. The Ki of erythromycin
for cyclosporin hydroxylation in human liver microsomes in vitro is
about 75 µM (Miller et al., 1990). The Imax of
erythromycin after a 1.1 g dose is 10-12 µM (Vereerstraeten
et al., 1987), and the absorption term [the second term in
equation (22)] was calculated to be 15-93 µM using
ka = 0.02-0.10 min1,
D = 1.1 g, Qh = 1610 ml/min, and
Fa = 0.58. In the calculation of ka
using kel(=0.693/t1/2) and
Tmax (Lensmeyer et al., 1988) based on equation
(23), the maximum and minimum values were obtained taking the
interindividual variation in Tmax into consideration. As
mentioned above (see Section III.D.2.), however, it would be preferable
to use the maximum value of ka in order to avoid a false
negative prediction of the possibility of a drug-drug interaction. Equation (22) gives an Iin,max of 25-105 µM. Because the
fu of erythromycin is 0.16, Iin,u and
Iin,u/Ki are calculated to be 4-17 µM and
0.05-0.23, respectively (table 8). The contribution of these metabolic
pathways of cyclosporin to the total elimination (CLoral=fb · CLint)
is about 76% (fh · fm = CLh,m/CLtot = 0.76) (table 8).
Therefore, if the maximum value of Iin,u/Ki
(0.23) is used in the evaluation of the inhibition of these pathways, the erythromycin-induced increase in the AUCpo of
cyclosporin can be predicted from equation (14) as follows:
|
|
|
). Therefore,
it may be inappropriate to estimate the pharmacokinetic alteration
based only on the same methodology used for competitive inhibitors.
Another methodology has to be developed for the prediction of in vivo
drug-drug interactions based on the so-called "mechanism-based inhibition" of P450 from in vitro data; it should include the possible effects of inhibitor exposure time and the turn-over of the
enzyme, as will be discussed later in details (see Section III.G.).
Table 9 summarizes the results of the
prediction of drug-drug interactions based on in vitro data. In some
cases, in vivo pharmacokinetic parameters (Cmax, AUC) were
changed to some extent by drug-drug interactions although the values of
Iu/Ki calculated from in vitro data were too
small to expect any metabolic inhibition in vivo. This finding suggests
that alterations in in vivo pharmacokinetic parameters may be caused by
other factors such as interactions involving absorption or excretion.
Furthermore, in the case of macrolide antibiotics such as erythromycin,
the model describing "the mechanism-based inhibition" should be
introduced to predict the drug-drug interaction.
|
F. Procedure for Predicting Inhibitory Effects of Coadministered Drugs on the Hepatic Metabolism of Other Drugs
The following is a proposed procedure for predicting the metabolic inhibition by one drug that is expected to be coadministered with the study drug being developed.
| 1) | Confirmation of the involvement of P450 by in vitro inhibition studies, e.g., using SKF-525A and CO. |
| 2) | Identification of the P450 isozyme by metabolic studies using human P450 expression systems and the inhibition studies using P450 antibodies or inhibitors specific for each isozyme. |
| 3) | Searching the in vivo pharmacokinetic data for the coadministered drug that possibly inhibits the P450 isozyme catalyzing the metabolism of the drug under investigation. The maximum plasma unbound concentration of the coadministered inhibitor (Iin,max,u) can be estimated by Imax (or Imax,ss), ka (or Tmax and t1/2), Fa, and fu as described by equation (22). |
| 4) | Evaluation of the unbound concentration of inhibitor in the liver, which may be larger than Iin,max,u in the case of an inhibitor that is actively transported into hepatocytes (fig. 3). The unbound concentration ratio (liver/plasma) should be measured by the method given below (see Section III.H.1.) using human hepatocytes (or rat isolated hepatocytes if human samples are not available). A 5- to 10-fold safety margin may also be considered for the concentration ratio if there are no experimental results available. |
| 5) | In vitro measurement of the Ki of the inhibitor for the metabolism of the study drug using human liver microsomes or human P450 expression systems. |
| 6) | Assessing the possibility of metabolic inhibition by comparing the values of Iin,max,u and Ki. If the Iin,max,u/Ki value is larger than 0.3-1, you may want to consider designing the in vivo drug interaction studies. The limit of Iin,max,u/Ki value should depend on the pharmacodynamic and/or toxicodynamic features and the therapeutic window of the drug investigated. |
Although a more precise and quantitative prediction requires the collection of more information and/or elaborate experiments, the authors think that the judgment of "absence of a metabolic drug-drug interaction" may be reliable if the interaction is not expected by this prediction method. The above metodology has been proposed based on the idea of avoiding false negative predictions. Therefore, it should be kept in mind that some of the predicted drug-drug interactions may not take place in vivo. We speculate that more than 80 combinations could be judged as "non-interacting" if 100 kinds of drug-drug interactions are investigated, at random, by this methodology. Of the less than 20 combinations involving possible interactions, cautious investigations using human in vivo studies would be necessary for some combinations, taking the therapeutic range, pharmacokinetic/pharmacodynamic characteristics, and severity of the adverse effects into consideration.
G. Mechanism-Based Inhibition
1. Characteristics of Mechanism-Based Inhibition.
In 1993, 15 Japanese patients with cancer and herpes zoster died from
5-fluorouracil (5-FU) toxicity caused by high blood concentrations
caused by an interaction between 5-FU and sorivudine, an antiviral drug
(Pharmaceutical Affairs Bureau, 1994). The interaction between
sorivudine and 5-FU is based on "mechanism-based inhibition", which
differs from the competitive or noncompetitive inhibition described so
far (Desgranges et al., 1986; Okuda et al.,
1997). A mechanism-based inhibitor is metabolized by an enzyme to form a metabolite which covalently binds to the same enzyme, leading to
irreversible inactivation of the enzyme. Several terms such as
"mechanism-based inactivation," "enzyme-activated irreversible inhibition," "suicide inactivation," and "kcat
inhibition" have all been used as alternatives to "mechanism-based
inhibition" (Silverman, 1988). It should be noted, however, that the
inhibition is not called "mechanism-based inhibition" when the
inhibitor is metabolically activated by an enzyme and inactivates
another. Sorivudine is converted by gut flora to 5-bromovinyluracil
(BVU), which is metabolically activated by dihydropyrimidine
dehydrogenase (DPD), a rate-limiting enzyme in the metabolism of 5-FU
(fig. 8) (Okuda et al., 1995).
Then, the activated BVU irreversibly binds to DPD itself. This type of
interaction needs more attention than the common type of inhibition,
because the inhibitory effect remains after elimination of the
inhibitor (sorivudine, BVU) from blood and tissue and this can lead
to serious side-effects.
View larger version (16K):
[in a new window]
Fig. 8.
Proposed mechanism for lethal toxicity exerted by
simultaneous oral administration of sorivudine and
1-(2-tetrahydrofuryl)-5-fluorouracil (FT), a prodrug of 5-FU (Okuda
et al., 1995 ).
), furafylline (against CYP1A2) (Kunze and Trager, 1993), and orphenadrine (against CYP2B1) (Murray and Reidy, 1990).
2. Kinetic Analysis of Mechanism-Based Inhibition: Analysis of In
Vitro Data.
Is it also possible to predict the extent of in vivo
interactions from in vitro data in the case of mechanism-based
inhibition? The first step in making such predictions is to construct a
model describing the inhibition. Waley (1985) proposed the model shown in figure 9 for mechanism-based
inhibition. Mass-balance equations for the enzyme-inhibitor complexes
(EI and EI') and the inactive enzyme (Einact) can be
expressed as follows:
![<UP>d</UP>(<UP>EI</UP>)<UP>/dt</UP>=<UP>k</UP><SUB>+<UP>1</UP></SUB> · [<UP>I</UP>] · <UP>E</UP>−(<UP>k</UP><SUB>−<UP>1</UP></SUB>+<UP>k</UP><SUB><UP>2</UP></SUB>) · <UP>EI</UP>](/content/vol50/issue3/fulltext/387/img058.gif)
|
(25) |
|
(26) |
|
(27) |
|
(28) |
![<UP>EI′</UP>=<UP>k</UP><SUB>+<UP>1</UP></SUB> · <UP>k<SUB>2</SUB></UP> · [<UP>I</UP>] · <UP>E<SUB>o</SUB>/</UP>((<UP>k</UP><SUB>+<UP>1</UP></SUB> · <UP>k<SUB>2</SUB></UP>+<UP>k</UP><SUB>+<UP>1</UP></SUB>(<UP>k<SUB>3</SUB></UP>+<UP>k</UP><SUB><UP>4</UP></SUB>))](/content/vol50/issue3/fulltext/387/img062.gif)
|
(29) |
![· [<UP>I</UP>]+(<UP>k<SUB>3</SUB></UP>+<UP>k</UP><SUB><UP>4</UP></SUB>)(<UP>k</UP><SUB>−<UP>1</UP></SUB>+<UP>k</UP><SUB><UP>2</UP></SUB>))](/content/vol50/issue3/fulltext/387/img063.gif)
|
![<UP>V<SUB>inact</SUB></UP>=<UP>k</UP><SUB>+<UP>1</UP></SUB> · <UP>k<SUB>2</SUB></UP> · <UP>k<SUB>4</SUB></UP> · [<UP>I</UP>] · <UP>E<SUB>o</SUB>/</UP>((<UP>k</UP><SUB>+<UP>1</UP></SUB> · <UP>k<SUB>2</SUB></UP>+<UP>k</UP><SUB>+<UP>1</UP></SUB>(<UP>k<SUB>3</SUB></UP>+<UP>k</UP><SUB><UP>4</UP></SUB>))](/content/vol50/issue3/fulltext/387/img064.gif)
|
(30) |
![· [<UP>I</UP>]+(<UP>k<SUB>3</SUB></UP>+<UP>k</UP><SUB><UP>4</UP></SUB>)(<UP>k</UP><SUB>−<UP>1</UP></SUB>+<UP>k</UP><SUB><UP>2</UP></SUB>))](/content/vol50/issue3/fulltext/387/img065.gif)
|
![=(<UP>k<SUB>2</SUB></UP> · <UP>k<SUB>4</SUB>/</UP>(<UP>k<SUB>2</SUB></UP>+<UP>k<SUB>3</SUB></UP>+<UP>k</UP><SUB><UP>4</UP></SUB>) · [<UP>I</UP>] · <UP>E</UP><SUB><UP>o</UP></SUB>)<UP>/</UP>((<UP>k<SUB>3</SUB></UP>+<UP>k</UP><SUB><UP>4</UP></SUB>)<UP>/</UP>(<UP>k<SUB>2</SUB></UP>](/content/vol50/issue3/fulltext/387/img066.gif)
|
![+<UP>k<SUB>3</SUB></UP>+<UP>k</UP><SUB><UP>4</UP></SUB>) · (<UP>k</UP><SUB>−<UP>1</UP></SUB>+<UP>k</UP><SUB><UP>2</UP></SUB>)<UP>/k</UP><SUB>+<UP>1</UP></SUB>+[<UP>I</UP>])](/content/vol50/issue3/fulltext/387/img067.gif)
|
|
(31) |
![<UP>k<SUB>obs</SUB></UP>=<UP>V<SUB>inact</SUB>/E<SUB>o</SUB></UP>=<UP>k<SUB>inact</SUB></UP> · [<UP>I</UP>]<SUB><UP>o</UP></SUB><UP>/</UP>(<UP>K<SUB>i,app</SUB></UP>+[<UP>I</UP>]<SUB><UP>o</UP></SUB>)](/content/vol50/issue3/fulltext/387/img069.gif)
|
(32) |
|
(33) |
|
(34) |
 1 Preincubate the enzyme suspension for an appropriate
period in the presence of various concentrations of inhibitor.
|
|
| 2 Mix the substrate solution with the enzyme suspension to
measure the initial metabolic rate of the substrate so that the
remaining enzymatic activity can be determined. The incubation time for
this measurement should be as short as possible (around 1-3 min)
compared with the preincubation time, in order to minimize the reaction
of the inhibitor with the enzyme during the incubation.
|
|
| 3 Plot the logarithm of the enzymatic activity against the
preincubation time. The apparent inactivation rate constant (kobs) can be determined from the slope of the initial
linear phase.
|
|
| 4 Obtain the parameters (kinact,
Ki,app) from the relationship between kobs and
the initial inhibitor concentration ([I]o) using the
nonlinear least squares regression method.
|
|
|
). The
"partition ratio" in table 10 is defined as k3/k4 and can be obtained as the ratio of the
amount of the inhibitor released as the product and the amount
covalently bound to the enzyme. The larger the contribution of this
inactivation pathway to the total elimination of the inhibitor in the
in vitro system, the smaller the partition ratio. The partition ratio
of the most powerful mechanism-based inhibitor is zero (every turnover
produces inactivated enzyme).
|
| a. | Preincubation time-dependent inhibition of the enzyme (time-dependence). |
| b. | No inhibition if cofactors necessary for producing the activated inhibitor (eg., NADPH for P450 metabolism) are not present in the preincubation medium. |
| c. | Potentiation of the inhibition depending on the inhibitor concentration (saturation kinetics). |
| d. | Slower inactivation rate of the enzyme in the presence of substrate compared with its absence (substrate protection). |
| e. | Enzyme activity not recovered following gel filtration or dialysis (irreversibility). |
| f. | 1:1 Stoichiometry of the inhibitor and the active site of the enzyme (stoichiometry of inactivation). |
3. Prediction of In Vivo Interactions from In Vitro Data in the Case of Mechanism-Based Inhibition. How can inhibitory effects in vivo be estimated from the microscopic inhibition parameters obtained from in vitro studies?
A simulation study was carried out using the perfusion model in figure 11 and the pharmacokinetic parameters in table 11. The inhibitor is assumed to inactivate a certain CYP isozyme in the liver in a "mechanism-based" manner. The differential equations for the substrate (S) and inhibitor (I) can be expressed as follows:
|
|
|
(35) |
|
|
(36) |
|
(37) |
|
(38) |
|
(39) |
|
(40) |
|
(41) |
|
(42) |
|
(43) |
|
(44) |
|
(45) |
| a. | S and I are simultaneously administered orally. |
| b. | Both S and I are eliminated only in the liver and their elimination can be described by the Michaelis-Menten equation. |
| c. | Distribution of S and I in the liver rapidly reaches equilibrium, and the unbound concentration in the hepatic vein is equal to that in the liver at equilibrium (well-stirred model). |
| d. | The unbound molecule in the liver is related to the elimination. |
| e. | The contribution of the CYP isozyme subject to inactivation is small in total elimination of the inhibitor in the liver (i.e., the elimination of the inhibitor itself is not altered by inactivation of the enzyme). |
| f. | Gastrointestinal absorption can be described by a first-order rate constant. |
|
(46) |
|
(47) |
|
1. As the inhibitor itself
is gradually eliminated from blood and liver, the enzyme level recovers
to reach its initial level by replacement of the inactivated enzyme by
newly synthesized enzyme (fig. 13). The
faster the turnover rate of the enzyme, the faster the enzyme level is
restored to its initial level.
|
|
|
H. Problems To Be Solved for the More Precise Prediction of
Drug-Drug Interactions
1. Estimation of the Tissue Unbound Concentration of the Inhibitor
That Is Actively Transported into Hepatocytes.
As described above
(see Section III.D.), in vivo drug-drug interactions based on
inhibition of hepatic metabolism can be predicted by the values of
Ki and the unbound concentration of the inhibitor in the
liver, which cannot be directly measured in vivo. The analyses have
been based on the assumption that the steady-state unbound concentration of the inhibitor in the liver is equal to that in the
hepatic capillary (sinusoid), because many drugs are transported into
hepatocytes by passive diffusion. However, in the case of an inhibitor
that is concentrated in hepatocytes by active transport (Yamazaki
et al., 1995, 1996), the extent of the interaction may be
underestimated if plasma concentrations are used in the prediction. investigated the effect of omeprazole on
diazepam metabolism using rat liver microsomes and hepatocytes. Omeprazole inhibited both 3-hydroxylation and N-demethylation of
diazepam, and the Ki in hepatocytes was smaller than that
in microsomes for both pathways (table
12). On the other hand, as shown in
figure 16, the in vivo clearance of
diazepam was reduced depending on omeprazole concentration, which was
maintained under steady-state conditions. In this in vivo study, the
Ki was calculated to be 57 µM from equation (48).
|
(48) |
|
(49) |
|
|
|
; Nakamura et al., 1994). Assuming both active transport and passive diffusion for the influx into hepatocytes and only passive diffusion for the efflux from the
hepatocytes (figure 18), the initial
uptake velocity in the presence of an adequate concentration of
ATP-depletor represents the uptake by passive diffusion, because the
active transport of the drug is completely inhibited. C/M ratio in the
steady-state can be described by equation (50):
|
(50) |
|
(51) |
|
|
|
reported that FCCP, rotenone,
or sodium azide causes a marked reduction in the uptake of
[3H]cimetidine into isolated rat hepatocytes that
parallel the reduction in cellular ATP (fig.
19). The unbound concentration ratio of
cimetidine in hepatocytes in this case can be calculated to be about
5.6 according to equation (50). If the unbound concentration ratio in a
linear condition (Cmedium Km) is calculated
from equations (50) and (52) using the values of Vmax,
Km, and PSpassive which were determined by
fitting the initial uptake velocity (vo) to equation (53),
a value of 7.1 can be obtained:
|
(52) |
|
(53) |
-hydroxylation and 4-hydroxylation of triazolam in human liver
microsomes are 600 µM, 36 µM, and 160 µM, respectively (Knodell
et al., 1991; Von Moltke et al., 1996). The
unbound concentration ratios (liver/plasma) when the intracellular
unbound concentration is 600 µM, 160 µM, and 36 µM are calculated
to be about 1.5, 3.7, and 6.4, respectively, using the parameters for
carrier-mediated transport reported by Nakamura et al.
(1994). This calculation indicates that the lower the plasma
concentration of inhibitor, the higher the liver-to-plasma unbound
concentration ratio, when the carrier-mediated active transport
(influx) is operating.
|
) that
concentration of pravastatin, an HMG-CoA reductase inhibitor, in
isolated rat hepatocytes was about 3- to 7-fold higher than that in
medium because of active transport. It was also suggested that
simvastatin and lovastatin, which competitively inhibited the hepatic
uptake of pravastatin, may be transported by the same carrier as
pravastatin (Yamazaki et al., 1993). Since the HMG-CoA
reductase inhibitors such as fluvastatin, simvastatin, and pravastatin
inhibit CYP2C9-mediated 4'-hydroxylation of diclofenac (Transon
et al., 1996), the concentrative uptake into hepatocytes
should be taken into consideration in predicting interactions involving
these drugs.
In the future, more accurate predictions of in vivo drug-drug
interactions may become possible by estimating the unbound
concentration of the inhibitor in the liver in in vitro studies.
2. Evaluation of Drug-Drug Interactions Involving Drug Metabolism
in the Gut.
CYP3A4, an enzyme that metabolizes many drugs,
including cyclosporin, exists not only in the liver but also in the
gut; it plays an important role in the first-pass metabolism after oral administration of its substrates (Kolars et al., 1991, 1992;
Thummel et al., 1996). De Waziers et al. (1990)
have used Western blot analysis and shown that CYP3A4 is highly
expressed in the duodenum and jejunum, secondly to the liver, in humans
(fig. 20).
|
; Gomez
et al., 1995; Gupta et al., 1989). Wu et
al. (1995) attempted to differentiate the absorption and first-pass gut and hepatic metabolism of cyclosporin in humans by a
kinetic analysis of the change in BA by rifampicin-induced induction
and ketoconazole- or erythromycin-induced inhibition of CYP3A4-mediated
metabolism. Based on the model shown in figure 22, BA after oral administration can be
expressed as follows using the fraction of the drug dose absorbed into
and through the gastrointestinal membranes (Fabs), the
fraction of the absorbed dose that passes through the gut into the
hepatic portal blood unmetabolized (Fg), and the hepatic
first-pass availability (Fh):
|
(54) |
|
|
(55) |
|
(56) |
Eh) at the time when the metabolism was
inhibited by the interacting drug (assuming
Fg = 1). As shown in table
13, the calculated extraction ratio in
the gut was always larger than that in the liver, irrespective of the
value of Fabs. Furthermore, Xg was larger than
Xh when Fabs<86% in the case of rifampicin coadministration and was smaller than Xh in the case of
erythromycin coadministration, indicating that the interaction observed
in the gut is about the same or larger than that observed in the liver.
|
|
|
; Terao et al., 1996). Recent studies have
revealed the overlapping substrate specificity of CYP3A4 and p-gp. As
shown in table 14, many substrates of
CYP3A4 are reported to be substrates or inhibitors of p-gp (Wacher
et al., 1995). In another study (Schuetz et al.,
1996) using a cell line derived from a human colon adenocarcinoma, it
was shown that many of the p-gp inducers also induce CYP3A4, suggesting
the possibility of common regulatory factors for these proteins (table
15).
|
|
, 1996) has pointed out the possibility that the synergistic
effects of CYP3A4-mediated metabolism and p-gp-mediated efflux in the
gut epithelium may result in an unexpectedly high first-pass effect in
the gut after oral administration. Thus, the inhibition or induction of
CYP3A4 and/or p-gp caused by drug-drug interactions may affect the
first-pass effect in the gut.
The effects of gut metabolism and efflux from epithelial cells to the
lumen on the absorption of orally administered drugs were investigated
by a simulation study. Based on the compartment model shown in figure
23, the fraction of the drug absorbed
into the portal vein (Fa) can be expressed as follows:
|
(57) |
1 is the membrane
permeation constant from the lumen into the cell. 1 is a
hybrid parameter with no dimensions, consisting of the transit time in
the lumen, diffusion in the unstirred water layer, and the permeability
through the brush-border membrane of the gut epithelial cells:
|
(58) |
represents
small intestinal transit time, and Vav represents the
average luminal volume.
|
|
(59) |
|
(60) |
1) is relatively small (fig. 24 right panel). The results in figure 24 suggest that the
effect of p-gp inhibition caused by drug-drug interactions on drug
absorption may depend on the relative extent of each process (influx
from the lumen into the epithelial cell, efflux from the cell to the
lumen, intracellular metabolism, and transport from the cell to the
portal vein).
|
| |
Footnotes |
|---|
a Address for correspondence: Yuichi Sugiyama, Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan, Phone: 81-3-5689-8094, Fax: 81-3-5800-6949, E-mail: sugiyama{at}seizai.f.u-tokyo.ac.jp.
| |
Abbreviations |
|---|
1, membrane
permeation constant from the lumen into the cell;
5, FU, 5-fluorouracil;
ATP, adenosine triphosphate;
AUC, area under concentration-time curve;
AUCIV, AUC after intravenous administration;
AUCpo, AUC after oral administration;
AUCu, AUC
for unbound drugs;
BA, bioavailability;
BBM, brush border membrane;
BLM, basolateral membrane;
BVU, 5-bromovinyluracil;
C/M ratio, cell-to-medium unbound concentration ratio;
Ccell, steady-state total drug concentration in the cell;
Ccell,free, steady-state unbound drug concentration in the
cell;
Ch, concentration in the liver;
CL, clearance;
CLh, hepatic clearance;
CLh,m, clearance for a
particular metabolic pathway;
CLint, intrinsic clearance
for metabolism;
CLint,all, overall intrinsic clearance;
CLoral, oral clearance;
CLr, renal clearance;
CLtot, total clearance;
Cmax, maximum
concentration;
Cmedium, steady-state total drug
concentration in the medium;
Cportal, concentration in
portal vein;
Css, steady-state concentration;
Csys, concentration in systemic blood;
Cu,ss, Css for unbound drugs;
D, dose;
DBSP, dibromosulfophthalein;
DPD, dihydropyrimidine dehydrogenase;
E, enzyme;
Eact, amount of active enzyme;
Eg, gut
extraction ratio;
Eh, hepatic extraction ratio;
EI, enzyme-inhibitor complex;
Einact, amount of inactive
enzyme;
EIS, enzyme-inhibitor-substrate complex;
Eo, total
concentration of the enzyme;
ES, enzyme-substrate complex;
Fa, fraction of drug absorbed from the gastrointestinal
tract into the portal vein;
Fabs, fraction of drug dose
absorbed into and through the gastrointestinal membranes;
fb, unbound fraction in blood;
Fg, fraction of
absorbed dose that passes through the gut into the hepatic portal blood
unmetabolized ;
fh, fraction of CLh in
CLtot;
Fh, hepatic availability;
fm, fraction of the metabolic process subject to inhibition
in CLh;
fT, unbound fraction in the cell;
fu, unbound fraction in plasma;
I, inhibitor;
Ih, concentration of inhibitor in the liver;
Iin,max, maximum concentration of inhibitor in portal vein;
Iin,max,u, maximum unbound concentration of inhibitor in
the portal vein;
Iin,u, unbound concentration of inhibitor
in the portal vein;
Imax, maximum concentration of inhibitor
in the systemic blood;
Imax,ss, steady-state maximum
concentration of inhibitor;
Iportal, concentration of
inhibitor in portal vein;
Iss, steady-state total plasma
concentration of inhibitor;
Isys, concentration of
inhibitor in systemic blood;
Iu, unbound concentration of
the inhibitor;
ka, first order absorption rate constant;
kdeg, degradation rate constant of the enzyme;
kel, elimination rate constant;
Ki, inhibition
constant;
Ki,app, apparent inactivation constant;
kinact, maximum inactivation rate constant;
Km, Michaelis constant;
kobs, apparent inactivation rate
constant;
Kp, liver-to-blood concentration ratio;
P, product;
p-gp, p-glycoprotein;
P1,app, apparent influx
clearance from the gut lumen into epithelial cells;
P2, efflux clearance from epithelial cells to the gut lumen;
P3, absorption clearance from the epithelial cells to the
portal vein;
PS, intrinsic clearance for membrane permeation;
PSactive, membrane permeation clearance by active
transport;
PSeff, intrinsic clearance for efflux;
PSinf, intrinsic clearance for influx;
PSpassive, membrane permeation clearance by passive
diffusion;
Qa, blood flow rate in the hepatic artery;
Qh, hepatic blood flow rate;
Qpv, blood flow
rate in the portal vein;
R, degree of inhibition;
Rc, degree of increase in CSS and AUC;
S, substrate;
t', time
after oral administration;
t1/2, elimination half life;
, small intestinal transit time;
Tmax, time to reach the maximum concentration;
Vabs, absorption rate;
Vav, average luminal
volume;
Vd, volume of distribution;
Vh, volume
of liver;
vin,max, maximum influx rate into the liver;
Vmax, maximum metabolic rate;
vo, initial
uptake velocity obtained in the absence of ATP-depletors;
vpassive, initial uptake velocity obtained in the presence
of ATP-depletors;
Vportal, volume of portal vein;
Xg, change in gut extraction ratio;
Xh, change
in hepatic extraction ratio.
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why are they still regarded as clinically important?
Br J Clin Pharmacol
37:
125-128[Medline].
0031-6997/98/503-0387$03.00/0
PHARMACOLOGICAL REVIEWS
Copyright © 1998 by The American Society for Pharmacology and Experimental Therapeutics
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|
|
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|
|
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|
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|
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|
|
|
|
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|
|
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
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K. Yamano, K. Yamamoto, M. Katashima, H. Kotaki, S. Takedomi, H. Matsuo, H. Ohtani, Y. Sawada, and T. Iga Prediction of Midazolam{---}cyp3a Inhibitors Interaction in the Human Liver from in Vivo/in Vitro Absorption, Distribution, and Metabolism Data Drug Metab. Dispos., April 1, 2001; 29(4): 443 - 452. [Abstract] [Full Text]
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M. Ishigami, T. Honda, W. Takasaki, T. Ikeda, T. Komai, K. Ito, and Y. Sugiyama A Comparison of the Effects of 3-Hydroxy-3-Methylglutaryl-Coenzyme A (HMG-CoA) Reductase Inhibitors on the CYP3A4-Dependent Oxidation of Mexazolam in Vitro Drug Metab. Dispos., March 1, 2001; 29(3): 282 - 288. [Abstract] [Full Text]
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J. Kajita, T. Kuwabara, H. Kobayashi, and S. Kobayashi CYP3A4 Is Mainly Responsibile for the Metabolism of a New Vinca Alkaloid, Vinorelbine, in Human Liver Microsomes Drug Metab. Dispos., September 1, 2000; 28(9): 1121 - 1127. [Abstract] [Full Text]
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M. B. Fisher, K. Campanale, B. L. Ackermann, M. VandenBranden, and S. A. Wrighton In Vitro Glucuronidation Using Human Liver Microsomes and The Pore-Forming Peptide Alamethicin Drug Metab. Dispos., May 1, 2000; 28(5): 560 - 566. [Abstract] [Full Text]
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J. H. Lin, I-W. Chen, M. Chiba, J. A. Nishime, and F. A. deLuna Route-Dependent Nonlinear Pharmacokinetics of a Novel HIV Protease Inhibitor: Involvement of Enzyme Inactivation Drug Metab. Dispos., April 1, 2000; 28(4): 460 - 466. [Abstract] [Full Text]
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S.-i. Kanamitsu, K. Ito, H. Okuda, K. Ogura, T. Watabe, K. Muro, and Y. Sugiyama Prediction of In Vivo Drug-Drug Interactions Based on Mechanism-based Inhibition from In Vitro Data: Inhibition of 5-Fluorouracil Metabolism by (E)-5-(2-Bromovinyl)uracil Drug Metab. Dispos., April 1, 2000; 28(4): 467 - 474. [Abstract] [Full Text]
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K. Komatsu, K. Ito, Y. Nakajima, S.-i. Kanamitsu, S. Imaoka, Y. Funae, C. E. Green, C. A. Tyson, N. Shimada, and Y. Sugiyama Prediction of in Vivo Drug-Drug Interactions between Tolbutamide and Various Sulfonamides in Humans Based on in Vitro Experiments Drug Metab. Dispos., April 1, 2000; 28(4): 475 - 481. [Abstract] [Full Text]
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K. Yamano, K. Yamamoto, H. Kotaki, S. Takedomi, H. Matsuo, Y. Sawada, and T. Iga Quantitative Prediction of Metabolic Inhibition of Midazolam by Erythromycin, Diltiazem, and Verapamil in Rats: Implication of Concentrative Uptake of Inhibitors into Liver J. Pharmacol. Exp. Ther., March 1, 2000; 292(3): 1118 - 1126. [Abstract] [Full Text]
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H. Takahashi, T. Kashima, S. Kimura, N. Murata, T. Takaba, K. Iwade, T. Abe, H. Tainaka, T. Yasumori, and H. Echizen Pharmacokinetic Interaction between Warfarin and a Uricosuric Agent, Bucolome: Application of In Vitro Approaches to Predicting In Vivo Reduction of (S)-Warfarin Clearance Drug Metab. Dispos., October 1, 1999; 27(10): 1179 - 1186. [Abstract] [Full Text]
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X.-J. Zhao, E. Koyama, and T. Ishizaki An In Vitro Study on the Metabolism and Possible Drug Interactions of Rokitamycin, a Macrolide Antibiotic, Using Human Liver Microsomes Drug Metab. Dispos., July 1, 1999; 27(7): 776 - 785. [Abstract] [Full Text]
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K. Ito, D. Hallifax, R. S. Obach, and J. B. Houston IMPACT OF PARALLEL PATHWAYS OF DRUG ELIMINATION AND MULTIPLE CYTOCHROME P450 INVOLVEMENT ON DRUG-DRUG INTERACTIONS: CYP2D6 PARADIGM Drug Metab. Dispos., June 1, 2005; 33(6): 837 - 844. [Abstract] [Full Text] [PDF]
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: Tolbutamide (500 mg p.o.) alone; 
: Tolbutamide (500 mg
p.o.)+Sulfaphenazole (500 mg p.o., q12h).
: control; 
: with FCCP (2 µM); 
: with sodium azide (30 mM).
