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Review ArticleReview

Prediction of Pharmacokinetic Alterations Caused by Drug-Drug Interactions: Metabolic Interaction in the Liver

K. Ito, T. Iwatsubo, S. Kanamitsu, K. Ueda, H. Suzuki and Y. Sugiyama
Pharmacological Reviews September 1998, 50 (3) 387-412;
K. Ito
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T. Iwatsubo
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S. Kanamitsu
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K. Ueda
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H. Suzuki
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Y. Sugiyama
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I. Introduction

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.

II. Drug-Drug Interactions Other Than Involving Metabolism

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,ssand 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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Table 1

Relationship between the area under concentration-time curve (AUC) or AUC for unbound drugs (AUCu) and the hepatic blood flow (Qh), the hepatic intrinsic clearance (CLint,h), and the blood unbound fraction (fb) based on the well-stirred model (Wilkinson, 1983)

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 fuwere 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.

Figure 1
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Figure 1

Effects of protein binding replacement on the blood concentration profile during a constant intravenous infusion of (a) a high clearance drug with a high Vd, (b) a high clearance drug with a low Vd, (c) a low clearance drug with a high Vd, or (d) a low clearance drug with a low Vd. The protein binding and the tissue distribution of the drug are assumed to reach the equilibrium rapidly. An ideal situation is assumed where the concentration of the interacting drug (the displacer of the protein binding) immediately reaches a constant value at 10 min.

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 (Horiet al., 1982; Takano et al., 1984; Tanigawaraet 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:CLint,all=PSinf·CLint/(PSeff+CLint) Equation 1where PSinf is intrinsic clearance for influx, and PSeff is intrinsic clearance for efflux.

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.

III. Drug-Drug Interactions Involving Metabolism in the Liver

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 (Bergstromet 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 figure2, P450 demethylates and oxidizes the macrolide antibiotic into a nitrosoalkane that forms a stable, inactive complex with P450 (Periti et al., 1992).

Figure 2
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Figure 2

Inhibition mechanism of P450 by macrolides (Peritiet al., 1992).

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: E+S⇄ES→E+P Km (Michaelis constant for S) E+I⇄EI Ki (Inhibition constant for I) where E is the enzyme, S is the substrate, ES is the enzyme-substrate complex, P is the product, I is the inhibitor, and EI is the enzyme-inhibitor complex. In the case of competitive inhibition, the metabolic rate (v) can be expressed by the following equation (2):v=Vmax·SKm(1+I/Ki)+S Equation 2where Vmax is the maximum metabolic rate.

It is clear from equation (2) that the inhibition by a given concentration of I is marked when the substrate concentration is low and becomes less marked with an increase in the substrate concentration.

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:E+S⇄ES→E+PKmE+I⇄EIKiEI+S⇄EISKmES+I⇄EISKi where EIS is the enzyme-inhibitor-substrate complex. It is assumed that the inhibitor binds to the free enzyme and the ES complex with the same affinity. In the case of noncompetitive inhibition, the metabolic rate can be expressed by the following equation (3):v={Vmax/(1+I/Ki)}·SKm+S Equation 3It is clear from equation (3) that the degree of inhibition does not depend on the substrate concentration.

3. Uncompetitive Inhibition.

Uncompetitive inhibition is a pattern of inhibition where the inhibitor binds only to the enzyme forming a complex with the drug:E+S⇄ES→E+PKmES+I⇄EISKi Unlike competitive and noncompetitive inhibition, the inhibitor cannot bind to the free enzyme. In the case of uncompetitive inhibition, the metabolic rate can be expressed by the following equation (4):v={Vmax/(1+I/Ki)}·SKm/(1+I/Ki)+S Equation 4It is clear from equation (4) that the inhibition becomes more marked with increasing substrate concentration.

The degree of inhibition depends on the inhibition pattern when the substrate concentration is high. However, when the substrate concentration is much lower than Km (Km ≫ 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):R=v(+inhibitor)v(−inhibitor)=11+I/Ki Equation 5In clinical situations, the substrate concentration is usually much lower than Km. In this review, we will discuss the most frequently observed case in which equation (5) can be applied.

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.

fh and fm are expressed as follows:fh=CLhCLh+CLr Equation 6 fm=CLint,1CLint,1+CLint,2 Equation 7where CLh is hepatic clearance, CLr is renal clearance, and CLint,1 and CLint,2represent the intrinsic clearance for the metabolic pathway inhibited and not inhibited by the inhibitor, respectively (CLint = CLint,1 + CLint,2). In equation (6), it is assumed that only the liver and kidney are the clearance organs. Equation (6) can be rearranged to give the following equation:CLr=CLh(1/fh−1) Equation 8Equation (7) can be rearranged to give the following equation:CLint,2=CLint,1(1/fm−1) Equation 9The fractional clearance for a particular metabolic pathway (CLh,m) can be expressed as fh multiplied by fm.

Rc, defined as the degree of increase in Css and AUC caused by the drug-drug interaction in vivo, can be calculated as shown below, depending on the route of administration.

a. Intravenous Administration.

The change in AUC after intravenous bolus administration (AUCiv) and Css during intravenous infusion can be expressed by the following equation, if the dose or infusion rate is constant:

Rc=AUCiv(+inhibitor)AUCiv(−inhibitor)=Css(+inhibitor)Css(−inhibitor) Equation 10 =CLtotCLtot′=CLh+CLrCLh′+CLr=CLh+CLh(1/fh−1)CLh′+CLh(1/fh−1) =CLh/fhCLh′+CLh/fh−CLh=1fh·CLh′/CLh+1−fh where ′ represents the value after alteration by the drug-drug interaction.

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: y=CLh′CLh=fb·CLint′fb·CLint=fb·(CLint,1′+CLint,2)fb·(CLint,1+CLint,2) Equation 11 =CLint,1′+CLint,1(1/fm−1)CLint,1+CLint,1(1/fm−1) =CLint,1′+CLint,1/fm−CLint,1CLint,1/fm =fm·CLint,1′/CLint,1+1−fm Combining equations (10) and (11) yields the following equation: Rc=1fh(fm·CLint,1′/CLint,1+1−fm)+1−fh Equation 12 =1fh·fm·CLint,1′/CLint,1+1−fh·fm Because Cu,ss encountered clinically is usually much less than Km, CLint,1 and CLint′,1can be expressed as follows:CLint,1=Vmax/Km and CLint,1′=Vmax/Km(1+Iu/Ki) where Iu is the unbound concentration of the inhibitor. Therefore,CLint,1′/CLint,1=11+Iu/Ki Equation 13can be derived. Combining equations (12) and (13) yields the following equation:Rc=1fh·fm·{1/(1+Iu/Ki)}+1−fh·fm Equation 14It is clear from equation (14) that, in the case of the intravenous administration of a low clearance drug, the degree of increase in AUCiv is determined not by Km or Cu,ss but by Ki, Iu, fh, and fm, if Km ≫ Cu,ss.

b. Oral Administration.

The change in AUCpoafter 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:

 Rc=AUCpo(+inhibitor)AUCpo(−inhibitor)=Css,av(+inhibitor)Css,av(−inhibitor) Equation 15 =CLoralCLoral′=(CLh+CLr)/(Fh·Fa)(CLh′+CLr)/(Fh′·Fa) ={CLh+CLh(1/fh−1)}/Fh{CLh′+CLh(1/fh−1)}/Fh′ =CLh/fh(CLh′+CLh/fh−CLh)·Fh′Fh =1fh·CLh′/CLh+1−fh·Fh′Fh where Fh is hepatic availability and Fa is the fraction absorbed from the gastrointestinal tract into the portal vein.

Some kind of mathematical model has to be introduced for the calculation of the hepatic intrinsic clearance (CLint) in vivo. In order to avoid a false negative prediction of drug-drug interactions, we tried to evaluate the maximum inhibitory effect expected. The well-stirred model was selected as one which can detect the maximum effect of the inhibitor. In the case of oral administration where D is dose, D/AUCpo = D/τ/Css,av = CLh/Fh = fb · CLintcan 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): Rc=Fh′Fh=CLintCLint′=CLint,1+CLint,2CLint,1′+CLint,2 Equation 16 =CLint,1+CLint,1(1/fm−1)CLint,1′+CLint,1(1/fm−1) =CLint,1/fmCLint,1′+CLint,1/fm−CLint,1 =1fm·CLint,1′/CLint,1+1−fm Furthermore, as Cu,ss encountered clinically is usually much less than Km, CLint,1 and CLint′,1 can be expressed as follows:CLint,1=Vmax/Km and CLint,1′=Vmax/Km(1+Iu/Ki) Therefore,CLint,1′/CLint,1=11+Iu/Ki Equation 17can be derived. Combining equations (16) and (17) yields the following equation:Rc=1fm·{1/(1+Iu/Ki)}+1−fm Equation 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:Fh=Qh/(Qh+fb·CLint) Equation 19It is clear from equation (19) that Fh is minimally affected by the change in CLint in the case of a low clearance drug (Qh ≫ fb · CLint: Fh = 1), but is inversely proportional to CLintin 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).

Figure 3
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Figure 3

Relationship between unbound drug concentration in the liver capillary (Cf,p) and that in the liver (Cf,T). Cin and Cout represent the drug concentration at the entrance (portal vein side) and the exit (hepatic vein side) of the liver, respectively.

Figure 4
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Figure 4

Model for estimating inflow concentration of the inhibitor into the liver after oral administration (Iin). Iout, I, and Iblood represent the inhibitor concentration at the exit of liver (hepatic vein side), the inhibitor concentration at the liver capillary, and inhibitor concentration in the systemic circulation, respectively. Qa, Qpvand Qh( = Qa +  Qpv) represent blood flow at hepatic artery, portal vein, and hepatic vein, respectively. ka, D, and Fa represent the absorption rate constant, dose, and the fraction absorbed from the gastrointestinal tract into the portal vein, respectively, of the inhibitor.

Practically, the maximum plasma concentration in the circulation (Imax) has been estimated for many inhibitors. When the value of Imax is not reported, it can be predicted from both the plasma concentration at a single time point after administration and the pharmacokinetic parameters such as the elimination half life (t1/2,β).

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:vin,max≦QaImax+QpvImax+ka·D·Fa·e−kat′ Equation 20where Qa and Qpv represent the blood flow rate in the hepatic artery and the portal vein, respectively, Fa is the fraction absorbed from the gastrointestinal tract into the portal vein, and t′ is the time after oral administration (after subtraction of the lag-time).

When the absorption rate is maximum (i.e., t′ = 0), the final term in equation (20) can be expressed as ka · D · Fa and thus:vin,max≦(Qa+Qpv)Imax+ka·D·Fa Equation 21As Qh =  Qa +  Qpv, the following equation can be derived: Embedded Image Equation 22 Therefore, Iin,max can be predicted if the parameters such as ka and Fa are available for the inhibitor. Taking the plasma protein binding into consideration, the unbound Iin,max (Iin,max,u) can be calculated as Iin,max · fu. Finally, comparing the value of Iin,max,u as Iin,u and that of Ki obtained in vitro allows the prediction of the maximum degree of in vivo drug-drug interaction caused by metabolic inhibition.

In general, the apparent absorption rate of the orally administered drug is maximum when the gastrointestinal absorption of the drug is so rapid that the rate-limiting step is the gastric emptying rate. A first order rate constant (ka) of about 0.1 min− 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:Tmax=ln(ka/kel)/(ka−kel) Equation 23In practice, however, because of the possible estimation error of Tmax caused by interindividual differences etc., the calculated value of ka sometimes exceeds 0.1 min− 1, though it should never exceed that, theoretically, for gastric emptying. Therefore, the theoretically maximum value of 0.1 min− 1 was used for ka when it was calculated to be larger than 0.1 min− 1. Moreover, in order to avoid a false negative prediction, the maximum ka of 0.1 min− 1 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. Veroneseet al. (1990) reported about a five-fold increase in both AUCpo and t1/2 of tolbutamide in humans following coadministration of 500 mg sulfaphenazole (table2, fig. 5).

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Table 2

Inhibition of tolbutamide metabolism (CYP2C9) by coadministration of sulfaphenazole

Figure 5
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Figure 5

Effect of sulfaphenazole coadministration on plasma concentration of tolbutamide (Veronese et al., 1990). ▵: Tolbutamide (500 mg p.o.) alone; ▴: Tolbutamide (500 mg p.o.) + Sulfaphenazole (500 mg p.o., q12h).

The t1/2 of intravenous tolbutamide is prolonged and the CLtot is reduced markedly in rats, too, by sulfaphenazole (Sugita et al., 1981). 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):CLtot=D/AUC=fb·CLint Equation 24 Sugita et al. (1984) 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 fbincreases 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).

Figure 6
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Figure 6

Effect of sulfaphenazole (SP) coadministration on plasma concentration of tolbutamide (TB) in rabbits (A) and rats (B) (Sugita et al., 1981, 1984). Open and closed symbols represent plasma concentrations of sulfaphenazole (or its metabolite, N4-Ac SP) and tolbutamide, respectively.

Figure 7
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Figure 7

Prediction of interspecies difference in the interaction of tolbutamide and sulfaphenazole (SP) or sulfadimethoxine (SDM) from in vitro data (Sugita et al., 1984).

The interaction between tolbutamide and sulfaphenazole involves both plasma protein binding and hepatic metabolism in humans, too. The main metabolic pathway of tolbutamide in vitro is CYP2C9-mediated hydroxylation, and the metabolite undergoes sequential metabolism forming a carboxylate in vivo (Thomas and Ikeda, 1966; 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 min− 1, 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/Kiwas 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 α-hydroxy (OH)- and 4-OH-triazolam. Ketoconazole is a powerful inhibitor of both these metabolic pathways, with a mean Kivalue 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 α- 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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Table 3

Comparison of the prediction of triazolam-ketoconazole interaction by von Moltke’s and our method

Using our method proposed above (see Section III.D.), the ketoconazole-triazolam interaction would be predicted as follows: The Imax,ss of ketoconazole during administration of 200 mg × 2/day was 6.6 μM (Daneshmend et al., 1981), 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 min− 1, 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 α-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:R=11+(13–15)×0.525+11+(3.5–4.0)×0.475 =0.128–0.143 Thus, an 85.7–87.2% reduction is predicted by this method, which is very close to the observed reduction (88%) (table 3). The degree of the inhibition should be larger if ketoconazole is actively transported into the liver.

Two cases have been shown here in which the interaction that had actually occurred in vivo was successfully predicted based on in vitro metabolism data. On the other hand, we believe that the ability to predict by the above-mentioned methods based on in vitro data should be very high in the case of drug combinations that do not interact with each other in vivo. In other words, the absence of in vivo drug-drug interactions should be successfully predicted, which has been partly confirmed in our study, though the data are not shown here.

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 (table4). 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 min− 1, 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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Table 4

Inhibition of sparteine metabolism (CYP2D6) by coadministration of quinidine

b. Terfenadine-ketoconazole.

Honig et al. (1993) 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 Kivalues 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 (Itoet 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.

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Table 5

Inhibition of terfenadine metabolism (CYP3A4) by coadministration of ketoconazole

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 AUCpoof imipramine (dose: 50 mg) is reported to increase about 1.9-fold after coadministration of 60 mg fluoxetine (table6) (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 min− 1, 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.

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Table 6

Inhibition of imipramine metabolism (CYP2D6) by coadministration of fluoxetine

b. Caffeine-ciprofloxacin.

Stille et al. (1987) 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 min− 1, 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:AUCpo(+inhibitor)/AUCpo(control) =1/{fh·fm/(1+Iu/Ki)+(1−fh·fm)}=1.1 A 1.6-fold increase was actually observed, which indicates a greater degree of inhibition than predicted. Possible reasons for this discrepancy include the estimation error of Ki, the possibility that other metabolic pathways may also be inhibited by ciprofloxacin, and the accumulation of ciprofloxacin in the liver because of active transport. Indeed, we have recently demonstrated that a new quinolone antibiotic, grepafloxacin, is actively taken up by the rat liver (Sasabe et al., 1997).

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Table 7

Inhibition of caffeine metabolism (CYP1A2) by coadministration of ciprofloxacin

c. Cyclosporin-erythromycin.

Vereerstraetenet al. (1987) reported that the AUCpo of cyclosporin (dose: 6 mg) increased about 1.6-fold after coadministration of 1.1 g erythromycin (table8). 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 (Vereerstraetenet 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 min− 1, D = 1.1 g, Qh = 1610 ml/min, and Fa = 0.58. In the calculation of kausing 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:AUCpo(+inhibitor)/AUCpo(control) =1/{fh·fm/(1+Iu/Ki)+(1−fh·fm)}=1.2 This value is smaller than the observed 1.6-fold increase in AUCpo.

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Table 8

Inhibition of cyclosporin metabolism (CYP3A4) by coadministration of erythromycin

As mentioned above (see Section III.B.), however, the inhibition by erythromycin may not be due only to a competitive inhibition mechanism. Its demethylated metabolite is known to inactivate P450 by forming a complex with this enzyme (Periti et al., 1992). 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.

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Table 9

Summary of the prediction of in vivo drug-drug interaction from in vitro data based on competitive or noncompetitive inhibition mechanism9-a

In order to avoid false negative predictions, one should not limit an interaction to a particular metabolic pathway, especially when the ratio of the pathway in question to the total clearance (fh · fm = CLh,m/CLtot) is small, as in the cases with the terfenadine-ketoconazole interactions. The most important factor in the prediction is the accurate estimation of Iu/Ki. In other words, if the calculated value of Iu/Ki for a particular metabolic pathway is high, the possible occurrence of drug interaction in vivo should be suspected, because it is likely that this inhibitor also inhibits another metabolic pathway(s) which has not been identified yet.

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 “kcatinhibition” 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.

Figure 8
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Figure 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 (Okudaet al., 1995).

Many drugs other than sorivudine also are reported to be mechanism-based inhibitors, including macrolide antibiotics such as erythromycin and troleandomycin (against CYP3A4) (Periti et al., 1992), 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:d(EI)/dt=k+1·[I]·E−(k−1+k2)·EI Equation 25 d(EI′)/dt=k2·EI−(k3+k4)·EI′ Equation 26 dEinact/dt=k4·EI′ Equation 27where E and [I] represent the concentration of the active enzyme and the mechanism-based inhibitor, respectively. Because the total concentration of the enzyme (Eo) is maintained at a constant level,Eo=E+EI+EI′ Equation 28Combining equations (25), (26), and (28) yields the following equation, assuming a steady-state for EI and EI′ (i.e., d(EI)/dt  =  0 and d(EI′)/dt  =  0): EI′=k+1·k2·[I]·Eo/((k+1·k2+k+1(k3+k4)) Equation 29 ·[I]+(k3+k4)(k−1+k2)) Therefore, the initial inactivation rate of the enzyme under steady-state conditions (Vinact =  dEinact/dt) can be expressed as follows using equations (27) and (29):Vinact=k+1·k2·k4·[I]·Eo/((k+1·k2+k+1(k3+k4)) Equation 30 ·[I]+(k3+k4)(k−1+k2)) =(k2·k4/(k2+k3+k4)·[I]·Eo)/((k3+k4)/(k2 +k3+k4)·(k−1+k2)/k+1+[I]) As the initial inactivation rate of the enzyme at steady-state is equal to the initial decreasing rate of the active enzyme,Vinact=−dE/dt(t=0)=kobs·E(t=0)=kobs·Eo Equation 31where kobs represents the apparent inactivation rate constant of the enzyme. The following equation can be derived from equations (30) and (31):kobs=Vinact/Eo=kinact·[I]o/(Ki,app+[I]o) Equation 32wherekinact=k2·k4/(k2+k3+k4) Equation 33 Ki,app=(k3+k4)/(k2+k3+k4)·(k−1+k2)/k+1 Equation 34Parameters (kinact, Ki,app) required for predicting this model can be determined by in vitro studies as follows (fig. 10):

〈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.

Figure 9
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Figure 9

Enzyme inhibition by a mechanism-based inhibitor (Waley 1985). E and Einact represent the active and inactive enzyme, respectively; I represents the mechanism-based inhibitor; EI and EI′ represent the enzyme-inhibitor complex I and II, respectively; and P represents the product.

Figure 10
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Figure 10

Method to obtain kinetic parameters in vitro for mechanism-based inhibition.

Table 10 summarizes the results of the analysis of various combinations of P450 isozymes and mechanism-based inhibitors (Chiba et al., 1995). 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).

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Table 10

Comparison of inactivation parameters for mechanism-based P450 inactivators (Chiba et al., 1995)

It is clear from this model analysis that, in the case of mechanism-based inhibition, the inhibitor is metabolically activated by an enzyme and irreversibly inactivates the same enzyme by covalent binding, exhibiting the following characteristics:

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).

Mechanism-based inhibitors should satisfy these criteria.

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 figure11 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:

Figure 11
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Figure 11

Physiological model for the description of the time profiles of substrate and inhibitor concentrations in the plasma and liver. See the legend of Table 11 for the abbreviations used.

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Table 11

Parameters used in the simulation of mechanism-based inhibition

For S;Vh·(dCh/dt) Equation 35 =Q·Cportal−Q·Ch/Kp−fb·CLint·Ch/Kp CLint=Vmax/(Km+fb·Ch/Kp) Equation 36 Vmax=Vmax(0)·Eact(t)/Eo Equation 37  Vportal·(dCportal/dt)=Q·Csys+Vabs−Q·Cportal Equation 38 Vabs=ka·D·F·e−ka·t Equation 39 Vd·(dCsys/dt)=Q·Ch/Kp−Q·Csys Equation 40For I;Vh·(dIh/dt)=Q·Iportal−Q·Ih/Kp−fb·CLint·Ih/Kp Equation 41 CLint=Vmax/(Km+fb·Ih/Kp) Equation 42 Vportal·(dIportal/dt)=Q·Isys+Vabs−Q·Iportal Equation 43 Vabs=ka·D·F·e−ka·t Equation 44 Vd·(dIsys/dt)=Q·Ih/Kp−Q·Isys Equation 45where Q represents blood flow rate, Csys and Isys represent concentration in systemic blood, Vd represents volume of distribution in the central compartment, Cportal and Iportal represent concentration in portal vein, Vportal represents volume of portal vein, Ch and Ih represent concentration in the liver, Vh represents volume of liver, fbrepresents unbound fraction in blood, CLint represents intrinsic metabolic clearance, Fa represents fraction absorbed from the gastrointestinal tract, Km represents Michaelis constant for the metabolic elimination, Vmaxrepresents maximum metabolic rate, and Kp represents liver-to-blood concentration ratio.

The following assumptions were made in the mass-balance equations (35 to 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.

Furthermore, it should be noted that Vmax of the substrate is assumed to be proportional to the amount of active enzyme (Eact) in equation (37).

The differential equations for active and inactive enzyme in the liver (Eact and Einact, respectively) can be described as follows: dEactdt=−kinact·Eact·fb·Ih/KpKi,app+fb·Ih/Kp+kdeg(Eo−Eact) Equation 46 dEinactdt=kinact·Eact·fb·Ih/KpKi,app+fb·Ih/Kp−kdeg·Einact Equation 47where kdeg represents the degradation rate constant (turnover rate constant) of the enzyme. The initial conditions (at t = 0) are Eact = Eo and Einact = 0. In the absence of an inhibitor, the enzyme level in the liver is at steady-state and the degradation rate (kdeg · Eo) is equal to the synthesis rate, which is assumed to be unaffected by an inhibitor.

a. Basic simulation.

Using the physiological model in figure 11 and the parameters in table 11, time courses of inhibitor blood concentrations, active enzyme levels in the liver (Eact), and substrate blood concentrations have been simulated with the dose of the inhibitor ranging from 0 to 50,000 μmol (fig. 12). The above eight differential equations (three for the substrate, three for the inhibitor, and two for the enzyme level in the liver) were numerically solved. The elimination rate of the active enzyme (Eact) increases with an increasing dose of the inhibitor, resulting in the prolonged elimination of the substrate.

Figure 12
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Figure 12

Effects of the dose of inhibitor on inhibitor blood concentration, the active enzyme level in the liver and substrate blood concentration. The substrate and the inhibitor were assumed to be administered simultaneously.

b. Effect of the turnover rate of the enzyme.

The effect of the turnover rate constant of the enzyme (kdeg) on the profiles of Eact and the substrate elimination were investigated in the simulation study. Basic parameters in table 11 were used except that kdeg was changed to cover the range of 0.00005–0.005 min− 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.

Figure 13
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Figure 13

Effects of turnover rate of the enzyme on the active enzyme level in the liver and substrate blood concentration.

c. Timing of inhibitor administration.

One of the characteristics of the mechanism-based inhibition is that the effect remains even after the inhibitor is eliminated from the body. Therefore, the inhibitory effect may depend on the timing of the substrate administration, even if the same dose of the inhibitor is administered. Simulations shown in figure14 indicate that the most potent inhibitory effect can be obtained by having an appropriate interval between administration of the inhibitor and the substrate. Too short an interval will lead to completion of the kinetic event of the substrate before sufficient inactivation of the enzyme occurs, and too long an interval will allow the enzyme to recover. Both situations will result in a reduction of the inhibitory effect.

Figure 14
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Figure 14

Effects of administration interval of the inhibitor and substrate on the active enzyme level in the liver and substrate blood concentration.

d. Possibility of in vitro/in vivo scaling.

Figure15 shows the effects of two parameters obtained in the in vitro studies (kinact and Ki,app) on the simulated in vivo profiles of Eact and substrate blood concentrations. As expected, in vivo inactivation of the enzyme and prolongation of substrate elimination become marked with a larger kinact and a smaller Ki,app. In the future, it will be necessary to examine whether this method can properly predict in vivo effects from in vitro data. What kind of approach should be taken to verify this methodology? For example, although estimation of the unbound concentration of inhibitor in the liver is important for any prediction, it is impossible to measure this, especially in humans. Instead, kinetic parameters can be determined to fit the inhibitor blood concentration profile, which is measured in most cases. However, undetermined parameters, such as the liver-to-plasma concentration ratio, need to be varied within certain limits in the simulation study so that the range of alteration in the profiles of Eact and substrate can be predicted.

Figure 15
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Figure 15

Effects of kinact and Ki,app on the active enzyme level in the liver and substrate blood concentration.

It is also important to confirm the validity of the prediction method in animal studies, where the inhibition studies can be performed both in vitro (using e.g. liver microsomes for P450) and in vivo. Because invasive experiments are possible in this case, including measurements of the distribution kinetics of the inhibitor in the liver, this may allow for more accurate predictions.

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 (Yamazakiet al., 1995, 1996), the extent of the interaction may be underestimated if plasma concentrations are used in the prediction.

Zomorodi and Houston (1995) 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 (table12). 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).CL=CLo/(1+Iss/Ki) Equation 48where CLo represents diazepam clearance in the absence of omeprazole, and Iss represents the steady-state total plasma concentration of omeprazole. The in vivo Ki showed closer agreement with the Ki values obtained in hepatocytes than with those observed in microsomes (table 12). Their results, however, should be interpreted cautiously, because the Kiwas calculated based on the amount of drug added to the medium instead of the unbound concentration, and the total plasma concentration in vivo was used as the Iss in equation (48) instead of the unbound concentration, which should be related to the metabolic inhibition. As shown in figure 17, Km or Ki values obtained in the metabolism studies based on the medium concentration of substrates and inhibitors, respectively, may be smaller in hepatocytes than in microsomes if the molecule is actively transported into hepatocytes. The difference is reflected in the cell-to-medium unbound concentration ratio (C/M ratio) as shown in equation (49): C/M ratio=Km(MS)/Km(Cell)=Ki(MS)/Ki(Cell) Equation 49where MS and Cell in parentheses indicate the values obtained in microsomes and in cells, respectively. Therefore, the difference in the Ki values of omeprazole obtained in liver microsomes and hepatocytes may be explained by the accumulation of omeprazole in hepatocytes by active transport.

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Table 12

Omeprazole inhibition of diazepam metabolism in hepatic microsomes and hepatocytes (Zomorodi and Houston, 1995)

Figure 16
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Figure 16

Relationship between diazepam clearance and steady-state concentration of omeprazole in rats (Zomorodi and Houston, 1995).

Figure 17
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Figure 17

The difference in Km or Ki values obtained in the metabolism studies using hepatocytes (Cell) and microsomes (MS) in the case of a drug which is actively transported into the liver. V represents the initial velocity of the metabolite formation.

The active transport of drugs can be evaluated by measuring the drug uptake into hepatocytes or liver slices in the presence and absence of adenosine triphosphate (ATP)-depletors such as rotenone and FCCP (Yamazaki et al., 1993; 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):C/M ratio=(PSactive+PSpassive)/PSpassive=vo/vpassive Equation 50where PSactive and PSpassive represent the membrane permeation clearance by active transport and passive diffusion, respectively; vo and vpassiverepresent the initial uptake velocity obtained in the absence and presence of ATP-depletors, respectively. This C/M ratio can also be calculated by measuring the steady-state drug concentration (sum of the bound and unbound forms) in the cell and that in the medium in the absence and presence of the ATP-depletor as follows: Ccell/Cmedium(control)Ccell/Cmedium(+ATP­depletor) Equation 51 =Ccell,free/fT/Cmedium(control)Ccell,free/fT/Cmedium(+ATP­depletor) =Ccell,free(control)Cmedium=C/M ratio where Ccell and Cmedium represent steady-state total drug concentration in the cell and that in the medium, respectively; Ccell,free represents steady-state unbound drug concentration in the cell; and fT represents the unbound fraction in the cell. It is assumed that fT is not affected by the ATP-depletor and that Ccell,free equals Cmedium in the presence of the ATP-depletor. Equation (51) can be used even when active transport is involved in the efflux process out of the hepatocytes, whereas equation (50) cannot be applied in such a case.

Figure 18
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Figure 18

A model for drug transport into and out of the hepatocyte. PSactive and PSpassive represent the membrane permeation clearance by active transport and passive diffusion, respectively.

Nakamura et al. (1994) 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:PSactive=Vmax/(Km+Cmedium) Equation 52  vo=VmaxCmedium/(Km+Cmedium)+PSpassiveCmedium Equation 53Here, in this calculation, it is assumed that carrier-mediated saturable uptake represents active transport. The Ki value of cimetidine for the metabolism of ethoxyresorufin and those values for α-hydroxylation and 4-hydroxylation of triazolam in human liver microsomes are 600 μM, 36 μM, and 160 μM, respectively (Knodellet 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.

Figure 19
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Figure 19

Effect of ATP depletors on cellular ATP content (a) and the initial uptake velocity (Vo) of [3H]cimetidine into hepatocytes (b) (Nakamura et al., 1994). •: control; □: with FCCP (2 μM); ▵: with rotenone (30 μM); ○: with sodium azide (30 mM).

We previously reported (Yamazaki et al., 1993) 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 (Transonet 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).

Figure 20
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Figure 20

Immunoquantification of P450s and epoxide hydrolase in human liver and extrahepatic microsomes using Western blots (De Waziers et al., 1990).

As shown in figure 21, the bioavailability (BA) of cyclosporin after oral administration was reduced by coadministration of rifampicin, an inducer of CYP3A4, and increased by coadministration of ketoconazole or erythromycin, which are inhibitors of CYP3A4 (Hebert et al., 1992; Gomezet 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 figure22, 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):BA=FabsFgFh Equation 54 =Fabs(1−Eg)(1−Eh) where Eg and Eh represent gut and hepatic extraction ratio, respectively. Assuming that Fabs is not altered by the enzyme inducer or the inhibitor, BA after coadministration of the interacting drug can be expressed as follows:BA=Fabs(1−XgEg)(1−XhEh) Equation 55where Xg and Xh represent the changes in the gut and hepatic extraction ratio, respectively, during coadministration of the interacting drug. Assuming that the drug is eliminated only by the liver and kidney, as is the case for many drugs, Eh can be calculated by the following equation:Eh=CLh/Qh=(CLtot−CLr)/Qh Equation 56where CLtot can be estimated from the dose divided by the AUC after intravenous administration and CLr can be estimated by multiplying CLtot and the urinary excretion rate of the parent compound. Furthermore, Xh in equation (55) can be obtained as the ratio of Eh in the presence and absence of the interacting drug. Equations (54) and (55) then contain three unknown parameters (Fabs, Eg, and Xg), allowing the calculation of Eg and Xg by fixing the value of Fabs. The maximum value of Fabs was set at 1 (complete absorption), and the minimum value was calculated by dividing BA by Fh(=1  −  Eh) at the time when the metabolism was inhibited by the interacting drug (assuming Fg =  1). As shown in table13, 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.

Figure 21
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Figure 21

Effects of rifampicin, ketoconazole, and erythromycin on the disposition of cyclosporin (Hebert et al., 1992; Gomez et al., 1995; Gupta et al.,1989). Open column: control; Closed column: with the interacting drug.

Figure 22
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Figure 22

Schematic diagram depicting the effects of absorption and gut and hepatic first-pass extraction on drug oral bioavailability (Wu et al., 1995). Fabs: Fraction of the drug dose absorbed into and through the gastrointestinal membranes; FG: fraction of the absorbed dose that passes through the gut into the hepatic portal blood unmetabolized; FH: the hepatic first-pass availability; BA: oral bioavailability; EG and EH: gut and hepatic extraction ratio, respectively.

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Table 13

Gut and hepatic extraction of cyclosporine and the effects of enzyme inducers and inhibitors at the boundary conditions for absorption (Wu et al., 1995)

Therefore, the drug-drug interaction based on metabolic inhibition in the gut after oral administration cannot be neglected, especially for the drugs metabolized by CYP3A4. The methodology for the prediction of in vivo first-pass gut metabolism from in vitro studies using human gut samples needs to be established.

On the other hand, it is known that p-gp exists in the luminal membrane of gut epithelial cells and acts as an efflux transporter (Saitoh and Aungst, 1995; 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 (Wacheret 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 (table15).

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Table 14

Substrates for and inhibitors of both CYP3A and P-gp (Wacher et al., 1995)

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Table 15

Effect of various drugs on expression of P-glycoprotein and CYP3A in LS180 cells (Schuetz et al., 1996)

Benet (1995, 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 figure23, the fraction of the drug absorbed into the portal vein (Fa) can be expressed as follows:Fa=P3CLint+P31−exp−α1×CLint+P3P2+CLint+P3 Equation 57where P2, P3, and CLintrepresent the clearance for efflux from the cell to the lumen, absorption from the cell to the portal vein, and intracellular metabolism, respectively, and α1 is the membrane permeation constant from the lumen into the cell. α1 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:α1=P1,app×tgi¯Vav Equation 58where P1,app represents the apparent influx clearance from the lumen into the cell, t̅g̅i̅ ̅ represents small intestinal transit time, and Vav represents the average luminal volume.

Figure 23
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Figure 23

A compartment model used in the simulation of the effects of gut metabolism and efflux to the lumen on the drug absorption. P1,app: apparent influx clearance from the lumen into the cell; P2: efflux clearance from the cell to the lumen; P3: absorption clearance from the cell to the portal vein; CLint: intracellular metabolic clearance.

The results are shown in figure 24. When CLint+ P3 is much larger than P2, equation (57) can be rearranged to yield equation (59):Fa=P3CLint+P3{1−exp(−α1)} Equation 59In this case, Fa is not affected by a change in P2 possibly caused by inhibition of p-gp (fig. 24 left panel). If CLint + P3 is much smaller than P2, equation (60) can be derived:Fa=P3CLint+P31−exp−α1×CLint+P3P2 Equation 60Here, the reduction of P2 is directly reflected in the change in Fa (fig. 24 right panel). This indicates that Fa is increased with the reduction in P2 when the initial value of P2 is large, i.e., in the case of the drugs extensively transported out of the cell into the lumen. This effect of reducing P2 is more marked if the influx clearance of the drug into the cell (α1) 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).

Figure 24
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Figure 24

Effect of the inhibition of efflux (reduction in P2) on drug absorption. Fa: fraction of the drug absorbed into the portal vein; α1: membrane permeation constant from the lumen into the cell; P2: efflux clearance from the cell to the lumen; P3( = 0.8): absorption clearance from the cell to the portal vein; CLint ( = 0.2): intracellular metabolic clearance.

In the future, in addition to metabolic studies using human gut samples, drug-drug interactions involving the efflux process should be quantitatively evaluated by transport studies using intestinal brush border membrane vesicles to allow for more precise predictions of in vivo drug-drug interactions.

Footnotes

  • ↵FNa 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:
    α1
    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
    t̅g̅i̅ ̅
    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
  • The American Society for Pharmacology and Experimental Therapeutics

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Pharmacological Reviews
Vol. 50, Issue 3
1 Sep 1998
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Prediction of Pharmacokinetic Alterations Caused by Drug-Drug Interactions: Metabolic Interaction in the Liver
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Prediction of Pharmacokinetic Alterations Caused by Drug-Drug Interactions: Metabolic Interaction in the Liver

K. Ito, T. Iwatsubo, S. Kanamitsu, K. Ueda, H. Suzuki and Y. Sugiyama
Pharmacological Reviews September 1, 1998, 50 (3) 387-412;

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Review ArticleReview

Prediction of Pharmacokinetic Alterations Caused by Drug-Drug Interactions: Metabolic Interaction in the Liver

K. Ito, T. Iwatsubo, S. Kanamitsu, K. Ueda, H. Suzuki and Y. Sugiyama
Pharmacological Reviews September 1, 1998, 50 (3) 387-412;
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  • Article
    • I. Introduction
    • II. Drug-Drug Interactions Other Than Involving Metabolism
    • III. Drug-Drug Interactions Involving Metabolism in the Liver
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    • References
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