I. Introduction

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Serious side-effects caused by drug interactions have attracted a great deal of attention and have become a social problem since the coadministration of ketoconazole and terfenadine was reported to cause potentially life-threatening ventricular arrhythmias (Monahan et al., 1990), and an interaction between sorivudine and fluorouracil resulted in fatal toxicity in Japan (Watabe, 1996; Okuda et al., 1997). The possible sites of drug-drug interaction which can change pharmacokinetic profiles include: (1) gastrointestinal absorption, (2) plasma and/or tissue protein binding, (3) carrier-mediated transport across plasma membranes (including hepatic or renal uptake and biliary or urinary secretion), and (4) metabolism. Pharmacodynamic interactions such as antagonism at the receptor may also increase or decrease the effects of a drug.

In this review, after brief comments on (2) and (3), we intend to focus on (4) and to discuss the possibility of the quantitative prediction of drug-drug interactions in vivo based on the analyses of data from literature obtained by in vitro experiments using human liver samples. Furthermore, strategic proposals for avoiding toxic interactions will be given from a pharmacokinetic point of view.

	II. Drug-Drug Interactions Other Than Involving Metabolism

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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 (f_u)^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 (V_d) of the drug. The effect on the steady-state concentration (C_ss) 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 C_ss and AUC for unbound drugs (C_u,ss and AUC_u) 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, C_u,ss and AUC_u 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).

View this table: [in this window] [in a new window]   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 V_d 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 V_d, V_d increases in parallel with f_u. 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 V_d, which depend on f_u to a lesser extent, the total blood concentration is not affected so much by the change in f_u, 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 f_u. In this simulation, changes in both CL and V_d associated with the change in f_u were considered. As just described above, the steady-state unbound concentration is altered with the change in f_u only for a high clearance drug. It is also clear from figure 1 that, in the case of drugs with a small V_d, 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.

View larger version (31K): [in this window] [in a new window]   Fig. 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.

Very few studies have focused on drug-drug interactions involving carrier-mediated transport across membranes, including the interactions involving renal secretion and reabsorption and those where p-glycoprotein (p-gp) plays a role (Tsuruo et al., 1981; Slater et al., 1986; Kusuhara et al., in press).

Along with metabolism, renal excretion is one of the most important processes affecting the total body clearance of a drug. Alterations in this process caused by drug-drug interactions should, therefore, be carefully considered. Secretion of drugs at the renal tubule is an active transport process, where organic anion transporters, organic cation transporters, and p-gp are known transport carriers (Hori et al., 1982; Takano et al., 1984; Tanigawara et al., 1992). The renal clearance of a drug is reduced by inhibition of these transport processes. It is known that both organic anion transporters and organic cation transporters exist on both the basolateral membrane (BLM) and the brush border membrane (BBM) and that they are different from each other, whereas p-gp is only present on the BBM. The inhibitors of these transporters interact with other drugs; for example, inhibition of the renal excretion of penicillin and other related drugs by probenecid (Hunter, 1951), methotrexate excretion by nonsteroidal anti-inflammatory drugs (Statkevich et al., 1993), and digoxin excretion by quinidine (Tanigawara et al., 1992) all involve this kind of interaction.

Most studies of pharmacokinetic drug-drug interactions reported so far have been limited to the analysis of hepatic metabolism. However, the hepatic clearance of many drugs has been found to be determined mainly by hepatic uptake (Yamazaki et al., 1995, 1996). The overall intrinsic clearance (CL_int,all) can be expressed using the intrinsic clearance for metabolism (CL_int) and that for membrane permeation (PS) as follows:

(1)

where PS_inf is intrinsic clearance for influx, and PS_eff is intrinsic clearance for efflux.

It is clear from equation (1) that CL_int,all equals CL_int in the case of drugs with large (PS  CL_int) and symmetrical (PS_inf = PS_eff) 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

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As a pharmacokinetic parameter directly related to the pharmacological and/or adverse effects of drugs, it is very important to predict the hepatic clearance. Because the use of animal scale-up is limited in the case of hepatic metabolic clearance due to large inherent interspecies differences, we have developed an alternative methodology to predict in vivo metabolic clearance in the liver; it is based on in vitro studies using mainly rat liver microsomes and isolated rat hepatocytes (Sugiyama and Ooie, 1993; Iwatsubo et al., 1996). Recently, with the greater availability of human liver samples, the method of in vitro/in vivo scaling can now be applied to human studies. We have already demonstrated that the method can be applied to P450 metabolism in humans based on in vitro and in vivo data obtained from the literature (Iwatsubo et al., 1997). However, the prediction of intrinsic clearance was not successful for some drugs, possibly because of the contribution of active transport into the liver and/or first-pass metabolism in the gut.

In order to prevent toxic drug-drug interactions, it is important to quantitatively predict pharmacokinetic changes caused by coadministration of drugs that are known to inhibit the hepatic metabolism of the drug under study (Sugiyama and Iwatsubo, 1996; Sugiyama et al., 1996). In this review, we have focused on the drug-drug interactions via inhibitory mechanisms and have tried to predict in vivo interactions from in vitro data on drug metabolism obtained from the literature.

Drug-drug interactions involving metabolism are one of the principal problems in clinical practice to evaluate the pharmacological and adverse effects of drugs. Parkinson (1996) summarized examples of substrates, inhibitors, and inducers of the major human liver microsomal P450 enzymes involved in drug metabolism. In the case of drugs that undergo metabolism by CYP3A4 and 2D6, particular attention should be paid to the interactions resulting in alterations in blood concentrations possibly accompanied by a change in its effects, because a number of drugs are metabolized by these enzymes (Bertz and Granneman, 1997). For example, blood concentrations of imipramine and desipramine, substrates for CYP2D6, are elevated several-fold by coadministration of fluoxetine, another substrate for CYP2D6 (Bergstrom et al., 1992). Similarly, concentrations of terfenadine, which is metabolized by CYP3A4, are increased in patients taking erythromycin, which is also a substrate for CYP3A4 (Honig et al., 1992). Quinidine is metabolized mainly by CYP3A4 but inhibits the metabolism of substrates for CYP2D6, such as sparteine, rather than those for CYP3A4 (Schellens et al., 1991). Furthermore, in the case of drugs whose metabolism is mediated by multiple isozymes (e.g., diazepam), drug-drug interaction may be complicated because of possible dose-dependent changes in the contribution of each isozyme to the overall metabolism (Iwatsubo et al., 1997).

The first is mutual competitive inhibition caused by coadministration of drugs metabolized by the same P450 isozyme, such as the above-mentioned (see Sec. A.) combinations of imipramine or desipramine and fluoxetine (CYP2D6). In this case, as reported for metoprolol and propafenone (CYP2D6) (Wagner et al., 1987), blood concentrations of both drugs may be increased.

The second is the inactivation of P450 by the drug metabolite forming a complex with P450. This type of inhibition is designated as "mechanism-based inhibition" (Silverman, 1988). Inhibition by macrolide antibiotics, such as erythromycin, is a typical example of this type of interaction. As shown in figure 2, P450 demethylates and oxidizes the macrolide antibiotic into a nitrosoalkane that forms a stable, inactive complex with P450 (Periti et al., 1992).

View larger version (13K): [in this window] [in a new window]   Fig. 2.   Inhibition mechanism of P450 by macrolides (Periti et 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).

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: 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):

(2)

where V_max is the maximum metabolic rate.

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: 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):

(3)

It 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: 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):

(4)

It is clear from equation (4) that the inhibition becomes more marked with increasing substrate concentration.

(5)

D. Prediction of In Vivo Drug-Drug Interactions Based on In Vitro Data

1. General Equations. The following factors determine the degree of change in C_ss 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.

(6)

(7)

(8)

(9)

(10)

i. High clearance drug. Because f_b · CL_int is much larger than the hepatic blood flow rate (Q_h) (Q_h  f_b · CL_int), CL_h is rate-limited by the flow rate (CL_h = Q_h). When the altered CL_h is still rate-limited by the flow rate (CL_h' = Q_h), i.e., Q_h  f_b. CL_int', then CL_h' equals CL_h. Thus, R_c can be calculated to be unity by equation (10), indicating no change in AUC_iv or C_ss. This is not the case when the inhibition is so extensive that CL_h is not limited by the flow rate any more.

ii. Low clearance drug. In the case of a low clearance drug, CL_h = f_b · CL_int and CL_h' = f_b. CL_int'. If the protein binding is not altered by the inhibitor, the ratio (y) of CL_h and CL_h' can be calculated as follows:

(11)

Combining equations (10) and (11) yields the following equation:

(12)

Because C_u,ss encountered clinically is usually much less than K_m, CL_int,1 and CL_int',1 can be expressed as follows: where I_u is the unbound concentration of the inhibitor. Therefore,

(13)

can be derived. Combining equations (12) and (13) yields the following equation:

(14)

It is clear from equation (14) that, in the case of the intravenous administration of a low clearance drug, the degree of increase in AUC_iv is determined not by K_m or C_u,ss but by K_i, I_u, f_h, and f_m, if K_m  C_u,ss.

(15)

i. High clearance drug. Because f_b · CL_int is much larger than the hepatic blood flow rate (Q_h  f_b · CL_int), CL_h is rate-limited by the flow rate (CL_h = Q_h). When the altered CL_h is still rate-limited by the flow rate (CL_h' = Q_h), i.e., Q_h  f_b · CL_int', then CL_h' equals CL_h. On the other hand, F_h = Q_h/(f_b · CL_int) and F_h' = Q_h/(f_b · CL_int'). Therefore, the following equation (16) can be derived from equation (15):

(16)

Furthermore, as C_u,ss encountered clinically is usually much less than K_m, CL_int,1 and CL_int'_,1 can be expressed as follows: Therefore,

(17)

can be derived. Combining equations (16) and (17) yields the following 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.

(19)

2. The evaluation of the unbound concentration of the inhibitor in vivo. Although I_u 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 I_u? An underestimation of I_u 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 I_u, 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 I_u and was used in the prediction (I_in,u; fig. 4).

View larger version (18K): [in this window] [in a new window]   Fig. 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.

View larger version (16K): [in this window] [in a new window]   Fig. 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, Qpv and 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.

1/2,

(20)

(21)

(22)

1

(23)

1

1

1

1

1. Successful Cases of In Vitro/In Vivo Prediction. a. TOLBUTAMIDE-SULFAPHENAZOLE. Interactions between tolbutamide and sulfa-agents cause serious side effects such as hypoglycemic shock in patients (Christensen et al., 1963) and exhibit the marked interspecies differences in animals. Veronese et al. (1990) reported about a five-fold increase in both AUC_po and t_1/2 of tolbutamide in humans following coadministration of 500 mg sulfaphenazole (table 2, fig. 5).

View larger version (17K): [in this window] [in a new window]   Fig. 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).

(24)

View larger version (15K): [in this window] [in a new window]   Fig. 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.

View larger version (31K): [in this window] [in a new window]   Fig. 7.   Prediction of interspecies difference in the interaction of tolbutamide and sulfaphenazole (SP) or sulfadimethoxine (SDM) from in vitro data (Sugita et al., 1984).

1

1

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 CL_oral of sparteine (dose: 50 mg) fell from 979 to 341 ml/min (35% of the control value) after coadministration of 200 mg quinidine (table 4). The main metabolic pathway of sparteine is CYP2D6-mediated dehydration. Because quinidine is a specific inhibitor of CYP2D6, it is reasonable that metabolic inhibition is involved in this quinidine-induced reduction in the CL_oral of sparteine. The K_i of quinidine for the CYP2D6-mediated metabolism in human liver microsomes in vitro is reported to be 0.06 µM. The I_max 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 k_a = 0.0027-0.069 min¹, D = 200 mg, Q_h = 1610 ml/min, and F_a = 0.83. I_in,max is, therefore, calculated to be 5-26 µM. Because the f_u of quinidine is 0.15, the I_in,u and I_in,u/K_i 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% (f_h · f_m = CL_h,m/CL_tot = 0.25) (table 4). Therefore, the complete inhibition of this dehydration pathway will reduce the CL_oral 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.