General Framework for the Quantitative Prediction of CYP3A4-Mediated Oral Drug Interactions Based on the AUC Increase by Coadministration of Standard Drugs
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- Ohno, Y., Hisaka, A. & Suzuki, H. Clin Pharmacokinet (2007) 46: 681. doi:10.2165/00003088-200746080-00005
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Cytochrome P450 (CYP) 3A4 is the most prevalent metabolising enzyme in the human liver and is also a target for various drug interactions of significant clinical concern. Even though there are numerous reports regarding drug interactions involving CYP3A4, it is far from easy to estimate all potential interactions, since too many drugs are metabolised by CYP3A4. For this reason, a comprehensive framework for the prediction of CYP3A4-mediated drug interactions would be of considerable clinical importance.
The objective of this study was to provide a robust and practical method for the prediction of drug interactions mediated by CYP3A4 using minimal in vivo information from drug-interaction studies, which are often carried out early in the course of drug development.
The analysis was based on 113 drug-interaction studies reported in 78 published articles over the period 1983–2006. The articles were used if they contained sufficient information about drug interactions. Information on drug names, doses and the magnitude of the increase in the area under the concentration-time curve (AUC) were collected.
The ratio of the contribution of CYP3A4 to oral clearance (CRCYP3A4) was calculated for 14 substrates (midazolam, alprazolam, buspirone, cerivastatin, atorvastatin, ciclosporin, felodipine, lovastatin, nifedipine, nisoldipine, simvastatin, triazolam, zolpidem and telithromycin) based on AUC increases observed in interaction studies with itraconazole or ketoconazole. Similarly, the time-averaged apparent inhibition ratio of CYP3A4 (IRCYP3A4) was calculated for 18 inhibitors (ketoconazole, voriconazole, itraconazole, telithromycin, clarithromycin, saquinavir, nefazodone, erythromycin, diltiazem, fluconazole, verapamil, cimetidine, ranitidine, roxithromycin, fluvoxamine, azithromycin, gatifloxacin and fluoxetine) primarily based on AUC increases observed in drug-interaction studies with midazolam. The increases in the AUC of a substrate associated with coadministration of an inhibitor were estimated using the equation 1/(1 - CRCYP3A4 · IRCYP3A4), based on pharmacokinetic considerations.
The proposed method enabled predictions of the AUC increase by interactions with any combination of these substrates and inhibitors (total 251 matches). In order to validate the reliability of the method, the AUC increases in 60 additional studies were analysed. The method successfully predicted AUC increases within 67–150% of the observed increase for 50 studies (83%) and within 50–200% for 57 studies (95%). Midazolam is the most reliable standard substrate for evaluation of the in vivo inhibition of CYP3A4. The present analysis suggests that simvastatin, lovastatin and buspirone can be used as alternatives. To evaluate the in vivo contribution of CYP3A4, ketoconazole or itraconazole is the selective inhibitor of choice.
This method is applicable to (i) prioritise clinical trials for investigating drug interactions during the course of drug development and (ii) predict the clinical significance of unknown drug interactions. If a drug-interaction study is carefully designed using appropriate standard drugs, significant interactions involving CYP3A4 will not be missed. In addition, the extent of CYP3A4-mediated interactions between many other drugs can be predicted using the current method.
Cytochrome P450 (CYP) 3A4 is the most prevalent CYP enzyme in the human liver. It accounts for ≈30% of the total CYP enzymes in hepatic microsomes and is involved in the metabolism of >50% of the drugs currently on the market.[1,2] CYP3A4 is also the target enzyme for a number of drug interactions of significant clinical concern. Drug interactions are one of the major sources of adverse events, and some have actually led to drug withdrawals in the past.[3–5] Even though there are numerous reports of CYP3A4 drug interactions, it is far from easy to estimate all potential interactions, since too many drugs are metabolised by CYP3A4. For this reason, a comprehensive framework for the prediction of drug interactions would be of significant clinical importance. In addition, pharmaceutical companies are encouraged to carry out many in vivo drug-interaction studies during the drug development process, and the cost of these studies is increasing. Consequently, it is important to prioritise significant drug interactions to be confirmed as early as possible during the course of development. A reliable method for the prediction of CYP3A4 drug interactions would be advantageous in such circumstances.
A great deal of effort has already been devoted to establish a method for the accurate prediction of in vivo drug interactions using in vitro experimental data.[6–11] These predictions in principle rely on the [I]/Ki ratio, i.e. a ratio of the unbound concentration of the inhibitor at the interaction site to the in vitro inhibition constant. The results of these studies have increased our understanding of the mechanisms of drug interactions. Nowadays, both human liver specimens and expressed human CYP enzymes are commercially available, and it is not difficult to determine a profile of metabolic drug interactions in vitro. However, the proper interpretation and quantitative extrapolation of in vitro data to in vivo situations require a detailed understanding of the overall pharmacokinetics of the drugs involved. Consideration should be given to the site of interaction, the time-courses of the unbound drug concentration at the site, the effects of drug transporters on the pharmacokinetics, and the possible contribution of metabolites to the interaction.
Intestinal CYP3A4 plays a significant role in the first-pass metabolism of orally administered drugs. For example, several human in vivo studies have shown that midazolam, felodipine, ciclosporin and buspirone are extensively metabolised in the intestine. Although Caco-2 cells are used in predicting the extent of intestinal absorption, it is difficult to predict the intestinal metabolism because of the very low expression of CYP3A4 in this cell line. It is also known that CYP3A4 does not distribute uniformly along the length of the intestine — it is expressed more in the jejunum than in the ileum. In addition, quantitative prediction of oral bioavailability is difficult because of the synergistic role of CYP3A4 and efflux transporters, such as multidrug resistance-1 (MDR1), in reducing the intestinal absorption of substrate drugs.[16–20] MDR1 is expressed more in the ileum than in the jejunum. Although the intestine is also considered an important site of drug interactions, the extent of intestinal metabolism is unpredictable in many cases.
CYP3A4 recognises a wide range of substrates, and some structural flexibility has been suggested at the substrate recognition site. Consequently, the enzyme kinetics of CYP3A4 are sometimes complicated. Indeed, simple competitive inhibition theory has often failed to explain interactions via CYP3A4.
A series of CYP3A4 substrates apparently act as mechanism-based inhibitors which covalently bind to the enzyme. For these drugs, the recovery of CYP3A4 activity depends on regeneration of the enzyme at the target site. For this reason, predictions of the mechanism-based interactions from in vitro data require more complicated kinetic models compared with reversible inhibitors.[7,8,13,24–26]
Considering these complex factors, it is reasonable to conclude that, by using only in vitro experimental data, precise prediction of in vivo drug interactions is not easy for the variety of drugs that are metabolised by CYP3A4. One of the practical methods to overcome this problem is to use in vivo information on interaction data of probe drugs of CYP3A4. This approach would enable the prediction of various drug interactions from results of a small number of drug-interaction studies carried out early in the course of drug development.
The objective of the present study was to construct a framework for the prediction of various drug interactions mediated by CYP3A4 using minimum in vivo information on drug interactions. We selected midazolam as a standard substrate and ketoconazole or itraconazole as a standard inhibitor. We aimed to keep the method as simple as possible from a practical viewpoint while, at the same time, remaining theoretically accurate.
Equation 5 is applicable to both competitive and non-competitive inhibitions, since the drug concentration in vivo is usually much lower than the Michaelis-Menten constant (Km) value.
It needs to be mentioned that the above theory may not be directly applicable to mechanism-based inhibitors. However, the final form of equation 11 can be accepted even for mechanism-based inhibitors by regarding the IRCYP3A4 values as overall inhibition ratios of CYP3A4 at the equilibrium state between inactivation and generation of the metabolising enzyme. From this viewpoint, the definition of IR by equation 10 is invalid for mechanism-based inhibitors.
We assumed that the CRCYP3A4 value of simvastatin is 1.0 since it has been reported that simvastatin is a selective substrate of CYP3A4, and a search of the literature showed that the plasma AUC of simvastatin tends to be increased most markedly following inhibition of CYP3A4. It was confirmed that a reduction of the CRCYP3A4 value of simvastatin to 0.95 did not affect the overall outcomes of the present analysis.
Once we assumed the CRCYP3A4 value for simvastatin, the IRCYP3A4 value of itraconazole, a typical inhibitor, was obtained based on equation 11, using the result of a drug-interaction study involving simvastatin and itraconazole.
The CRCYP3A4 value of midazolam, a typical substrate, was calculated from studies with midazolam and itraconazole using the calculated IRCYP3A4 value of itraconazole with equation 11. An algebraic mean of the AUC increase was used for the calculation, whenever the results of multiple studies are reported for an interaction set of interest.
The IRCYP3A4 values of the other inhibitors including ketoconazole, another typical inhibitor, were calculated from interaction studies between the inhibitor and midazolam, using the calculated CRCYP3A4 value of midazolam with equation 11.
The CRCYP3A4 values of other substrates were calculated from interaction studies between the substrate and itraconazole or ketoconazole whenever possible, using the calculated IRCYP3A4 value of itraconazole or ketoconazole, respectively, with equation 11.
For nifedipine, no interaction study with itraconazole or ketoconazole has been reported. Accordingly, the CRCYP3A4 value of nifedipine was calculated from the study with nifedipine and diltiazem, using the calculated IRCYP3A4 value of diltiazem, with equation 11. Diltiazem was selected, since the AUC of nifedipine was most significantly increased by the administration of diltiazem.
CYP3A4 is the most important drug-metabolising enzyme, which preferentially oxidises relatively large, lipophilic, neutral to basic molecules. Therefore, CYP3A4 is recognised as a key enzyme that determines the clearance of various drugs and, in some cases, has a major effect on their safety and efficacy. Although no major polymorphism in the CYP3A4 gene has been identified, marked interindividual differences have been reported in the activity of CYP3A4. One possible reason for such differences in the activity is that CYP3A4 is inducible by various diets and drugs, such as rifampicin (rifampin) and carbamazepine, via the mechanism mediated by pregnane X receptor.[111–113] Furthermore, CYP3A4 is the predominant metabolising enzyme not only in the liver but also in the intestine. It has been reported that intestinal metabolism is the major factor determining the bioavailability of some drugs.[16,114–116] However, as far as we know, nobody has succeeded in predicting the extent of the first-pass effect on metabolism by intestinal CYP3A4 from in vitro data. Although there are some established methods to determine the activity of CYP3A4 in vivo, including evaluation of the metabolic ratio of selective substrates (midazolam, testosterone and cortisol) and the erythromycin breathe test, it has been reported that these methods do not offer consistent results, possibly due to differences in the organ of metabolism (liver or intestine) and/or the presence of multiple recognition sites in the CYP3A4 molecule.
Despite these issues regarding the in vivo evaluation of CYP3A4 activity, the current rather simple method gave satisfactory predictions in most cases. The following issues may contribute to this success. First, uncertain factors were avoided since the current method relies primarily on an overall in vivo evaluation. For example, the present method satisfactorily predicted drug interactions with mechanism-based inhibitors such as azithromycin, clarithromycin, diltiazem, erythromycin and roxithromycin (figure 4), which frequently exhibit complicated kinetics. Accurate predictions have been achieved recently from in vitro data for mechanism-based inhibitors by sophisticated analysis. For the successful prediction, it has been reported that evaluation of the unbound fraction of the drug in the incubation medium is important.[13,119] Moreover, the analysis requires a turnover rate of the metabolising enzyme and a rate constant for the irreversible reaction, both of which are not easy to estimate from in vitro experiments.
Second, we used simvastatin as a selective substrate and ketoconazole and itraconazole as selective inhibitors of CYP3A4, although these drugs are not absolutely specific for CYP3A4. For example, although we assumed that the CRCYP3A4 value of simvastatin is 1.0, this drug is also metabolised by CYP2C8 to a minor extent. Ketoconazole is a well known selective inhibitor of CYP3A4, but this drug also inhibits the activities of CYP2C8, CYP2C9 and MDR1, which may also affect the disposition of substrate drugs analysed in the present study. Despite these defects, the success in the prediction of drug interactions with the present method (figure 1 and figure 2) suggests that CYP3A4 plays a crucial role in most of the drug interactions analysed in the present study.
A number of probe drugs have been used to study the activity of CYP3A4, including midazolam, nifedipine, simvastatin and erythromycin. Among them, it is generally recognised that the most reliable probe drug is midazolam for CYP3A4.[108,124] The plasma AUC of midazolam is increased significantly by coadministration of various CYP3A4 inhibitors (figure 1, figure 2, figure 3 and figure 5). In our preliminary analysis, we found that the rank order of the AUC increase of typical substrates, such as simvastatin, lovastatin and buspirone, by a series of inhibitors was generally in good agreement with the rank order of the AUC increase of midazolam produced by these inhibitors. These results suggest that the extent of CYP3A4 inhibition after administration of each inhibitor is almost the same among substrates. From this analysis, we hypothesised that calculation of AUC increases from IRCYP3A4 values should be possible.
It has often been reported that in vitro Ki values vary significantly among the CYP3A4 substrates used, which contradicts our hypothesis that the IRCYP3A4 value is the same for any substrate. For example, nifedipine was allocated to a different group from midazolam and triazolam by a cluster analysis of the victim profile of in vitro drug interactions. However, as represented in figure 3, no clear discrepancy was observed for the predicted AUC increases of any particular substrate assuming a single IRCYP3A4 value for each inhibitor. It is therefore possible that the in vivo Ki value of each inhibitor is not affected by the substrate drugs analysed in the present study. This result may be because the number of available drug-interaction studies is limited for each inhibitor. Accordingly, we should be cautious in predicting the increase in the AUC for a novel substrate drug by using the IRCYP3A4 values determined in the present study.
In the validation process of our study, the method provided successful predictions in 57 of 60 cases. Telithromycin is a particular example of an accurate prediction. The CRCYP3A4 value of telithromycin was calculated to be 0.49 from the results of an interaction study with ketoconazole. The AUC increase produced by interaction with itraconazole, which has an IRCYP3A4 value of 0.95, was predicted to be 1.85, which was in good agreement with the observed increase of 1.60. Telithromycin also acts as an inhibitor of CYP3A4. The IRCYP3A4 value of telithromycin was 0.91 and an AUC increase of simvastatin produced by interaction of telithromycin was predicted to be 11.1, which was also in good agreement with the observed increase of 10.8.
In contrast, we had difficulties in predicting three reports of drug interactions, i.e. ciclosporin-voriconazole, triazolam-itraconazole, and one of two reports for a triazolam-erythromycin interaction. In reference, it was reported that the AUC of ciclosporin was increased 1.70-fold by the administration of voriconazole, whereas we predicted a 4.61-fold increase (figure 1 and figure 5). In the same manner, although the AUC of triazolam was reported to be increased 27.1-fold by administration of itraconazole, we predicted an 8.85-fold increase (figure 1 and figure 5). Concerning the interaction between triazolam and erythromycin, there was a deviation in the increase in the AUC of triazolam by erythromycin. In reference, a 3.65-fold increase was reported whereas a 2.06-fold increase was reported in reference. Our prediction was 4.32-fold (figure 1 and figure 5) and was in accord with the former article. The reason for these deviations is unknown. Further studies will help to investigate whether there was some mechanistic reason or whether there was simply some unavoidable variability. The factors that need to be considered include the contributions by other metabolising enzymes and transporters, and the variety of enzyme kinetics of CYP3A4 inhibition.
In the present study, midazolam, itraconazole and ketoconazole were used as a standard substrate or an inhibitor because they are used most commonly in drug-interaction studies. As a result, overall AUC increases were successfully predicted, indicating that the standard drugs were selected appropriately. It may be possible to use other commonly used substrates of CYP3A4 such as simvastatin, lovastatin and buspirone to calculate IRCYP3A4 values, because no deviation was observed in the predictability of AUC increase for these substrate drugs when coadministered with a wide range of inhibitors (figure 3).
To prioritise drug-interaction studies during the course of drug development, Obach et al. have recently proposed a rank-order approach in which the mechanism of possible interactions is explored by in vitro experiments and then the most probable interactions are evaluated in vivo using typical substrates or inhibitors. The results of the present study support their approach. If a drug-interaction study is carefully designed using the appropriate standard drugs, significant interactions via CYP3A4 will not be missed. In addition, the extent of CYP3A4-mediated interactions between many other drugs will be able to be predicted using the current method, as suggested by the results in figure 5.
We have constructed a general framework for prediction of the increase in AUC mediated by CYP3A4. The precision and robustness of the method have been demonstrated satisfactorily. Several standard substrates and inhibitors are proposed for the evaluation of drug interactions via CYP3A4. This method would be applicable to (i) prioritise clinical trials for investigating drug interactions during the course of drug development and (ii) estimate the clinical significance of unknown drug interactions.
Dr Akihiro Hisaka now works in Pharmacology and Pharmacokinetics at the University of Tokyo Hospital Faculty of Medicine, University of Tokyo, Tokyo, Japan.
This study was supported by Health and Labor Sciences Research Grants for Research on Regulatory Science of Pharmaceuticals and Medical Devices from the Ministry of Health, Labor and Welfare, Japan. The authors have no conflicts of interest that are directly relevant to the content of this study.