ORIINAL RESARCH ARTILE
`
`
`Clin Phormocokinei 2007: 46 (8): 681—696
`03126963/07/0008—0681 /3411 95/0
`© 2007 Acts Data Information BV. All rights resen/ed
`
`General Framework for the
`
`Quantitative Prediction of
`CYP3A4-Mediated Oral Drug
`Interactions Based on the AUC
`Increase by Coadministration
`of Standard Drugs
`
`Yoshiyuki Chi/10,1 Akihiro Hisozka1 and Hiroshi Suzukz‘lr2
`
`1 Department of Pharmacy, University of Tokyo Hospital Faculty of Medicine, University of
`Tokyo, Tokyo, Japan
`2 Center for Advanced Medical Engineering and Informatics, Osaka University, Osaka, Japan
`
`
`Abstract
`
`Background: 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 interac-
`tions would be of considerable clinical importance.
`Objective: 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.
`Data sources: 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 concentra—
`tion-time curve (AUC) were collected.
`Methods: The ratio of the contribution of CYP3A4 to oral clearance (CRCYP3A4)
`was calculated for 14 substrates (midazolam, alprazolam, buspirone, cerivas-
`tatin, 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, von'conazole,
`itraconazole,
`telithromycin,
`clarithromycin, saquinavir, nefazodone, erythromycin, diltiazem, fluconazole,
`verapamil, cimetidinc, ranitidine, roxithromycin, fluvoxamine, azithromycin, gat—
`ifloxacin and fluoxetine) primarily based on AUC increases observed in drug-
`interaction studies with midazolam. The increases in the AUC of a substrate
`
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`associated with coadministration of an inhibitor were estimated using the equation
`1/(] — CRCYP3A4 . IRCYP3A4), based on pharmacokinetic considerations.
`Results: 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 viva inhibition of CYP3A4. The present analysis
`suggests that simvastatin, lovastatin and buspirone can be used as alternatives. To
`evaluate the in viva contribution of CYP3A4, ketoconazole or itraconazole is the
`selective inhibitor of choice.
`
`Conclusion: This method is applicable to (i) prioritise clinical trials for investi-
`gating 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.
`
`Background
`
`Cytochrome P450 (CYP) 3A4 is the most preva—
`lent CYP enzyme in the human liver. It accounts for
`=30% of the total CYP enzymes in hepatic micro—
`somes and is involved in the metabolism of >50% of
`
`the drugs currently on the market?!” CYP3A4 is
`also the target enzyme for a number of drug interac—
`tions of significant clinical concern. Drug interac—
`tions 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 viva drug-
`interaction studies during the drug development pro—
`cess, and the cost of these studies is increasing.
`Consequently, it is important to prioritise significant
`drug interactions to be confirmed as early as possi—
`ble during the course of development. A reliable
`method for the prediction of CYP3A4 drug interac-
`tions 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
`
`viva drug interactions using in vitra experimental
`data.[6““] 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
`vitra. However, the proper interpretation and quanti-
`tative extrapolation of in vitra data to in viva situa-
`tions require a detailed understanding of the overall
`pharmacokinetics of the drugs involved. Considera-
`tion 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 interactionm]
`
`the quantitative prediction of drug
`Moreover,
`interactions is difficult for the following reasons:
`
`0
`
`Intestinal CYP3A4 plays a significant role in the
`first—pass metabolism of orally administered
`drugs. For example, several human in viva stud-
`ies have shown that midazolam,
`felodipine,
`ciclosporin
`and
`buspirone
`are
`extensively
`metabolised in the intestinelm Although Caco—Z
`
`© 2007 Adis Data Information BV, All rights reserved.
`
`Clin Pharmacokinei 2007: 46 (8)
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`683
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`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 linen“ 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 ileumils] In addition,
`quantitative prediction of oral bioavailability is
`difficult because of the synergistic role of
`CYP3A4 and efflux transporters, such as mul—
`tidrug resistance-l (MDRI), in reducing the in—
`testinal
`absorption of
`substrate
`drugs.[16'20]
`MDRl is expressed more in the ileum than in the
`jejunum?” Although the intestine is also consid-
`ered an important site of drug interactions, the
`extent of intestinal metabolism is unpredictable
`in many cases.
`0 CYP3A4 recognises a wide range of substrates,
`and some structural flexibility has been suggest-
`ed at the substrate recognition siteml Conse—
`quently,
`the enzyme kinetics of CYP3A4 are
`sometimes complicated. Indeed, simple competi—
`tive inhibition theory has often failed to explain
`interactions via CYP3A4.[23]
`
`0 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 vitra data require more complicated ki—
`netic models compared with reversible inhibi-
`tors.[7,8,l3,24-26]
`
`Considering these complex factors, it is reasona—
`ble to conclude that, by using only in vitra experi-
`mental data, precise prediction of in viva drug inter—
`actions is not easy for the variety of drugs that are
`metabolised by CYP3A4. One of the practical meth—
`ods to overcome this problem is to use in viva
`information on interaction data of probe drugs of
`CYP3A4. This approach would enable the predic—
`tion 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 con—
`struct a framework for the prediction of various drug
`interactions mediated by CYP3A4 using minimum
`in viva information on drug interactions. We select—
`
`ed midazolam as a standard substrate and ketocona—
`zole 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,
`re—
`maining theoretically accurate.
`
`Methods
`
`The analysis is based on 113 in viva studies
`reported in 78 articles published over the period
`1983—2006 (table I). Based on information from a
`comprehensive review,[9] we added some new data
`from the literature. Some substrates and inhibitors
`
`information be-
`were removed from the original
`cause of the small contribution or low selectivity for
`CYP3A4. Studies were used if the report included
`information on the dosage regimen and the increase
`in the area under the concentration-time curve
`
`(AUC). A survey of a series of articles revealed that,
`in general, inhibitor drugs were administered con-
`secutively for more than several days to attain
`steady—state conditions, and substrate drugs were
`given as a single—dose administration.
`The oral clearance (CLorai) of drugs is given by
`equation 1, where CLtot, CLH, CLR, Fa, PG and FH
`are the total body clearance, hepatic clearance, renal
`clearance, absorption ratio, and gastrointestinal and
`hepatic availabilities, respectively. The reason why
`Fa is located at the left side in equation 1 is that the
`current analysis (right—side terms) should focus on
`events after absorption.
`
`CLtot _ CLH+ CLR
`CLoral ' Fa: m -
`FG . FH
`
`(Eq- 1)
`Assuming the well—stirred organ modellmsl equa-
`tion 1 can be converted to equation 2 where fu and
`CLim(H) are the unbound fraction of the drug in the
`blood and the intrinsic clearance of the liver, respec—
`tively. This equation represents a general relation-
`ship between the oral clearance and the intrinsic
`clearance of the liver.
`
`CLoral ° Fa =
`
`fu’ €14an)
`F6
`
`
`+ CLR
`F6 ° FH
`
`(1301.2)
`In the present study, two simplifications were
`made with respect to equation 2. First, we assumed
`that CLR can be ignored, which is frequently the
`case for lipophilic CYP3A4 substrates. Second, we
`
`© 2007 Adis Data Information BV. All rights reserved.
`
`Clin Photmocokinet 2007: 46 (8)
`
`3
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`

`

`684
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`Ohno et al.
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`© 2007 Adis Data Informofion BV. All rights reserved.
`
`Clin Phormocokine‘r 2007,- 46 (8)
`
`4
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`

`

`General Prediction of CYP3A4 Drug Interactions
`
`
`
`
`
`
`assumed that FG can be regarded as 1.0 hypotheti-
`cally. This simplification does not necessarily mean
`that the gastric metabolism is insignificant. Rather,
`it means that the gastric metabolism is assumed to
`occur in proportion to the hepatic metabolism as if it
`is a part of the liver. This may allow some overesti-
`mation of CLim(H) as a consequence. Significant
`gastric first-pass effects are well established facts
`for some CYP3A4 substrates; FG values of midazo—
`lam, felodipine, ciclosporin and buspirone are re-
`ported to be 0.57, 0.45, 0.39 and 0.21, respective—
`ly.[13’16] In the future, it may be advantageous to
`distinguish between gastric and hepatic metabolism
`by CYP3A4. At present, however, we have no alter-
`native but to accept this simplification, since we do
`not know the extent of the in viva gastric metabolism
`for all of the drugs analysed in the present study. In
`this connection, it is worth noting that incorporation
`of gut wall CYP3A4 inhibition did not result in a
`general improvement in drug—interaction predictions
`in a recent reportiml Overall, equation 2 becomes
`equation 3 with these simplifications.
`
`CLoral ' Fa 5 1cu ' CLint(H)
`
`(Eq- 3)
`
`We then considered a relationship between the
`intrinsic clearance and metabolic drug interactions
`under an assumption of the rapid equilibrium of the
`drug concentration between the blood and the
`hepatocyte.
`It
`is often the case that a drug is
`metabolised by more than two pathways. In equa-
`tion 4, we assume two intrinsic metabolic clear—
`
`ances, CLint(Cyp3A4) and CLint(others), which re-
`present the metabolism mediated by CYP3A4 and
`the sum of other metabolic pathways, respective—
`1y.[11,1o7]
`
`CLint(H) = CLint(CYP3A4) + CLint(others)
`
`(Eq. 4)
`
`In the following equations, asterisks denote pa-
`rameters altered by a drug interaction. When the
`metabolism of CYP3A4 is inhibited by a drug inter—
`action, the altered clearance is given by equation 5.
`
`Equation 5 is applicable to both competitive and
`non—competitive inhibitions, since the drug concen—
`tration in vivo is usually much lower than the
`Michaelis-Menten constant (Km) value.
`Here, we define the ratio of the contribution of
`
`CYP3A4 to oral clearance (CRCYP3A4) by equation
`6:
`
`CRCYP3A4 =
`
`CLorai ‘ CLoral(—CYP3A4)
`CL 1Ora
`
`(Eq. 6)
`
`is an altered in viva oral
`where CLoraK—CYP3A4)
`clearance when CLim(cyp3A4) is blocked complete-
`1y. CLoraK—CYP3A4) is given by equation 7, based on
`equations 3 and 4.
`
`CLora1(—CYP3A4) ' Fa = fu ° (€14er
`
`CLint(CYP3A4))
`
`(Eq. 7)
`
`From equations 6 and 7, CRCYP3A4 is given by
`equation 8, which indicates that the ratio of the in
`viva contribution of CYP3A4 to oral clearance has
`
`the same value as the fraction metabolised by
`CYP3A4 to inhibition (fm(CYP3A4), Which is deter—
`mined by examining the effect of CYP3A4 selective
`inhibitors/antibodies on drug metabolism by human
`liver microsomesmg’mgl when the urinary excretion
`is very low and rapid equilibrium is achieved in the
`liver. These relationships have been widely used for
`prediction of in viva clearances in the presence of
`drug interactions or CYP enzyme polymorphisms,
`as reported by other groups.[“’1071
`
`CRCYP3A4=
`
`CLint(CYP3A4)
`in
`CL- [(H)
`
`(Eq. 8)
`
`Equation 5 can be converted to equation 9 using
`equations 6 and 8.
`C
`I CLin
`CLiiHKH) :W + (1 _ CRCYP3A4) . CmeH)
`1 + E
`
`= (1 — CRCYP3A4 '
`
`
`I
`[Il[+]Ki
`
`) CLint(H)
`
`(EQ- 9)
`
`CL*int(CYP3A4) =
`
`CLint(CYP3A4)
`1+%]i
`
`To estimate AUC from equation 9, the equation
`needs to be integrated with time. For this pur-
`pose,
`the time—averaged apparent inhibition ratio
`(IRCYP3A4) is defined by equation 10:
`
`(Eq- 5)
`
`© 2007 Adis Data Information BV. All rights resen/ed.
`
`Clin Phormocokine‘r 2007; 46 (8)
`
`5
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`

`

`Ohm) et al.
`686
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`Happl
`IRCYP3A4 =W
`
`(Eq. 10)
`
`where [Iapp] is the time-averaged apparent unbound
`concentration of the inhibitor in the liver. The in—
`
`crease in the ratio of the AUC caused by a drug
`interaction (equation 10) is derived from equations 9
`and 10. We assumed here that the value of IRCYP3A4
`
`for an inhibitor is the same for any substrate.
`
`Equation ll indicates that an AUC increase by a
`drug interaction between any oral drug via CYP3A4
`can be predicted if the CRCYP3A4 of the substrate
`and the IRCYP3A4 of the inhibitor are available.
`
`AUC*ora1 : CLoral - Fa _ CLint(H)
`AUCma]
`CL*OmloFa
`CL*int(H)
`
`_
`—
`
`l
`
`l — CR
`
`[lapp]
`' —'—‘-"‘
`[lappl +Ki
`
`CYP3A4
`
`= ____l___
`1 — CRCYP3A4 ° IRCYP3A4
`
`(Eq. ll)
`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 inhibi—
`tors 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 defi—
`nition of IR by equation 10 is invalid for mecha—
`nism—based inhibitors.
`
`The values of CRCYP3A4 and IRCYP3A4 for vari—
`ous substrates and inhibitors were calculated se-
`
`quentially according to the following steps based on
`AUC increases in the 53 interaction studies, which
`are indicated in table I.
`
`0 We assumed that the CRCYP3A4 value of simvas—
`tatin is 1.0 since it has been reported that simvas-
`tatin 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.
`
`0 Once we assumed the CRCYP3A4 value for
`simvastatin, the IRCYP3A4 value of itraconazole,
`a typical inhibitor, was obtained based on equa-
`tion 11, using the result of a drug—interaction
`study involving simvastatin and itraconazole.
`o 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 ll.
`An algebraic mean of the AUC increase was used
`for the calculation, whenever the results of multi—
`ple studies are reported for an interaction set of
`interest.
`
`0 The IRCYP3A4 values of the other inhibitors in-
`cluding ketoconazole, another typical inhibitor,
`were calculated from interaction studies between
`
`the inhibitor and midazolam, using the calculated
`CRCYP3A4 value of midazolam with equation ll.
`0 The CRCYP3A4 values of other substrates were
`calculated from interaction studies between the
`substrate and itraconazole or ketoconazole when—
`
`ever possible, using the calculated chyp3A4
`value of itraconazole or ketoconazole, respec-
`tively, with equation 11.
`e For nifedipine, no interaction study with itra—
`conazole or ketoconazole has been reported. Ac-
`cordingly, 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 se—
`lected, since the AUC of nifedipine was most
`significantly increased by the administration of
`diltiazem.
`
`Results
`
`We surveyed 113 in viva drug—drug interaction
`studies published in 78 articles
`(table I). The
`CRCYP3A4 values of 14 substrates and the IRCYP3A4
`values of 18 inhibitors were calculated using equa—
`tion ll based on the results of 53 clinical studies
`
`(the estimation set), which are indicated in table I.
`As shown in table 11, high CRCYP3A4 values of
`>095 were calculated for simvastatin,
`lovastatin,
`
`buspirone and nisoldipine, 0.85—0.94 for triazo—
`lam, midazolam and felodipine, and 0.70—0.84
`for ciclosporin, nifedipine and alprazolam. High
`IRCYP3A4 values of >095 were estimated for
`
`© 2007 Adis Data information BV. All rights reserved.
`
`Clin Phormocokiner 2007; 46 (8)
`
`6
`
`

`

`
`W Wfide— e. aam am: 25;; 242, was”
`General Prediction of CYP3A4 Drug Interactions
`
`
`
`Table ll. Calculated ratios of the contribution of CYP3A4 to the oral
`clearance (CRCYP8A4) of substrates
`Substrate
`CRCYP3A4
`Simvastatin
`1.00
`Lovastatin
`1 .00
`Buspirone
`0.99
`Nisoldipine
`0.96
`Triazolam
`0.93
`Midazolam
`0.92
`Felodipine
`0.89
`Ciclosporin
`0.80
`Nifedipine
`0.78
`Alprazolam
`0.75
`Atorvastatin
`0.68
`Telithromycin
`0.49
`Zolpidem
`0.40
`
`Cerivastatin 0.18
`
`ketoconazole (daily dose 200—400mg), voriconazole
`(400mg) and itraconazole (100—200mg), 0.85—0.94
`for
`telithromycin
`(800mg),
`clarithromycin
`(SOO—lOOOmg),
`saquinavir
`(3600mg) and nefa-
`zodone (400mg), and 0.70—0.84 for erythromycin
`(lOOO—ZOOOmg), diltiazem (90—270mg),
`flucona-
`zole (200mg) and veraparnil (240—480mg).
`
`The current method enabled predictions of the
`AUC increase caused by drug-drug interactions of
`any combination of the substrates and inhibitors. In
`order
`to validate the suitability of the present
`method, the extent of the increase in the AUC by
`drug interaction was predicted for the remaining 60
`clinical studies (the validation set), which are indi—
`cated in table I, and the results were compared with
`the observed values (figure 1). This prediction was
`performed by substituting the values of CRCYP3A4
`and IRCYP3A4 shown in table II and table III, respec—
`tively, in equation 11. As shown in figure 1, with the
`current method we could predict the increase in the
`AUC within 67—150% of the observed AUC in—
`
`crease for 50 clinical studies (83%) and within
`50-200% for 57 clinical studies (95%).
`
`In the calculation of CRCYP3A4 and IRCYP3A4
`values, the algebraic mean of the increase in the
`AUC was used when more than one article was
`
`available for the same interaction set (table I). In
`. these cases, however, significant deviation was ob—
`served in the AUC increase between or among re-
`ports. For the analysis, we often combined the data
`of clinical studies with different doses of the inhibi~
`
`tor. Since the lower doses frequently gave more
`AUC increase of a substrate, it is possible that the
`deviation of the inhibitor dose may not largely affect
`the results, as far as the dose of the inhibitor is set
`within the therapeutic range. The extent of this
`deviation is shown in figure 2, which was prepared
`in the same style as figure 1. Each circle and vertical
`bar in figure 2 represents the mean + SD reported in
`each article of the estimation set in table I. If the SD
`
`values were not reported in articles listed in table I,
`the reported mean values are shown by squares. As
`shown by the dotted lines in figure 2, for most of the
`articles the increase in the AUC of substrate drugs
`caused by drug inhibition deviated by 67—150% of
`the algebraic mean values. The predictions within
`50—200% of the observed AUC increase in figure 1
`were regarded as successful, since the correspond-
`ing variation of the AUC in figure 2 (the estimation
`set) was within this range.
`The data shown in figure 1 and figure 2 were then
`reorganised to indicate the relationships between the
`IRCYP3A4 values and the increase in AUC of each
`substrate (figure 3). It was found that
`the AUC
`increased steeply as the IRCYP3A4 value increased
`for highly CYP3A4—dependent substrates, such as
`simvastatin, lovastatin and buspirone, whereas only
`minimal increases were observed for poor CYP3A4
`substrates, such as zolpidem and cerivastatin (figure
`
`Table III. Calculated ratios of the time-averaged apparent inhibition
`ratio of CYP3A4 (IRCYP3A4) for inhibitors
`Inhibitor
`Daily dose (mg)
`Ketoconazole
`200—400
`Voriconazole
`400
`ltraconazole
`100—200
`Telithromycin
`800
`Clarithromycin
`500—1000
`Saquinavir
`3600
`Neiazodone
`400
`Erythromycin
`1000—2000
`Diltiazem
`90—270
`Fluconazoie
`200
`Verapamil
`240—480
`Cimetidine
`800—1200
`Ranitidine
`300—600
`Roxithromycin
`300
`Fluvoxamine
`100—200
`Azithromycin
`250—500
`Gatifloxacin
`400
`Fluoxetine
`20—60
`
`chvp3A4
`1 .00
`0.98
`0.95
`0.91
`0.88
`0.88
`0.85
`0.82
`0.80
`0.79
`0.71
`0.44
`0.37
`0.35
`0.30
`0.1 1
`0.08
`0.00
`
`© 2007 Adis Data lnformotion BV. All rights reserved.
`
`Ciin Phormocokine’r 2007; 46 (8)
`
`7
`
`

`

`Ohm) et al.
`688
`
`
`100
`
`
`
`
`
`ObservedAUCincrease(fold)
`
`0.5
`
`Calculated AUC increase (fold)
`
`Fig. 1. Relationship between the observed and calculated increase in the area under the concentration-time curve (AUC) by drug
`interactions. Using the ratio of the contribution of cytochrome P450 (CYP) 3A4 to oral clearance (CRCYP3A4) and the time-averaged
`apparent inhibition ratio of the CYP3A4 (IRCYP3A4) values shown in table it and table III, respectively, the increase in the AUC of substrate
`drugs by drug interactions reported in 60 clinical studies (the validation set; table I) was predicted with equation 11. Each circle and vertical
`bar represents the mean + SD values of subjects reported in each article. A dashed bar represents the range. Where the SD values or the
`ranges were not reported in the article, the reported mean value is shown by a square. The solid and dotted lines represent 50—200% and
`67—150% ranges, respectively, of the calculated increase.
`
`3). In the same manner, potent inhibitors, such as
`azole antifungals, increased the blood levels of a
`number of CYP3A4 substrates markedly, whereas
`no or only very minor increases were observed for
`weak inhibitors, such as azithromycin, gatifloxacin
`and fluoxetine (figure 4).
`
`Finally, the data were reorganised to show that
`the increase in the AUC in 251 kinds of drug interac-
`tions between 14 substrate diugs and 18 inhibitors
`could be predicted (figure 5; note that telithromycin
`is included with both the substrates and the in—
`
`hibitors). The nomogram in figure 5 indicates that a
`
`very marked increase in the AUC is anticipated
`when substrate drugs with high CRCYP3A4 values
`are administered with potent inhibitors with high
`IRCYP3A4 values.
`
`Discussion
`
`CYP3A4 is the most important drug-metabolis—
`ing enzyme, which preferentially oxidises relatively
`large, lipophilic, neutral to basic molecules. There-
`fore, 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
`
`© 2007 Adis Data Information BV. All rights reserved.
`
`Cltn Phormocokinet 2007; 46 (8)
`
`8
`
`

`

`General Prediction of CYP3A4 Drug Interactions
`
`
`689
`
`
`efficacy. Although no major polymorphism in the
`CYP3A4 gene has been identified, marked inter—
`individual differences have been reported in the
`activity of CYP3A4.[1101 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}1 ”'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 drugsimdlim] However, as
`100
`
`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 viva, including evaluation of
`the metabolic ratio of selective substrates (midazo—
`
`lam, testosterone and cortisol) and the erythromycin
`breathe test, it has been reported that these methods
`
`do not offer consistent results,m7] possibly due to
`differences in the organ of metabolism (liver or
`
`intestine) and/or the presence of multiple recogni-
`tion sites in the CYP3A4 moleculemsi
`
`50*
`
`_. 0
`
`U'l
`
`
`
`0.5
`
`
`
`
`ObservedAUCincrease(fold)
`
`
`
`,.
`
`Calculated AUC increase (fold)
`
`p
`
`Fig. 2. Relationship between the observed and calculated increase in the area under the concentration-time curve (AUC) by drug
`interactions. This figure was prepared in the same style as figure 1 for the purpose of demonstrating the deviation of AUC values among 53
`clinical studies (the estimation set; table I), the mean values of which were used to determine the CRCYP3A4 and IRcyp3A4 values. Each
`~circle and vertical bar represents the mean + SD values of subjects reported in each article. A dashed bar represents the range. Where the
`SD values or the ranges were not reported in the article, the reported mean value is shown by a square. The solid and dotted lines represent
`50—200% and 67—150% ranges, respectively, of the calculated increase.
`
`© 2007 Adis Data Information BV. All rights reserved.
`
`Clin Pharmacokinet 2007; 46 (8)
`
`9
`
`

`

`
`
`Ohno et al.
`“WWWEWWWMWM”€7MWWWMWSam
`
`100 I Simvastatin
`
`100 ' Lovastatin
`
`100.
`
`Buspirone
`
`100-
`
`Nisordipine
`
`10 '
`
`1
`
`1 r—fifi—T—rfi
`0 0.2 0.4 0.6 0.8 1.0
`
`1o —
`
`10 *
`
`l
`1- 1-
`
`0 0.2 0.4 0.6 0.8 1.0
`0 0.2 0.4 0.6 0.8 1.0
`O 0.2 0.4 0.6 0.8 1.0
`
`
`
`100 -
`
`1o -
`
`Triazolam
`
`I
`
`4
`E,
`r——~—.——n'—i—~—r—u
`I;
`0 0.2 0.4 0.6 0.8 1.0
`g
`'
`‘5
`5 100 Nifedipine
`10 -
`
`1
`
`3<
`
`1
`
`/r‘I/§/
`-
`O 0.2 0.4 0.6 0.8 1.0
`
`100 ' Zolpidem
`
`10 '
`
`10° ' Atorvastatin
`
`10 -
`
`100 _ Telithromycin
`
`10 '
`
`~
`
`1
`
`///‘§“
`0 0.2 0.4 0.6 0.8 1.0
`
`‘
`—
`.3 .
`0 0.2 0.4 0.6 0.8 1.0
`
`
`1 l
`.
`I M 1 Fifi—’4
`O 0.2 0.4 0.6 0.8 1.0
`0 0.2 0.4 0.6 0.8 1.0
`
`Fig. 3. Increase in the area under the concentration—time curve (AUC) reorganised for each substrate drug as a function of the inhibition
`ratio (IR) of the inhibitors. The data shown in figure 1 and figure 2 were reorganised to show the increase in the AUC of each substrate drug
`as a function of the time-averaged apparent inhibition ratio of CYP3A4 (IRCYP3A4) values of the inhibitors. The closed and open symbols
`represent the dataset shown in figure 1 and figure 2, respectively. See figure 1 and figure 2 legends for details.
`
`I RCYP3A4
`
`Despite these issues regarding the in viva evalua—
`tion 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 cur—
`rent method relies primarily on an overall in viva
`evaluation. For example, the present method satis-
`factorily predicted drug interactions with mecha-
`nism-based
`inhibitors
`such
`as
`azithromycin,
`clarithromycin, diltiazem, erythromycin and rox—
`ithromycin (figure 4), which frequently exhibit com-
`plicated 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 eval-
`uation of the unbound fraction of the drug in the
`
`incubation medium is importantm’m] 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 sub-
`strate and ketoconazole and itraconazole as selective
`
`inhibitors of CYP3A4, although these drugs are not
`absolutely specific for CYP3A4. For example,
`al—
`though we assumed that the CRCYP3A4 value of
`simvastatin is 1.0, this drug is also metabolised by
`CYP2C8 to a minor extent.[1°5] Ketoconazole is a
`well known selective inhibitor of CYP3A4, but this
`drug also inhibits the activities of CYP2C8,[120]
`CYP2C9ml] and MDR1,[m] which may also affect
`the disposition of substrate drugs analysed in the
`
`© 2007 Adis Data Information BV. All rights reserved.
`
`1 0
`
`Clin Phormocokinet 2007; 4(2 (8)
`
`
`
`
`
`
`
`1001
`
`Midazolam
`
`.13i
`
`1o —
`
`100 -
`
`Felodipine
`
`10'
`
`100 -
`
`Ciclosporin
`
`10
`
`1
`
`0 0.2 0.4 0.6 0.8 1.0
`
`O 0.2 0.4 0.6 0.8 1.0
`
`:/rf
`/1
`
`.
`.
`.
`.
`.
`1 s
`0 0.2 0.4 0.6 0.8 1.0
`-i
`100 Alprazolam
`1o .
`
`1
`
`i
`‘
`.
`0 0.2 0.4 0.6 0.8 1.0
`
`1
`
`100 ' Cerivastatin
`
`10 ‘
`
`
`
`10
`
`

`

`
`i... $9M . 427A» ”m laws/W a“
`. 4A
`General Prediction of CYP3A4 Drug Interactions
`
`691
`
`
`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 midazo—
`lam, nifedipine, simvastatin and erythromycinim]
`Among them,
`it
`is generally recognised that
`the most reliable probe drug is midazolam for
`CYP3A4.[