Modeling, Prediction, and in Vitro in Vivo Correlationof CYP3A4 Induction

Magang Shou, Mike Hayashi, Yvonne Pan, Yang Xu, Kari Morrissey,Lilly Xu, and Gary L. Skiles

Department of Pharmacokinetics and Drug Metabolism, Amgen, Inc., Thousand Oaks, California

Received January 25, 2008; accepted July 29, 2008

ABSTRACT:

CYP3A4 induction is not generally considered to be a concern forsafety; however, serious therapeutic failures can occur with drugswhose exposure is lower as a result of more rapid metabolicclearance due to induction. Despite the potential therapeutic con-sequences of induction, little progress has been made in quanti-tative predictions of CYP3A4 induction-mediated drug-drug inter-actions (DDIs) from in vitro data. In the present study, predictivemodels have been developed to facilitate extrapolation of CYP3A4induction measured in vitro to human clinical DDIs. The followingparameters were incorporated into the DDI predictions: 1) EC50

and Emax of CYP3A4 induction in primary hepatocytes; 2) fractionsunbound of the inducers in human plasma (fu, p) and hepatocytes(fu, hept); 3) relevant clinical in vivo concentrations of the inducers([Ind]max, ss); and 4) fractions of the victim drugs cleared by CYP3A4

(fm, CYP3A4). The values for [Ind]max, ss and fm, CYP3A4 were obtainedfrom clinical reports of CYP3A4 induction and inhibition, respec-tively. Exposure differences of the affected drugs in the presenceand absence of the six individual inducers (bosentan, carbamaz-epine, dexamethasone, efavirenz, phenobarbital, and rifampicin)were predicted from the in vitro data and then correlated withthose reported clinically (n � 103). The best correlation was ob-served (R2 � 0.624 and 0.578 from two hepatocyte donors) whenfu, p and fu, hept were included in the predictions. Factors that couldcause over- or underpredictions (potential outliers) of the DDIswere also analyzed. Collectively, these predictive models couldadd value to the assessment of risks associated with CYP3A4induction-based DDIs by enabling their determination in the earlystages of drug development.

Drug drug interactions (DDIs) are a major source of clinical prob-lems leading either to severe adverse drug reactions (Honig et al.,1993; Gomez et al., 1995; Floren et al., 1997; Greenblatt et al., 1998;Backman et al., 2002, 2006) or a reduction in pharmacological effects(Back et al., 1979; Daniels et al., 1984; Heimark et al., 1987). Themost common DDIs are associated with alterations in drug clearance,primarily due to inhibition of drug-metabolizing enzymes (DMEs),particularly cytochromes P450 (P450s). Predictions of in vivo DDIsbased on in vitro drug metabolism data are increasingly being inte-grated into decision making about the development of potential drugsduring the early stages of drug discovery. The success in thesepredictions is due largely to an increased understanding of DMEs atthe molecular level, which has provided insight into the mechanismsof drug biotransformation and disposition. Substantial progress hasbeen made in extrapolating in vitro inhibition data for the predictionof clinical DDIs, but fewer advances in prediction of induction-basedDDIs have been realized.

Induction of CYP3A4 is especially important because the enzymeis involved in the metabolism of approximately 50% of marketed

drugs (Guengerich, 1999). Unlike P450 inhibition, CYP3A4 inductionis not as frequent a problem and is not normally thought to be a safetyconcern. Induction can lead to inadequate efficacy of coadministereddrugs, and it is therefore an undesirable property (Dickinson et al.,2001; Grub et al., 2001). Induction studies have generally been moredifficult to conduct experimentally than inhibition studies becauseinduction is an indirect and slow process of gene up-regulation andincreased protein expression; however, the advent of newer technol-ogies such as nuclear receptor-reporter and mRNA analyses haspartially overcome this problem. Although the concept of induction ofDMEs has been known for several decades (Conney, 1967), anunderstanding of the mechanisms of P450 induction has only beendeveloped slowly. The major mechanism of CYP3A4 induction hasbeen determined to occur via activation of a human orphan nuclearreceptor known as the pregnane X receptor (PXR) (Kliewer et al.,1998). Inducer binds to PXR in the cytosol and together they trans-locate to the nucleus where a heterodimer with retinoid X-receptor isformed. The receptor complex binds to the DNA-responsive element(DNARE) upstream of the CYP3A4 gene and activates its promoter,leading to transcription of the CYP3A4 gene. PXR also induces thetranscription of a number of other drug-metabolizing enzymes includ-ing less potent induction of CYP2B and CYP2C9 (Kliewer et al.,

Article, publication date, and citation information can be found athttp://dmd.aspetjournals.org.

doi:10.1124/dmd.108.020602.

ABBREVIATIONS: DDI, drug-drug interaction; DME, drug-metabolizing enzyme; P450, cytochrome P450; PXR, pregnane X receptor; DMSO,dimethyl sulfoxide; RIF, rifampin; PB, phenobarbital; DEX, dexamethasone; BST, bosentan; EFA, efavirenz; CMZ, carbamazepine; KHB, Krebs-Henseleit buffer; b-DNA, branched-DNA; GADPH, glyceraldehyde-3-phosphate dehydrogenase; AUC, area under the curve; RSS, residual sumof squares; P-gp, P-glycoprotein; CAR, constitutive androstane receptor; OATP, organic anion-transporting polypeptide; DNARE, DNA-responsiveelement; IVIVE, in vitro in vivo extrapolations.

0090-9556/08/3611-2355–2370$20.00DRUG METABOLISM AND DISPOSITION Vol. 36, No. 11Copyright © 2008 by The American Society for Pharmacology and Experimental Therapeutics 20602/3392177DMD 36:2355–2370, 2008 Printed in U.S.A.

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1998). The extent of induction depends on the concentration of theinducer and on the duration of exposure.

Induction has been well characterized with a number of drugs, suchas rifampicin (Backman et al., 1996), phenobarbital (Rutledge et al.,1988), troglitazone (Prueksaritanont et al., 2001), and bosentan (vanGiersbergen et al., 2002), examples that are mainly confined toinduction of CYP3A4. CYP3A4 is induced by a wide range ofstructurally diverse compounds that reflect a broad range of bindingspecificity for PXR. Inducers of CYP3A4 also induce CYP3A in otherspecies but with major differences in the degree of response (Jones etal., 2000; Moore and Kliewer, 2000). For example, the prototypicalCYP3A4 inducer rifampicin is a potent activator of human and rabbitPXR but has little activity on the rat and mouse receptor (Jones et al.,2000; Vignati et al., 2004). These species differences are known to besubstantial, both in the spectrum of the enzymes induced and theextent of induction, and make nonhuman in vivo models inappropriatefor induction studies intended to understand the risk of human induc-tion. Because of these limitations, there is an increasing demand forthe use of human in vitro systems (Masimirembwa et al., 2001). Acommonly used technique is based on immortalized cell lines withengineered receptor and reporter systems (Goodwin et al., 1999).Another emerging technique is the use of immortalized human hepa-tocytes (Mills et al., 2004; Ripp et al., 2006). Despite these advances,primary human hepatocyte cultures have been and remain the goldstandard in vitro system for investigating potential P450 induction(LeCluyse et al., 2000; Hewitt et al., 2007). Induction can be assessedwith these cultures by measurements of both mRNA level and P450functional enzymatic activity using probe substrates. The hepatocyte-based assays are, however, relatively low throughput and impracticalfor screening of large numbers of compounds because of the com-plexity of the assays and the need for a continuous supply of hepa-tocytes. Comparisons of induction measured in cell-based reporterassays and human hepatocytes have shown some degree of correlation(Luo et al., 2002).

The importance of CYP3A4 induction has prompted the search foralternative approaches to predicting DDIs using in vitro data obtainedduring the drug discovery stage. Unfortunately, little progress hasbeen made in this regard. Development of predictive models of DDIsdue to P450 inhibition have been based on fundamental principles andassumptions of in vitro in vivo extrapolations (IVIVE) and have beenessential for predicting the magnitude of clinical DDIs. The success ofthese models demonstrates that in vitro data can be integrated intopredictive models for in vivo DDI predictions and that useful predic-tions can be performed relatively early in the drug discovery process.These predictions aid in 1) selecting compounds for further develop-ment, 2) developing structure-activity relationships to avoid the po-tential for DDIs, and 3) planning of clinical DDI studies for com-pounds that are advanced into further drug development. In thepresent study, predictive models were developed and used for IVIVEof induction-based DDIs. Correlations of the predicted DDIs withobserved in vivo DDIs were also analyzed.

Materials and Methods

Materials. The test articles were obtained from the following sources: di-methyl sulfoxide (DMSO), rifampin (RIF), phenobarbital (PB), dexamethasone(DEX), testosterone, 6�-hydroxytestosterone, Dulbecco’s modified Eagle’s me-dium (plating medium), and serum-free Williams’ Medium E (culture medium)from Sigma-Aldrich (St. Louis, MO); bosentan (BST) and efavirenz (EFA) fromBIOMOL International LP (Plymouth Meeting, PA); carbamazepine (CMZ) fromMP Biomedicals (Solon, OH); fetal bovine serum from Invitrogen (Carlsbad, CA);sandwich medium from BD Biosciences (Bedford, MA); and Krebs-Henseleitbuffer (KHB) from Amgen (Thousand Oaks, CA). Freshly isolated human hepa-tocytes were purchased from CellzDirect (Pittsboro, NC).

Human Hepatocyte Culture and Experimental Procedure. Fresh humanhepatocytes from two donors were purchased from CellzDirect: donor 1, a36-year-old white female (height 5 ft 3 in., body weight 160 lb, lot 0624) withno record of medications or of substance or alcohol abuse; and donor 2, a41-year-old white female (height 5 ft 7 in., body weight 152 lb, lot 0697) witha history of smoking but no alcohol abuse. On day 1, fresh hepatocytes werereceived and suspended in plating medium (0.75 � 106 cells/ml). Hepatocyteswere counted and plated in collagen-coated 24-well plates with a density of0.4 � 106 cells/well (BD Biosciences). The hepatocytes were placed in a 37°Cincubator (Steri-Cult CO2 Incubator, model 3310; Thermo Fisher Scientific,Waltham, MA) under an atmosphere of 95% air/5% CO2 and 90% relativehumidity and allowed a 3- to 5-h attachment period. After the attachmentperiod, the plating medium and unattached cells were removed by aspiration,sandwich medium was applied (0.5 ml/well), and the cells were incubatedovernight. On day 2, the sandwich medium was aspirated and culture medium(0.5 ml/well) was applied for an overnight acclimation period. On days 3 and4, culture medium containing either DMSO (0.1%), BST (0.5–50 �M), CMZ(1.0–100 �M), DEX (1.0–100 �M), EFA (0.5–20 �M), PB (15.6–1000 �M),or RIF (0.05–5 �M) was applied on each day (0.5 ml/well). The test articleswere prepared in DMSO stock solutions, resulting in final incubation concen-trations of 0.1% DMSO. Compound treatment was maintained for a total of48 h. On day 5, hepatocytes were gently washed three times with KHB (0.5ml/well, 37°C) and allowed to acclimate for an additional 10 min. P450enzyme activities were subsequently determined by the addition of the markersubstrate testosterone (200 �M; CYP3A4) dissolved in KHB (0.5 ml/well, 37°C).After a 15-min incubation, the medium was removed and stored at �80°C untilanalyzed. Hepatocytes used for mRNA analysis were washed once with phosphatebuffered saline (0.5 ml/well, 25°C) containing calcium and magnesium andaspirated. Lysis mixture (0.5 ml) (Panomics, Inc., Fremont, CA) was added toeach well, and the mixture was then stored at �80°C until assayed.

mRNA Analysis. CYP3A4 mRNA content was determined with branchedDNA (b-DNA) signal amplification technology using the Panomics Discover XLKit (Panomics, Inc.) with assays performed according to the manufacturer’sinstructions. b-DNA probe sets containing capture extender, label extender, andblocking probes for human CYP3A4 and GAPDH were also purchased fromPanomics, Inc. Plate washing steps were performed on an Elx405 automatedmicroplate washer (BioTek Instruments, Inc., Winooski, VT), and luminescencewas analyzed on a Luminoskan Ascent microplate luminometer (Thermo Lab-systems, Helsinki, Finland). P450 mRNA levels were normalized to the mRNAlevels of the housekeeping gene GAPDH.

Enzyme Activity. After the induction treatment period, the metabolism rateof the CYP3A4 marker substrate, testosterone, by the cell cultures was deter-mined. Analysis and quantification of 6�-hydroxytestosterone, the major tes-tosterone metabolite in hepatocyte cultures, was performed by liquid chroma-tography-tandem mass spectrometry on a system comprising a reverse-phasehigh-performance liquid chromatograph (Shimadzu, Kyoto, Japan) and a triplequadrupole mass spectrometer (API 5000; Applied Biosystems, Foster City,CA) using Turbo IonSpray (Applied Biosystems) via multiple reaction mon-itoring. Samples (25 �l) were loaded on a C18 column (Onyx Monolithic C18,100 � 3.0 mm, P/No. CHO-8158; Phenomenex, Torrance, CA), and analyteswere eluted with a linear gradient of mobile phase A (H2O with 0.1% aceticacid and 5% methanol) to B (H2O with 0.1% acetic acid and 95% methanol)in 4.6 min. The flow rate was 1 ml/min. The metabolite was quantified bycomparison of peak area ratios of metabolite to internal standard (prazosin) toa standard curve prepared using authentic 6�-hydroxytestosterone.

Fractions Unbound in Plasma and Hepatocytes. Frozen human plasmawas thawed, aliquots (2 ml) were preheated to 37°C, and then the individualinducers were incubated at 1 and 10 �M in polypropylene centrifuge tubes(Corning Inc., Corning, NY) for 15 min. After this incubation period, 800-�laliquots (n � 2) were transferred into polyallomar tubes (Beckman Coulter,Fullerton, CA), and then the tubes were placed in a MLA-130 rotor andcentrifuged at 16,128g for 3 h at 37°C in an Optima Max Ultracentrifuge(Beckman Instruments, Palo Alto, CA). Similarly, the inducers at varyingconcentrations (5 �M BST, 20 �M CMZ, 100 �� DEX, 10 �M EFA, 250 �MPB, and 1 �M RIF) were incubated with human hepatocytes at 37°C for 5 min.After incubation, the tubes were centrifuged at 500g for 5 min. Aliquots (0.1ml; n � 3) of the supernatants (the unbound fractions) were then removed fromeach tube. To precipitate proteins and prepare the samples for analysis, 50 �l

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of aliquoted supernatant from the plasma or hepatocyte samples was mixedwith 200 �l of acetonitrile containing 0.125 �g/ml internal standard (propri-etary Amgen compound). The samples were thoroughly mixed by vortexingand centrifuged for 10 min at 405g, and then 200 �l of the resultant supernatantwas transferred into 96-well plate. Solvent was removed under a stream of N2

gas, and the samples were reconstituted with 100 �l of 50% MeOH/H2O forliquid chromatography-tandem mass spectrometry analysis. Standard curvesand quality controls were prepared in the same manner. Concentrations ofanalytes were determined using a standard curve prepared in the same matrix.The unbound fraction in plasma ( fu, p) or hepatocytes ( fu, hept) was determinedas a ratio of the concentration measured relative to total concentration. Re-covery of drug was also determined by measuring the initial concentration ofdrug in the plasma or hepatocytes and comparing it to the nominal concentra-tions. All measurements were conducted in triplicate.

Data Analysis. In vitro induction assays were performed in triplicate.Concentration-response data sets of each inducer for mRNA and enzymeactivity were plotted and fitted to the Hill equation (eq. 1) using Sigmaplot10.0 (Systat Software Inc., Chicago, IL),

E �Emax � [Ind]n

EC50n � [Ind]n (1)

where Emax is the maximum response (net maximum fold increase), EC50 is theinducer concentration at 50% Emax, [Ind] is the inducer concentration, and n isthe sigmoidity of the fitted curve.

CYP3A4 Induction Modeling. Induction of CYP3A4 enzyme by an inducerin hepatocytes is described as a receptor-mediated (e.g., PXR) process that triggerstranscriptional activation of the gene coding for the protein. The fraction ofoccupancy of the DNA promoter elements by inducer complex (inducer-receptor)is expressed by eq. 2 and related to the concentration of the inducer:

FO �[Ind � r � DNARE]

[DNARE]�

[Ind]n

[Ind]n � EC50n (2)

where FO is fractional occupancy, [Ind] is the inducer concentration, and[Ind � r � DNARE] is the concentration of inducer, receptor, and DNARE com-plex. [DNARE] is the response element concentration, n is the Hill coefficient,and EC50 is the concentration of inducer at 50% maximal effect of induction.

Hepatic CYP3A4 content in the absence of inducer is governed by the rateof de novo enzyme synthesis (K0) and the rate of enzyme degradation (Kdeg) asshown in eq. 3. At steady state, the rate of de novo biosynthesis of the enzymeequals the degradation rate (K0 � Kdeg � [E]ss). It follows that the enzymecontent ([E]ss) and intrinsic clearance (CLint) of a substrate of the enzyme aredescribed by eqs. 4 and 5, respectively,

d�E�

dt� K0 � Kdeg�E� (3)

�E�ss �K0

Kdeg(4)

CLint ��E�ss � kcat

Km(5)

where K0 and Kdeg are the rates of enzyme synthesis and degradation, respec-tively; [E]ss is the enzyme content at steady state; kcat is the rate constant formetabolism of a substrate by the enzyme, and Km is the substrate-enzymedissociation constant.

When an inducer is present, the enzyme level is raised to a new steady-statelevel as described in eq. 6. At the new steady state the enzyme level ([E]�ss) isdefined by eq. 7 and intrinsic clearance (CL�int) for a substrate is determinedby eq. 8:

d�E��

dt� K0 � KInd � �E�� � Kdeg � �E��

� K0 � �Kmax � K0 � FO � Kdeg � �E�� (6)

�E��SS �K0 � �Kmax � K0 � FO

Kdeg(7)

CL�int ��E��SS � kcat

Km(8)

where Kind is the rate constant for enzyme induction, Kmax is the maximumvelocity of enzyme synthesis, and [E]�ss is the enzyme content at steady statein the presence of an inducer. The ratio of CLint in the absence and presenceof an inducer is expressed by eq. 9, where Emax is the maximum induction ofenzyme (net fold increase of enzyme):

CLint

CL�int�

�E�ss

�E��ss�

1

1 ��Kmax � K0,invitro � [Ind]n

K0,invitro�EC50n � [Ind]n

�1

1 �Emax � [Ind]n

�EC50n � [Ind]n

(9)

Prediction of DDIs from in Vitro CYP3A4 Induction. Based on phar-macokinetic principles, the AUC ratio of a substrate after intravenous admin-istration in the presence and absence of an inducer is expressed by eq. 10. Withoral administration the AUC of a substrate in the presence of an inducer ismainly affected by changes in hepatic bioavailability (F�h) and clearance(CL�h), assuming that gastrointestinal absorption and metabolic extraction (Eg)are not altered significantly [Fab � 1 (fraction of drug absorbed in gut) andFg � 1 (gut bioavailability)] (eq. 11). Therefore, the AUC ratio of the dose isexpressed in eq. 12:

AUC�IV

AUCIV�

CLsyst

CL�syst�

CLh � CLr

CL�h � CLr

�

CLh � CLh � � 1

fh� 1�

CL�h � CLh � � 1

fh� 1��

1

fh �CL�h

CLh� �1 � fh

(10)

Fh � �1 � Eh �Qh

Qh � fu � CLint(11)

AUC�PO

AUCPO�

CLPO

CL�PO�

F�h � CLh

Fh � CL�h

�

� Qh

Qh � fu � CL�int� � � Qh � fu � CLint

Qh � fu � CLint�

� Qh

Qh � fu � CLint� � � Qh � fu � CL�int

Qh � fu � CL�int��

CLint

CL�int(12)

To predict the potential for drug interactions caused by CYP3A4 induction, arelationship of hepatic clearance to CYP3A4 intrinsic clearance (CLint, CYP3A4)is derived in eq. 13. If a compound is highly cleared by the liver(Qh fu � CLint or fu � CL�int) and CLh (or CL�h) is limited by the hepatic flowrate, then CLh is equal to CL�h (CLh/CL�h � 1). For a low-clearance drug( fu � CLint or fu � CL�int Qh), however, the ratio of CLh to CL�h can beexpressed by eq. 13. The change in AUC ratio is then predicted by eq. 14 (eqs.10 and 13 combined) for intravenous administration and by eq. 15 (eqs. 12 and13 combined) for oral administration:

CLh

CL�h�

� Qh � fu � CLint

Qh � fu � CLint�

� Qh � fu � CL�int

Qh � fu � CL�int��

CLint

CL�int� �Qh � fu � CL�int

Qh � fu � CLint�

�CLint

CL�int�

CLint, CYP3A4 � CLint, otherP450s

CL�int, CYP3A4 � CLint, otherP450s

�

CLint, CYP3A4 � CLint, CYP3A4 � � 1

fm, CYP3A4� 1�

CL�int, CYP3A4 � CL�int, CYP3A4 � � 1

fm, CYP3A4� 1�

�1

fm, CYP3A4

CL�int, CYP3A4

CLint, CYP3A4� �1 � fm, CYP3A4

(13)

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AUC�IV

AUCIV�

CLsyst

CL�syst�

1

fh � fm, CYP3A4 �CL�int, CYP3A4

CLint, CYP3A4� �1 � fh � fm, CYP3A4

�1

fh � fm, CYP3A4 � �1 �Emax � [Ind]n

�EC50n � [Ind]n�� �1 � fh � fm, CYP3A4

or � fh � 1 �1

fm, CYP3A4 � �1 �Emax � [Ind]n

�EC50n � [Ind]n�� �1 � fm, CYP3A4

(14)

AUC�PO

AUCPO�

CLint

CL�int�

1

fm, CYP3A4 � �1 �Emax � [Ind]n

�EC50n � [Ind]n�� �1 � fm, CYP3A4

(15)

where [Ind] is the inducer plasma concentration achieved at steady state,usually Cmax, ss.

In many cases, Emax and EC50 are not readily obtained from concentration-response curves measured by in vitro induction assays because of limitationsimposed by drug solubility, cell permeability, or toxicity. In these instances theslope of the induction response curve (equivalent to Emax/EC50) at a moreexperimentally feasible low concentration range of the inducer can be used forthe prediction (eq. 16). This equation is, however, only applicable if in vivoconcentrations of an inducer are low ([Ind] EC50). Eq. 17 shows thepredictive model with inclusion of fractions unbound in plasma ( fu, p) andhepatocytes ( fu, hept), based on the hypothesis that only unbound drug (inducer)can access the intracellular nuclear receptor responsible for induction.

AUC�

AUC�

1

fm, CYP3A4 � �1 � slope � [Ind]n � �1 � fm, CYP3A4(16)

AUC�

AUC�

1

fm, CYP3A4 � �1 �Emax � � fu,p � Ind�n

��EC50 � fu,hept]n � � fu,p � Ind�n�� �1 � fm, CYP3A4

(17)

Contribution of CYP3A4 to Drug Clearance in Vivo ( fm, CYP3A4). Frac-tions of the drugs cleared by CYP3A4 ( fm, CYP3A4) were collected fromclinical DDI studies reported in the literature. The extent of decreased clear-ance (or increased AUC) of a substrate drug in the presence of a selectiveCYP3A4 inhibitor ( fi, CYP3A4) is used as an estimate of fm, CYP3A4 and calcu-lated by eq. 18. In theory, an accurate value of fm, CYP3A4 should be given bythe maximum decrease in clearance of a victim drug that is administered i.v.from clinical DDI studies (complete inhibition of the target enzyme by aninhibitor). However, most DDI studies reported in literature were performed bythe oral administration of victim drugs, in which the first-pass metabolism ofthe drugs in the gut (Fg) could be affected (eq. 19) (Obach et al., 2006).Unfortunately, the estimated ratios of Fg�/Fg (Fg� and Fg represent gut bio-availability in the presence and absence of inhibitor) in literature reports werenot readily available for most drugs. Thus, the estimations of fm, CYP3A4 for thevictim drugs of oral administration from clinical DDI data are based onassumptions that fg, CYP3A4 (eq. 19) is not changed in the presence of CYP3A4inhibitor and that the inhibition of the CYP3A4 pathway is complete. Inaddition, when the fm, CYP3A4 values were calculated in the present study forthe victim drugs given intravenously it is also assumed that the inhibition ofintrinsic clearance of the CYP3A4 pathway by the inhibitors is complete(perhaps close enough for approximation), even for the high extraction drugs.

fm, CYP3A4 � fi, CYP3A4 � 1 �CL�[I]

CL[ctr]� 1 �

AUC[ctr]

AUC�[I](18)

CL�IV

CLIV�

AUCIV

AUC�IV�

AUCPO

AUC�PO�Fg�

Fg�

AUCPO

AUC�PO(assuming Fg� � Fg) (19)

Correlation Analysis. The correlation analyses between predicted andclinical DDIs were performed by linear regression with analysis of variance. R2

(correlation coefficient) and residual sum of squares (RSS) (a measure of thesize of the residuals, which are the differences of the actual data points fromregression modeled values) were determined. In addition, weighted residuals

(the residual divided by the standard error of the estimate) were analyzed toassess the distribution (normal or outlying) of the residuals.

Results

Concentration-Response Profiles of in Vitro CYP3A4 Induc-tion. Human hepatocytes are routinely used as an experimental systemfor the evaluation of P450 induction potential. Selection of the modelcompounds, BST, CMZ, DEX, EFA, PB, and RIF, was based on 1)known CYP3A4 induction in vitro and in vivo, 2) minimal potentialfor mechanism-based inhibition of CYP3A4, and 3) known clinicallyrelevant concentrations. Hepatocytes were prepared from fresh humanlivers (two donors), cultured for 2 days, and then treated with theinducers. After 48-h incubations with the inducers at varying concen-trations, mRNA was analyzed using b-DNA technology, and CYP3A4activity was determined by turnover of the marker substrate testos-terone. The measured levels of induction in mRNA and enzymeactivity were normalized against solvent control and recorded as thenet fold increase over basal levels measured in control incubations.Full concentration-response curves were generated to provide com-plete kinetic characterization of each inducer (Fig. 1).

EC50 and Emax values for mRNA and enzyme activity were gen-erated by fitting the observed data to the Hill equation (Table 1). Allof the inducers were found to be capable of inducing CYP3A4 mRNAand activity. Moreover, there was general agreement between the twodonors and between mRNA and activity parameters. As is commonlyobserved, however, some differences in the induction kinetics wereobserved. These differences occurred between the two donors andbetween the inductions measured by mRNA or activity levels. Gen-erally, the enzyme activity Emax values for donor 1 were approxi-mately 2- to 3-fold higher than those for donor 2, with the exceptionof RIF, which induced activity to a similar extent for both donors.Between the two donors, enzyme activity EC50 values for all of theinducers were similar with the exception of EFA, which had anapproximately 5-fold higher EC50 in hepatocytes from donor 1 thanthat from donor 2. In hepatocytes from donor 1, the EC50 and Emax

values for both mRNA and enzyme activity agreed within a 2-foldrange except for a 6.7-fold difference in EC50 for EFA and a 4.7-folddifference in Emax for RIF. In donor 2 there was greater variability inboth EC50 and Emax, and mRNA Emax values were generally higherthan those for activity.

The greater CYP3A4 activity measured as testosterone 6�-hydroxylation after exposure to the inducers indicated that greateramounts of enzyme were present as a result of the induction. Therelative in vitro induction potential of the inducers was also evaluatedby determining changes in their intrinsic clearance (CLint � Emax/EC50 for enzyme activity). The analysis resulted in a rank order of RIF(42.0 and 24.5) � BST (19.6 and 4.1) � CMZ (1.03 and 0.17), EFA(0.58 and 1.45), DEX (0.33 and 0.25), and PB (�0.1 and 0.07) fordonors 1 and 2, respectively. The experimentally determined Emax andEC50 were incorporated into the prediction models together withfm, CYP3A4, clinical relevant concentration ([Ind]max, ss), protein bind-ing in plasma ( fu, p), and membrane partitioning in hepatocytes( fu, hept), as described below. Unbound fractions of the inducers inhepatocytes were measured to correct the EC50 values for free con-centration as required for eq. 17. fu, hept was measured at inducerconcentrations similar to their corresponding EC50 values in hepato-cytes. The fractions unbound were widely variable among the com-pounds (Table 2). Interestingly, binding of the inducers to plasma andhepatocytes was observed to have a similar trend. As reported previ-ously (Austin et al., 2002; Lobell and Sivarajah, 2003), this probablyreflects a relationship between binding in either plasma or membranesand logD (or logP).

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CYP3A4 Reaction Phentotyping ( fm, CYP3A4). The degree towhich CYP3A4 contributes to the clearance of a drug, that is, thefraction of the drug metabolized by this isozyme ( fm, CYP3A4), can be

obtained from in vivo data (e.g., human radiolabeled compounddisposition studies) or estimated by drug interaction studies with P450isoform-selective inhibitors ( fi, CYP3A4, eq. 18). fm, CYP3A4 is a critical

FIG. 1. Curves of CYP3A4 response (fold increase) to inducer concentration (micromolar). IC50 and Emax values (Table 1) were generated by fitting of the observed datain hepatocytes to the Hill equation (eq. 1). Donor 1: A, mRNA; B, enzyme activity. Donor 2: C, mRNA; D, enzyme activity.

TABLE 1

EC50 and Emax values of the inducers for CYP3A4 mRNA and activity in hepatocytes generated from the concentration vs. response curves in Fig. 1

S.D. shown in parentheses.

Inducer Conc.mRNA Activitya

EC50 Emax nb R2 EC50 Emax n R2

�M �M �M

Donor 1 BST 0.51–50 1.07 (0.48) 11.40 (3.47) 1.3 (0.5) 0.953 0.78 (0.27) 14.75 (0.57) 1.6 (0.8) 0.995CMZ 1.02–100 20.55 (3.03) 14.30 (0.89) 1.2 (0.1) 0.997 15.30 (0.79) 15.73 (0.57) 2.1 (0.3) 0.994DEX 1.02–100 50.80 (11.60) 11.10 (1.70) 1.14 (0.2) 0.994 33.8 (5.3) 12.62 (3.43) 1.2 (0.3) 0.992EFA 0.51–20 1.860 (1.01) 5.80 (0.34) 3.4 (2.6) 0.994 12.50 (3.5) 7.27 (0.77) 1.8 (0.5) 0.987PB 15.6–1000 155.50 (20.90) 17.90 (1.07) 1.5 (0.2) 0.993 250.00 (92.1) 22.65 (5.57) 0.9 (0.2) 0.985RIF 0.05–5 0.33 (0.12) 49.70 (5.47) 1.1 (0.4) 0.919 0.25 (0.04) 10.60 (0.60) 1.2 (0.2) 0.987

Donor 2 BST 0.51–50 5.78 (3.88) 17.76 (3.59) 0.8 (0.2) 0.971 1.13 (0.20) 4.63 (0.24) 1.1 (0.2) 0.9889CMZ 1.02–100 14.37 (4.43) 6.04 (0.79) 1.4 (0.40) 0.976 27.70 (2.72) 4.57 (0.28) 2.6 (0.7) 0.991DEX 6.25–200 75.20 (14.81) 15.12 (1.48) 0.8 (0.1) 0.960 22.00 (2.40) 5.41 (0.78) 1.2 (0.7) 0.918EFA 0.51–20 15.74 (5.89) 9.74 (3.08) 0.7 (0.2) 0.986 2.18 (1.40) 3.15 (0.78) 1.2 (0.7) 0.917PB 15.6–1000 86.19 (7.41) 6.94 (0.25) 1.9 (0.3) 0.992 120.30 (7.9) 8.65 (0.18) 1.4 (0.1) 0.998RIF 0.01–20 0.22 (0.05) 81.61 (3.54) 1.2 (0.2) 0.981 0.51 (0.08) 12.51 (0.69) 1.2 (0.3) 0.975

a CYP3A4 activity (testosterone 6�-hydroxylation) measured in hepatocytes.b Sigmoidity of the curves in Fig. 1.

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parameter for the DDI prediction because, as shown in eq. 17, themagnitude of the DDI is directly proportional to this parameter. Theprecise role of an individual P450 in drug clearance, however, isdifficult to ascertain because of multiplicity and overlapping substratespecificities for various P450 isoforms. In the present study,fm, CYP3A4 values were determined from clinical DDIs associated withCYP3A4 inhibition. The extent to which systemic or oral clearance ofa drug is altered in the presence of an isozyme-specific inhibitorreveals the fraction of that drug cleared by CYP3A4. A number ofCYP3A4-selective inhibitors have been widely used for coadminis-tration with drugs suspected to be CYP3A4 substrates. These includethe competitive inhibitors ketoconazole, fluconazole, itraconazole,and voriconazole and the mechanism-based inhibitors mibefradil,ritonavir, diltiazem, clarithromycin, and saquinavir (Table 3). Thefm, CYP3A4 values were calculated by eq. 18 for the drugs used in thisstudy and are shown in Table 3. As seen in Table 3, the fm, CYP3A4

values from many drug interaction studies ranged markedly from 0.05(ropivacaine) to 0.95 (buspirone). High fractions indicate that thedrugs are cleared mainly by CYP3A4, and low fractions imply that thedrugs are cleared not only by CYP3A4 but also possibly by otherroutes including conjugative enzyme- or transporter-mediated path-ways or renal or hepatic excretion.

In Vivo Inducer Concentrations. To achieve a steady state ofP450 induction, a human inducing agent must typically be chronicallyadministered. Accordingly, for induction-mediated DDI studies sub-jects usually receive multiple doses of the inducer over consecutivedays. After a steady state of induction is reached, the maximumconcentration of the inducer ([Ind]max, ss) can then be measured.Ideally, for purposes of correlating a clinical DDI with [Ind]max, ss, theinducer concentration should be measured directly in the DDI study.In many of the studies cited herein, however, these measurementswere not performed. The [Ind]max, ss values were therefore obtainedfrom separate studies (Table 4) where the dose regimens were similaror identical to those used in the clinical DDI studies (Table 5). In theDDI studies the inducers were generally administered orally forseveral consecutive days followed by administration of the substratedrugs.

In Vivo Drug-Drug Interactions. The decreased AUC ratios(AUC�/AUC) of the affected drugs in response to the inducers (Table5) were reported within the cited references and resulted in 103 datapoints of drug/inducer interactions. The drugs represented in Table 5are chemically and pharmacologically diverse with a wide range inmagnitude of DDIs caused by CYP3A4 induction. This was thereforeconsidered to be a suitable data set for the purposes of comparingactual DDIs with those predicted by eq. 17.

In Vitro-in Vivo Correlations. Various in vitro data were used fora model-directed prediction of DDIs with eq. 17. These data includedthe in vitro induction data (Table 1), unbound fractions of drug inhepatocyte incubations ( fu, hept) and in plasma ( fu, p) shown in Table

2, the fm, CYP3A4 values obtained from clinical CYP3A4 inhibitionstudies (Table 3), and the in vivo inducer concentrations at dosesrelevant to the DDI studies (Table 4). The predicted DDI values fromthe in vitro enzyme activity induction data in hepatocytes of the twodonors for which fu, p and fu, hept of the inducers were included in theprediction are listed in Table 5. The measured clinical DDIs are alsoshown in Table 5, and these data sets (n � 103) and correspondingcorrelation analyses are shown in Figs. 2A and 3A. In addition to thepredicted DDIs shown in Table 5 in which fractions unbound wereincluded, correlation analyses were also performed without inclusionof the unbound fractions in the DDI prediction. These additionalcomparisons were performed to improve the understanding of theimpact of these binding parameters on the correlations (Figs. 2, B–D,and 3, B–D). The correlation analyses included the 95% confidenceinterval (95% probability that the predicted DDI value will occur). Aunity line (or unit correlation) is included in each figure to illustrateany difference from the observed correlation.

The best correlations were obtained when both fu, p and fu, hept wereincluded in the analyses, leading to R2 � 0.624 for donor 1 (RSS �3.05) and 0.578 for donor 2 (RSS � 3.41), respectively (Figs. 2A and3A). The regression lines of correlation were very similar to the lineof unity (unity correlation). When fu, p or fu, hept or both were excludedfrom the calculation, the correlations were found to be relatively poor(R2 � 0.310–0.577; RSS � 3.42–5.82) (Figs. 2, B–D, and 3, B–D).As might be expected, overpredictions of the DDIs were observed inthe absence of the fu, p (Figs. 2, B and D, and 3, B and D). The degreeof the overpredictions was dependent on the level of inducer bindingto plasma. When fu, hept was ignored, the drug interactions appeared tobe largely underpredicted, particularly for the inducers that werehighly bound in hepatocytes such as EFA (Figs. 2C and 3C). Figure4 shows the distributions of the weighted residuals of the correlationsbetween the predicted and observed DDIs when both fu, p and fu, hept

were included in the calculations. Most weighted residuals for theindividual inducers were distributed around zero (unity regression)within 2 units. The distribution ratios of positive to negative residualwere 43:60 for donor 1 and 44:59 for donor 2, respectively. Theseshow that the data were normally distributed around the unity.

When concentration-induction relationships are evaluated in hepa-tocytes, it is not uncommon to have difficulty determining EC50 andEmax values for P450 inducers that are poorly soluble or toxic. Thisdifficulty arises because poor solubility and toxicity limit the ability tofully determine the concentration-response curves. This limitation canpotentially be overcome by using the slope (Emax/EC50) of the induc-tion-response curves obtained in the linear range at low concentra-tions. Using the slope, eq. 16 can then be used for the prediction. Thisapproach is, however, only valid when in vivo concentrations of aninducer are much lower than its EC50, the conditions required for eq.17 to be simplified to eq. 16. In the present study, EC50 and Emax

values could be obtained for all of the inducers; however, it wasobserved that many of the in vivo concentrations of the inducers werehigher than the EC50 values. For example, in vivo RIF concentrationswere 10 to 18 �M ( fu, p � 0.175), but the EC50 values for activitywere 0.25 to 0.51 �M. When predictions by eq. 16 were attemptedusing the slope values for the inducers such as RIF, the DDIs weresignificantly overpredicted (data not shown). Predictions of druginteractions were also attempted from the in vitro EC50 and Emax

values for mRNA induction shown in Table 1. The correlation anal-yses are shown in Fig. 5. The correlations were poor for both donors(R2 � 0.359 and 0.436), and for RIF in particular the DDIs weregreatly overestimated. This overestimation was due predominantly tothe much greater Emax observed for mRNA induction in both donorscompared with enzyme activity.

TABLE 2

Unbound fractions of the inducers in human plasma and hepatocytes

Data are mean values in triplicate (S.D.).

InducerPlasma Hepatocytes

Conc. fu, p Conc. fu, hept

�M �M

BST 1,10 0.0074 (0.003) 1 0.189 (0.010)CMZ 1,10 0.041 (0.002) 20 0.520 (0.021)DEX 1,10 0.300 (0.011) 100 0.589 (0.043)EFA 1,10 0.029 (0.004) 5 0.063 (0.006)PB 1,10 0.700 (0.092) 250 1.000 (0.074)RIF 1,10 0.175 (0.011) 1 0.419 (0.052)

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Discussion

Marked induction of CYP3A4 by a drug is undesirable because ofthe large number of drugs that are dependent on CYP3A4 for theirclearance. Induction by a drug can affect not only the clearance of aconcomitantly administered medication but also its own clearance (auto-induction) by the induced enzyme (Tran-Johnson et al., 1987). Simpleassays, such as reporter gene assays, that detect binding of inducers toreceptors such as PXR have been used for measuring the capability ofa drug to activate induction pathways; however, binding to PXR alonedoes not necessarily result in concomitant levels of enzyme induction(LeCluyse et al., 2000; Luo et al., 2002; Sinz et al., 2006). Freshhuman hepatocytes maintained in primary culture are preferred for thedirect assessment of the functional consequences of PXR activation,as measured by induction of mRNA and enzyme activity.

In the studies described here, differences in the kinetic character-istics of induction were observed between the two donors (Table 1).Such interindividual variability is not uncommon and imposes adegree of uncertainty in predicting the clinical consequences of in-duction. Differences in Emax values between mRNA induction andenzyme activity suggested that in some instances the parallel relation-ship between these two measures of induction can be altered byfactors such as variations in rates of transcription, translation, andprotein synthesis. Additional uncertainty in predictions also resultsfrom the metabolic instability of inducers in the cell cultures duringthe incubation time (48 h). Consumption of substrate could decrease theactual concentrations in the medium and thus differ from the nominalconcentrations used for the calculation of kinetic parameters. In thisregard, three inducers used here, BST (BST phenol metabolite), CMZ

TABLE 3

Fractions of the drugs cleared by CYP3A4 (fm, CYP3A4) estimated by fi, CYP3A4 from clinical DDIs associated with CYP3A4 inhibition (eq. 18)

Substrate fma Reference Substrate fm Reference

Alfentanilb 871 Kharasch et al., 2005 Midazolam 89 Chen et al., 2006Alprazolam 69 Greenblatt et al., 1998 Nifedipine 634 Kremens et al., 1999Amprenavir 22 Polk et al., 1999 Omeprazole 37 He et al., 2003Antipyrine 25 D’Mello et al., 1985 Pravastatinb 526 Jacobson, 2004Atorvastatin 605 Mazzu et al., 2000 Prednisoloneb 27 Zurcher and Frey, 1989Budesonide 765 Raaska et al., 2002 Quetiapine 84 Grimm et al., 2006Buspirone 93–955 Kivistö et al., 1997 Quinidine 615 Damkier et al., 1999Cyclophosphamideb 434 Yule et al., 1999 Quinine 461 Wanwimolruk et al., 2002Cyclosporine 79 Gomez et al., 1995 Repaglinideb 306 Niemi et al., 2001Diazepam 245 Ahonen et al., 1996 Ritonavir 23 Khaliq et al., 2000Ethinyl estradiol 194 Hilbert et al., 2001 Ropivacaine 55 Jokinen et al., 2001aEtizolam 355 Araki et al., 2004 Saquinavir 66 Grub et al., 2001Everolimus 778 Kovarik et al., 2005 Sildenafilb 667 Muirhead et al., 2000Gefitinib 445 Swaisland et al., 2005 Simvastatin 915 Neuvonen et al., 1998Glyburide 256 Lilja et al., 2007 Tacrolimus 58 Tuteja et al., 2001Imatinib 29 Dutreix et al., 2004 Tirilazadb 41 Fleishaker et al., 1996Indinavir 67–902 Hsu et al., 1998 Triazolam 91 Greenblatt et al., 1998Losartan 21 Kaukonen et al., 1998 Verapamil 309 Fuhr et al., 2002Methadone 254 Cobb et al., 1998 Zolpidem 262 Greenblatt et al., 2000

a fm, CYP3A4 values were calculated (eq. 18) by decreased oral clearance (or systemic clearance) or increased AUC of the drugs in the presence of ketoconazole or other individual CYP3A4inhibitors ( fi, CYP3A4) as denoted by 1troleandomycin, 2ritonavir, 3mibefradil, 4fluconazole, 5itraconazole, 6clarithromycin, 7saquinavir, 8erythromycin, and 9grapefruit juice.

b Drugs were given i.v.

TABLE 4

In vivo concentrations of the inducers at different dose regimens.

Inducer Dose Regimen Cmax, ss Reference

�M

BST 125 mg b.i.d. 5 days 2.0 Dingemanse et al., 2003BST 500 mg b.i.d. 7 days 14.4 Binet et al., 2000DEX 8 mg, b.i.d., 5 days 0.1 McCune et al., 2000CMZ 50 mg b.i.d. 2 wk 8.0 Yasui-Furukori et al., 2003CMZ 100 mg t.i.d. 10 days 17.8 Furukori et al., 1998CMZ 150 mg b.i.d. 2 wk 17.7 Yasui-Furukori et al., 2003CMZ 200 mg b.i.d. 4 days or q.d. 5 days 13.5–22.8 Otani et al., 1996a; Masui et al., 2006CMZ 300 mg b.i.d. 2 wk 25.0 Yasui-Furukori et al., 2003CMZ 400 mg q.d. 4 wk 20.3–23.3 Otani et al., 1996b; Schlienger et al., 2000CMZ 600 mg q.d. 9 wk 22.0–33.0 de Leon and Bork, 1997; Licht et al., 2000CMZ 800 mg q.d. 7 days 31.3 Hillebrand et al., 1987CMZ 942 mg 6 wk 39.8 Ketter et al., 1995CMZ 1400–1650 mg (chronic) 40.8–41.7 May et al., 1996, 2003EFA 200 or 400 mg q.d. 10 days 6.9 Mouly et al., 2002EFA 600 mg q.d. 10 days 10.6–14.9 Veldkamp et al., 2001; la Porte et al., 2004b; Weiner et al., 2005EFA 800 mg q.d. 7 days 11.31 Lopez-Cortes et al., 2002PB 30 mg b.i.d. (chronic) 31.2–36.6 Back et al., 1980PB 90 mg q.d. 29 days 56.8–66.3 Ferron et al., 2003PB 100 mg q.d. 21 days 59.0–65.0 Kapil et al., 1987; Rutledge et al., 1988PB 140 mg q.d. 19 days 123.6 Saccar et al., 1985PB 200 mg q.d. 7 days 105.0 Williams et al., 1983RIF 300 mg b.i.d. 13.5 days 4.1–8.9 Drusano et al., 1986;Chandler et al., 1990RIF 600 mg q.d. 10 days 10.2–18.2 Boyd et al., 2003b; la Porte et al., 2004a; Stone et al., 2004; Droste et al., 2005RIF 900 mg q.d. 10 days 12.1 Humbert et al., 1991

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TABLE 5

Comparisons of the predicted DDIs from the in vitro CYP3A4 induction in hepatocytes of the two liver donors (PRED1 and PRED2)with the clinical observed DDIs reported in cited references

Inducer Dose Regimena Substrate Dose RegimenbAUC Ratioc

ReferencesPRED1 PRED2 OBSd

BST 125 mg b.i.d. 7 days Ethinyl estradiol 35 �g SD 0.87 0.94 0.69 van Giersbergen et al., 2006BST 125 mg b.i.d. 4.5 days Glyburide 2.5 mg b.i.d. 9.5 days 0.84 0.92 0.6 van Giersbergen et al., 2002BST 62.5 mg b.i.d. 4 wk Sildenafil 100 mg SD 0.66 0.81 0.47 Paul et al., 2005BST 62.5 mg b.i.d. 8 wk Sildenafil 100 mg SD 0.66 0.81 0.31 Paul et al., 2005BST 125 mg b.i.d. 5.5 days Simvastatin 40 mg q.d. 5.5 days 0.59 0.75 0.66 Dingemanse et al., 2003DEX 10 mg q.d. 2 days Cyclophosphamide 350–420 mg infusion 2 days 1.00 1.00 0.57 Moore et al., 1988DEX 1.5 mg q.d. 4 days Triazolam 0.5 mg SD 0.87 0.69 0.81 Villikka et al., 1998EFA 600 mg, q.d. 7 days Amprenavir 1200 mg b.i.d. 0.71 0.69 0.76 Falloon et al., 2000EFA 600 mg, q.d. 2 wk Amprenavir 600 mg b.i.d. 5.8 mo 0.71 0.69 0.6 Goujard et al., 2003EFA 600 mg q.d. 20 days Amprenavir 600 mg b.i.d. 10 days 0.63 0.67 0.43 Morse et al., 2005EFA 600 mg q.d. 15 days Atorvastatin 10 mg SD 0.63 0.61 0.59 Gerber et al., 2005EFA 600 mg q.d. 14 days Indinavir 800 mg b.i.d. 30 days 0.56 0.46 0.75 Aarnoutse et al., 2002EFA 600 mg q.d. 4 wk Indinavir 800 mg b.i.d. 4 wk 0.56 0.46 0.93 Boyd et al., 2003aEFA 600 mg q.d. 14–21 days Methadone 35–100 mg daily (maintenance therapy) 0.60 0.64 0.43 Clarke et al., 2001EFA 600 mg q.d. 15 days Pravastatin 40 mg SD 0.51 0.49 0.44 Gerber et al., 2005EFA 600 mg q.d. 10 days Ritonavir 200 mg q.d. 20 days 0.79 0.72 0.8 la Porte et al., 2004bEFA 600 mg q.d. 14 days Ritonavir 200 mg q.d. 28 days 0.79 0.72 0.78 Wire et al., 2004EFA 600 mg q.d. 14 days Ritonavir 100 mg b.i.d. 30 days 0.70 0.68 0.64 Aarnoutse et al., 2002EFA 600 mg q.d. 2 wk Ritonavir 100 mg b.i.d. 5.8 mo 0.70 0.68 0.45 Goujard et al., 2003EFA 600 mg q.d. 14 days Amprenavir 1395 mg q.d. 28 days 0.80 0.72 0.87 Wire et al., 2004EFA 600 mg q.d. 14 days Amprenavir 700 mg b.i.d. 28 days 0.80 0.72 0.93 Wire et al., 2004EFA 600 mg q.d. 14 days Amprenavir 700 mg b.i.d. 28 days 0.80 0.72 0.91 Wire et al., 2004EFA 600 mg q.d. 4 wk Ritonavir 100 mg b.i.d. 4 wk 0.79 0.72 0.98 Boyd et al., 2003aEFA 600 mg q.d. 14 days Ritonavir 100 mg b.i.d. 28 days 0.79 0.72 0.76 Wire et al., 2004EFA 600 mg q.d. 35 days Ritonavir 100 mg b.i.d. 35 days 0.79 0.72 0.95 Hsu et al., 2003PB 100 mg q.d. 14 days Antipyrine 1 g SD 0.53 0.72 0.79 Leclercq et al., 1989PB 100 mg q.d. 8 days Antipyrine 250 mg SD 0.53 0.72 0.66 Schellens et al., 1989PB 100 mg q.d. 8 days Nifedipine 20 mg SD 0.31 0.50 0.39 Schellens et al., 1989PB 50–90 mM (SS) Quinidine 300 mg SD 0.32 0.51 0.34 Data et al., 1976PB 100 mg q.d. 8 days Tirilazadc 105 mg i.v. QID 7 days 0.41 0.61 0.8 Fleishaker et al., 1996PB 100 mg q.d. 8 days Tirilazadc 105 mg i.v. q.i.d. 7 days 0.41 0.61 0.76 Fleishaker et al., 1996PB 100 mg q.d. 21 days Verapamil 1.05 mg SD 0.42 0.62 0.66 Rutledge et al., 1988PB 100 mg q.d. 21 days Verapamil 80 mg SD 0.40 0.59 0.23 Rutledge et al., 1988PB 100 mg q.d. 21 days Verapamil 80 mg q.i.d. 5 days 0.40 0.59 0.21 Rutledge et al., 1988RIF 100 mg q.d. 14 days Antipyrine 1 g SD 0.53 0.72 0.84 Leclercq et al., 1989RIF 100 mg q.d. 16 days Losartan 50 mg q.d. 7 days 0.65 0.83 0.97 Goldberg et al., 1996RIF 600 mg q.d. 12 days (R)-Verapamil 10 mg SD 0.20 0.18 0.5 Fromm et al., 1996RIF 600 mg q.d. 12 days (R)-Verapamil 120 mg b.i.d. 24 days 0.20 0.18 0.02 Fromm et al., 1996RIF 600 mg q.d. 12 days (R)-Verapamil 120 mg b.i.d. 16 days 0.20 0.18 0.01 Fromm et al., 1998RIF 600 mg q.d. 12 days (S)-Verapamil 120 mg b.i.d. 16 days 0.20 0.18 0.03 Fromm et al., 1998RIF 600 mg q.d. 12 days (S)-Verapamil 120 mg b.i.d. 24 days 0.20 0.18 0.03 Fromm et al., 1996RIF 600 mg q.d. 5 days Alfentanil 1.125 mg SD 0.10 0.09 0.43 Phimmasone and Kharasch, 2001RIF 600 mg q.d. 5 days Alfentanil 1.125 mg SD 0.10 0.09 0.38 Kharasch et al., 2004bRIF 600 mg q.d. 5 days Alfentanil 4.5 mg SD 0.10 0.09 0.05 Kharasch et al., 2004bRIF 450 mg q.d. 4 days Alprazolam 1 mg SD 0.12 0.11 0.12 Schmider et al., 1999RIF 600 mg q.d. 18 days Amprenavir 1200 mg b.i.d. 8 days 0.30 0.27 0.18 Polk et al., 2001RIF 300 mg b.i.d. 8 days Antipyrine 1000 mg SD 0.29 0.28 0.61 Leclercq et al., 1989RIF 300 mg b.i.d. 8 days Antipyrine 1000 mg SD 0.29 0.28 0.53 Leclercq et al., 1989RIF 600 mg q.d. 15 days Antipyrine 1200 mg SD 0.28 0.26 0.41 Bennett et al., 1982RIF 600 mg q.d. 5 days Atorvastatin 40 mg SD 0.23 0.21 0.2 Backman et al., 2005RIF 600 mg q.d. 7 days Budesonide 3 mg SD 0.11 0.10 0.01 Dilger et al., 2005RIF 600 mg q.d. 7 days Budesonide 3 mg SD 0.11 0.10 0.003 Dilger et al., 2005RIF 600 mg q.d. 5 days Buspirone 30 mg SD 0.09 0.08 0.09 Kivistö et al., 1999RIF 600 mg q.d. 5 days Buspirone 30 mg SD 0.09 0.08 0.08 Lamberg et al., 1998RIF 600 mg q.d. 11 days Cyclosporine 750 mg SD 0.11 0.10 0.27 Hebert et al., 1992RIF 600 mg b.i.d. 7 days Diazepam 10 mg SD 0.29 0.26 0.27 Ohnhaus et al., 1987RIF 300 mg b.i.d. 7 days Diazepam 10 mg SD 0.29 0.27 0.24 Ohnhaus et al., 1987RIF 300 mg b.i.d. 10 days Ethinyl estradiol 35 �g q.d. (2 cycles) 0.34 0.31 0.36 LeBel et al., 1998RIF 600 mg q.d. 14 days Ethinyl estradiol 35 �g (chronic treatment) q.d. 0.34 0.31 0.34 Barditch-Crovo et al., 1999RIF 600 mg q.d. 13 days Everolimus 4 mg SD 0.11 0.10 0.38 Kovarik et al., 2002RIF 600 mg q.d. 16 days Gefitinib 500 mg SD 0.18 0.16 0.17 Swaisland et al., 2005RIF 600 mg q.d. 5 days Glyburide 1.75 mg SD 0.28 0.26 0.61 Niemi et al., 2001RIF 600 mg q.d. 11 days Imatinib 400 mg SD 0.25 0.23 0.26 Bolton et al., 2004RIF 450 mg q.d. 6 wk Irinotecan 75 mg/m2 q.d. (4 cycles) 0.77 0.77 0.94 Yonemori et al., 2004RIF 300 mg b.i.d. 7 days Losartan 50 mg q.d. 7 days 0.33 0.32 0.65 Williamson et al., 1998RIF 600 mg q.d. 10 days Methadone 5.5 mg SD 0.28 0.25 0.32 Kharasch et al., 2004aRIF 600 mg q.d. 10 days Methadone 11.2 mg SD 0.28 0.25 0.23 Kharasch et al., 2004aRIF 600 mg q.d. 14 days Midazolam 2 mg SD 0.10 0.09 0.14 Adams et al., 2005RIF 600 mg q.d. 9 days Midazolam 5.5 mg SD 0.10 0.09 0.12 Chung et al., 2006RIF 600 mg q.d. 7 days Midazolam 6 mg SD 0.10 0.09 0.1 Gorski et al., 2003

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(CMZ-10,11-epoxide), and DEX (3-methoxymorphinan), have been re-ported to be CYP3A4 substrates (Gorski et al., 1994; Wang et al., 1997;Furukori et al., 1999; van Giersbergen et al., 2002; Kim et al., 2005).

In vivo fm, CYP3A4 is estimated by fi, CYP3A4 (eq. 18) as a change inthe ratio of either AUC or clearance of a victim drug in the presenceand absence of a coadministered CYP3A4 inhibitor (Brown et al.,2005; Venkatakrishnan and Obach, 2005; Galetin et al., 2006). Theratio ideally represents the proportion of the total clearance of a drugmediated by CYP3A4 at a relevant in vivo concentration of theinhibitor (Table 3). Under specific conditions fm, CYP3A4 will be equalto fi, CYP3A4; however, if those conditions are not met, fm, CYP3A4

could be either over- or underestimated. When a victim drug isadministered orally and the AUC changes after inhibition, fm, CYP3A4

can be equal to fi, CYP3A4 only if the bioavailability (F) of the victimdrug did not change. Any increase in F will result in a decrease inapparent clearance (CL/F), and fm, CYP3A4 will be overestimated com-pared with what would be derived from a true clearance value ob-tained if the victim drug was given intravenously. Many DDI studiesare performed with oral dosing regimens, and, therefore, DDI predic-tions based on eq. 18 could be affected by potential errors offm, CYP3A4 determined from fi, CYP3A4. This is particularly true fordrugs with low bioavailability due to high first-pass extraction. Inaddition, some CYP3A4 inhibitors such as ketoconazole and indinaviralso inhibit P-glycoprotein (P-gp). The inhibition of P-gp has beenshown to increase oral absorption of substrates (Kim et al., 1998;Zhang et al., 1998; Achira et al., 1999) and would be another mech-anism by which F could be affected.

Another requirement for fm, CYP3A4 to be equal to fi, CYP3A4 iscomplete inhibition of CYP3A4 in the inhibition studies in whichfi, CYP3A4 is determined. Several of the inhibitors used to assessfm, CYP3A4 that are listed in Table 3 were recommended by the U.S.Food and Drug Administration draft guidance for use in DDI studies(http://www.fda.gov/cder/drug/drugInteractions/tableSubstrates.htm).These inhibitors were classified as either strong (ketoconazole, ritona-vir, itraconazole, clarithromycin, and saquinavir) or moderate (flucon-azole, grapefruit juice, and erythromycin) inhibitors. An assessment ofwhether CYP3A4 was indeed completely inhibited is dependent onthe in vivo concentration (Cmax, ss) of the inhibitor that was achievedand the potency of the inhibitor (Ki, KI, and kinact). For example, theextent of inhibition achieved by ketoconazole (Cmax, ss � 8.9–13.1�M, fu, p � 0.01, Ki � 0.015 �M, and ratio of unbound Cmax, ss/Ki �5.9–8.7) (Varhe et al., 1994) and ritonavir (Cmax, ss � 7.8 �M, fu, p �0.015, K� � 0.1 �M, and ratio of unbound Cmax, ss/Ki � 1.2) (Hsu etal., 1998) were likely to have been high. The inhibitors ritonavir,clarithromycin, saquinavir, and erythromycin have been shown to bepotent and selective mechanism-based inhibitors of CYP3A4 (Green-blatt et al., 1999) and the extent of inhibition with these compoundswas likely to have also been high. Mibefradil (KI � 2.3 �M andkinact � 0.4 min�1) has been shown to have an in vitro potencygreater than clarithromycin (KI � 5.5 �M, kinact � 0.072 min�1)(Prueksaritanont et al., 1999), and clinical DDIs caused by mibefradil(1.2- to 7.8-fold) were comparable with those caused by clarithromy-cin (1.3- to 8.5-fold), depending on the extent to which the victimdrugs were cleared by CYP3A4 (Jacobson, 2004). The final require-

TABLE 5—Continued.

Inducer Dose Regimena Substrate Dose RegimenbAUC Ratioc

ReferencesPRED1 PRED2 OBSd

RIF 300 mg b.i.d. 7 days Midazolam 8 mg SD 0.10 0.09 0.06 Gurley et al., 2006RIF 600 mg q.d. 5 days Midazolam 3 mg SD 0.10 0.09 0.05 Kharasch et al., 2004bRIF 450 mg q.d. 5 days Midazolam 7.5 mg SD 0.10 0.09 0.05 Eap et al., 2004RIF 600 mg q.d. 5 days Midazolam 15 mg SD 0.10 0.09 0.04 Backman et al., 1996RIF 600 mg q.d. 5 days Midazolam 15 mg SD 0.10 0.09 0.02 Backman et al., 1998RIF 450 mg q.d. 4 days Nifedipine 20 mg q.i.d. (stable therapy) 0.14 0.14 0.54 Tsuchihashi et al., 1987RIF 450 mg, q.d. �30 days Nifedipine 20 mg b.i.d. (stable therapy) 0.14 0.14 0.39 Tada et al., 1992RIF 600 mg q.d. 7 days Nifedipine 20 mg SD 0.13 0.12 0.08 Holtbecker et al., 1996RIF 600 mg q.d. 6 wk Prednisolone 10 mg i.v. 0.27 0.24 0.67 Powell-Jackson et al., 1983RIF 300–450 mg q.d. 3 wk Prednisolone 20 mg infusion 0.28 0.27 0.53 Bergrem and Refvem, 1983RIF 600 mg q.d. 7 days Quinidine 450 mg SD 0.14 0.12 0.27 Twum-Barima and Carruthers, 1981RIF 600 mg q.d. 7 days Quinidine 450 SD 0.14 0.12 0.17 Twum-Barima and Carruthers, 1981RIF 600 mg q.d. 2 wk Quinine 600 mg SD 0.17 0.16 0.17 Wanwimolruk et al., 1995RIF 600 mg q.d. 7 days Repaglinide 4 mg SD 0.24 0.22 0.52 Bidstrup et al., 2004RIF 600 mg q.d. 5 days Repaglinide 37.5 mg SD 0.24 0.22 0.43 Niemi et al., 2000RIF 600 mg q.d. 7 days Repaglinide 4 mg SD 0.24 0.22 0.2 Bidstrup et al., 2004RIF 600 mg q.d. 5 days Ropivacainec 45 mg SD 0.68 0.65 0.62 Jokinen et al., 2001bRIF 600 mg q.d. 5 days Ropivacainec 45 mg SD 0.68 0.65 0.48 Jokinen et al., 2001bRIF 600 mg q.d. 14 days Saquinavir 1200 mg t.i.d. 14 days 0.21 0.19 0.3 Grub et al., 2001RIF 600 mg q.d. 5 days Simvastatin 40 mg SD 0.10 0.09 0.14 Kyrklund et al., 2000RIF 600 mg q.d. 9 days Simvastatin 40 mg SD 0.10 0.08 0.09 Chung et al., 2006RIF 600 mg, q.d. �31 days Tacrolimus 15 mg SD 0.25 0.23 0.45 Kuypers et al., 2005RIF 600 mg q.d. 18 days Tacrolimus 75 mg SD 0.25 0.23 0.32 Hebert et al., 1999RIF 600 mg q.d. 5 days Triazolam 0.5 mg SD 0.10 0.09 0.05 Villikka et al., 1997aRIF 600 mg q.d. 15 days Verapamil 120 mg SD 0.20 0.18 0.07 Barbarash et al., 1988RIF 600 mg q.d. 5 days Zolpidem 20 mg SD 0.27 0.25 0.28 Villikka et al., 1997bCMZ 600 mg q.d. 21 days Ethinyl estradiol 35 �g q.d. 21 days 0.67 0.71 0.58 Doose et al., 2003CMZ 300–600 mg q.d. 8–12 wk Ethinyl estradiol 50 �g SD 0.67 0.71 0.58 Crawford et al., 1990CMZ 200 mg q.d. 6 days Etizolam 1 mg SD 0.55 0.68 0.54 Kondo et al., 2005CMZ 400 mg q.d. 28 days Mirtazapine 30 mg SD 7 days 0.40 0.20 0.39 Sitsen et al., 2001CMZ 400 mg q.d. 21 days Mirtazapine 30 mg SD 28 days 0.40 0.20 0.37 Sitsen et al., 2001CMZ 400–600 mg b.i.d. 3 wk Omeprazole 20 mg SD 0.50 0.52 0.6 Laroudie et al., 2000CMZ 600–1750 mg 2–12 mo Prednisolone 35 mg i.v. SD 0.49 0.30 0.67 Olivesi, 1986CMZ 300 mg b.i.d. 14 days Simvastatin 80 mg SD 0.30 0.34 0.26 Ucar et al., 2004

SD, single dose used for the substrate drug.a Dose regimens of the inducers, e.g., 125 mg b.i.d. 7 days represents a dose (125 mg) that was given orally twice a day for 7 days.b Dose regimens of the CYP3A4 substrate drugs.c AUC ratios of the drugs in the presence and absence of the inducers predicted from the in vitro induction data in hepatocytes of donor 1 (PRED 1) and donor 2 (PRED 2), respectively.d Observed in vivo DDIs from the corresponding references.

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ment for fm, CYP3A4 to be equal to fi, CYP3A4 is selective inhibition ofCYP3A4 in the DDI clinical trial. The CYP3A4 selectivities of someinhibitors (e.g., ketoconazole and itraconazole) have been reported tobe concentration- and substrate-dependent (Galetin et al., 2005). Ifother P450s responsible for the clearance of the victim drug are alsoinhibited, the fm, CYP3A4 can be overestimated.

The dependence of DDI susceptibility on fm, CYP3A4 and [Ind]max, ss

is illustrated in simulated RIF- and PB-mediated drug interactions,respectively (Fig. 6). Several laboratories have attempted to predict invivo DDIs using a relative induction score (Ripp et al., 2006) or aninduction ratio (Kato et al., 2005) measured in hepatocytes. In neithercase, however, was fm, CYP3A4 incorporated into the models. Figure 6clearly illustrates the importance of that parameter in any DDI pre-diction. The observed in vivo fm, CYP3A4 varied widely for the sub-strates examined, from 0.05 (ropivacaine) to 0.95 (buspirone). Lowfm, CYP3A4 values suggest possible involvement of other clearancepathways of the drugs rather than CYP3A4, such as glucuronidation(ethinyl estradiol), CYP2C9 (losartan), and CYP2C9/19 and CYP2B6(diazepam). Those drugs (e.g., alfentanil, simvastatin, and midazo-lam) with high fm, CYP3A4 values are cleared more extensively byCYP3A4, leading to a high degree of susceptibility to a DDI (Table5). An example of this is the RIF-midazolam interactions, in which themidazolam AUC was reduced to approximately one-tenth the AUC in

the noninduced state. Because of its potent enzyme induction, RIFcaused large interactions with other drugs; however, the clinicallyobserved decreases in exposure due to coadministration with RIFwere greater than those predicted in many cases, particularly for drugswith relatively low fm, CYP3A4 values. This is because the CYP3A4-dependent portion of the total clearance of the drug increases uponinduction. In addition, the clearance of drugs by CYP3A4 inducerssuch as RIF can be affected by increased expression of other enzymesand transporters regulated by PXR transactivation or other inductionmechanisms such as CAR (Hewitt et al., 2007). For example, PB wasonly a moderate inducer of CYP3A4 in primary human hepatocytesand human PXR transactivation assays at its Cmax concentrations (Luoet al., 2002; Sinz et al., 2006), yet it has shown clinically significantDDIs (Table 5). This is probably because PB also induced CYP2B6and CYP2C, contributing to the clearance of the affected drugs by itsCAR-mediated induction and, to a lesser extent, PXR-mediated in-duction (Luo et al., 2002).

To predict the magnitude of in vivo DDIs, the most appropriateinducer concentrations to use in the calculation would be the concen-tration achieved in vivo at the target enzyme in the liver. Many drugsenter hepatocytes by passive diffusion, which allows projecting intra-cellular hepatic concentrations on the basis of systemic unboundconcentrations ([Ind]max, ss � fu, p). This projection may not be appro-

FIG. 2. Correlation analyses of predicted DDIs (Donor 1) with observed in vivo DDIs. A, with fu, p and fu, hept. B, without fu, p and fu, hept. C, with fu, p and without fu, hept.D, without fu, p and with fu, hept. LR, linear regression; R2, correlation coefficient; 95% CI, 95% confidence interval; 95% PI, 95% prediction interval; unity, unity correlation;RSS, residual sum of squares.

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priate if 1) the inducers are actively transported into or out of hepa-tocytes, 2) the inducers or affected drugs are inhibitors of transportersexpressed in the liver that transport the inducers (e.g., EFA, vera-

pamil, ritonavir, and cyclosporine), or 3) the inducers are extensivelymetabolized by CYP3A4 (e.g., DEX, BST, and CMZ) and the sys-temic concentration is merely a fraction of the liver exposure. Clearly

FIG. 3. Correlation analyses of predicted DDIs (donor 2) with observed in vivo DDIs. A, with fu, p and fu, hept. B, without fu, p and fu, hept, C, with fu, p and without fu, hept.D, without fu, p and with fu, hept. LR, linear regression; R2, correlation coefficient; 95% CI, 95% confidence interval; 95% PI, 95% prediction interval; unity, unity correlation;RSS, residual sum of squares.

FIG. 4. Weighted residual distributions (difference of the weighted residual from zero, the perfect linear regression) of the correlations (corrected by fu, p and fu, hept) betweenpredicted and observed DDIs.

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FIG. 5. Correlations of the DDIs predicted from the in vitro mRNA induction data (including fu, p and fu, hept) in donors 1 (A) and 2 (B) with the clinically observed DDIs.LR, linear regression; R2, correlation coefficient; 95% CI, CYP3A4; 95% PI, 95% prediction interval; unity, unity correlation; RSS, residual sum of squares.

FIG. 6. Simulated relationship of predicted DDIs as a function of in vivo free concentration of inducer ([Ind]max, ss) and fraction of drug cleared by CYP3A4 ( fm, CYP3A4)using eq. 17. RIF (A and B): EC50 � 0.25 �M, Emax � 10.6 (fold increase), fu, p � 0.179 and fu, hept � 0.419; PB (C and D): EC50 � 250 �M, Emax � 22.6 (fold increase),fu, p � 0.7 and fu, hept � 1.0.

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there are multiple variables that can confound the assumptions in eq.17, and, accordingly, under- or overprediction of the DDIs can beexpected. For example, BST has been reported to be a substrate forhepatic OATP1B1 and OATP1B3 (Treiber et al., 2007). The under-predictions of the DDIs by BST with several CYP3A4 substrates, e.g.,ethinyl estradiol, glyburide, and sildenafil were probably due to higherconcentrations of the inducer in hepatocytes than in plasma. Similarly,RIF has been shown to be a substrate for and inhibitor of the uptaketransporters OATP-C and OATP1B1 in the liver (Tirona et al., 2003).In addition, the liver inlet concentration of inducer after oral ad-ministration ([Ind]inlet, ss) is a function of the concentration in both the he-patic artery ([Ind]max, ss) and the portal vein after gut absorption (eq. 20):

[Ind]inlet, SS � [Ind]max, ss �Fa � Ka � dose

QH(20)

where Fa, Ka, and QH are the fraction of dose absorbed from the gutinto the portal vein, absorption rate constant of inducer, and hepaticblood flow rate, respectively. For example, [Ind]max, ss (0.1 �M) ofDEX is lower than the [Ind]inlet, ss (0.25 �M), calculated by eq. 20with Ka � 2.68 h�1, Fa � 0.88, and dose � 1.5 mg (q.d., 4 days). Thisdifference could in part explain the underpredictions of DEX interac-tions with cyclophosphamide and triazolam, respectively, in Table 5.

The gut may contribute a large portion of the first-pass extraction ofCYP3A4 substrates, particularly for those substrates extensivelycleared by that isozyme (Thummel et al., 1996). For example, thebioavailability (F) of cyclosporine was reduced by the coadministra-tion of RIF (Ito et al., 1998) by increased metabolism in both gut andliver and also by induction of P-gp (Wacher et al., 1995). The latterhighlights an additional complexity: RIF, PB, and DEX are alsoinducers of P-gp via the same nuclear receptor (PXR) that mediatesthe induction of CYP3A4 (Schuetz et al., 1996). Thus, the additiveeffects of increased CYP3A4-mediated metabolism and P-gp-medi-ated efflux by the intestinal epithelium may result in a substantiallyhigher first-pass effect in the gut after oral administration. Withoutconsideration of the gut first-pass effect, DDIs can be underpredicted.In addition, many drugs act as both inducers and inhibitors ofCYP3A4 (ritonavir and EFA). When EFA was coadministered withritonavir (KI � 0.04 �M), indinavir, or amprenavir (a potent mech-anism-based inhibitor) in combination therapy (Luo et al., 2003),induction-mediated DDIs were overpredicted as a result of the potentCYP3A4 inhibition by the interacting drugs (Table 5).

A high degree of interindividual variability of DDIs has beencommonly observed in the clinic. This variability could be due togenetic polymorphisms of P450s and transporters. For example,whereas CYP3A5 and CYP3A4 share common substrates and induc-ers, CYP3A5 is less inducible, differs in its tissue expression pattern,and is subject to genetic polymorphism (Kuehl et al., 2001). CYP3A4can also vary in its abundance within a population. Combined, thesefactors result in the potential for a large degree of interindividualvariability of DDIs after treatment with CYP3A inducers (Lamba etal., 2002). Similar genetic polymorphisms may also affect OATP-Cdrug disposition. OATP-C expression is potentiated by PXR activa-tion; however, it has a number of naturally occurring variant formsthat have markedly impaired function (Tirona et al., 2003).

In addition to the CYP3A4 induction-mediated DDIs describedhere, the predictive models may also be useful for the evaluation ofpotential DDIs from induction of other P450 isoforms such asCYP1A2, CYP2B6, and CYP2C9. The induction of these other P450sis regulated by different nuclear receptors (i.e., aryl hydrocarbonreceptor and CAR); however, the fundamental principles of the po-tential of a drug to induce enzyme activity and mRNA in hepatocytes

and the endpoint measurements of Emax and EC50 are identical tothose used for CYP3A4.

The results of the analyses described here reveal some valuableinsight into the correlation between in vitro induction and clinicalDDIs. The draft U.S. Food and Drug Administration guidance fordrug interaction studies (http://www.fda.gov/cber/guidelines.htm)recommends using induction of �40% of the positive control as oneendpoint for prediction of clinically relevant enzyme induction. Theabsolute level of induction that a value of 40% represents is, of course,relative to the extent of induction caused by the positive control,which in turn is dependent on the potency of the positive control (Emax

and EC50) and the concentration tested. The results described hereshow that 40% of Emax for the potent inducers RIF and PB corre-sponded to 4.2- and 9.1-fold increases in CYP3A4 activity in donor 1,respectively. The respective reductions of midazolam AUC predictedby eq. 17 for these levels of induction were 78 and 89%. This findingsuggests that induction by a test drug that is 40% of the response forpotent inducers that are tested at or near their Emax concentrations mayresult in an excessively large DDI.

The model has the potential to be used for classification of aninducer. For example, cutoffs that are proportionately similar to thoseused to rank P450 inhibitors might be applied. Using those criteria,induction could be based on the AUC in the induced state comparedwith the control and ranked as none (�75%), weak (50–75%), mod-erate (20–50%), or strong (�20%). A corresponding set of in vitrovalues of potency can be calculated by eq. 21 assuming fm, CYP3A431. Using this approach, induction of none, weak, moderate, and stronghas a ratio of �0.33, 0.33 to 1.0, 1.0 to 4.0, and �4.0, respectively.Similarly, if Emax and EC50 for a drug cannot be obtained, the slopeof the response-concentration curve in the linear range measured inhepatocytes can be also used, providing the projected [Ind] EC50:

ratio �Emax � [Ind]n

EC50n � [Ind]n � slope � [Ind]n (21)

In conclusion, measurement of in vitro induction parameters for adrug candidate at an early stage of drug discovery can providevaluable information about the potential of a drug to cause clinicalDDIs. If in vitro induction is observed and DDIs are predicted usingthe models described here, an informed decision about termination oradvancement of the molecule can be made with a better understandingof the associated risk than has previously been available. Should themolecule be advanced, better planning and timing of clinical DDIstudies may be possible. Confidence in the DDI prediction couldallow for more rapid lead optimization by avoiding unnecessaryoptimization efforts or more rapid termination decisions. Ultimately,the decision of whether a drug candidate with a potential of inductionshould be advanced into development will depend on the magnitudeof the predicted DDIs, the clinical indication, the dosing regimen, andconcomitant medications that may be P450 inhibitors or predomi-nantly cleared by the induced enzyme.

Acknowledgments. We thank Drs. Kenneth Korzekwa and PaulPearson for valuable comments on the work.

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Address correspondence to: Dr. Magang Shou, Department of Pharmaco-kinetics and Drug Metabolism, 30E-2-B, Amgen, Inc., One Amgen Center Drive,Thousand Oaks, CA 91320-1799. E-mail: mshou@amgen.com

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