Selection of Alternative CYP3A4 Probe Substrates for ...dmd.aspetjournals.org/content/dmd/early/2010/03/04/dmd.110.0320… · develop a strategy for selecting alternate CYP3A4 probe

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Selection of Alternative CYP3A4 Probe Substrates for Clinical Drug

Interaction Studies Using In Vitro Data and In Vivo Simulation

Robert S. Foti, Dan A. Rock, Larry C. Wienkers and Jan L. Wahlstrom*

Pharmacokinetics and Drug Metabolism, Amgen, Inc, Seattle, WA 98119

DMD Fast Forward. Published on March 4, 2010 as doi:10.1124/dmd.110.032094

Copyright 2010 by the American Society for Pharmacology and Experimental Therapeutics.

This article has not been copyedited and formatted. The final version may differ from this version.DMD Fast Forward. Published on March 4, 2010 as DOI: 10.1124/dmd.110.032094

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Running title:

Alternative CYP3A4 DDI Probe Substrates

Corresponding author:

*To whom correspondence should be addressed: Jan L Wahlstrom Pharmacokinetics and Drug Metabolism Amgen, Inc 1201 Amgen Court West Mail Stop AW2/D2262 Seattle, WA 98119 Phone: (206)265-7423 FAX: (206)217-0494 [email protected]

Manuscript metrics:

Text pages: 25

Tables: 2

Figures: 3

References: 25

Words in the abstract: 232

Words in the introduction: 459

Words in the results and discussion: 2444

Abbreviations:

DDIs, drug-drug interactions; HPLC, high-performance liquid chromatography,

LC-MS/MS, liquid chromatography/tandem mass spectrometry


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Abstract

Understanding the potential for P450-mediated drug-drug interactions

(DDI) is a critical step in the drug discovery process. DDI of CYP3A4 is of

particular importance, due to the number of marketed drugs which are cleared by

this enzyme. In response to studies suggesting the presence of several binding

regions within the CYP3A4 active site, multiple probe substrates are often used

for in vitro CYP3A4 DDI studies, including midazolam (the clinical standard),

felodipine/nifedipine and testosterone. However, design of clinical CYP3A4 DDI

studies may be confounded for cases such as AMG 458, where testosterone is

predicted to exhibit a clinically relevant DDI (AUCI/AUC ≥ 2), while midazolam

and felodipine/nifedipine are not. In order to develop an appropriate path forward

for such clinical DDI studies, the inhibition potency of twenty known inhibitors of

CYP3A4 were measured in vitro using eight clinically relevant CYP3A4 probe

substrates and testosterone. Hierarchical clustering suggested four probe

substrate clusters: testosterone; felodipine; midazolam, buspirone, quinidine and

sildenafil; and simvastatin, budesonide and fluticasone. The in vivo sensitivities

of six clinically relevant CYP3A4 probe substrates (buspirone, cyclosporine,

nifedipine, quinidine, sildenafil and simvastatin) were determined in relation to

midazolam from literature DDI data. Buspirone, sildenafil and simvastatin

exhibited similar or greater sensitivity than midazolam to CYP3A4 inhibition in

vivo. Finally, SimCYP was used to predict the in vivo magnitude of CYP3A4 DDI

caused by AMG 458 using midazolam, sildenafil, simvastatin and testosterone as

probe substrates.


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Introduction

The cytochrome P450 superfamily of drug metabolizing enzymes is

involved in the metabolism of the majority of currently prescribed drugs and new

chemical entities. Within the P450 superfamily, CYP3A4 is responsible for the

metabolism of approximately 55% of marketed drugs (Wienkers and Heath,

2005). Due to its general importance in drug clearance, assessment and

modeling of CYP3A4 inhibition is a critical part of the drug discovery and

development process. Probe substrate-dependent inhibition profiles have been

observed in vitro with CYP3A4, possibly due to the presence of multiple probe

substrate binding regions within the CYP3A4 active site (Kenworthy et al., 1999).

In response, a standard approach when testing for CYP3A4 inhibition has been

to use multiple probe substrates such as midazolam (the clinical standard),

felodipine/nifedipine or testosterone for in vitro experiments (Wienkers and

Heath, 2005). Extrapolation of the in vitro data to the in vivo situation may be

confounded in the multiple probe substrate scenario if a probe substrate that is

not clinically relevant, such as testosterone, is markedly more susceptible to

inhibition than midazolam (the clinical standard) and felodipine/nifedipine (Obach

et al., 2005). Our first aim was to measure the in vitro inhibition profiles of eight

clinically relevant CYP3A4 probe substrates (budesonide, buspirone, felodipine,

fluticasone, midazolam, quinidine, sildenafil and simvastatin) and testosterone

versus a panel of twenty known CYP3A4 inhibitors and to determine the similarity

of the probe substrates based upon hierarchical clustering of the inhibition data.


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Recent draft guidance from the U.S. Food and Drug Administration

outlining the design of P450-mediated drug interaction experiments suggests that

sensitive CYP3A4 probe substrates other than midazolam may be used for

clinical DDI studies (Huang et al., 2007). However, few studies have compared

the in vivo sensitivity of CYP3A4 probe substrates based upon clinical DDI data

from the literature (Ragueneau-Majlessi et al., 2007). Our second aim was to

mine the literature for clinical CYP3A4 DDI data and correlate the in vivo DDI

sensitivity of clinically relevant probe substrates with midazolam. Our third aim

was to integrate the in vitro correlation results and in vivo sensitivity analysis to

develop a strategy for selecting alternate CYP3A4 probe substrates for the

testosterone-selective inhibition situation, if needed.

Our fourth and final aim was to demonstrate a case study where

evaluation of alternate CYP3A4 probe substrates was warranted based upon in

vitro inhibition data. AMG 458 (Liu et al., 2008), a potent inhibitor of the receptor

tyrosine kinase c-Met, exhibits markedly more potent inhibition of testosterone

6β-hydroxylation than midazolam 1’-hydroxylation and felodipine

dehydrogenation in vitro. We used SimCYP, a physiologically-based modeling

tool, to simulate the magnitude of effect AMG 458 would have on the alternate

CYP3A4 probe substrates and to select an appropriate probe substrate for the

clinical DDI study based upon the in silico predictions.


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Materials and Methods

Materials. Pooled human liver microsomes (15 individual donors) were

purchased from CellzDirect (Durham, NC). AMG 458 was obtained from the

Amgen Sample Bank (Thousand Oaks, CA). Ammonium formate, HPLC-grade

acetonitrile and HPLC-grade methanol were obtained from Alfa Aesar (Ward Hill,

MA). Cyclosporine A was obtained from Biomol International (Plymouth Meeting,

PA). Felodipine was obtained from Alltech Associates (Deerfield, IL).

Dehydrofelodipine, fluticasone 17β-carboxylic acid, N-desmethylsildenafil, 6β-

hydroxybudesonide, 6’-hydroxybuspirone and sildenafil were obtained from

Toronto Research Chemicals, Inc (North York, ON, Canada). 3’-

Hydroxysimvastatin was obtained from USBiological (Swampscott, MA). NADPH

was purchased from EMD Biosciences (San Diego, CA). All other chemicals

used were purchased from Sigma-Aldrich (St. Louis, MO) and were of the

highest purity available.

Ki Determination. Incubations were carried out using eight probe substrates of

CYP3A4: budesonide, buspirone, felodipine, fluticasone, midazolam, quinidine,

sildenafil, simvastatin and testosterone. Twenty known inhibitors of CYP3A4

exhibiting a wide range of inhibition potencies were selected for the in vitro

studies. Stock solutions of all the inhibitors were made in dimethylsulfoxide

(DMSO) and then diluted 10-fold with acetonitrile prior to addition to the

incubation mixtures to minimize DMSO content. Four concentrations of each

probe substrate [0.5*Km, Km, 2*Km and 4*Km: 0.5, 1, 2, and 4 µM for budesonide;


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4, 8, 16, and 32 µM for buspirone; 1.0, 2.0, 4.0 and 8.0 µM for felodipine; 0.3,

0.6, 1.2 and 2.4 µM for fluticasone; 0.75, 1.5, 3.0, and 6.0 µM for midazolam; 15,

30, 60 and 120 µM for quinidine; 4.5, 9, 18, and 36 µM for sildenafil; 1, 2, 4, and

8 µM for simvastatin; and 25, 50, 100 and 200 µM for testosterone] and five

concentrations of each inhibitor (spanning a ten-fold range of the expected Ki)

were used for determination of Ki in a 96-well plate format. Briefly, each reaction

was carried out in duplicate containing 0.1 mg/mL human liver microsomal

protein per incubation. Each incubation reaction mixture contained enzyme,

probe substrate and inhibitor suspended in phosphate buffer (100 mM, pH 7.4)

containing 3 mM MgCl2 and was preincubated for three minutes in an incubator-

shaker at 37 °C. The reactions were initiated by the addition of NADPH (1 mM

final concentration). DMSO concentrations did not exceed 0.1% v/v and total

organic solvent concentrations did not exceed 1% v/v. Solvent concentrations

were the same for all experiments and turnover rates did not differ significantly

from minimal solvent controls. The reactions were terminated with 100 µl of

acetonitrile containing 0.1 µM of tolbutamide (internal standard). Length of the

incubations were 20 min, except for midazolam, which was carried out for 5 min.

The incubation time and protein concentrations used were within the linear range

for each respective CYP probe reaction.

Liquid Chromatography/Tandem Mass Spectral Analysis. All analytical methods

were conducted using HPLC-MS/MS technology. In brief, the LC-MS/MS system

was comprised of an Applied Biosystems 4000 Q-Trap (operated in triple


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quadrupole mode) equipped with an electrospray ionization source (Applied

Biosystems, Foster City, CA). The MS/MS system was coupled to two LC-20AD

pumps with an in-line CBM-20A controller and DGU-20A5 solvent degasser

(Shimadzu, Columbia, MD) and a LEAP CTC HTS PAL autosampler equipped

with a dual-solvent self-washing system (CTC Analytics, Carrboro, NC). The

injection volume was 10 µL for each sample. For all assays except simvastatin

hydroxylation, HPLC separation was achieved using a Gemini C18 2.0 x 30 mm

5 µm column (Phenomenex, Torrance, CA). Gradient elution (flow rate = 500

µL/min) was carried out using a mobile phase system consisting of (A) 5 mM

ammonium formate with 0.1% formic acid and (B) acetonitrile with 0.1% formic

acid. HPLC flow was diverted from the MS/MS system for the first 20 seconds to

remove any non-volatile salts. For simvastatin hydroxylation, a Luna C18 (5 μm,

30 x 3.0 mm; Phenomenex, Torrance, CA) HPLC column was used. Gradient

conditions utilized were similar to those used above, though the initial percentage

of acetonitrile in the mobile phase was 50%. MS/MS conditions were optimized

for individual analytes accordingly. Generic MS parameters included the curtain

gas (10 arbitrary units), CAD gas (medium), ionspray voltage (4500 V), source

temperature (450 °C) and ion source gas 1 and gas 2 (40 arbitrary units, each).

Interface heaters were kept on for all analytes. Analysis masses were (positive

ionization mode): dehydrofelodipine, m/z 382.1→354.2; N-desmethylsildenafil,

m/z 461.1→283.3; 6β-hydroxybudesonide, m/z 447.0→339.2; fluticasone 17β-

carboxylic acid, m/z 453.2→293.0; 6’-hydroxybuspirone, m/z 402.3→122.3; 1’-

hydroxymidazolam, m/z 342.1→203.1; 3-hydroxyquinidine, m/z 341.2→226.1; 3’-


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hydroxysimvastatin, m/z 435.1→199.2; 6β-hydroxytestosterone, m/z

305.0→269.0; tolbutamide, m/z 271.2→91.1.

Statistical Analysis. Standard curve fitting was performed using Analyst (version

1.4; Applied Biosystems, Foster City, CA). In general, standard curves were

weighted using 1/x. Substrate saturation curves and inhibition data were plotted

and analyzed using GraphPad Prism (version 4.01; GraphPad Software Inc., San

Diego, CA). Data was then fitted to either a competitive (Equation 1), non-

competitive (Equation 2), or linear-mixed inhibition model (Equation 3):

(1) ][)

][1(

][max

SK

IK

SVv

im ++

•=

(2) )

][1]([)

][1(

][max

iim K

IS

K

IK

SVv

+++

•=

(3) )'][

1]([)][

1(

][max

iim K

IS

K

IK

SVv

+++

•=

In the preceding equations, Km is equal to the substrate concentration at half

maximal reaction velocity, [I] is the concentration of inhibitor in the system, Ki is

the dissociation constant for the enzyme-inhibitor complex and Ki’ is the

dissociation constant for the enzyme-substrate-inhibitor complex. Note that in

the above equations, Km, Ki and Vmax were treated as global parameters. The

mechanism of inhibition was determined by visual inspection of the data using


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Dixon ([I] vs 1/v) and Lineweaver-Burke (1/[S] vs 1/v) plots and comparative

model assessment using the Akaike Information Criteria.

Hierarchical Clustering Analysis. Statistical and clustering analysis of the

inhibition potency data was performed using Statistica 8.0 (StatSoft, Tulsa, OK).

An UPGMA (Unweighted Pair Group Method with Arithmetic mean) clustering

algorithm was used to determine similarity between the inhibition data sets and

form successively larger clusters using a Euclidean distance similarity measure.

Data were entered as inhibition potency (Ki) values. Compounds that exhibited

activation or Ki values above 50 µM were entered as a Ki of 50 µM. For the

purposes of hierarchical clustering, all probe substrate-effector pairings must

have a numerical value; this poses an issue for instances where the probe

substrate is also the effector (Kumar et al., 2006). For those instances, an

average value of Ki for that effector obtained with the other probe substrates was

calculated and used.

Correlation Analysis of In Vivo Drug Interaction Potential. Literature data for

AUCI/AUC were obtained using the University of Washington Metabolism and

Transport Drug Interaction Database™, where AUCI is defined as the area under

the plasma concentration-time curve for a given probe substrate in the presence

of an inhibitor and AUC is defined as the area under the plasma concentration-

time curve for a given probe substrate in the absence of inhibitor. Studies were

considered comparable if they had a similar dose regimen for both inhibitor and


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probe substrate. For instances where multiple AUCI/AUC values were available

in the literature, the AUCI/AUC values were averaged. A minimum of four shared

AUCI/AUC values were deemed necessary to carry out the correlation analysis.

Linear regression was carried out on untransformed data.

Prediction of In Vivo Drug Interactions. SimCYP (Version 8.01) was used to

predict the in vivo drug interactions between AMG 458 and the probe substrates

midazolam, sildenafil, simvastatin and testosterone. Drug interaction potentials

were predicted for 500, 1000 and 2000 mg doses of AMG 458 based upon the

anticipated therapeutic range (Liu et al., 2008); the following data for AMG 458

was entered into SimCYP: mw (molecular weight, 539.2 amu), logP (3.4), fa

(fraction absorbed, 1.0), fmCYP3A4 (fraction metabolized by CYP3A4, 0.99), fu

(fraction unbound in plasma, 0.01), fumic (fraction unbound in microsomes, 0.9), in

vitro microsomal clearance (18 µL/min/mg) and predicted Vdss (0.88 L/kg), where

Vdss is defined as the volume of distribution at steady state. Physicochemical

properties and dosing regimens for midazolam, sildenafil and simvastatin were

taken directly from SimCYP default values. For testosterone, the following data

was obtained from the literature and entered into SimCYP (White et al., 1998;

Patki et al., 2003): mw (288.4 amu), logP (3.5), fmCYP3A4 (0.99), fu (0.08,

predicted), in vitro microsomal clearance (101 µL/min/mg) and Vdss (1.0 L/kg).

The remaining physiological and ADME parameters were predicted with SimCYP

on the basis of the physicochemical data input using a one compartment

distribution model. The pharmacokinetic simulations were designed to represent


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100 healthy volunteers ranging in age from 18 to 65 and divided into 10 trials of

10 subjects each. Female subjects represented approximately 34% of the

simulated population.


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Results

The inhibition constants (Ki) for a set of twenty effectors were determined

for the probe substrates budesonide, buspirone, felodipine, fluticasone,

midazolam, quinidine, sildenafil, simvastatin and testosterone (Table 1).

Competitive, noncompetitive and linear-mixed inhibition profiles were observed,

depending on the probe substrate-inhibitor combination. Felodipine and

fluoxetine exhibited linear-mixed inhibition using midazolam as a probe substrate;

nifedipine exhibited linear-mixed inhibition using quinidine as a probe substrate.

Eight effectors (cyclosporine, felodipine, fluoxetine, haloperidol, ketoconazole,

nifedipine, sertraline and terfenadine) exhibited noncompetitive inhibition using

midazolam as a probe substrate. Five effectors (fluoxetine, fluvoxamine,

itraconazole, ketoconaozle and sertraline) exhibited noncompetitive inhibition

using buspirone as probe substrate. Four effectors (AMG 458,

dextromethorphan, haloperidol and simvastatin) exhibited noncompetitive

inhibition using felodipine as a probe substrate. Three effectors (felodipine,

sertraline and simvastatin) exhibited noncompetitive inhibition using quinidine as

a probe substrate. One effector exhibited noncompetitive inhibition using

fluticasone (e.g. cyclosporine) and sildenafil (e.g. sertraline) as probe substrates,

respectively.

Hierarchical clustering analysis was performed on the non-transformed

inhibition potency data using an UPGMA clustering algorithm to obtain a

Euclidean distance similarity measure. Results from the clustering analysis for

the CYP3A4 data were visualized as a dendrogram (Figure 1), where the


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horizontal axis of the dendrogram represents the Euclidean linkage distance

between probe substrate clusters. Relative to midazolam, the average fold

decrease in inhibition potency with standard error for each probe substrate was:

buspirone (3 ± 1), quinidine (4 ± 6), sildenafil (5 ± 2), budesonide (8 ± 4),

fluticasone (12 ± 7), felodipine (14 ± 5), simvastatin (22 ± 44) and testosterone

(106 ± 45).

In vivo DDI data for CYP3A4 probe substrates was collected from the

literature and compiled when similar study conditions were used relative to a

midazolam comparator study (Table 2). For probe substrates with four or more

DDI studies in common with midazolam, a linear correlation analysis was carried

out (Figure 2). The line of unity of the correlation analysis is represented by a

dashed line. Buspirone and simvastatin exhibited correlations that were greater

than unity (2.7 and 1.8, respectively), sildenafil exhibited a correlation that was

near unity (0.81), and cyclosporine, nifedipine and quinidine exhibited

correlations that were markedly lower than unity (0.38, 0.01 and 0.25,

respectively). Correlation analysis for budesonide, felodipine, fluticasone and

erythromycin were not carried out as the literature contained less than four DDI

studies in common with midazolam.

Prediction of the magnitude of in vivo DDI due to AMG 458 was obtained

using SimCYP (Figure 3). Doses of 500, 1000, and 2000 mg of AMG 458 were

chosen based upon coverage of the anticipated therapeutic range. Midazolam,

simvastatin, sildenafil and testosterone were predicted to exhibit AUCI/AUC

values of 1.1, 1.2, 2.0 and 2.1 at 500 mg doses of AMG 458; 1.2, 1.4, 2.6 and 3.0


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at 1000 mg doses of AMG 458; and 1.5, 1.8, 3.8 and 4.9 at 2000 mg doses of

AMG 458, respectively.


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Discussion

Screening for and predicting the magnitude of P450-mediated DDIs is a

crucial part of the drug discovery and development paradigm, potentially

influencing both patient safety and product differentiation. Recent examples of

drugs withdrawn from the market due to drug interactions include mibefradil (Po

and Zhang, 1998) and cerivastatin (Davidson, 2002). Within the P450

superfamily, CYP3A4 is responsible for the metabolism of a majority of marketed

drugs. As a result, assessment and modeling of CYP3A4 inhibition is a key

component of reversible inhibition testing (Wahlstrom et al., 2006). While

advancements have been made in the design of in vitro DDI experiments, the

prediction of in vivo DDIs and database analysis of P450-mediated DDIs, a

comprehensive understanding of probe substrate selection for CYP3A4 based

upon both in vitro correlation data and in vivo sensitivity analysis has been

lacking.

The selection of appropriate CYP3A4 probe substrates for in vitro studies

has led to substantial debate. CYP2C9 (Kumar et al., 2006), CYP2C19 (Foti and

Wahlstrom, 2008) and CYP3A4 (Kenworthy et al., 1999; Stresser et al., 2000)

have exhibited probe substrate-dependent inhibition for in vitro studies. The

correlation analysis of CYP3A4 DDI data from in vitro experiments has

suggested that at least three probe substrate classes may exist for CYP3A4:

benzodiazepine-like, dihydropyridine-like and testosterone-like, possibly due to

the presence of multiple binding regions within the CYP3A4 active site

(Kenworthy et al., 1999). While the use of midazolam, testosterone and


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felodipine/nifedipine as probe substrates has been suggested based upon

hierarchical clustering of in vitro data, implementation of a three probe substrate

approach may be prohibitive depending on the number of candidates to be

tested. Results from in vitro studies assaying the inhibition potential of forty-two

marketed therapeutics against CYP3A4 using multiple probe substrates generally

suggest the rank ordering of midazolam > testosterone > felodipine in terms of in

vitro sensitivity to inhibition (Obach et al., 2006). Accuracy of the prediction of in

vivo DDI magnitude from this data was dependent upon the statistical method

used. None of the three probe substrates was clearly superior based upon

performance of the in vivo DDI prediction. The use of quinidine as a CYP3A4 in

vitro probe substrate has also been suggested based upon its kinetic properties

and selectivity for CYP3A4 over CYP3A5 (Galetin et al., 2005).

Our selection of in vitro probe substrates was based upon the availability

of clinical DDI data and structural characteristics of the probe substrate. Probe

substrates with known correlation to midazolam and testosterone (e.g.

cyclosporine, erythromycin and nifedipine) were excluded from the in vitro

analysis. Although the correlation of quinidine to midazolam and testosterone

has previously been determined using azole-type inhibitors (Galetin et al., 2005),

quinidine was included in the in vitro portion of this study in order to understand

its correlation to the other probe substrates based upon a chemically diverse set

of inhibitors. Due to a general industry paradigm using testosterone as a

CYP3A4 probe substrate, we were particularly interested in identifying clinically

relevant steroids for in vitro testing. Fluticasone and budesonide were selected


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as steroid probe substrates based upon the availability of clinical DDI data.

Eplerenone, a steroid with clinical DDI data (Ragueneau-Majlessi et al., 2007),

exhibited linear kinetics in our hands and was therefore unsuitable for use as an

in vitro probe substrate. Other probe substrates with a steroid chemotype, such

as prednisolone, were excluded from consideration because a significant

contribution to their clearance is mediated by enzymes other than P450s

(Zurcher et al., 1989).

The correlation analysis of our in vitro inhibition data suggested four

clusters: felodipine-like, midazolam-like, simvastatin-like and testosterone-like.

Testosterone was the least similar probe based upon both correlation analysis

and average inhibition potency when compared to midazolam. A feature that

differentiated testosterone from the other probe substrates was the number of

effectors that caused activation rather than inhibition. While activation indicates

interaction between the effector and probe substrate, the result is difficult to

context within an in vivo setting (Tracy, 2003). Based upon these results, we did

not find a clinically relevant replacement for testosterone for use with in vitro

assays.

The in vivo sensitivity of the CYP3A4 probe substrates is another criterion

for probe substrate selection. The sensitivity of midazolam and simvastatin as

CYP3A4 probe substrates has been directly compared in a clinical study using

ketoconazole as the inhibitor (Chung et al., 2006). The authors concluded that

simvastatin was a suboptimal in vivo probe substrate due to its lack of CYP3A4

selectivity, as demonstrated by a marked increase in pharmacokinetic variability


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within the same patient population. Retrospective analysis of literature in vivo

DDI studies, however, suggested that simvastin may generally exhibit increased

sensitivity to inhibition in vivo based upon results from multiple CYP3A4 inhibitors

(Ragueneau-Majlessi et al., 2007).

The availability of sufficient clinical CYP3A4 DDI data in the literature

relative to midazolam (n = 4 studies) was our selection criteria for inclusion in the

in vivo correlation analysis. Although previous comparisons to in vivo midazolam

DDI data have been made for buspirone and simvastatin, they were included in

this analysis because we averaged DDI data for instances where multiple clinical

studies for the same inhibitor and probe substrate combination were available

and carried out using similar conditions. Of the probe substrates tested in vitro,

felodipine, fluticasone and budesonide were not included in the sensitivity

analysis since they had less than four clinical DDI studies in common with

midazolam. Cyclosporine, nifedipine and quinidine exhibited reduced sensitivity

when compared to midazolam in vivo, sildenafil exhibited similar sensitivity and

buspirone and simvastatin exhibited enhanced selectivity. The effect of this

enhanced selectivity on the accuracy of in vivo DDI predictions is unclear. The

estimation of inhibitor concentration used in the DDI prediction (total systemic

Cmax, free systemic Cmax, total hepatic inlet Cmax or the free hepatic inlet Cmax) has

a profound impact on the prediction and has resulted in either underestimation or

overestimation of DDI magnitude for these probe substrates depending on the

methodology chosen (Galetin et al., 2005; Obach et al., 2006).


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The reasons for the differences in probe substrate sensitivity may include

competing clearance mechanisms that are not mediated by CYP3A4,

experimental variability in the in vivo DDI studies, or differences in the

susceptibility of each probe substrate to intestinal CYP3A4 inhibition. The fmCYP

value may have a marked impact on DDI predictions. Probe substrates with an

fmCYP value of 0.5 for a particular P450-mediated pathway may experience a

maximal increase in AUCI/AUC of 2-fold theoretically; as fmCYP increases, the

effect on the magnitude of AUCI/AUC increases (Ito et al., 2005). The three

probe substrates with the lowest in vivo sensitivity (cyclosporine, nifedipine and

quinidine) have fmCYP3A4 values lower than 0.8 (0.71, 0.71 and 0.76, respectively).

Intestinal first pass metabolism may also have a marked impact on DDI

prediction. For drugs with an intestinal extraction ratio less than 50%, a maximal

increase in AUCI/AUC of 2-fold is expected (Galetin et al., 2008). For drugs with

a high extent of intestinal extraction, increases in AUCI/AUC of 4-fold or more

may be expected if maximal enzyme inhibition in the gut is achieved.

Midazolam is a clear CYP3A4 DDI probe substrate choice for most

instances based upon its in vitro and in vivo characteristics, such as CYP3A

selectivity and the availability of both intravenous and oral formulations.

However, probe substrate selection for clinical CYP3A4 DDI studies may be

confounded for cases such as AMG 458, where a probe substrate that is not

clinically relevant (e.g. testosterone) has been tested in vitro, demonstrates

markedly increased CYP3A4 inhibition potential relative to midazolam and

felodipine/nifedipine, and is predicted to exhibit a clinically relevant DDI


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(AUCI/AUC ≥ 2), while midazolam and felodipine/nifedipine are not. Due

diligence suggests that a strategy is needed to evaluate whether additional or

alternative clinical studies to a midazolam or felodipine/nifedipine DDI study may

be necessary based upon in vitro results.

Ideal characteristics of a probe substrate for DDI studies include formation

of a primary metabolite that is selectively mediated by the P450 of interest, the

observation of Michaelis-Menten kinetics in vitro and a lack of confounding

transporter activity in vivo. When identifying a potential alternative probe

substrate to midazolam based upon in vitro inhibition potency, the most likely

candidates would demonstrate inhibition profiles unique from midazolam. Based

upon their in vitro inhibition profiles, we would select primary midazolam

alternatives from the testosterone (cyclosporine and erythromycin), felodipine or

simvastatin clusters. The low therapeutic index of cyclosporine makes it an

undesirable probe substrate in vivo (Jorga et al., 2004). Erythromycin is often

used in a single time point breath test (Frassetto et al., 2007), limiting the amount

of clinical DDI data available for a full time course and may exhibit confounding

transporter activity (Obach et al., 2005). These characteristics hindered our

ability to create a direct correlation between the in vitro and in vivo data for

cyclosporine and erythromycin, and were part of the rationale for excluding them

from consideration as alternate probe substrates. Nifedipine is often cited as a

probe substrate for in vitro CYP3A4 inhibition studies. However, it exhibits

reduced sensitivity in vivo when compared to midazolam and has not been tested

using a potent CYP3A4 inhibitor clinically to our knowledge. Felodipine has a


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somewhat higher fmCYP3A4 value (0.81) than nifedipine that should result in

increased in vivo sensitivity and has been tested in vivo using a potent CYP3A4

inhibitor: AUCI/AUC = 6.3 at 200 mg itraconazole (Jalava et al., 1997).

Simvastatin demonstrates unique inhibition profiles from midazolam in

vitro, has been clinically tested with potent CYP3A4 inhibitors in vivo and

demonstrates enhanced in vivo sensitivity when compared to midazolam. Based

upon these characteristics, simvastatin is our primary choice as an alternative

probe substrate when testosterone-selective inhibition of CYP3A4 is observed.

Testing the inhibition potential of other probe substrates may be considered

based upon in vitro results. However, since potent inhibitors are expected to be

identified in vitro and testable in vivo using midazolam, alternative probe

substrates should exhibit similar or better in vitro and in vivo sensitivity than

midazolam for consideration. Although they are in the same in vitro inhibition

cluster as midazolam, buspirone and sildenafil may be considered based upon

acceptable in vitro characteristics and in vivo sensitivity.

The ability to predict in vivo exposure levels of a given drug (or inhibitor)

using modeling and simulation programs such as SimCYP is a useful tool in the

design of drug efficacy and safety studies (Rostami-Hodjegan and Tucker, 2007).

Using the case study of AMG 458, the magnitude of in vivo DDI caused by AMG

458 was predicted for midazolam, sildenafil and simvastatin using SimCYP.

Predictions based upon testosterone are shown for comparative purposes.

Buspirone exhibited activation with AMG 458 and was therefore not included for

the in silico predictions. Predictions for felodipine were not included since it was


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inhibited less potently than midazolam by AMG 458. Sildenafil was predicted to

exhibit clinically relevant DDI (AUCI/AUC ≥ 2) across the anticipated dose range

(500-2000 mg), while midazolam and simvastatin were not. At the lowest dose

(500 mg), midazolam and simvastatin were predicted to demonstrate a < 1.2-fold

increase in DDI magnitude; this magnitude of change may be difficult to detect

based upon pharmacokinetic variability within an in vivo DDI study. Since clinical

DDI studies may be carried out at low doses of drug, often lower than anticipated

efficacious doses, these predictions suggest that sildenafil would be an

acceptable clinical CYP3A4 probe substrate for DDI studies using AMG 458.

While simvastatin is our recommended probe substrate for testosterone-selective

inhibition, the in vivo predictions using AMG 458 demonstrate that other

alternatives may be considered.

The selection of a CYP3A4 probe substrate for clinical DDI studies may be

unclear for cases where a probe substrate, such as testosterone, is predicted to

exhibit clinically significant DDI, while clinically relevant probe substrates, such

as midazolam and felodipine/nifedipine, are not. Based upon hierarchical

clustering of in vitro data and correlation analysis of clinical DDI data, we

recommend the use of simvastatin as a primary alternative CYP3A4 probe

substrate for testosterone-selective inhibition. Buspirone or sildenafil may serve

as useful secondary probe substrates for the testosterone-selective inhibition

situation. The complexity of CYP3A4-mediated reactions suggests it is unlikely

that a universal alternative to midazolam will be available in the near future.


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Figure Legends

Figure 1. CYP3A4 Probe Substrate Hierarchical Clustering Dendrogram

Figure 2. Correlation of Literature AUCI/AUC Values for CYP3A4 Probe

Substrates Relative to Midazolam: A) Buspirone, b) Cyclosporine, C) Nifedipine,

D) Quinidine, E) Sildenafil, F) Simvastatin

Figure 3. SimCYP Predicted AUCI/AUC Values for Midazolam, Simvastatin,

Sildenafil and Testosterone at AMG 458 Doses of 500, 1000 and 2000 mg


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Table 1. Ki Values (µM) Obtained Using Pooled HLMsa

Probe Substrateb Effector TST MDZ SIL FLU BUD QUI BUS SIM FEL

AMG 458 0.55 7.3c 1.0 3.3 7.3 5.6 ACTd 7.7 8.6e Budenoside ACT 0.10 0.17 0.14 X 0.18 0.038 1.3 13.1 Buspirone 50 0.54 2.1 9.3 13.2 1.3 X 10.3 50 Clozapine 7.4 0.42 17.6 9.3 2.1 5.4 0.74 17.1 10.4 Cyclosporine 24.2 3.1e 3.5 6.9e 1.5 0.36 1.1 12.7 4.3 Dextromethorphan 50 8.6 50 50 50 50 26.2 50 24.8e Felodipine ACT 0.25e 0.65 0.44 0.085 0.20e 0.87 0.43 X Fluoxetine 8.7 4.2e 3.3 50 47.0 2.7 5.5e ACT 50 Fluticasone ACT 0.085 0.39 X 0.16 0.93 0.080 17.1 0.79 Fluvoxamine 20.4 2.6 2.7 50 0.68 1.6 5.3e 16.1 50 Haloperidol 27.4 2.3e 2.4 3.1 0.84 3.6 0.61 9.4 2.9e Itraconazole 0.013 0.013 0.016 0.012 0.044 0.016 0.016e 0.052 0.045 Ketoconazole 0.023 0.014e 0.017 0.044 0.011 0.009 0.021e 0.072 0.079 Midazolam 6.4 X 1.8 0.74 4.2 0.97 0.85 8 30.5 Nifedipine ACT 1.8e 8.7 0.46 0.27 1.9c 0.95 1.7 14.9 Sertraline 10.4 3.1e 2.1e 12.6 0.20 1.2e 2.1e 1.6 50 Sildenafil 50 0.71 X 3.9 2.1 1.1 2.0 14.6 50 Simvastatin 35.0 0.16 0.37 0.38 0.31 0.81e 0.54 X 16.5e Terfenadine 11.2 1.0e 0.21 0.31 0.055 0.066 0.13 1.3 1.2 Testosterone X 2.7c 9.5 1.8 5.0 50 3.7 1.2 ACT

aGlobal standard error for data fitting was less than 20% and r2 > 0.80 for each effector bAbbreviations: Testosterone (TST); Midazolam (MDZ); Sildenafil (SIL); Fluticasone (FLU); Budesonide (BUD); Quinidine (QUI); Buspirone (BUS); Simvastatin (SIM); Felodipine (FEL) cLinear-mixed inhibition dActivation eNoncompetitive inhibition


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Table 2. AUCI/AUC Values Obtained from the Literature

AUCI/AUCa

Inhibitor (mg/day) BUD BUS CYC FEL MDZ NIF QUI SIL SIM

Cimetidine (800-1200)b 1.1 1.8 1.8 1.5 1.6 Clarithromycin (500)b 3.6 2.3 Clarithromycin (1000)b 8.4 10.0 Diltiazem (60-120)b 5.5 1.4 3.8 1.4 1.5 2.0 Erythromycin (800-1000)b 3.4 1.0 2.8 Erythromycin (1500)b 6.0 2.2 2.5 4.0 6.2 Fluconazole (200) 1.8 3.6 Fluvoxamine (100-200) 2.4 1.7 1.4 1.4 Grapefuit juice (rsc)b 2.0 1.6 2.0 2.0 1.0 1.2 3.6 Grapefruit juice (dsd)b 9.2 6.0 1.5 13.5 Itraconazole (100) 4.2 5.8 2.5 Itraconazole (200) 16.9 1.4 6.3 6.6 18.6 Ketoconazole (200) 6.8 6.1 11.8 Ketoconazole (400) 11.0 12.4 Mibefradil (50-100)b 2.3 8.9 Ranitidine (300) 1.5 1.2 Ritonavir (500 bid)b 13.6 11.0 Saquinavir (600-1200 tid)b 5.1 3.1 Verapamil (80)b 3.4 1.5 2.9 1.5 4.6

aAbbreviations: Budesonide (BUD); Buspirone (BUS); Cyclosporine (CYC); Felodipine (FEL); Midazolam (MDZ); Nifedipine (NIF); Quinidine (QUI); Sildenafil (SIL); Simvastatin (SIM); bTime dependent inactivator of CYP3A4 cRS = regular strength grapefruit juice dDS = double strength grapefruit juice


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Figure 1

20 40 60 80 100 120 140

Linkage Distance

Felodipine

Simvastatin

Budesonide

Fluticasone

Quinidine

Sildenafil

Buspirone

Midazolam

Testosterone

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