Generation and evaluation of a CYP2C9...

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Generation and evaluation of a CYP2C9 heteroactivation pharmacophore

Ann-Charlotte Egnell, Cecilia Eriksson, Nan Albertson, Brian Houston and Scott

Boyer

EST Chemical Computing, AstraZeneca R&D, S-431 83 Mölndal, Sweden

(A.E.,S.B.); High Throughput Screening, AstraZeneca R&D, S-431 83 Mölndal,

Sweden (C.E., N.A) and School of Pharmacy and Pharmaceutical Sciences,

University of Manchester, Manchester M13 9PL, UK (A.E., B.H.)

Copyright 2003 by the American Society for Pharmacology and Experimental Therapeutics.

JPET Fast Forward. Published on October 13, 2003 as DOI:10.1124/jpet.103.054999This article has not been copyedited and formatted. The final version may differ from this version.JPET Fast Forward. Published on October 13, 2003 as DOI: 10.1124/jpet.103.054999

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a) Running title: CYP2C9 heteroactivation pharmacophore

b) Corresponding author:

Ann-Charlotte Egnell, AstraZeneca R&D, EST Chemical Computing, S-431

83 Mölndal

Telephone: +46 31 706 52 84

Fax: +46 31 776 37 92

Email: [email protected]

c) No of text pages: 19

No of tables: 4

No of figures: 6

No of references: 51

No of words in Abstract: 229

Introduction: 627

Discussion: 1815

d) List of non-standard abbreviations:

CYP, cytochrome P450; HLM, human liver microsomes; P450, cytochrome P450;

HFC, 7-hydroxy-4-trifluoromethyl-coumarin; HTS, high throughput screening; MFC,

7-methoxy-4-trifluoromethyl-coumarin; NSAID, non steroidal anti-inflammatory

drug; QSAR, quantitative structure activity relationship; rCYP, recombinantly

expressed P450.

e) Recommended section assignment:

Absorption, Distribution, Metabolism & Excretion

This article has not been copyedited and formatted. The final version may differ from this version.JPET Fast Forward. Published on October 13, 2003 as DOI: 10.1124/jpet.103.054999

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Abstract

Positive cooperativity (auto- and heteroactivation) of drug oxidation, a potential cause

of drug interactions, is well established in vitro for cytochrome P450 (CYP, P450)

3A4 but to a much lesser extent for other drug metabolising P450 isoforms. Using a

high throughput fluorescent based CYP2C9 effector assay, we identified >30

heteroactivators from a set of 1504 structurally diverse compounds. Several potent

heteroactivators of CYP2C9 mediated 7-methoxy-4-trifluoromethyl-coumarin

metabolism are marketed drugs or endogenous compounds (amiodarone,

niclosamide, liothyronine, meclofenemate, zafirlukast, estropipate, and

dichlorphenamide yielding 150% control reaction velocity (v150%) at 0.04µM,

0.09µM, 0.5µM, 1µM, 1.2µM, 1.5µM and 2.5µM respectively). Some

heteroactivators are also known CYP2C9 substrates or inhibitors, suggesting potential

multiple binding sites and substrate dependent effects. v150%, the concentration of

effector giving 150% of control reaction velocity , was used as pharmacophore

modelling parameter based on enzyme kinetic assumptions. The generated

pharmacophore, (training set: n=36, v150% 0.04 – 150µM) contains one hydrogen

bond acceptor, one aromatic ring and two hydrophobes. v150%s for 94% of the training

set heteroactivators were predicted within 1 log unit for the residual (r [log observed

v150%] vs [log predicted v150%] = 0.71; r2: 0.50). The model also correctly identifies

close to 70% of potent inhibitors (IC50<1µM) as high affinity CYP2C9 binders,

suggesting that heteroactivators and inhibitors share some common structural

CYP2C9 binding features, supporting the previously suggested hypothesis that

CYP2C9 heteroactivators can bind within the active site.


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Heteroactivation of P450 mediated drug metabolism is a well-known

phenomena for CYP3A4, the major drug metabolising P450 isoform in humans

(Benet et al., 1996), with extensive in vitro evidence supporting the idea of two or

more binding sites for substrates, inhibitors and heteroactivators within or near the

active site (Shou et al., 1994; Ueng et al., 1997; Korzekwa et al., 1998; Domanski et

al., 2000; Hosea et al., 2000; Kenworthy et al., 2001). Such non-Michaelis Menten

kinetics presents a deviation from the assumptions traditionally made in quantitative

prediction of in vivo drug clearance and drug interaction potential (Houston and

Kenworthy, 2000). Apart from potentially introducing errors in in vitro - in vivo

correlations, positive cooperativity of drug metabolism, leading to increases in affinity

and/or catalytic efficiency, is a potential cause of drug interactions if translated to in

vivo events (Egnell et al., 2003). Reports of atypical enzyme kinetics for other

relevant drug metabolising P450 isoforms are rare and often only supported by single

observations. Such scarce data on atypical kinetics have been reported for CYP3A5

(Korzekwa et al., 1998), CYP1A2 (Ekins et al., 1998), CYP2D6 (Kudo and Odomi,

1998), CYP2B6 (Korzekwa et al., 1998), CYP2C8 (Korzekwa et al., 1998) and to an

increasing extent for CYP2C9 (Korzekwa et al., 1998; Hutzler et al., 2001; Hutzler et

al., 2002; Hutzler et al., 2003). For CYP2C9, as for CYP3A4, experimental

observations seem to indicate simultaneous binding of heteroactivator and substrate

within or near the active site (Hutzler et al., 2001).

CYP2C9 is believed to be one of the more important P450s in drug

metabolism, and is involved in a number of clinical drug-drug interactions (Miners

and Birkett, 1998). Several attempts at obtaining quantitative structure-activity

relationship (QSAR) models for CYP2C9 ligand binding have been reported,

reflecting the desire of early identification of CYP2C9 substrates and effectors in drug


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discovery. Although no universally predictive model is currently available, the

presence of a hydrogen bond donor in the CYP2C9 ligand (Jones et al., 1993; Jones et

al., 1996a), in many cases an anionic feature (Mancy et al., 1995), has been supported

also by pharmacophore studies (Ekins et al., 2000) and is suggested to be situated

approximately 7 to 8 Å from the site of metabolism (Mancy et al., 1995; Jones et al.,

1996a). Further, a hydrophobic zone between the site of metabolism and a potential

cationic site on the protein has been suggested to be important for binding of the

potent CYP2C9 inhibitor sulphaphenazole, a compound which is hypothesized to

interact directly to the P450 heme iron via the aniline nitrogen (Mancy et al., 1996).

The importance of this heme interaction has however been disputed based on tight

binding analogues of sulphaphenzole not containing a basic nitrogen (Rao et al.,

2000).

Few studies have addressed the potential common structural features

conferring activation potency toward P450s. The only pharmacophore attempt to our

knowledge was based on three autoactivators of CYP3A4 (compounds displaying

sigmoidal velocity vs substrate curves, supposedly resulting from simultaneous,

cooperative binding in the active site leading to conformational change) (Ekins et al.,

1999). Further, one attempt has been made to study structural requirements for

CYP2C9 heteroactivation, by use of structural derivatives of dapsone, a

heteroactivator of several CYP2C9 substrates (Hutzler et al., 2002). Electronic effects

were suggested to be of importance for heteroactivation, since changing one of the

electron donating para amino groups of dapsone to a nitro group, changed the effect

from activation to inhibition (Hutzler et al., 2002).

The main purpose of this work was to test the hypothesis of common

pharmacophore features within a diverse set of CYP2C9 heteroactivators, and to


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compare heteroactivator pharmacophore features with those of inhibitors. We report

the identification of previously unknown heteroactivators of CYP2C9 by use of a high

throughput P450 effector screen.


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Materials and Methods

Materials

Previously characterised (Masimirembwa et al., 1999) recombinant human

cytochrome P450 (rCYP) 2C9 expressed in yeast was from AstraZeneca Biotech

Laboratory (Södertälje, Sweden). 7-methoxy-4-trifluoromethyl-coumarin (MFC) was

from Sigma (St Louis, MO), hydroxypropyl-β-cyclodextrin was from Aldrich

(Steinheim, Germany), dimethylsulphoxide (DMSO) was from Lab-Scan (Dublin,

Ireland) and black Greiner 384-well plates were from Greiner labortechnik

(Frickenhausen, Germany).

CYP2C9 high throughput effector screening

The effect of a set of 1504 compounds (oral drugs, known P450 inhibitors, and

compounds chosen for maximum diversity using Diverse Solutions;

http://www.tripos.com/sciTech/inSilicoDisc/comboChem/dvs.html) on CYP2C9

mediated metabolism of 7-methoxy-4-trifluoromethyl-coumarin (MFC) to 7-hydroxy-

4-trifluoromethyl-coumarin (HFC) (fig 1) was investigated using recombinantly

expressed CYP2C9. Reaction conditions were as previously described (Bapiro et al.,

2001) except for some modifications to allow for a high throughput approach. Briefly,

compounds were dissolved at a concentration of 10mM in a 1:1 mix of

dimethylsulphoxide and 50 mM cyclodextrin, and were serially diluted in1/3

increments by an automatic liquid handling system (CyBi-Well, Cybio Northern

Europe Ltd, Jena, Germany) to yield seven different stock concentrations. 1µL of

each stock concentration was transferred by the automatic liquid handling system, to

the reaction plate and left at room temperature until evaporation of

dimethylsulphoxide was complete. A mix of rCYP2C9, substrate and buffer were


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added to each well to yield final concentrations of 30 pmol/mL rCYP2C9, 50µM

MFC and 0.025µM KPO4 (pH7.4). Final organic solvent concentration from addition

of substrate from a stock solution was 0.3% acetonitrile. After a 10 min preincubation

at 37°C, reactions were started by the addition of NADPH (final concentration 1mM).

Metabolite formation was measured after 35 min incubation at 37°C by fluorescence

measurement (excitation: 405 nm ; emission: 535nm) in a Spectra Max Gemini plate

reader (Molecular Devices Corp., Sunnyvale, CA). Metabolite formation was linear

with respect to time and protein concentration under the conditions used. To ensure

that compounds tested were not fluorescent at the wavelengths used, the fluorescent

emission of the compounds was measured in buffer. For heteroactivators, v150% values

(concentration resulting in 150% of control reaction velocity ) were deduced from the

plot of activity of control vs effector concentration by optical examination. Inhibitors

identified in the effector screen were used in part of the evaluation of the

pharmacophore. IC50 values (concentration resulting in 50% inhibition) were

calculated by fitting the data (using XLfit) to the following equation:

slope

conc

IC

ABAinhibition

+

−+=50log

1

% (eq 1)

where A is the minimum % inhibition and B is the maximum % inhibition. In cases

when there were not enough data points to characterize the entire curve, B was set to

100 and A was set to 0.

Rationalising the use of v150% as pharmacophore modelling parameter

The experimental end-point used for building the heteroactivator pharmacophore

was the concentration yielding 150% of control reaction velocity, v150%. Due to the

lack of detailed mechanistic knowledge about the molecular interactions leading to


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heteroactivation of this P450 isoform, the use of experimental parameter reflecting

pure affinity (in analogy with IC50 or Ki for competitive inhibitors) is not possible to

derive. A theoretical relation between v150% and the heteroactivator affinity constant

Ka, can be made if making an analogy to the enzyme kinetic approach that has been

employed for CYP3A4 (Kenworthy et al., 2001; Galetin et al., 2002; Houston and

Kenworthy, 2000). If assuming CYP2C9 mediated MFC metabolism and its

heteroactivation can be described by parameters Vmax, the maximum velocity of the

reaction in absence of heteroactivation, S, the substrate concentration, A, the

heteroactivator concentration, Ks, the affinity constant for binding of MFC, Ka, the

affinity constant for binding of the heteroactivator to an hypothesized allosteric site,

α, a value between 0 and 1 defining the change in affinity for the metabolic site

induced by the heteroactivator (α <1 for activation), and β, a value ≥ 0 defining the

change in catalytical efficiency of carbamazepine-epoxide formation induced by the

heteroactivator (β >1 for activation), then the velocity in presence of a heteroactivator

(vactivation) can be described as a function of Ka, Vmax, S, A, Ka, Ks1, α and β , and

the velocity in abscence of a heteroactivator (vcontrol) can be described as a function of

Vmax, S and Ks. At a fixed increase in velocity, [vactivation / vcontrol] takes a nominal

value, for example for the modelling parameter used here, v150%, [vactivation / vcontrol] =

1.5. Heteroactivator affinity Ka can then be expressed as a function of v 150%, S, Ks1,

Vmax, α and β. Since substrate concentration S was held constant between

experiments, and since parameters Vmax and Ks can be regarded as constants

describing the interaction between the MFC and CYP2C9 in absence of

heteroactivator, it follows that if assuming that all heteroactivators are characterised

by the same α and β, any change in Ka between heteroactivators would be reflected

by a change in v 150%.. The quantitative relationship between Ka and v150% will differ


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depending on the mechanistic assumptions used to describe the interaction between

the P450 and the substrate and heteroactivator. If assuming a simplistic case in which

all CYP2C9 heteroactivators have the same maximal potency of changing substrate

affinity (ie all heteroactivators have the same α) and catalytic efficiency (ie all

heteroactivators have the same β) v150% would change proportionally with Ka and as

such be a pure surrogate measure for ranking heteroactivator affinity.

The endpoint used to classify inhibitors in potency categories for validation of

the heteroactivation pharmacophore, was the concentration resulting in 50%

inhibition, the IC50 value, a pure surrogate measure for ranking inhibitor affinity if

assuming competitive inhibition (Todhunter et al., 1979).

Molecular modelling and pharmacophore generation

3D structures of CYP2C9 MFC-heteroactivators were generated in

CONCORD (Tripos Associates Inc.) and imported into Catalyst (Accelrys) where all

subsequent modelling and pharmacophore generation was performed. For chiral

compounds where racemates were used to generate experimental data, all possible

isomers were included in the training-set, and assigned the same v150% value. 3D-

minimization and conformer generation were performed within Catalyst which uses a

forcefield with a parameter set derived from the CHARMm force field. For each

structure, a maximum of 255 conformers were generated using the “Best Quality”

feature to maximise the coverage of conformational space. The generation of

conformers in Catalyst is performed using the poling method (Smellie et al., 1995a;

Smellie et al., 1995b; Smellie et al., 1995c), designed to cover as broad low energy

conformational space as possible (Barnum et al., 1996), by introducing a so called


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poling function in the forcefield calculation in order to favour generation of

conformers from previously unexplored conformational space.

Hypothesis generation was performed using all possible chemical features of

the compounds in the training set as possible features of the pharmacophore. Features

included were hydrogen bond acceptors, hydrogen bond donors, hydrophobes,

negative ionizable features and aromatic rings.

All molecular and pharmacophore modelling was performed on a Silicon

Graphics O2 station (Silicon Graphics Inc., Mountain View, CA, USA).

Pharmacophore validation

To validate that the quantitative heteroactivation pharmacophore chosen for

further validation was not generated by chance, the pharmacophore generation was

repeated using identical conditions except for permutation of v150% values.

Permutation was performed by randomizing values, making sure no compound

retained its original v150% value. Comparison of significance between pharmacophores

was judged by the difference in cost between the lowest cost hypothesis and the

related null hypothesis, as well as on correlation coefficients.

For external validation, five different modified training-sets were used, four

different structures being left out in each, resulting always in leaving out one structure

from within potency categories v150% ≤ 1µM, 1-20µM, 20-60µM, and > 60µM.

Removal of heteroactivators from the training-set was performed by random within

these predefined potency groups.

Due to the lack of potent heteroactivators for validating external predicitivity,

and in order to compare heteroactivator affinity features with those of inhibitors, a set

of CYP2C9 inhibitors identified from the same experimental data set were tested for


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fit to the CYP2C9 heteroactivation pharmacophore. Inhibitors were chosen without

prior knowledge of the structure, to cover defined IC50 ranges (IC50<1µM, n=52 ; 1-

10µM, n=32 , >10µM, n=124). A set of non-actives (no effect at 125µM, n=49 ) were

included in the evaluation. 3D structures of the compounds for inhibitors and non-

actives were generated in CONCORD and imported in Catalyst for conformer

generation as described above for heteroactivators. Compounds were fit to the

CYP2C9 heteroactivator pharmacophore using the Fast Fit feature of the software.


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Results

CYP2C9 effector screening

Out of the 1504 compounds tested in the CYP2C9 high throughput effector

screen, 37 (2.5%) were identified as heteroactivators of CYP2C9 mediated HFC

formation (>20% activation at ≤ 125µM). Heteroactivator structures and their

respective observed v150% values are shown in table 1. Representative experimental

data are shown in fig 2 for the four most potent heteroactivators (v150% ≤ 1µM). The

drop in activation potency observed at high concentrations of some compounds,

possibly due to solubility problems at high effector concentrations, did not affect the

estimation of v150%. Although the absence of a common level of maximum activation

by all heteroactivators (fig 2) could theoretically be due to solubility problems, it can

not be excluded that the effect reflects a true drop in activation potency, in which case

the modelling parameter, v150%, would not reflect pure affinity but would contain

information also about potency. Further insight into the mechanism of

heteroactivation of CYP2C9 would be needed to allow derivation and in vitro

measurement of an experimental parameter purely reflecting heteroactivator affinity.

Several of the most potent heteroactivators of MFC metabolism are oral drugs

and endogenous compounds, including in decreasing potency amiodarone (anti-

arrythmic), niclosamide (anti-parasitic), liothyronine (thyroidal hormone, T3),

meclofenemate (NSAID), zafirlukast (anti-asthmatic), estropipate (estrogen

replacement), dichlorphenamide (carbonic anhydrase inhibitor), hydroflumethiazide

(diuretic), mefenamic acid (NSAID) and warfarin (anticoagulant), all with v150%

values below 10µM. Of these compounds, only niclosamide has been previously

reported as a CYP2C9 heteroactivator (Bapiro et al., 2001).


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CYP2C9/MFC heteroactivation pharmacophore

Initially, two different training sets were used for pharmacophore generation,

one excluding the structurally deviating compound no 5 (table 1), in order to

investigate the impact of this compound on the final pharamacophore. Excluding

compound 5 gave slightly better estimates of its prediction (data not shown), a higher

r value for internal prediction of training-set compounds, as well as a slightly higher

difference in cost to the corresponding null hypothesis (table 2). The next highest

scored pharmacophore when including compound 5 had identical (table 2) and

superimposable (not shown) features to the highest scored hypothesis generated when

excluding this compound. The highest scored pharmacophore generated when

excluding compound 5 was taken further for validation of statistical validity and

predictive power. There was a significant difference in features, cost and correlation

coefficients of the heteroactivation pharmacophore as compared to the lowest cost

pharmacophore based on permuted v150% values (table 2), suggesting that the

pharmacophore is a statistically valid representation of CYP2C9 heteroactivation of

HFC formation. The geometric details of the pharmacophore is shown in fig 3A and

B, and superimposed with the 5 most potent heteroactivators in fig 4.

Of the training set compounds, 94% (34 out of 36) were predicted within 1

log unit of the residual (r for [log observed v150%] vs [log predicted v150%] = 0.71; r2:

0.50). The categorical nature of the model, as suggested by fig 5A, is supported by

the results of external validation attempts. The lowest cost external validation

pharmacophore models (table 3) generated by compound exclusion from the training

set (table 4) were all similar to the pharmacophore based on the complete training set

(n=36). The hydrogen bond acceptor/aromatic ring feature is conserved and in each

case superimposable with the pharmacophore in fig 3 (not shown). Only four out of


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five external validation models predicted the v150% within 1 log residual for 3 out of 4

compounds, with only one model (model 4) ranking all compounds (table 4). Due to

the skewed distribution of v150% values in the training set, using a default v150%

uncertainty value of 3 (defined by Catalyst as the measured value being within 3 times

higher or 3 times lower of the true value) only the two most potent molecules in the

training set are classified by Catalyst as actives to be used in the initial step of

pharmacophore generation. As such, exclusion models 2 and 4 (table 4) involve

excluding one of the two compounds on which the original model was heavily

dependent. The potent compounds excluded in models 3 and 5 were also outliers

when included in the original model. In model 1, where both potent heteroactivators

are kept in the training set, the two excluded potent heteroactivators were well

predicted by the original model. It does however generate a false positive (table 4).

The ability of the CYP2C9 heteroactivation pharmacophore (fig 3) to classify

potent inhibitors as tight CYP2C9 binders is shown in fig 5B. A high percentage of

the most potent heteroactivators (fig 5A) and inhibitors (fig 5B) are predicted in the

<1µM range, while there is a steady decrease in this percentage as IC50 increases.

Few hits (4%) are seen for non-actives (fig 5B).


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Discussion

One limiting factor in structural investigations of CYP2C9 heteroactivators is

the scarcity of experimental data on which to base computational methods. In an

effort to overcome this limitation, we have used a high throughput approach to screen

a large number of compounds (>1500) from which we have been able to identify 36

previously unknown heteroactivators of CYP2C9 mediated HFC formation, with v150%

values in the range 0.04µM to 150µM (table 1).

Although several of the most potent heteroactivators identified are marketed

drugs or endogenous compounds (table 1), the possible clinical relevance of the

findings is difficult to gauge, since our data, in combination with others (Hutzler et

al., 2002) , suggests that CYP2C9 heteroactivation is strongly substrate dependent.

This phenomena has been extensively studied with CYP3A4 (Kenworthy et al., 1999;

Wang et al., 2000; Kenworthy et al., 2001), for which the substrate dependent effects

have been rationalised as a result of multiple and cooperative binding sites within or

near the active site, as supported by enzyme kinetic modelling, spectral binding

studies and site directed mutagenesis (Shou et al., 1994; Ueng et al., 1997; Korzekwa

et al., 1998; Domanski et al., 2000; Hosea et al., 2000; Kenworthy et al., 2001;

Dabrowski et al., 2002; Galetin et al., 2002). Two clear examples of substrate

dependency with CYP2C9 are with effectors amiodarone and zafirlukast. Both are

potent heteroactivators of MFC metabolism (v150% = 0.04µM and 1.2 µM respectively,

table 1) but are reported to be CYP2C9 inhibitors of other substrates

(amiodarone/warfarin; zafirlukast/warfarin; zafirlukast/tolbutamide) (O'Reilly et al.,

1987; Heimark et al., 1992; Suttle et al., 1997; Vargo et al., 1997; Shader et al., 1999).

Similarly, niclosamide, a potent activator of MFC metabolism (Bapiro et al., 2001)

(table 1), is a potent inhibitor of CYP2C9 mediated diclofenac-4-hydroxylase activity


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(Bapiro et al., 2001). Further, dapsone, a heteroactivator of CYP2C9 mediated

metabolism of flurbiprofene, naproxene and piroxicam (Korzekwa et al., 1998;

Hutzler et al., 2001) was observed here to be a weak inhibitor of MFC metabolism

(IC50 145µM, data not shown). Another example is sulphamethoxazole, not studied in

this work but reported by others to weakly inhibit tolbutamide metabolism (Back et

al., 1988) but to have no effect on flurbiprofene- or naproxene metabolism (Hutzler et

al., 2002). Due to these evident substrate dependent effects, direct extrapolation of the

results presented here to other, potentially more clinically relevant substrates may not

be valid. Further, for clinical relevance to be assessed, a number of pharmacokinetic

factors must be considered, such as therapeutic concentrations in relation to

heteroactivation potency, hepatic extraction ratio and administration route, since P450

activity is expected to influence systemic drug clearance only when the extraction

ratio is relatively low, and could potentially influence pre-systemic clearance to lower

the bioavailability of an orally administered drug of any extraction ratio (Pang and

Rowland, 1977; Egnell et al., 2003). For example, even if the potent heteroactivating

effect on MFC metabolism by niclosamide and zafirlukast (table 1) was also relevant

for other substrates, these drugs would not be expected to increase hepatic in vivo

CYP2C9 activity because of very low systemic availability (Tracy and Webster,

1996) and low plasma concentrations (Shader et al., 1999) respectively.

Many of the heteroactivators are also known substrates for CYP2C9 (such as

mefenemic acid, v150%: 5µM; warfarin, v150%: 5.5µM; flurbiprofene, v150%: 35µM; and

alprazolam, v150%: 42µM) (Leemann et al., 1992; Takahashi et al., 1998;

Venkatakrishnan et al., 1998; Hutzler et al., 2002) further supporting the possibility of

different bindging modes and/or binding sites.


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The main purpose of this work was to investigate if CYP2C9 heteroactivator

affinity can be related to structure, information that would be potentially useful for

identification at an early stage of drug discovery. The generated pharmacophore (fig

3), based on 36 heteroactivators (v150% 0.04 – 150 µM), was a significantly better

representation of data than a pharmacophore based on permuted v150% values (table 2).

The features defined by the pharmacophore – a hydrogen bond acceptor, an aromatic

ring and two hydrophobes - are in accordance with structural features previously

suggested to be important for CYP2C9 active site binding. For CYP2C9 inhibitors,

the presence of at least one hydrogen bond acceptor and one hydrophobic region,

separated by 3 to 5.8 Å, has been suggested (Ekins et al., 2000). For these inhibition

pharmacophores, aromatic rings were not included as possible features, and it seems

possible that the hydrogen bond acceptor – hydrophobe feature of the heteraoctivation

pharmacophore, separated by 4.8Å (fig 3A), could correspond to the hydrogen bond

acceptor – hydrophobe feature in the reported inhibition pharmacophore (Ekins et al.,

2000). The importance of hydrogen bonding and hydrophobic interactions for

CYP2C9 binding is further supported by CYP2C9 homology modelling and the

reported correspondance of active site amino residues to grid interaction energies in a

3D QSAR CYP2C9 inhibition model (Afzelius et al., 2001), as well as by an inverse

pharmacophore based on analysis of selective regions in the active sites of different

human CYP2C isoforms (Ridderström et al., 2001). The presence of an aromatic ring

in the heteroactivation pharmacophore is in accordance with previous suggestions of

the importance of aromatic stacking interactions for CYP2C9 binding, as based on a

ligand based 3D QSAR combined with a CYP2C9 homology model directed site

directed mutagenesis study showing the decrease in affinity of warfarin, diclofenac

and sulphaphenazole of a Phe114Leu mutation. (Jones et al., 1996b; Korzekwa et al.,


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1993; Haining et al., 1999). An additional pharmacophore model for CYP2C9

inhibition (Rao et al., 2000) supports the pharmacophoric features in our

heteroactivation pharmacophore in that it suggest the importance of aromatic stacking

interactions in relatively close proximity to partially negatively and/or positively

charged groups, as well as enhanced affinity with increased steric interactions further

away from the aromatic ring, possibly corresponding to the additional hydrophobic

features in the heteroactivation pharmacophore (fig 3).

The positive impact of halogenation of aromatic structures of benzbromarone

derivatives on CYP2C9 affinity has been recently reported as based on inhibition of

CYP2C9 mediated warfarin metabolism (Locuson et al., 2003). Electronic effects

have been previously suggested to be of importance for CYP2C9 binding. In the case

of a dapsone derivative, electron donation was suggested to be a determinant of

whether the effect of the binder is activation or inhibition of flurbiprofene 4-

hydroxylation (Hutzler et al., 2002), and similarly, compounds with similar steric bulk

but different electrostatics varied greatly in their ability to inhibit S-warfarin

metabolism (Rao et al., 2000). The importance of such effects for heteroactivation

needs further experimental and computational investigation. The similarity in

structure of the benzbromarone derivatives (Locuson et al., 2003) to amiodarone, the

most potent heteroactivator in our training set, makes them interesting candidates for

investigating structural and electronic effects of CYP2C9 binders on different marker

substrates. The majority of these benzobromarone derivatives overlap with

amiodarone to fit the hydrogen bond acceptor – aromatic ring feature of our

heteroactivation pharmacophore as the lowest possible energy fit when using the Best

Fit feature (data not shown).


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Although quantitative internal predictivity of the presented CYP2C9

heteroactivation model (r of [log observed v150%] vs [log predicted v150%] = 0.71; r2:

0.50) is in the range of that reported for CYP2C9 inhibition pharmacophores (Ekins et

al., 2000), fig 5A suggests that the heteroactivation model is of categorical nature,

with a higher degree of correct classification of high affinity and low affinity

compounds than for intermediate potency compounds. One reason for the lack of

continuous quantitative internal predictivity of the model might be the skewed

distribution of the training set, the difference in v150% of the most potent and the third

most potent compound being more than 3.5 orders of magnitude. Since this causes

Catalyst to use only the two most potent heteroactivators in the initial steps of

pharmacophore generation, it is possible that the structural information is not

optimally extracted. Increasing the number of compounds used by Catalyst in initial

pharmacophore generation, by increasing the assumed uncertainty range of

experimental values, yielded a hypothesis with similar features as that in fig 3,

superimposable with the hydrogen bond acceptor - aromatic ring feature, but with

higher cost than the null hypothesis (the theoretical cost for generating a hypothesis

that contains no pharmacophore features, and that estimates all activities to be the

average activity), and lower internal predictivity (r log [observed] vs log [predicted] =

0.59; r2: 0.35) than the model shown in fig 3 (data not shown).

Exclusion of compounds from the training set (table 4), confirmed the

expected lack of continuous quantitative predictivity of the model. Due to the scarcity

of known potent CYP2C9 heteroactivators, the external categorical predictivity of

such compounds using the model generated here can not be properly tested. However,

challenging the model with potent inhibitors as well as non-actives, all identified in

the same HTS assay, reveals a clear relationship between predicted and observed


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CYP2C9 affinity (fig 5B). It is also interesting to note that the most potent of the

coumarin set of CYP2C9 inhibitors previously reported (Jones et al., 1996b) although

not correctly classified (predicted affinity in the range of 14µM for observed Ki ≤

1µM) fit well the hydrogen bond acceptor – aromatic ring feature of the

heteroactivation pharmacophore presented here (fig 6).

The CYP2C9 heteroactivation model presented here, although not intended to

be a high-resolution pharmacophore for CYP2C9 binding, still does reveal some

important information. The clear trends in classification of CYP2C9 inhibitors (fig

5B) based on a model generated on heteroactivation data strongly indicates that potent

heteroactivators and potent inhibitors do contain some common structural features

conferring CYP2C9 active site binding, supporting previously reported experimental

observations that positive cooperativity of CYP2C9 can be described by a model

assuming two binding sites within the active site (Hutzler et al., 2001). Further, the

fact that important structural information on CYP2C9 affinity could be obtained based

on high throughput screening heteroactivation data using a quick and simple

pharmacophore approach based on a number of simplifying but controlled

assumptions regarding affinity for active site binding is promising and suggests the

future potential of quantitatively predictive heteroactivation QSAR models. It is

tempting to speculate a future possible structure-based distinction between

heteroactivators and inhibitors. Any generalisation of such a distinction model would

probably be limited by the substrate dependent effects.

In conclusion, by use of a high throughput approach, we have identified a

number of previously unknown heteroactivators of CYP2C9 mediated MFC

metabolism. The evident substrate dependent effects suggest that it is advisable to use

several substrates in CYP2C9 effector screens aimed at predicting clinical drug


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interaction potential. We propose that our data, in combination with others (Hutlzer et

al., 2001) suggests that potent heteroactivation of a screening marker substrate can be

a potential sign of high CYP2C9 active site affinity, potentially causing inhibition

with other substrates. The presented CYP2C9 heteroactivation pharmacophore is

predictive for high active site affinity, suggesting that improvements of experimental

and computational approaches may make possible the generation of continuously

quantitative prediction models of CYP2C9 heteroactivator potency from chemical

structure.


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References:

Afzelius L, Zamora I, Ridderstrom M, Andersson TB, Karlen A and Masimirembwa

CM (2001) Competitive CYP2C9 inhibitors: enzyme inhibition studies,

protein homology modeling, and three-dimensional quantitative structure-

activity relationship analysis. Mol Pharmacol 59:909-919.

Back DJ, Tjia JF, Karbwang J and Colbert J (1988) In vitro inhibition studies of

tolbutamide hydroxylase activity of human liver microsomes by azoles,

sulphonamides and quinolines. Br J Clin Pharmacol 26:23-29.

Bapiro TE, Egnell AC, Hasler JA and Masimirembwa CM (2001) Application of

higher throughput screening (HTS) inhibition assays to evaluate the

interaction of antiparasitic drugs with cytochrome P450s. Drug Metab Dispos

29:30-35.

Barnum D, Greene J, Smellie A and Sprague P (1996) Identification of common

functional configurations among molecules. J Chem Inf Comput Sci 36:563-

571.

Benet LZ, Kroetz DL and Sheiner LB (1996) Pharmacokinetics: The dynamics of

drug absorption, distribution, and elimination, in Goodman & Gilman's The

Pharmacological Basis of Therapeutics. (Hardman JG, Goodman Gilman A

and Limbird LE eds) pp 3-27, McGraw-Hill, New York.

Dabrowski MJ, Schrag ML, Wienkers LC and Atkins WM (2002) Pyrene.pyrene

complexes at the active site of cytochrome P450 3A4: evidence for a multiple

substrate binding site. J Am Chem Soc 124:11866-11867.

Domanski TL, He YA, Harlow GR and Halpert JR (2000) Dual role of human

cytochrome P450 3A4 residue Phe-304 in substrate specificity and

cooperativity. J Pharmacol Exp Ther 293:585-591.


at ASPE



ownloaded from


JPET#54999

24

Egnell AC, Houston JB and Boyer CS (2003) In vivo CYP3A4 heteroactivation is a

possible mechanism for the drug interaction between felbamate and

carbamazepine. J Pharmacol Exp Ther 305:1251-1262.

Ekins S, Bravi G, Binkley S, Gillespie JS, Ring BJ, Wikel JH and Wrighton SA

(2000) Three- and four-dimensional-quantitative structure activity relationship

(3D/4D-QSAR) analyses of CYP2C9 inhibitors. Drug Metab Dispos 28:994-

1002.

Ekins S, Bravi G, Wikel JH and Wrighton SA (1999) Three-dimensional-quantitative

structure activity relationship analysis of cytochrome P-450 3A4 substrates. J

Pharmacol Exp Ther 291:424-433.

Ekins S, Ring BJ, Binkley SN, Hall SD and Wrighton SA (1998) Autoactivation and

activation of the cytochrome P450s. Int J Clin Pharmacol Ther 36:642-651.

Galetin A, Clarke SE and Houston JB (2002) Quinidine and haloperidol as modifiers

of CYP3A4 activity: multisite kinetic model approach. Drug Metab Dispos

30:1512-1522.

Haining RL, Jones JP, Henne KR, Fisher MB, Koop DR , Trager WF and Rettie AE

(1999) Enzymatic determinants of the substrate specificity of CYP2C9: role of

B'-C loop residues in providing the pi-stacking anchor site for warfarin

binding. Biochemistry 38:3285-3292.

Heimark LD, Wienkers L, Kunze K, Gibaldi M, Eddy AC, Trager WF, O'Reilly RA

and Goulart DA (1992) The mechanism of the interaction between amiodarone

and warfarin in humans. Clin Pharmacol Ther 51:398-407.

Hosea NA, Miller GP and Guengerich FP (2000) Elucidation of distinct ligand

binding sites for cytochrome P450 3A4. Biochemistry 39:5929-5939.


at ASPE



ownloaded from


JPET#54999

25

Houston JB and Kenworthy KE (2000) In vitro-in vivo scaling of CYP kinetic data

not consistent with the classical Michaelis-Menten model. Drug Metab Dispos

28:246-254.

Hutzler JM, Hauer MJ and Tracy TS (2001) Dapsone activation of CYP2C9-mediated

metabolism: evidence for activation of multiple substrates and a two-site

model. Drug Metab Dispos 29:1029-1034.

Hutzler JM, Kolwankar D, Hummel MA and Tracy TS (2002) Activation of

CYP2C9-mediated metabolism by a series of dapsone analogs: kinetics and

structural requirements. Drug Metab Dispos 30:1194-1200.

Hutzler JM, Wienkers LC, Wahlstrom JL, Carlson TJ and Tracy TS (2003) Activation

of cytochrome P450 2C9-mediated metabolism: mechanistic evidence in

support of kinetic observations. Arch Biochem Biophys 410:16-24.

Jones BC, Hawksworth G, Horne VA, Newlands A, Morsman J, Tute MS and Smith

DA (1996a) Putative active site template model for cytochrome P4502C9

(tolbutamide hydroxylase). Drug Metab Dispos 24:260-266.

Jones BC, Hawksworth G, Horne VA, Newlands A, Tute MS and Smith DA (1993)

Putative action site model for CYP2C9 (tolbutamide hydroxylase). Br J Clin

Pharmacol 34:P143-P144.

Jones JP, He M, Trager WF and Rettie AE (1996b) Three-dimensional quantitative

structure-activity relationship for inhibitors of cytochrome P4502C9. Drug

Metab Dispos 24:1-6.

Kenworthy KE, Bloomer JC, Clarke SE and Houston JB (1999) CYP3A4 drug

interactions: correlation of 10 in vitro probe substrates. Br J Clin Pharmacol

48:716-727.


at ASPE



ownloaded from


JPET#54999

26

Kenworthy KE, Clarke SE, Andrews J and Houston JB (2001) Multisite kinetic

models for CYP3A4: simultaneous activation and inhibition of diazepam and

testosterone metabolism. Drug Metab Dispos 29:1644-1651.

Korzekwa KR and Jones JP (1993) Predicting the cytochrome P450 mediated

metabolism of xenobiotics. Pharmacogenetics 3:1-18.

Korzekwa KR, Krishnamachary N, Shou M, Ogai A, Parise RA, Rettie AE, Gonzalez

FJ and Tracy TS (1998) Evaluation of atypical cytochrome P450 kinetics with

two-substrate models: evidence that multiple substrates can simultaneously

bind to cytochrome P450 active sites. Biochemistry 37:4137-4147.

Kudo S and Odomi M (1998) Involvement of human cytochrome P450 3A4 in

reduced haloperidol oxidation. Eur J Clin Pharmacol 54:253-259.

Leemann T, Kondo M, Zhao J, Transon C, Bonnabry P and Dayer P (1992) [The

biotransformation of NSAIDs: a common elimination site and drug

interactions]. Schweiz Med Wochenschr 122:1897-1899.

Locusson CW, Wahlstrom JL, Rock DA, Rock DA and Jones JP (2003) A new class

of CYP2C9 inhibitors: Probing 2C9 specificity with high-affinity

benzbromarone derivatives. Drug Metab Dispos 31:967-971.

Mancy A, Broto P, Dijols S, Dansette PM and Mansuy D (1995) The substrate

binding site of human liver cytochrome P450 2C9: an approach using

designed tienilic acid derivatives and molecular modeling. Biochemistry

34:10365-10375.

Mancy A, Dijols S, Poli S, Guengerich P and Mansuy D (1996) Interaction of

sulfaphenazole derivatives with human liver cytochromes P450 2C: molecular

origin of the specific inhibitory effects of sulfaphenazole on CYP 2C9 and


at ASPE



ownloaded from


JPET#54999

27

consequences for the substrate binding site topology of CYP 2C9.

Biochemistry 35:16205-16212.

Masimirembwa CM, Otter C, Berg M, Jonsson M, Leidvik B, Jonsson E, Johansson

T, Backman A, Edlund A and Andersson TB (1999) Heterologous expression

and kinetic characterization of human cytochromes P-450: validation of a

pharmaceutical tool for drug metabolism research. Drug Metab Dispos

27:1117-1122.

Miners JO and Birkett DJ (1998) Cytochrome P4502C9: an enzyme of major

importance in human drug metabolism. Br J Clin Pharmacol 45:525-538.

O'Reilly RA, Trager WF, Rettie AE and Goulart DA (1987) Interaction of

amiodarone with racemic warfarin and its separated enantiomorphs in humans.

Clin Pharmacol Ther 42:290-294.

Pang KS and Rowland M (1977) Hepatic clearance of drugs. I. Theoretical

considerations of a "well-stirred" model and a "parallel

tube" model. Influence of hepatic blood flow, plasma and blood cell

binding, and the hepatocellular enzymatic activity on hepatic drug clearance. J

Pharmacokinet Biopharm 5:625-653.

Rao S, Aoyama R, Schrag M, Trager WF, Rettie A and Jones, JP (2000) A Refined 3-

Dimensional QSAR of Cytochrome P450 2C9: Computational Predictions of

Drug Interactions. J Med Chem 43:2789-2796.

Ridderström M, Zamora I, Fjellström O and Andersson TB (2001) Analysis of

Selective Regions in the Active Sites of Human Cytochromes P450, 2C8, 2C9,

2C18, and 2C19 Homology Models Using GRID/CPCA. J Med Chem 44:

4072-4081.


at ASPE



ownloaded from


JPET#54999

28

Segel IH (1975) Enzyme Kinetics, Behavior and Analysis of Rapid Equilibrium and

Steady-State Enzyme Systems. John Wiley & Sons, Inc., New York.

Shader RI, Granda BW, von Moltke LL, Giancarlo GM and Greenblatt DJ (1999)

Inhibition of human cytochrome P450 isoforms in vitro by zafirlukast.

Biopharm Drug Dispos 20:385-388.

Shou M, Grogan J, Mancewicz JA, Krausz KW, Gonzalez FJ, Gelboin HV and

Korzekwa KR (1994) Activation of CYP3A4: evidence for the simultaneous

binding of two substrates in a cytochrome P450 active site. Biochemistry

33:6450-6455.

Smellie A, Kahn SD and Teig SL (1995a) Analysis of Conformational Coverage. 1.

Validation and Estimation of Coverage. J Chem Inf Comput Sci 35:285-294.

Smellie A, Kahn SD and Teig SL (1995b) Analysis of Conformational Coverage. 2.

Applications of Conformational Models. J Chem Inf Comput Sci 35:295-304.

Smellie A, Teig SL and Towbin P (1995c) Poling: Promoting Conformational

Variation. J Comp Chem 16:171-187.

Suttle AB, Vargo DL, Wilkinson LA and Birmingham BK (1997) Effect of zafirlukast

on the pharmacokinetics of R- and S-warfarin in healthy men. Clin Pharmacol

Ther 61:186.

Takahashi H, Kashima T, Nomoto S, Iwade K, Tainaka H, Shimizu T, Nomizo Y,

Muramoto N, Kimura S and Echizen H (1998) Comparisons between in-vitro

and in-vivo metabolism of (S)-warfarin: catalytic activities of cDNA-

expressed CYP2C9, its Leu359 variant and their mixture versus unbound

clearance in patients with the corresponding CYP2C9 genotypes.

Pharmacogenetics 8:365-373.

Todhunter JA (1979) Reversible enzyme inhibition. Methods Enzymol 63:383-411.


at ASPE



ownloaded from


JPET#54999

29

Tracy JW and Webster LT (1996) Drugs Used in the ChemoTherapy of

Helminthiasis., in Goodman & Gilman's The Pharmacological Basis of

Therapeutics. (Hardman JG, Goodman Gilman A and Limbird LE eds) pp

1009-1026, McGraw-Hill, New York.

Ueng YF, Kuwabara T, Chun YJ and Guengerich FP (1997) Cooperativity in

oxidations catalyzed by cytochrome P450 3A4. Biochemistry 36:370-381.

Wang RW, Newton DJ, Liu N, Atkins WM and Lu AY (2000) Human cytochrome P-

450 3A4: in vitro drug-drug interaction patterns are substrate-dependent. Drug

Metab Dispos 28:360-366.

Vargo DL, Yeh C, Lasseter K, Shambien EC and Birmingham BK (1997) Effect of

zafirlukast on phrothrombin time and area under the curve of warfarin. J Clin

Pharmacol 37:870.

Venkatakrishnan K, Greenblatt DJ, von Moltke LL and Shader RI (1998) Alprazolam

is another substrate for human cytochrome P450-3A isoforms. J Clin

Psychopharmacol 18:256.


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Footnotes

Reprint requests can be sent to Ann-Charlotte Egnell, AstraZeneca R&D, EST Comp

Chem, S-431 83 Mölndal, Sweden.


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Figure legends

Fig 1

CYP2C9 mediated formation of HFC (7-hydroxy-4-trifluoromethyl-coumarin) from

MFC (7-methoxy-4-trifluoromethyl-coumarin) used as marker reaction for the high

throughput effector screening.

Fig 2

Representative plots of experimental data for the four most potent heteroactivators

(v150% ≤ 1µM) of CYP2C9 mediated HFC formation from MFC as identified by the

high throughput screen.

Fig 3

A. CYP2C9 heteroactivation pharmacophore. Green sphere represents hydrogen bond

acceptor, orange sphere represents aromatic ring and blue spheres represent

hydrophobic features. Angles a = 62.2°, b=24.4°, c=67.7°, d=24.0°, e=15.9°.

Distances u=11.6Å, v=11.8Å, w=3.0Å, x=10.9Å, y=10.3Å, z=4.8Å. B. Coordinates of

the pharmacophore. Arrows indicate that coordinates are for both vector points.

Fig 4

CYP2C9 heteroactivation pharmacophore superimposed with the 5 most potent

heteroactivators in the training set (table 1). Compounds were fit to the

pharmacophore using the “Best Fit” feature of the software, allowing flexibility of

individual conformers within 20 kcal/mol. Compound 1 (amiodarone, v150% 0.04µM)

is shown in red, compound 2 (niclosamide, v150% 0.09µM) in cyan, compound 3 (v150%


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0.5µM) in black, compound 4 (liothyronine, v150% 0.5µM) in blue and compound 6

(meclofenemate, v150% 1.0µM) in green.

Fig 5

A. Ability of the CYP2C9 heteroactivation pharmacophore to classify heteroactivators

within affinity categories (internal predictivity). (v150% <1µM, n= 4; v150% 1-10µM,

n=32; v150% 10-150µM, n=28; n=number of compounds.)

B. Ability of the CYP2C9 heteroactivation pharmacophore to classify inhibitors and

non-actives within affinity categories, suggesting that heteroactivators and inhibitors

share important features for CYP2C9 active site binding. (IC50 <1µM, n=52, IC50 1-

10µM, n=32; IC50 >10µM, n=124; non-actives, n=49; n = number of compounds.)

Fig 6

The CYP2C9 heteroactivator pharmacophore fit to coumarins (Ki <1µM) from the

CYP2C9 inhibitor training set reported by Jones et al., 1996b. Fitting of compounds

was performed using the “Best Fit” feature, allowing individual conformers to flex

within a 20 kcal/mol energy range.


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Table 1

Heteroactivators of CYP2C9 mediated MFC metabolism and their respective v150%

values (concentration giving 150% of control reaction velocity).

compound trivial name

(if applicable)

structure v150%

(µM)

1 amiodarone O

O

I

I

O

N

0.04a

2 niclosamide N

+

N

O

O

O

Cl

O

Cl

0.09a

3 N

N

0.5

4 liothyronine (T3)

O

O

I I

I

N

O

O

0.5

5 perylene

1.0b

6 meclofenemate

N

O

Cl

OCl

1.0


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7 zafirlukast S

N

N

O

O

N

O

O

O

O

1.2

8 estropipate

SO

O

O

O

O

H

H H

1.5

9 dichlorphenamide

SS

O

O

O

O

NN

Cl

Cl

2.5

10

N

N+

3

11 hydroflumethiazide

S

S

N

OO

N

O

O

N

F

F

F

4.6

12 mefenamic acid N

O O

5

13 warfarin

O O

OO

5.5

14 in-house 9

15

N

Cl

O

O

10

16 diflunisal O

F

O O

F

14


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17 in-house 14

18 N

N

14

19 in-house 20

20 in-house 20

21 in-house 20

22 O

O

I

I

OO

20

23 in-house 25

24 in-house 30

25 in-house 35

26 flurbiprofene O

F

O

35

27

OO

40

28

N

ON

42

29 alprazolam N

N NN

Cl

42

30 in-house 60

31 O

Br

O

75

32 nifedipine N

N+

O

OO O

OO

125


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33 hydrochlorotiazide SS

N

OO

N

O

O

N

Cl

125

34 aminosalicylic acid O

OO

N

125

35 tolmetin sodium N

O

O

O

125

36 in-house 130a

37 ketoprofene

O

O

O

150a

a Extrapolation beyond the actual concentration range used (0.17µM – 125µM).

b Not included in the final CYP2C9 heteroactivation pharmacophore model training set.


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Table 2

Details of highest scored pharmacophores generated for CYP2C9 heteraoctivators

when excluding and including compound 5, and when permuting v150% values. A =

hydrogen bond acceptor, H = hydrophobe, R = aromatic ring. Hypo 1= highest scored

pharmacophore, Hypo 2 = next highest scored pharmacophore.

Including compound 5 Features Total cost Corr coefficient

r (r2)

Cost difference to

null hypothesis

Hypo 1 AHHH 203.2 0.70 (0.49) 16.0

Hypo 2 AHHR 209.0 0.64 (0.41) 10.2

Excluding compound 5 Features Total cost Corr coefficient

r (r2)

Cost difference to

null hypothesis

Hypo 1 AHHR 197.0 0.71 (0.50) 16.1

Excluding compound 5

and

permuting v150%

values.

Features Total cost Corr coefficient

r (r2)

Cost difference to

null hypothesis

Hypo 1 AAR 206.7 0.52 (0.27) 1.5

Hypo 2 AAR 207.9 0.50 (0.25) 0.3


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Table 3

Highest scored pharmacophores generated for external validation. See table 4 for

which compunds were left out in each model. A = hydrogen bond acceptor, H =

hydrophobe, R = aromatic ring.


r (r2)

Cost difference to

null hypothesis

Model 1

AHHR 174.1 0.73 (0.53) 12.4

Model 2 Features Total cost Corr coefficient

r (r2)

Cost difference to

null hypothesis

AHHR 168.9 0.73 (0.53) 8.8


r (r2)

Cost difference to

null hypothesis

Model 3:

AHHHR 168.3 0.81 (0.66) 22


r (r2)

Cost difference to

null hypothesis

Model 4:

AHR 171.8 0.70 (0.49) 11


r (r2)

Cost difference to

null hypothesis

Model 5:

AHHR 169.9 0.75 (0.56) 14.9


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Table 4

Results of external validation of CYP2C9 heteroactivation pharmacophore by five

times leaving out 4 random compounds with large differences in potency.

Compounds excludeda Observed v150% (µM) Predicted v150% (µM) Log residual Internally predicted

v150% with original

pharmacophore (µM)

Model 1:

4 0.5 2.3 0.66 0.66

7 1.2 0.39 -0.49 0.25

26 35 22-23b -0.20 to -0.18b 14

32 125 0.56 -2.35 20

Model 2:

2 0.09 7.5 1.92 0.44

17 14 19 0.13 15

24 30 22 -0.13 14

36 130 19 -0.84 15

Model 3:

6 1.0 25 1.40 14

13 5.5 26-27b 0.67 to 0.69b 20-24b

25 35 50 0.15 76

35 125 4.3 -1.46 15

Model 4:

1 0.04 1.9 1.68 0.052

22 20 5.5 -0.56 15

29 42 21 -0.3 15

34 125 380 0.48 85

Model 5:

3 0.5 26 1.72 17

19 20 17 -0.07 15

27 40 17, 18, 20 µMb -0.37 to -0.30b 15-16b

33 125 21 -0.77 37

a Numbers are corresponding to table 1.

b Depending on isomer


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O O

F FF

O

CH3

O O

F FF

OH

recombinant CYP2C9

7-hydroxy-4-trifluoromethyl-coumarin (HFC)7-methoxy-4-trifluoromethyl-coumarin (MFC)

Fig 1

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Fig 2

effector concentration (µM)

0.01 0.1 1 10 100

% c

han

ge

in r

eac

tion

vel

ocit

y

0

200

400

600

800

1000

1200

1400

1600

amiodarone

niclosamide

liothyronine

compound 3

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Fig 3

Features

5.76

4.43

-1.50

1.25

6.50

4.05

2.950.04-2.021.95Z

3.48-7.04-5.503.90Y

-1.97-1.32-2.862.72X

AHHHBAcoordinates

Features

5.76

4.43

-1.50

1.25

6.50

4.05

2.950.04-2.021.95Z

3.48-7.04-5.503.90Y

-1.97-1.32-2.862.72X

AHHHBAcoordinates

B

u v

z

y

x

a

c

w

b

d

e

u v

z

y

x

a

c

w

b

d

e

A


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Fig 5

A

B

observed

v 15

0%

<1µM

v 15

0% 1

-10µ

M

v 15

0% >

10µM

% p

redi

cted

with

in e

ach

pote

ncy

cat

ego

ry

0

20

40

60

80

100

<1µM

predicted v 150%

1-10µM

>10µM

observed

IC50

<1µ

M

IC50

1-1

0µM

IC50

>10

µM

no

n-a

ctiv

es% p

redi

cted

wit

hin

eac

h po

ten

cy c

ateg

ory

0

20

40

60

80

100

<1µM

predicted v 150%

1-10µM

>10µM

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Fig 6 JPET #54999


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