Generation and evaluation of a CYP2C9...
Transcript of 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.
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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
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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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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.
Features Total cost Corr coefficient
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
Features Total cost Corr coefficient
r (r2)
Cost difference to
null hypothesis
Model 3:
AHHHR 168.3 0.81 (0.66) 22
Features Total cost Corr coefficient
r (r2)
Cost difference to
null hypothesis
Model 4:
AHR 171.8 0.70 (0.49) 11
Features Total cost Corr coefficient
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 4
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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
JPET #54999
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Fig 6 JPET #54999
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