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Vol. 59, Issue 4, 909-919, April 2001
Department of Drug Metabolism and Pharmacokinetics & Bioanalytical Chemistry, AstraZeneca R&D, Mölndal, Sweden (L.A., I.Z., M.R., T.B.A., C.M.M.); and Department of Organic Pharmaceutical Chemistry, Biomedical Center, Uppsala University, Uppsala, Sweden (L.A., A.K.)
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Abstract |
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 Top
 Abstract
 Introduction
Experimental Procedures
Results and Discussion
References
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This study describes the generation of a three-dimensional quantitative structure activity relationship (3D-QSAR) model for 29 structurally diverse, competitive CYP2C9 inhibitors defined experimentally from an initial data set of 73 compounds. In parallel, a homology model for CYP2C9 using the rabbit CYP2C5 coordinates was built. For molecules with a known interaction mode with CYP2C9, this homology model, in combination with the docking program GOLD, was used to select conformers to use in the 3D-QSAR analysis. The remaining molecules were docked, and the GRID interaction energies for all conformers proposed by GOLD were calculated. This was followed by a principal component analysis (PCA) of the GRID energies for all conformers of all compounds. Based on the similarity in the PCA plot to the inhibitors with a known interaction mode, the conformer to be used in the 3D-QSAR analysis was selected. The compounds were randomly divided into two groups, the training data set (n = 21) to build the model and the external validation set (n = 8). The PLS (partial least-squares) analysis of the interaction energies against the Ki values generated a model with r2 = 0.947 and a cross-validation of q2 = 0.730. The model was able to predict the entire external data set within 0.5 log units of the experimental Ki values. The amino acids in the active site showed complementary features to the grid interaction energies in the 3D-QSAR model and were also in agreement with mutagenesis studies.
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Introduction |
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Top
Abstract
Introduction
Experimental Procedures
Results and Discussion
References
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An understanding of the drug-metabolizing enzymes' role in the clearance of compounds and of drug-drug interactions caused by coadministered medications is a key issue in both the drug discovery process and in the use of therapeutics. Advances in analytical and in vitro metabolism technologies have made it possible to determine metabolic stability, identify metabolites, and specify enzymes involved in compound biotransformation. Genomic research will lead to an increased number of therapeutic targets, and combinatorial chemistry has increased the number and chemical diversity of compounds that can be tested against these targets. High-throughput screening approaches in metabolism research, therefore, are being developed to meet this challenge.
The use of computational techniques to predict metabolic properties has
experienced a recent resurgence as attested by the increased number of
publications (Ekins et al., 2000
; Rao et al., 2000; Williams et al.,
2000). This interest is based on the idea that such approaches will add
chemical knowledge to the empirical data obtained with in vitro
systems. The computer-based models could enable the prediction of
substrates or inhibitors of specific enzymes, ensuring that only those
with desirable properties are synthesized. During lead optimization,
predictions of the sites of oxidation and chemical features that cause
metabolic instability or increased inhibitory potency would assist
medicinal chemists in making necessary chemical modifications. Efforts
to develop such predictive models have intensified on cytochromes P450
(P450s) because they are an important family of
drug-metabolizing enzymes.
Current models range from descriptive structure activity relationship
studies (Smith et al., 1997a,b; Lewis et al., 1999), to
three-dimensional quantitative structure activity relationship (3D-QSAR) models. 3D-QSAR models for CYP2B6 (Ekins et al., 1999c) and
CYP3A4 (Ekins et al., 1999d) substrates have been published. For P450
inhibitors, models have been proposed for CYP2D6 (Strobl et al., 1993;
Ekins et al., 1999b), CYP1A2 (Moon et al., 2000), CYP3A4 (Ekins et al.,
1999a) and CYP2C9 (Jones et al., 1996b; Ekins et al., 2000; Rao
et al., 2000). Other models use electronic parameters to predict likely
rates and sites of oxidation (Korzekwa et al., 1996), two-dimensional
descriptors to predict likely substrates and sites of oxidation (Lewis
et al., 1999), 3D descriptors to identify substrates and sites of
oxidation [CYP2D6 (de Groot et al., 1999a,b), CYP2C9 (Mancy et al.,
1995; Jones et al., 1996a)] and NMR studies for CYP2C9
substrates (Poli-Scaife et al., 1997). The challenges to model P450
substrates and inhibitors include the uncertainty in human P450 active
site structure, the large chemical diversity of compounds that interact
with each of the P450s, and the possible activation of some P450s
because of homotropic and heterotropic cooperativity (Domanski et al.
1999; Hutzler and Tracy, 2000).
With no human P450 yet crystallized, homology modeling has been
performed using coordinates of bacterial P450 crystal structures (CYP101, -102, -107, and -108) (Peterson and Graham, 1998). There is
only up to 19% amino acid sequence identity of these bacterial P450s
with the human P450s, which makes homology modeling difficult (Ridderström et al., 2000). Recently, a mammalian membrane-bound P450 (rabbit CYP2C5) has been crystallized (Williams et al., 2000), which improves our ability to model human P450s because the sequence identity between human CYP2C9 and rabbit CYP2C5 is 77%. Efforts to
generate pharmacophore models without taking into account the active
site geometry and chemistry can have a number of difficulties associated with the selection of the most likely active conformers and
identifying the different modes ligands can bind in the active site.
Most receptor-ligand modeling in drug design is performed using
compounds with a common core structure (Kubinyi, 1997a,b). P450s, on
the other hand, have the capacity to interact with compounds with
extremely diverse chemical features, some of which are specific for
some P450 isoforms (Lewis et al. 1999) but not easily discernible for
others. Substrates and inhibitors interact with P450s in a stereospecific manner, and most of the compounds are also very flexible
(with at least three rotatable bonds), which increases the number of
possible conformers from which one has to select the one likely to
interact with the P450. These features make it difficult to generate
models based on alignment rules such as comparative molecular field
analysis (CoMFA) as previous studies have done (Jones et al.,
1996b; Rao et al., 2000). To overcome this problem, efforts are
being made to use alignment-independent techniques with software like
ALMOND, based on GRid-INdependent Descriptors (Pastor et al., 2000) and
CATALYST (Molecular Simulations, San Diego, CA).
When identifying the site of metabolism, it must be noted that the
metabolism of a compound by a single P450 can occur at many sites and
can also be done by other P450s and at different rates. Identification
of the site of metabolism, therefore, is noninformative about the
importance of that route of biotransformation. Use of the apparent
Km value, which reflects affinity but not rate, to infer metabolic stability can also be misleading. The Vmax/Km ratio,
which estimates clearance and significance of pathway, might be a
better parameter to model against. With respect to inhibitors, their
interaction can be through reversible inhibition (competitive,
noncompetitive, uncompetitive, or mixed type) or irreversible, such as
metabolite complexion or reaction with the heme iron (Murray and Reidy,
1990). The quality of the data used in modeling is also crucial because
there is great variability in kinetic constants for the same compounds
between laboratories and, correspondingly, when different sources of
enzyme such as recombinant P450s, human liver microsomes, hepatocytes,
and liver slices are used (Boobis et al., 1998; Iwata et al., 1998;
Thummel and Wilkinson, 1998; Ekins et al., 1999d). Use of such
variable literature data could have contributed to previous modeling
efforts that have an error in predicting Ki
or Km values of 1 logarithmic unit (Ekins
et al., 1999a,b). Such deviation causes significant difference in the
prediction of the biologic effects of a test compound (e.g., a
Ki value of 100 µM when the actual one is
10 µM) and are therefore of limited value.
CYP2C9 is one of the most important drug-metabolizing P450s. It is
responsible for the metabolism of up to 15% of currently used
therapeutics. Among them are low therapeutic index drugs such as
warfarin, for which drug-drug interactions can involve enzyme
inhibition (Miners and Birkett, 1998). CYP2C9 also shares high sequence
identity (77%) with the recently crystallized mammalian CYP2C5 and
therefore a greater chance to generate a reliable protein homology
model. In this study, we attempt to generate a 3D-QSAR model for CYP2C9
inhibitors covering a large chemical space, taking into account
important parameters such as the mechanism of inhibition and the
stereochemistry of the inhibitors.
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Experimental Procedures |
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Top
Abstract
Introduction
Experimental Procedures
Results and Discussion
References
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Materials
Chemicals.
CYP2C9 substrates and their
metabolites: Diclofenac and 7-methoxy-4-trifluoromethylcoumarin
(MFC) were purchased from SIGMA (St. Louis, MO) and 4'-OH-diclofenac,
7-OH-4-trifluoromethylcoumarin, and 7-OH-warfarin were purchased from
GenTest (Woburn, MA). NADPH was bought from Sigma Aldrich Research
(Stockholm, Sweden). Recombinant human CYP2C9 expressed in yeast was
produced in-house (AstraZeneca R & D, Mölndal, Sweden).
CYP2C9 inhibitors: Sulfaphenazole was purchased from
Ultrafine (Manchester, UK). Dicoumarol, kaempferol, phenytoin,
phenylbutazone, progesterone, pyrimethamine, quercetin, quinine, and
thiabendazole were purchased from Sigma.
(3R,5S)-Fluvastatin sodium and
(3S,5R)-fluvastatin sodium [racemate from Sandoz
(Summit, NJ), enantiomers separated at AstraZeneca R & D];
fluvoxamine was from Chemtronica (Ballwin, MO); (R)-warfarin
and (S)-warfarin were from Ubichem (Eastleigh, UK);
zafirlukast was from AstraZeneca R & D; (R)-omeprazole,
(R)-pantoprazole, (S)-pantoprazole,
(R)-rabeprazole, (S)-rabeprazole, omeprazole
sulfone, spiro (fluoren-9,4-imidazolidine)-2',5'-dione (SFID), A-44338,
and D-62126 were from AstraZeneca R & D; and (
)-pyranocoumarin,
(+)-pyranocoumarin, (+)-miconazole, and () miconazole were from Sigma
(enantiomers separated at AstraZeneca R & D, Sweden)
Equipment, Software, and Databases.
MFC dealkylation was
assessed by a fluorometric method (Bapiro et al., 2001) and diclofenac
hydroxylation by a HPLC method (Masimirembwa et al., 1999). Enzyme
kinetic analysis was done using GraFit 4.0.12 (Erithacus Software
Limited, Middlesex, UK) and SIMFIT 5.3 (Bardsley et al., 1995). Protein
homology and QSAR modeling were done on Silicon Graphics Octane and
O2 workstations, respectively (Silicon Graphics
Inc., Mountain View, CA). Insight II 98.0 (Molecular Simulations Inc.)
was used in the protein homology modeling. The software utilized in the
computational analysis were: GOLD 1.1 (Dr Gareth Jones, University of
Sheffield, U.K.), GRID (Molecular Discovery Ltd., University of Oxford,
U.K.), GOLPE VOLSURF (MIA, Perugia, Italy), SYBYL 6.5.3, and CONCORD
(Tripos Associates Inc., St. Louis, MO). Chemical structures were
imported from ISIS-BASE database or drawn in ISIS-Draw (MDL information Systems Inc., San Leandro, CA).
Methods
Overview. The present work was initiated by performing inhibition studies to establish a data set of structurally diverse competitive CYP2C9 inhibitors. Simultaneously, a protein homology model was built based on the coordinates of the crystallized rabbit CYP2C5 structure. Mutagenesis experiments were performed guided by the homology model to enable validation of the final enzyme model. The homology model was then used for active site docking of compounds with known orientation toward the heme and the selected conformers were used as templates in a similarity analysis. The remaining compounds were then docked into the homology model and the resulting conformers were evaluated in the similarity analysis (based on grid interaction energies) to choose conformers similar to the templates. This approach enabled a conformer selection that constrained the conformational space to the active site of the homology model. The grid interaction fields for the selected conformers were statistically analyzed together with the experimental data to derive a 3D QSAR model. Data not incorporated in the model (external test set) was then used for external prediction to validate the model.
Inhibition Studies.
Drug-like compounds known from the
literature or suspected to interact with CYP2C9, as substrates or
inhibitors, were compiled. The initial screen of 73 compounds as
potential inhibitors of CYP2C9 was performed using MFC dealkylation
(Fig. 1), which is a fast screening assay
with good correlation with conventional HPLC assays. (Bapiro et al.,
2001). The inhibition assays were done with the substrate, MFC, at
Km concentration (50 µM). The inhibitors
were serially diluted from 200 to 0.009 µM in the 96-microtiter well
plate assay design with sulfaphenazole used as the positive control
inhibitor. This experimental set-up allowed for the use of the
relationship Ki = IC50 / 2 to estimate inhibitory potency assuming
competitive inhibition at [S] = Km. Of
the 73 compounds, 40 compounds with a defined stereochemistry showed
inhibitory effect and were investigated further.
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), competitive inhibitors (Jones et al., 1996b) and
compounds believed to be substrates, inhibitors, or mentioned in the
context of CYP2C9. In the principal component analysis (PCA) plot (Fig.
2), compounds used in this study
(squares) are well scattered over the chemical space.
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Protein Homology Modeling.
The crystal structure of CYP2C5
was used as template in the modeling of CYP2C9 (Williams et al., 2000).
The sequence identity between CYP2C9 and CYP2C5 is 77% (similarity,
83%) which makes CYP2C5 a good template for the modeling of CYP2C9.
Table 3 shows the amino acid alignment of
the 2C5_3LVN and CYP2C9 because the alignment is done to the mutated
protein (see Williams et al., 2000).
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Mutagenesis Experiments.
The mutants were generated as
described by Ridderström et al. (2000). The primers for
site-directed mutagenesis used in the mutagenic PCR were (forward
primers): Leu362Ala,
5'-ATACATTGACCTTGCCCCCACCAGCCTGCC-3' and for
Leu362Ile, 5'-GATACATTGACCTTATCCCCACCAGCCTG-3'. The nucleotides that introduced changes in the cDNA sequence are bold
and underlined. The reverse primers were complementary to the forward primers.
). The assays for (S)-warfarin hydroxylation,
diclofenac hydroxylation, and the HPLC-UV analysis were carried out as
described previously (Jung et al., 1998; Masimirembwa et al., 1999).
Active Site Docking and Conformer Selection.
Because the
crystal structure of CYP2C5 on which we base our CYP2C9 homology model
was crystallized with water hexacoordinated to the heme, we
carried out the docking of template molecules with the water still
resident in the active site. Because `nonligand' or `ligand-type'
compounds displace water when they interact with P450s (Klaassen,
1996), we are therefore modeling for the stage just before the
displacement of water as the sixth heme ligand. The distances of docked
compounds' site of interaction to the heme are therefore likely to be
different from when the water is displaced, a process that is probably
accompanied by conformational changes. The possibility of such
conformational changes is to some extent supported by the fact that we
could not dock tienilic acid or diclofenac into the active site of our
CYP2C9 homology model active site using the distances of these
molecules to the heme as measured by NMR (Poli-Scaife et al., 1997).
; He et al., 1999; Payne et al., 1999). Of the 10 best
solutions found by GOLD for each of these inhibitory substrates, the
conformers with the hydroxylation site in close proximity to the heme
were chosen (Fig. 2). The distances between the water and the
7-position of (S)-warfarin, progesterone, and phenytoin were
2.79 Å, 3.62 Å, and 3.02 Å, respectively. For the ligand-type
inhibitor sulfaphenazole, solutions with the ligating nitrogen closest
to the heme were chosen. In this study, we chose the nitrogen in the
pyrazole ring at a distance of 3.98 Å based on a better alignment with
the other template molecules. There are, however, two studies giving
different suggestions as to which nitrogen of sulfaphenazole ligates
with the heme. Mancy et al. (1995) postulate that it is the terminal
anilinic nitrogen whereas Rao et al. (2000) proposed the pyrazole ring nitrogen.
Derivation of a 3D-QSAR Model.
The grid interaction field
(Goodford, 1985) for all 155 GOLD solutions for all compounds docked
into the active site were calculated with the DRY (for hydrophobic
interactions) and the OH (for polar interactions) probe in a grid box
extending 4 Å beyond all molecules. The grid interaction energies were
calculated as defined in the program and imported into GOLPE
[Generating Optimal Linear Partial Least-Squares Analysis (PLS)
Estimations] (Massimo et al., 1993), a chemometric toolbox for 3D-QSAR
enabling statistical analysis such as PCA and PLS analysis (Wold et
al., 1983, 1987) for data sets with thousands of variables. The
x-matrix was pretreated by deleting the points with a standard
deviation lower than 0.02 kcal/mol and with an energy of interaction
lower than 0.02 kcal/mol. A PCA analysis was performed for all docked conformers and a clustering of one or few conformers of each compounds close to the selected template ones for (S)-warfarin,
phenytoin, progesterone, and sulfaphenazole was identified in the score
plot by a similarity analysis. The distances in the four principal component score plot of each GOLD conformer solution toward the selected template molecules were calculated and used as the similarity index. The one with the shortest distance to the templates was chosen
as the "active conformer" for each compound and was used for
further analysis.
) were
applied to the PLS loading space. This is a statistical method that
enables the selection of variables that are the most spread and the
least complementary to each other to increase the signal-to-noise
ratio. This process was run using the two first components in the PLS
model in an iterative manner and reducing 50% of the variables in each
iteration. Two D-optimal design iterations were used, yielding
1900 variables. Second, a smart region definition selection (Pastor et
al., 2000) was made with a critical distance set to 2.0 grid units (2 Å) and the collapsing distance to 25.0 grid units (25 Å). The smart
region definition is a mathematical technique that groups variables
that are correlated to each other and close in the space. The third
step was a fractional factorial design variable selection. The
variables were selected to increase the predictive power of the
model and the technique used fractional factorial design matrix and
Student t test cut-off values (Massimo et al., 1993). The
resulting model had one principal component and a correlation
coefficient (r2) of 0.947 and a predictive power
(q2) of 0.730. The internal cross-validation of
the model was done using the leave one out approach, in which one
object at a time is excluded from the model and predicted, the
procedure is repeated until all objects have been held out once. A more
rigorous measurement of internal predictive power was also performed by
leaving out 20% and 50% of the compounds, producing
q2 values of 0.672 and 0.497, respectively. The
external prediction capabilities of the model were checked by the
eight-compound validation set, giving a maximum error of 0.36 log units.
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Results and Discussion |
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Top
Abstract
Introduction
Experimental Procedures
Results and Discussion
References
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This study describes the generation of a 3D-QSAR model for CYP2C9 inhibitors using a combination of QSAR and protein homology modeling. The model captures the complexity of enzyme-inhibitor interactions by taking into account the mechanism of inhibition and the issue of active conformer selection. The good quality (r2 = 0.947, q2 = 0.730) and capacity to predict external data sets within 0.5 logarithmic units of the experimental value attest to the validity of our 3D-QSAR modeling approach.
CYP2C9 Inhibitor Data Set. Starting with a set of 73 compounds and ending up with 29 compounds suitable for use in the modeling work means that to get more compounds in the data set, one needs to screen a large number of compounds. Of the 40 compounds that inhibited CYP2C9, 29 were competitive inhibitors and 11 were either uncompetitive or of unclear mechanism of inhibition. The model developed for competitive inhibitors presented here can only be used for inhibitors with defined types of inhibition. This is valuable information in both modeling work and application of models to predict biological effect. The use of literature inhibition data of unspecified mechanism or Ki values approximated from IC50 determinations should be practiced with caution.
This modeling work describes a procedure to find an alignment although the compounds might have different binding modes in the CYP2C9 active site despite an identical mechanism of inhibition (competitive inhibition). In this study (as well as that of Rao et al., 2000) the
stereochemistry of the molecules was taken into account by only
including nonchiral compounds and enantiomers of chiral compounds.
The inhibitors included in our model include very potent inhibitors
(Ki <1 µM; n = 1),
potent inhibitors (Ki = 1-10 µM;
n = 6), medium potency inhibitors
(Ki = 10-20 µM (n = 4),
and poor inhibitors Ki > 20 µM
(n = 10) when classified according to their potential
to cause clinically significant inhibitory effects. It would have been
ideal to have at least 10 compounds in each category, which also
exhibit wide intracategory spread of values. To obtain such a data set,
a very large number of compounds need to be screened. This will
probably be possible in the near future using high-throughput screening
procedures. Data used in this study was generated using uniform
conditions to avoid the sometimes more than 10-fold variations in
Ki values for the same compounds generated
in different laboratories or using different sources of enzyme (Thummel
and Wilkinson, 1998).
CYP2C9 Homology Model.
A CYP2C9 homology model was constructed
using coordinates of the recently crystallized mammalian CYP2C5
(Williams et al., 2000). The root mean square distance in the
polypeptide backbone of the CYP2C9 model was 0.18 Å in relation to the
CYP2C5 crystal structure. SYBYL ProTable was used to calculate the
-
angles for the Ramachandran plot. Most of the - angles
(83%) were located in the core region of the plot. The MatchMaker
average energy score for the model was 0.09 kT (for the CYP2C5
template, 0.13 kT). Comparison of the MatchMaker energy graphs for
the model and the template did not reveal any poorly modeled regions.
Analysis of the dihedral angles 
showed that two angles were larger
than 5 S.D. from the reference value, where one of these angles was correspondingly high in the CYP2C5 template. Because CYP2C9 and CYP2C5
share 77% amino acid identity and 83% amino acid similarity, this
model is a substantial improvement compared with the model of CYP2C9
based on the bacterial CYP102 (Ridderström et al., 2000).
Mutagenesis Data. The rationale for making Leu362Ile and Leu362Ala mutants was 2-fold: in the protein homology model, leucine points into the active site, and in the same position, the isoform CYP2C19, which has different substrate specificity, has an isoleucine. These mutations resulted in increased Km and reduced Vmax values of CYP2C9 toward diclofenac (Table 5). The alanine mutant had a Km value eight times higher than that of the wild-type enzyme. The isoleucine and alanine mutations also resulted in reduced (S)-warfarin 7-hydroxylation. The Leu362Ile and Leu362Ala mutations had 90 and 20%, respectively, of the wild-type activity. The effects of the mutations on CYP2C9 function are in line with the QSAR model of CYP2C9-inhibitor interactions where hydrophobic interactions at that site contribute to more potent inhibitory effects (see section below).
3D-QSAR Model.
The 3D-QSAR model should have a bearing on
appropriate amino acids in the active site of the CYP2C9 model to make
biochemical sense. The examination of the PLS coefficients of the grid
interaction fields, proposed for the model, in comparison with the
active site of the homology model showed credible interaction patterns. The amino acids directed into the active site have representatives from
all substrate recognition sites SRS 1 to 6 except for SRS 3 (Gotoh,
1992; Williams et al., 2000). Of the 17 amino acids present in the
active site, two were polar (Thr-301 and Thr-304), two were acidic
(Asp-293 and Glu-300), and the rest were hydrophobic (Table
4). The hydrophobic grid interactions of
the PLS coefficients (PC1) are shown in Fig. 4. In the
SRS-1 region (B-C loop), Phe-114 shows a considerable
contribution consistent with mutagenesis data (Haining et al., 1999)
and could provide 
- stacking interactions with ligands. It
contributes to a hydrophobic pocket for the most potent inhibitor,
sulfaphenazole, together with Leu-366 (SRS-5) and Phe-476
(SRS-6). Leu-201 and Ile-205 (SRS-2) in the
F-helix constitute another hydrophobic pocket for interaction with
sulfaphenazole. The backbone of Ile-205 that is in close proximity
probably forms a hydrogen bond to the anilino nitrogen of
sulfaphenazole. (S)-Warfarin, which has a higher
Ki value, does not show this interaction.
The amino acid Ala-297 in region SRS-4 forms a third
hydrophobic pocket together with Val-113 (SRS-1) and
Leu-366. A direct hydrophobic interaction is suggested from Leu-362
(SRS-5) toward both (S)-Warfarin and
sulfaphenazole and is strongly supported by the mutagenesis data (Table
4). The Ala-477 in the SRS-6 region (
2) also shows very
strong hydrophobic interactions and could be considered a mutagenesis
target for SAR analysis. Leu-102 builds a fourth hydrophobic pocket
together with Val-292 in the I helix. The polar grid interactions
determined by the OH probe (Fig. 5) showed
directionally dependent favorable interactions with Asp-293 in SRS-4
and for the more potent inhibitors also Asp-204 in the
F-helix (SRS-2). An unexpected interaction between a methyl group and
the OH probe was observed. The amino acid associated with this
interaction was shown to be threonine (Thr 301), which could explain
the contradictory finding, in that the interaction could be with the
oxygen of threonine due to amino acid conformational flexibility.
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) and the ligation of the nitrogen with the
heme (Mancy et al., 1995), will only be possible when there are crystal
structures of the CYP2C9 with and without inhibitors.
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and Rao et al. (2000) have published CYP2C9
inhibitor models based on CoMFA. These models were based on a data set
of coumarins and sulfaphenazole-like compounds and the researchers were
able to find alignment rules and generate models with good predictive
power. Compounds in our study are well spread over the chemical space,
whereas those used by Jones et al. are confined to two quadrants of the
first two components of the PCA space (Fig. 2). We could not find any
alignment rules for our training data set using DISCO. Compared with
the CoMFA models, our 3D-QSAR study provides a way to tackle the
alignment problems associated with chemically diverse data sets.
Although Rao et al. (2000) combined the pharmacophore and protein
modeling, in our study we had the advantage of having a closely related P450 crystal structure to model the active site of CYP2C9. Table 6
shows the results we obtained using our model to predict the Ki values of the molecules in Jones et al.
(1996b). Compounds with an experimental
Ki value below 1.0 µM were not included
because our model has an endpoint at 0.5. We were able to predict 19 of 24 compounds within 0.5 log units and none of the other five were above
1.0 log units (Table 6). It should be noted that these are literature data and that the worst predicted compound (number 10)
has a Ki value of 1.0 µM, which is near
the endpoint of our model where the information is scarce. Our results
are comparable with those obtained by Jones et al. (1996b).
Their mean S.D. error of cross validation was between 0.21 and 0.28 for the four different models for comparable compounds,
whereas our mean S.D. error of prediction is 0.34. This
further validates the robustness of our model and also the good quality
of data generated under uniform conditions in Jones et al.
(1996b) study.
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,b,c,d) used CATALYST to generate a model for
CYP2C9 inhibitors. The CATALYST is a nonalignment dependent program for
generating 3D pharmacophore models. The model generated did not have
very good predictive power (predictive to 1.0 log unit of actual
values). We believe that for models to be of any value, they should
predict to within at least 0.5 log units of the actual value, otherwise
one obtains values that are associated with significantly different
biologic effects. Although CATALYST probably represents a powerful
modeling tool to handle chemically diverse data sets, the use of highly
variable literature data might have contributed to its failure to
produce robust models. We think, therefore, that our approach
represents a significant contribution toward 3D-QSAR modeling efforts
in the P450 field and CYP2C9 in particular.
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Acknowledgments |
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We thank our colleagues Hanna Nelander, for separation of chiral compounds, and Marie Ahlström, for technical assistance.
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Footnotes |
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Received October 3, 2000; Accepted December 20, 2000
Send reprint requests to: Collen M. Masimirembwa, Department of DMPK & Bioanalytical Chemistry, AstraZeneca R & D Mölndal, S-431 83 Mölndal, Sweden. E-mail: collen.masimirembwa{at}astrazeneca.com
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Abbreviations |
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P450, cytochrome P450; 3D, three-dimensional; QSAR, quantitative structure activity relationship; CoMFA, comparative molecular field analysis; MFC, 7-methoxy-4-trifluoromethylcoumarin; HPLC, high-performance liquid chromatography; PCA, principal component analysis; GOLPE, generating optimal linear partial least-squares analysis estimations; PLS, partial least-squares analysis.
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References |
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Top
Abstract
Introduction
Experimental Procedures
Results and Discussion
References
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- stacking anchor site for warfarin binding.
Biochemistry
38:
3285-3292[Medline].This article has been cited by other articles:
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, Model compounds; 
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al. (1996b)
, Miners and Birkett (1998)
, external test set.