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					Research Article                                                                                       ISSN: 2321-2969
Received: 20 April 2013, Accepted: 29 April 2013                                        Int. J. Pharm. Biosci. Technol.

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      International Journal of Pharma Bioscience and Technology; Volume 1, Issue 1, May 2013, Pg 34-49

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                                              GYRASE INHIBITORS

                           Arundhati C. Lele, Nilesh R. Tawari, Mariam S. Degani*

      Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Sciences and Technology,
                  Institute of Chemical Technology, N. P. Marg, Matunga, Mumbai 400019, India

                                                 Corresponding Author*
                                         E-mail address-

Pharmacophore mapping studies were undertaken for a series of molecules belonging to indazole,
pyrazole and their congeners as inhibitors of S. Aureus DNA gyrase. A four point pharmacophore with
two aromatic rings (R), one lipophilic/hydrophobic group (H) and one positive ionic feature (P) as
pharmacophoric features was developed. The pharmacophore hypothesis yielded a statistically
significant 3D-QSAR model, with correlation coefficient of r2 = 0.681 for training set molecules. The
model so generated showed good predictive power with correlation coefficient for an external test set of
fifteen molecules. The geometry and features of the pharmacophore will be useful for ligand based
design of potential DNA gyrase inhibitors.

Key words: Pharmacophore, 3D-QSAR, DNA gyrase, S. Aureus.

                                                                 negative supercoils into bacterial DNA as well as
Antimicrobial resistance is a growing threat
                                                                 the decatenation and unknotting of DNA. [5]
worldwide. Staphylococcus aureus infections are a
major cause of morbidity and mortality in                        Ligand based drug design approaches like
community      and    hospital   settings.[1] The                pharmacophore mapping and QSAR can be used
emergence       of   methicillin   resistant   and               in drug discovery in several ways; the
vancomycin resistant strains of S. Aureus                        rationalization of activity trend in molecules under
represents an enormous threat to public health                   study, to predict the activity for new compounds,
with MRSA accounting for as many as 50% of                       for database search studies in search of new hits
infections.[2] S. Aureus strains that combine                    and to identify important features for activity. This
resistant and virulent genes have become a major                 paper describes the development of a robust
treatment problem in Europe and the U.S.[3]                      ligand based 3D- pharmacophore hypothesis
Hence there is an ever increasing need for the                   using Pharmacophore Alignment and Scoring
development of newer antibiotics to treat S. aureus              Engine (PHASE) for S. aureus DNA gyrase. Ligands
infections.                                                      selected     for    the     development     of    the
                                                                 pharmacophore model and the subsequent
DNA gyrase is a prokaryotic specific type ΙΙ
                                                                 generation of atom based 3D-QSAR were
topoisomerase and has attracted considerable
                                                                 indazoles and pyrrazoles analogues acting as DNA
attention from the pharmaceutical industry ever
                                                                 gyrase inhibitors and the alignment obtained from
since the discovery of Nalidixic acid (DNA gyrase
                                                                 the generated pharmacophoric points is used to
inhibitor) in the early 1960’s. Other well-known
                                                                 derive an atom based 3D-QSAR model. The
DNA gyrase inhibitors include quinolones,
                                                                 pharmacophore         thus     developed     imparts
coumarins and cyclothialidines.[4] Bacterial DNA
                                                                 information about important features for DNA
gyrase is involved in the vital processes of DNA
                                                                 gyrase inhibitors and the geometry has the ability
replication, transcription, and recombination and
                                                                 to mine 3D virtual databases of drug like
catalyzes the ATP-dependent introduction of

Lele et al                                                                                                   Pg. 34
                                                                                   Int. J. Pharm. Biosci. Technol.

                                                           Table 1(m), belonging to the family of indazole
molecules. Furthermore, the contours generated
                                                           and pyrrazole analogues reported as DNA gyrase
from QSAR studies highlight the structural features
                                                           inhibitors [6-10] were used for the pharmacophore
required for DNA gyrase inhibitory activity and
                                                           generation. The negative logarithm of the
are useful for further design of more potent
                                                           measured IC50 value as pIC50 was used in the 3D-
                                                           QSAR study. The complete set was divided into a
                                                           training set (48 compounds) and a test set (15
                                                           compounds) using randomization as well as
                                                           chemical        and       biological     diversity.
Biological data
A set of 63 compounds shown in Table 1(a) to

Table 1. Structures and the inhibitory concemtrations of the set of molecules utilized for generation
                                     of pharmacophore model.

Table 1(a) [6]

         No                                     Structure                                    MIC (µg/ml)

             1.                                                                                      64

             2.                                                                                    0.25


Table 1(b)
General structure 1.[6]

                  No.                                 R1                                  MIC (µg/ml)

                  3.                                                                            32


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                                          Int. J. Pharm. Biosci. Technol.

             6.                                        64

             7.                                        32

             8.                                        64

             9                                         32

             10.                                       32

             11.                                       16

             12.                                       16

             13.                     N                  4

Table 1(c)
General structure 2 [6]

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                                   Int. J. Pharm. Biosci. Technol.

       Structure No.         R1            MIC (µg/ml)

              14.                                64

              15.                                128

              16.                                32

              17.                                 4

Table 1(d) [7]

  Structure No.        Structure             MIC (µg/ml)

        18.                                           4

        19.                                           4

        20.                                           1

        21.                                           1

Lele et al                                                Pg. 37
                                                Int. J. Pharm. Biosci. Technol.

Table 1(e)
General structure 3 [7]

 Structure No.              R3                           MIC (µg/ml)

       22.                                                       4
                          (E Stereochemistry)

       23.                                                       4
                          (Z Stereochemistry)

       24.                                                       8

                          (E Stereochemistry)

       25.                                                       4

                          (Z Stereochemistry)

Table 1(f)
General structure 4 [8]

 Structure No.               R                            MIC (µg/ml)

       26.                                                        2

       27.                                                        8

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                                         Int. J. Pharm. Biosci. Technol.

       28.                                                 2

       29.                                                 2

       30.                                                32

       31.                                                16

       32.                                                 4

       33.                                                 1

       34.                                              0.125


Table 1(g)
General Structure 5 [8]

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                                         Int. J. Pharm. Biosci. Technol.

 Structure No.                 R                   MIC (µg/ml)

       35.                                                 2

       36.                                                128

       37.                                                32

       38.                                                 4

Table 1(h)
General Structure 6 [9]

  Structure No.           R1       R2                MIC (µg/ml)

        39.                        H                        64

        40.                        H                        64

        48.                        CH3                      32

        42.                                                    4

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                                     Int. J. Pharm. Biosci. Technol.

        43.                                              4

        44.                                              4

        45.                                              4

        46.                                              1

Table 1(i)
General Structure 7 [9]

 Structure No.            R1   R2               MIC (µg/ml)

       47.                     CH3                     64

       48.                                              8

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                                                   Int. J. Pharm. Biosci. Technol.

Table 1(j) [10]

  Structure No.                 Structure                     MIC (µg/ml)

       49.                                                          64

Table 1(k)
General Structure 8 [10]

 Structure No.             R1               H-R2               MIC (µg/ml)

      50.                                                             4

      51.                                                             2

      52.                                                             16

      53.                                                             2

      54.                                                             4

      55.                                                             64

       56                                                             2

Lele et al                                                               Pg. 42
                                                  Int. J. Pharm. Biosci. Technol.

Table 1(L)
General Structure 9 [10]

  Structure No.             R1           H-R2                MIC (µg/ml)


        58.                                                         8

        59.                                                         4

Table 1(m)
General Structure 10 [10]

  Structure No.     X             R1            H-R2

       60.          O                                                   4
                                                3- Cl

       61.          O                                                   2
                                 3- Cl

       62.         NH                                                   2
                                                3- Cl

       63.         NH                                                   2
                                 3- Cl

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                                                                                  Int. J. Pharm. Biosci. Technol.

Generation of Common Pharmacophore                     S= WsiteSsite + WvecSvec + WvolSvol + WselSsel + Wrewm
Hypothesis (CPH)
                                                       Where W’s are weights and S’s are scores
The pharmacophore hypothesis and alignment
                                                       Ssite represents alignment score, the root mean
based on it was carried out using PHASE, version
                                                       square deviation in the site point position.
2.0, Schrödinger, LLC, New York, NY, 2005
installed on AMD Athelon workstation.[11] The          Svec represents vector score, and averages cosine
structures were imported from the project table in     of the angles formed by corresponding pairs of
the “Develop Pharmacophore Hypothesis” panel           vector features in the aligned structures.
and geometrically refined (cleaned) using
                                                       Svol represents volume score based on overlap of
Ligprep. Conformations were generated by
                                                       Van der Waals models of non hydrogen atoms in
MCMM/LMOD method using maximum of 2000
                                                       each pair of structures.
steps with distance dependent dielectric solvent
model and OPLS-2005 force field. All the               Ssel represents selectivity score, and accounts for
conformers were subsequently minimized using           what fractions of molecules are likely to match the
truncated Newton conjugate gradient minimization       hypothesis regardless of their activity towards
up to 500 iterations. For each molecule a set of       receptor.
conformers with maximum energy difference of 30
                                                       Wsite, Wvec, Wvol, Wrew have default value of 1.0
kcal/mol relative to the global energy minimum
                                                       while Wsel has default value of 0.0.
were retained. A redundancy check of 2 Å in the
heavy atom positions was applied to remove             In the hypothesis generation default values have
duplicate conformers. Pharmacophore features;          been used. Wrewm represents reward weights
hydrogen bond acceptor (A), hydrogen bond              defined by m-1 where m is the number of actives
donor (D), hydrophobic group (H), negatively           that match the hypothesis.
charged group (N), positively charged group (P),
                                                       The assessment of the generated CPHs was
aromatic ring (R), were defined by a set of 4
chemical structure patterns as SMARTS queries          performed by correlating the observed and
and assigned one of three possible geometries,         estimated activity for the internal set of 48 training
which define physical characteristics of the site:     molecules and external set of 15 test molecules
                                                       using partial least square analysis. The partial
Point—the site is located on a single atom in the      least square (PLS) regression was carried out
SMARTS query.                                          using PHASE with maximum of 4 PLS factors using
                                                       pharmacophore based model, with grid spacing of
Vector—the site is located on a single atom in the
                                                       1Å. The CPH with best predictive value and
SMARTS query, and assigned directionality
                                                       significant statistical data was chosen for
according to one or more vectors originating from
                                                       alignment of molecules and used for further 3D-
the atom.
                                                       QSAR studies.
Group—the site is located at the centroid of a
group of atoms in the SMARTS query. For aromatic       QSAR Model Building
rings, the site is assigned directionality, defined
                                                       A training set of 48 molecules were selected
by a vector that is normal to the plane of the ring.
                                                       randomly, incorporating biological and chemical
Active and inactive thresholds were applied to the     diversity, and were used to generate atom-based
dataset (Table 1.) to yield 11 actives and 12          QSAR models for all hypotheses using a grid
inactives which were used for the pharmacophore        spacing of 1.0 Ǻ. Models containing four or more
generation and subsequent scoring. Common              PLS factors tended to fit the pIC50 values beyond
pharmacophoric features were then identified           their experimental uncertainty and thus, only one,
from a set of variants, a set of feature types that    two and three factor models were considered.
define a possible pharmacophore, using a tree          Each of these models were validated using an
based partitioning algorithm with maximum tree         external test set of fifteen molecules which were
depth of 4. The final size of the pharmacophore        not considered during model generation.
box was 1Å to optimize the number of final
common pharmacophore hypotheses (CPH).                 RESULTS AND DISCUSSION
These CPHs were examined using a scoring
                                                       Pharmacophore models containing three and four
function to yield the best alignment of the active
                                                       sites were generated using a terminal box size of 1
ligands using overall maximum RMSD value of 1.2
                                                       Å with twelve highly active molecules selected [6-
Å with default options for distance tolerance. The
                                                       10] belonging to indazole, pyrazole and their
quality of alignment was measured by a survival
score, which was defined using following               congeners, using a tree based partition algorithm.
                                                       The three featured CPHs were rejected, as they
equation. [12]

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                                                                                  Int. J. Pharm. Biosci. Technol.

were unable to define the complete binding space       Hypotheses emerging from this process were
of the selected molecules. A total of 10,487           subsequently scored with respect to the seven
probable four featured CPHs belonging to seven         inactives, using a weight of 1.0. The hypotheses
types HHPR, AHPR, HHRR, AHHR, AHRR, AHHP and           which survived the scoring process were used to
HPRR were subjected to stringent scoring function      build an atom-based QSAR model. The summary
analysis, with respect to actives using default        of statistical data of the best CPHs labeled CPH1 to
parameters for site, vector, and volume.               CPH6 with their survival scores is listed in Table 2.

        Table 2. Summary of QSAR results for six best CPHs with survival scores
                         CPH1              CPH2            CPH3              CPH4                 CPH5
                        (HPRR)            (HHPR)          (HHRR)            (AHHR)               (AHRR)
  Survival score            3.136          3.136            3.092            3.026                2.872
         SD                 0.422          0.444            0.372            0.443                0.386
         r2                 0.681          0.647            0.729            0.648                0.699

          F                 31.3           26.9             35.1             27.1                  33.4
          P            5.413e-11         4.949e-10       3.686e-11         4.429e-10            2.644e-11
        RMSE                0.452          0.466            0.486            0.471                0.501

         Q2                 0.525          0.510            0.449           0.4842                0.414
    Pearson-R               0.741          0.720            0.754            0.736                0.646

    SD = Standard deviation of the regression, r2 = Value of r2 for the regression, F = Variance ratio
   P = Significance level of variance ratio, RMSE = Root-mean-square error, Q2 = Value of Q2 for the
predicted activities, Pearson-R = Correlation between the predicted and observed activity for the test set.

Consistent external predictivity was observed for      set and test set molecules are given in Table 3.
CPH1 for each combination as compared to others.       Figure 1 shows the alignment of the molecules
The CPH1 showed a statistically significant r2 value   under study along with CPH1 while Figure 2 shows
for training set (0.681), excellent predictive power   the gemotry of the pharmacophore generated. The
with Q2 of 0.525 and a Pearson-R value of 0.741.       distances        and     angles      between    the
Hence, the hypothesis CPH1 with two aromatic           pharmacophoric features are depicted in Figure
ring (R), one lipophilic/hydrophobic group (H)         3(a) and 3(b) respectively. Figures 4(a) and Figure
and one positive ionic feature (P) as                  4(b) shows the graphs of actual v/s. predicted
pharmacophoric features was retained for further       activity for training and test set molecules.
studies. Actual and predicted values of the training

                            Table 3. Actual and predicted values of the dataset

Structure       Biological           Predicted           Structure       Biological            Predicted
   No.           activity             activity              No.           activity              activity

    1              -2.283              -2.34                 33            -0.359                  -0.87
    2               0.389              0.31                  34             0.497                  0.98
    3              -1.995              -1.80                 35            -0.669                  -1.28
    4              -1.626              -1.76                 36            -2.599                  -1.49
    5              -2.166              -1.56                 37            -1.994                  -1.53
    6              -2.166              -1.92                 38            -1.094                  -1.51
    7              -1.894              -1.72                 39            -2.302                  -2.34
    8              -2.195              -1.83                 40            -2.363                  -2.29
    9              -1.866              -1.72                 41            -1.964                  -2.25

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                                                                  Int. J. Pharm. Biosci. Technol.

    10         -1.866           -1.56           42           -1.013                -1.11
    11         -1.551           -1.50           43           -0.969                -1.11
    12         -1.577           -1.56           44            -0.99                -1.04
    13         -0.949           -1.42           45           -0.966                -0.96
    14         -2.211           -1.80           46           -0.359                -0.99
    15         -2.474           -1.41           47           -2.265                -2.06
    16         -1.874           -1.84           48           -1.267                -0.97
    17         -0.937           -1.22           49           -2.283                -2.34
    18         -0.949           -1.57           50           -0.969                -0.93
    19         -0.937           -1.38           51           -0.668                -0.93
    20         -0.359           -0.87           52           -1.571                -1.25
    21         -0.345           -0.90           53            -0.68                -0.73
    22         -0.991           -1.41           54           -1.011                -1.30
    23         -0.991           -1.10           55           -2.138                -1.25
    24         -1.273           -0.99           56           -0.654                -0.86
    25         -0.972           -1.47           57           -0.969                -1.12
    26          -0.57           -0.93           58           -1.312                -0.98
    27         -1.267           -0.87           59           -0.955                -1.13
    28         -0.646           -0.90           60           -0.968                -1.14
    29          -0.66           -0.87           61           -0.653                -1.21
    30         -1.867           -0.97           62           -0.668                -0.98
    31         -1.561           -0.97           63           -0.654                -1.35
    32         -0.966           -1.07

  Fig. 1. CPH1 based alignment DNA gyrase   Fig. 2. Geometry of the pharmacophore- P9 is
                 inhibitors.                the positive ionic feature represented as blue
                                            sphere; R11 and R12 are the aromatic ring
                                            features represented in orange torus and H4 is
                                            the hydrophobic feature represented in green

Lele et al                                                                            Pg. 46
                                                                          Int. J. Pharm. Biosci. Technol.

Fig. 3(a) CPH1 features and distances.

                                                 Fig. 4(b). Scatter plots for the predicted and
                                                 experimental pIC50 values for the DNA gyrase
                                                 QSAR model applied to the test set.
                                                 The pharmacophore contains two aromatic ring (R)
                                                 features mapping on the pyrazole heterocycle and
                                                 benzene ring, one positive ionic feature mapping
                                                 on one of the nitrogens in the piperazine ring and
                                                 one lipophilic/ hydrophobic feature mapping on
                                                 the para-chloro atom in the distal part of molecule.
                                                 When the QSAR model was visualized in the
                                                 context of one of the most active molecule
                                                 (Structure 21) blue cubes, favorable for the
                                                 activity, were observed in context of the
                                                 pharmacophoric features thus corroborating the
                                                 findings of pharmacophore as observed in Fig. 5.
Fig. 3(b) CPH1 features and angles.

                                                 Fig. 5. QSAR model visualization for DNA
                                                 gyrase B of S. aureus.

                                                 The current work involves development of
                                                 predictive pharmacophore hypothesis (CPH) for S.
Fig. 4(a). Scatter plots for the predicted and   aureus DNA gyrase inhibitors using PHASE and
experimental pIC50 values for the DNA gyrase     performing the QSAR analysis of the model. Best
QSAR model applied to the training set.          CPH was selected primarily on the basis of its
                                                 internal and external predictive capabilities. The

Lele et al                                                                                    Pg. 47
                                                                                Int. J. Pharm. Biosci. Technol.

generated CPH consists of two aromatic rings, one     ACKNOWLEDGEMENTS
lipophilic/hydrophobic group and one positive
                                                      Author A. C. Lele is thankful to Council of Scientific
ionic feature. The pharmacophore hypothesis
                                                      and Industrial Research, Delhi, India and N. R.
yielded statistically significant 3D-QSAR model.
                                                      Tawari is thankful to Department of Biotechnology,
The developed pharmacophore model will aid in
                                                      Delhi, India for financial assistance.
the rational design of DNA gyrase inhibitors.

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                                                    Int. J. Pharm. Biosci. Technol.

              How to cite this article

                       APA style
   Lele, A. C., Tawari, N. R., & Degani, M. S.
   (2013). Development of pharmacophore
   model for S. Aureus DNA-gyrase inhibitors.
   International Journal of Pharma Bioscience and
   Technology, 1(1), 34–49.
               Elsevier Harvard style
   Lele, A.C., Tawari, N.R., Degani, M.S., 2013.
   Development of pharmacophore model for S.
   Aureus DNA-gyrase inhibitors. Int. J. Pharm.
   Biosci. Technol. 1, 34–49.
                 Vancouver Style
   Lele AC, Tawari NR, Degani MS. Development
   of pharmacophore model for S. Aureus DNA-
   gyrase inhibitors. Int. J. Pharm. Biosci.
   Technol. 2013;1(1):34–49.

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Description: Key words: Pharmacophore, 3D-QSAR, DNA gyrase, S. Aureus.