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AJ CSIAN OURNAL OF HEMISTRY
AJ CSIAN OURNAL OF HEMISTRY
https://doi.org/10.14233/ajchem.2021.23143
INTRODUCTION
Docking methodologies have been used to predict the
experimental binding modes and affinities of small molecules
within the binding site of particular targets like receptors and
enzymes and are currently being used in virtual screening studies
as a standard computational tool in drug design for lead comp-
ound optimization and to find novel biologically active comp-
ounds. The search algorithm and energy scoring functions are
used as fundamental tools for generating the different poses
for ligand and also its evaluation in docking studies [1]. Ligand-
protein docking is performed to predict the main binding modes
of a ligand with a protein with a defined three-dimensional
structure. Successful docking methods search high-dimensional
spaces effectively and use a scoring function and give the ranking
based on the binding modes [2,3]. By using docking appli-
cations, virtual screening on large libraries of analogues can
be performed so that the results can be graded and structural
hypotheses can be formulated on inhibition of the target by
ligands, which is helpful in optimizing leads. In addition, the
Molecular Docking Studies, Analgesic and Anti-inflammatory
Screening of Some Novel Quinazolin-4-one Derivatives
K.N. RAJINI KANTH1,*, , CH. JASWANTH KUMAR1, , D. ESWAR TONY1, , SK. MUNWAR1,
RAMA RAO NADENDLA1 and CHANDRAGIRI SIVA SAI2,
1Chalapathi Institute of Pharmaceutical Sciences, Chalapathi Nagar, Lam, Guntur-522034, India
2Amity Institute of Pharmacy, Amity University, Uttar Pradesh, Gonti Nagar, Lucknow-226028, India
*Corresponding author: E-mail: knrkanth2009@gmail.com
Received: 29 January 2021; Accepted: 25 February 2021; Published online: 16 April 2021; AJC-20325
Molecular docking studies was performed on 20 analogous novel quinazolin-4-one derivatives as cox-2 inhibitors using glide tool of
maestro 11.4 application of Schrodinger software. Anti-inflammatory and analgesic activities were further evaluated for the compounds.
Based on docking studies, the binding affinity of QZN-16 was found to be -10.32 kcal/mol. In order to understand the significance of
R-substituents on the quinazoline-4-one nucleus, the findings of hydrogen bonding interactions between designated ligands with binding
site region of 4cox were studied. The ligands which are having high docking score were subjected to pharmacological screening. The
compound QZN-16 has shown analgesic and anti-inflammatory activity at a dose level of 50 and 100 mg/kg body weight, respectively
when compared with standard drug indomethacin. The newly designed quinazoline-4-one derivatives may serve as lead molecules for
further development.
Keywor ds: Structure based drug design, Quinazolin-4-one derivatives, Molecular docking, Analgesic, Anti-inflammatory, Cox-2 inhibitor.
Asian Journal of Chemistry; Vol. 33, No. 5 (2021), 1058-1062
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characterization of the binding activity plays an important role
in both the logical design of drugs and the elucidation of funda-
mental biochemical processes [4-6].
Non-steroidal anti-inflammatory medications are among
the most widely used therapeutics (NSAIDs). The pharmacol-
ogical target is cyclooxygenase, which catalyzes the first and
key step in arachidonic-acid metabolism. They represent a choice
of treatment for different inflammatory diseases viz., arthritis,
rheumatism and relaxation through their anti-inflammatory,
antipyretic and analgesic activities. The constitutive isozyme
COX-1 plays a role in the cytoprotective mechanism of GIT
and in normal functioning of the renal system [7]. In response
to a pro-inflammatory stimulus, COX-2 is an inducible and
short-lived enzyme that is expressed. The biosynthesis of prost-
aglandin requires COX-2 in inflammatory cells. Classical non-
steroidal anti-inflammatory agents inhibit both isozymes to
varying degrees, a characteristic that has been directly linked
to the corresponding differential distribution in tissue and also
represents the shared therapeutic properties and side effects
of these agents [8,9]. The basis of the inhibition of COX-2
selectively is clearly demonstrated by the structural features
of certain newer heterocyclic derivatives. Few COX-2 inhibitors
which are selective like celecoxib are available in the market
and, thus, an important objective in medicinal chemistry is the
production of novel drugs operating with high efficacy using
a similar overall mechanism. Based on the facts given above,
we have selected some newer quinazolinone derivatives as
attractive candidates to exhibit both anti-inflammatory and
analgesic activities [10,11]. These derivatives have been tested
biologically for anti-inflammatory and analgesic activities in
vivo. To define the requirements of structure for inhibitory
activity of these novel compounds on COX-2 [12-14], docking
studies was performed.
EXPERIMENTAL
Selection of protein: While selecting protein for the dock-
ing studies, the resolution must be minimal and X-ray diffraction
should be used to assess the structure in order to ensure better
protein consistency and reliable docking performance. The
protein structure must have a resolution of 2.0-3.0 Å and protein
breaks should not be observed in the 3D structure. The protein
structure should contain co-crystallized ligand which is subjected
to docking simulation to study the effector and inhibitor chara-
cteristics towards ligand and it must possess complete domain
along with sidechains otherwise it leads to false interpretation
in the docking studies [15].
Methodology: In our molecular docking studies, software
Maestro along with the Glide algorithm which was provided
by Schrodinger small drug discovery suite was employed. The
selected protein for the docking studies was prepared using a
tool called protein preparation wizard. Ligands designed for
docking on the target were prepared by using a tool called
ligprep. The grid was generated for the selected binding site
by using the tool receptor grid generation. Glide tool was utilized
to calculate the docking score and different binding modes
for the ligands, the binding modes generated by the glide was
evaluated by using tools called pose organizer and ligand inter-
action diagram generated by Maestro.
Protein preparation: The RCSB domain of PDB was
used to get the X-ray crystal structure of protein PDB id: 4COX
[https://www.rcsb.org/structure/4COX] used in the docking
study. In the present docking study, protein preparation wizard
panel of Maestro was used to resolve common structural issues
by way of pre-processing, review and modification and
refinement. In the pre-processing step, the bond orders were
assigned to the protein, polar hydrogens were added to the
protein structure, zero-order bonds were created to the metals,
if necessary, disulfide bonds are created in the protein structure
and water molecules present around the co-crystalized ligand
(indomethacin) beyond 5.0 Å was determined. In the review
and modify step the water molecules, which are present around
the co-crystallized ligand and beyond the 5.0 Å were removed
in order to determine during the pre-processing step. Proto-
nation states were generated for the co-crystallized ligand and
the lowest penalty state was automatically selected. In the
refinement step, the overlapping atoms have been corrected,
side chains that have been flipped are labelled as flip and
hydrogen bonds assigned were adjusted in the protein by using
the force field OPLS in interactive optimizer tool [16,17].
Software method validation: Maestro was the program
used to validate the X-ray crystal structure. 4COX, collected
from the Protein data bank, was the protein used in the current
docking analysis (PDB). In the PDB file format, the X-ray crystal
structure of 4COX that was co-crystallized with ligand indo-
methacin was retrieved from the protein data bank. In the binding
site region of 4COX, the co-crystallized ligand bound with
protein was split and docked. The 4COX X-ray crystal structures
have a resolution of 2.9 Å, suggesting that the parameters for
the docking analysis are excellent for reproducing the X-ray
crystal structure. Ramachandran plot is considered as an impor-
tant statistical criterion for the X-ray crystal structure by consi-
dering allowed and disallowed regions.
Ligand preparation: The structure of quinazolin-4-one
derivatives (Fig. 1) was converted into a 3D model by using a
graphical user interface provided by Schrodinger and finally
subjected to energy minimization using the tool ligprep. A
series of steps are involved in the ligand preparation process
including corrections, generate variations, eliminate unwanted
moieties and optimize the structures. Hydrogen atoms are added
which is consistent with a particular force field before the 3D
structures can be minimized by applying the program htreat of
the maestro. Before the generation of ionization states, charged
groups must be neutralized, after which the ionizer generates
various ionization states, tautomerize generates various tautomer’s
ligfilter filters the structures possessing molecular weight greater
than 1000 or the specific functional groups, which may be present
or absent in the structure. The chirality of the atoms are varied to
generate possible structures using stereoizer, low-energy ring
conformations generated by ring_conf, followed by short confor-
mational search to adopt proper chirality’s in 3D structure to
optimize the geometries. The OPLS force field was used for the
preparation of ligands [18].
N
N
O
CH3
H
N
O
N
N
R
Fig. 1. General structure of quinazolin-4-one derivatives
Receptor grid generation: The grid was generated around
the active binding site of protein where the co-crystallized ligand
was present using the tool called receptor grid generation panel.
The binding site was defined in the grid box, which surrounds
the bound co-crystallized ligand atom within 15 Å dimensions.
According to the given dimensions, the receptor grid gene-
ration panel runs calculation for the target binding site and
generated a grid file. This grid file was uploaded into the glide
to perform the docking simulation.
Ligand docking: In this present study, we have used a
docking algorithm called Glide. Glide works on the Emodel1
scoring function, which gives ranking based on the coulomb-
Vol. 33, No. 5 (2021) Molecular Docking Studies, Analgesic & Anti-inflammatory Screening of Some Novel Quinazolin-4-one Derivatives 1059
vdW energy of protein-ligand complexes of a set of ligands
with a small contribution from Glide Score. The compounds
possessing strong binding affinity relative to those possessing
little to no binding affinity can be separated by using Glide-
Score2, which is an empirical scoring function. It gives a ranking
between the docked ligands based on the active and inactive
compounds [19,20]. Docking simulation was performed to
dock the designed quinazolin-4-one derivatives QZN1–QZN-
20 against cyclooxygenase-2 (4COX) using glide to study the
interaction between quinazoline-4-one derivatives and cyclo-
oxygenase-2 enzyme. The ligands, which were prepared and
the grid file which was generated before docking was uploaded
into glide. In this present docking study, the virtual screening
was performed using the extra precision mode to study the
physico-chemical descriptors. We used glide because it gives
fast and higher docking accuracy results than other docking
software (Glide: 82%, Surflex: 75%, FlexX:58%) [21-23].
Anti-inflammatory activity
Experimental animals: Albino rats of Wistar were
collected from an animal house attached to the Department of
Pharmacology from strains of either sex between 150 and 250
g. Throughout the study, the animals were fed a regular diet of
rats and water adlibitum. In the laboratory state, they were
acclimatized for two weeks before the experimentation. The
housing is maintained with the following conditions: 12:12 h
light and dark cycle regulated lighting at 25 ºC and around
50% relative humidity.
IAEC approval: Pharmacological evaluation of quina-
zoline-4-one derivatives for various screening methods has
been approved (approval No: 16/IAEC/CLPT/2018-19) by the
Institutional Animal Ethics Committee (IAEC) of Chalapathi
Institute of Pharmaceutical Sciences, Guntur, India (Reg.No.:
1048/PO/Re/S/07/CPCSEA).
Animals: Adult Wistar albino rats of either sex with a
weight of 180 to 220 g were divided into five separate groups
each of four rats. The control group was administered with
normal water at a dosage of 10 mL/kg for 0.5% gum acacia,
the regular group was administered with indomethacin 100
mg/kg and the test groups were administered with quinazoline-
4-one derivatives at a dose of 100 mg/kg body weight.
Acute anti-inflammatory activity: The hind paw edema
test induced by Carrageenan was performed in rats using the
method of Winter et al. [24]. The acute anti-inflammatory
activity in Wistar albino rats was assessed using the carrag-
eenan induced paw edema technique. By injecting 0.1 mL of
1% carrageenan solution into the sub-plantar surface of the
hind paw of the rat, acute inflammation was produced. The group
specific drugs were administered 1 h before the carrageenan
injection. A Plethismometer (PLM-01 PLUS Orchid Scientifics)
was used to measure the paw volume up to the tibiotarsal arti-
culation at baseline, 30, 60, 90 and 120 min after injection of
carrageenan. Anti-inflammatory behaviour was expressed by
measuring the percentage decrease in paw edema (V-paw volume).
Analgesic activity
Animals: Using Swiss male albino mice weighing 20 to
25 g, pharmacological studies were carried out. In the animal
house attached to the Pharmacology Department, mice were
raised. The animals were grouped randomly in polyacrylic cages
and held under normal conditions of animal housing (25 ± 2
ºC) and relative humidity (40-70%) with dark-light cycles (12/
12 h). Water ad libitum and regular laboratory chow were been
fed to the mice. For 1 day prior to experimentation, the mice
were acclimatized to laboratory conditions. Throughout the
entire day of the experiment, animals had no access to food.
Adult Swiss albino mice of either sex weighing between
20 and 25 g were divided into five groups each consisting of
four animals. The control group was administered with normal
water at a dosage of 10 mL/kg in 0.5% gum acacia and the
standard group was administered with pentazocine 6 mg/kg
in 0.5% gum acacia suspension. Quinazoline-4-one derivatives
at a dosage of 50 mg/kg were administered as a suspension in
0.5% gum acacia to the test groups.
Eddy’s hot plate method: The analgesic efficacy of quin-
azoline-4-one derivatives was tested using the Eddy & Leimbach
hot plate technique [25]. A temperature of 55 ± 0.2 ºC was
maintained. As a sign of discomfort, animals licked their limbs
and jumped. These mice were treated with suspension as
follows: in 0.5% of gum acacia the control group were adminis-
tered with regular water and 50 mg/kg of quinazoline-4-one
derivatives was administered to the test groups. Pentazocine
(6 mg/kg) standard was administered to the regular group via
oral route. Mice were put on the hot plate 1 h after dosing group
specific drugs and the time was monitored by a stopwatch
before either licking or jumping occurred. The latency period
was reported after oral administration of group-specific drugs,
before and after 1, 2, 3 and 4 h. The 12 s cut-off time was used
for the hot plate test [26].
RESULTS AND DISCUSSION
Docking studies: Extra precision docking approach (XP
mode) has been used as a tool in the present docking studies.
This method was employed to study the physico-chemical
properties of designed ligands. The binding of small molecules
into the enzyme complex was studied by utilizing glide, which
gives a reliable prediction and also in identifying the active
ligands. The hydrogen bond interactions of the ligand with
macromolecule were represented in the form of a ligand inter-
action diagram. Evaluation based on the various R-substituents,
which contribute to hydrogen bond interactions with the enzyme
was considered. The functional groups responsible were analyzed
on the basis of the ligand’s interaction with the enzyme’s bin-
ding site and the result obtained was interpreted. The molecule
QZN-16 was found to possess minimum energy of interaction
with a docking score of -10.36 kcal/mol in comparison with
standard indomethacin with a docking score of -12.09 due to
the additional availability of amino acid residue Arg-120. The
binding site interactions with the ligands are represented in
Fig. 2 and results are tabulated in Table-1.
The activities of quinazoline-4-one derivatives were studied
using standard drug. Even though the derivatives QZN-16,
QZN-08, QZN-20 differ in chemistry, they showed a positive
result for anti-inflammatory and analgesic activities (Table-2).
The difference between the values of paw edema for the anti-
1060 Kanth et al. Asian J. Chem.
TABLE-1
DOCKING RESULTS OF NEWER
QUINAZOLIN-4-ONE DERIVATIVES
R group
substituent’s
Binding
affinity H-bond interacting residues
-C6H5 -9.16 TYR-355, ARG-120
2-Cl C6H4 -9.21 TYR-385, TRP-387, SER-530, TYR-
355
3-Cl C6H4 -9.11 TYR-385, SER-530
4-Cl C6H4 -8.44 TYR-385, SER-530, TYR-355
2,4-Cl C6H3 -9.04 ARG-120, TYR-355
2-F C6H4 -9.45 TRP-387, TYR-385, SER-530
4-F C6H4 -9.66 TYR-385, TYR-355
2-NO2C6H4 -9.47 SER-530, TYR-385, ARG-120
4-NO2C6H4 -8.45 TYR-385, TYR-355, SER-530,
ARG-120
3-NO2C6H4 -8.72 TYR-385
2-OHC6H4 -9.29 ARG-120, TYR-385, SER-530,
4-OHC6H4 -9.17 TYR-385, TYR-355
2-OMeC6H4 -9.34 TYR-385, ARG-120, SER-530
4-OMeC6H4 -8.89 SER-530, TYR-355, TYR-385
2,4,6 OMeC6H3 -6.01 TYR-355, ARG-120
4-OH,3-OMeC6H4 -10.36 TYR-385, TYR-355, SER-530
(CH3)2N-C6H4 -7.66 ARG-120, TYR-355
2-Pyridyl -9.33 TYR-385, SER-350
4-Pyridyl -8.3 TYR-385, SER-530
2-Furanyl -9.17 TYR-385, SER-530
Indomethacin -12.09 TYR-385, TYR-355, SER-530,
ARG-120
inflammatory activity of all the three derivatives was found to
be similar to the standard drug. Also, much variation was not
observed between all the three derivatives as the changes with
time interval was found to be 2% at 30 min, 4% at 60 min, 6%
at 90 min and 3% at 120 min. The observed experimental values
of the test were compared with the standard and found to be
very similar as there is a slight variation between the values
and were found to be 7% at 30 min, 5% at 60 min, 5% at 90 min
and 9% at 120 min. In case of analgesic activity, the time to
withstand pain by the animal was also increased and the values
are nearer for three derivatives when compared with standard.
The observed experimental values of the standard to withstand
pain was found to be 2.28 s at 3 h as threshold and 1.63 s for
QZN-16, 0.66 s for QZN-8 and 0.72 s for QZN-20 as depicted
in Table-3. On an average, the test compounds possess the
ability to withstand pain for 1.00 s and 56.15% similar to that
of standard. In case of independent compounds, QZN-16 is
71.49% similar to that of standard, QZN-08 is 28.94% similar
to that of standard, QZN-20 is 31.57% similar to that of standard.
Conclusion
By using the Schrodinger small drug discovery suite using
glide algorithm, novel quinazoline-4-one derivatives were
subjected to docking studies. Cyclooxygenase-2 (PDB ID-
4COX) from the protein data bank was the enzyme selected
Fig. 2. 2D and 3D structures of interaction of QZN-16 and indomethacin with active site of 4COX
Vol. 33, No. 5 (2021) Molecular Docking Studies, Analgesic & Anti-inflammatory Screening of Some Novel Quinazolin-4-one Derivatives 1061
TABLE-2
ANTI-INFLAMMATORY SCREENING OF NEWER DERIVATIVES
Reduction in paw volume per time (min) and percentage difference
Treatment
groups Basal 30 min Change (%) 60 min Change (%) 90 min Change (%) 120 min Change (%)
Control 0.86 0.84 2 0.86 0 0.83 3 0.81 5
Standard 0.93 0.58 35 0.54 39 0.49 44 0.45 48
QZN-16 0.74 0.45 29 0.39 35 0.32 42 0.33 41
QZN-08 0.81 0.54 27 0.51 30 0.46 35 0.44 37
QZN-20 0.78 0.50 28 0.43 35 0.39 39 0.41 37
TABLE-3
SCREENING OF NEWER DERIVATIVES
FOR ANALGESIC ACTIVITY
Time (s) when paw licking and jumping was observed
Treatment
groups Basal 1 h 2 h 3 h 4 h
Control 5.71 5.54 5.42 5.45 5.18
Standard 5.12 5.43 6.30 7.40 6.04
QZN-16 6.02 6.41 7.18 7.65 7.07
QZN-08 5.86 6.04 6.31 6.52 6.11
QZN-20 5.78 5.91 6.12 6.50 6.03
for the study. The protein under study was subjected to the 3-
step refinement process for energy minimization of protein.
The ligands understudy was drawn and converted into 3D struc-
tures using a tool called 2D sketcher and subjected to energy
minimization. The receptor grid generation was done to define
the binding site for the ligands. The grid file generated was
uploaded into the glide to perform ligand docking. After the
completion of docking, the score was generated for the ligand
based on the interaction with the active site of the enzyme. The
ligands with high docking scores were selected and subjected
to binding site interaction and the results were interpreted and
compared with the standard drug. The docking score of QZN-
16 was found to be -10.32 kcal/mol. Compound QZN-16 showed
analgesic and anti-inflammatory activity nearer to the standard
drug at dose 50 mg/kg and 100 mg/kg body weight, respectively.
ACKNOWLEDGEMENTS
The authors acknowledge Schrodinger Inc. for providing
the software to carryout molecular docking studies.
CONFLICT OF INTEREST
The authors declare that there is no conflict of interests
regarding the publication of this article.
REFERENCES
1. I.A. Guedes, C.S. de Magalhaes and L.E. Dardenne, Biophys. Rev., 6,
75 (2014);
https://doi.org/10.1007/s12551-013-0130-2
2. J. Fan, A. Fu and L. Zhang, Quant. Biol., 7, 83 (2019);
https://doi.org/10.1007/s40484-019-0172-y
3. T. Lengauer and M. Rarey, Curr. Opin. Struct. Biol., 6, 402 (1996);
https://doi.org/10.1016/S0959-440X(96)80061-3
4. L.G. Ferreira, R.N. Dos Santos, G. Oliva and A.D. Andricopulo,
Molecules, 20, 13384 (2015);
https://doi.org/10.3390/molecules200713384
5. B.K. Shoichet, S.L. McGovern, B. Wei and J.J. Irwin, Curr. Opin. Chem.
Biol., 6, 439 (2002);
https://doi.org/10.1016/S1367-5931(02)00339-3
6. V. Salmaso and S. Moro, Front. Pharmacol., 9, 923 (2018);
https://doi.org/10.3389/fphar.2018.00923
7. R. Norregaard, T.H. Kwon and J. Frokiaer, Kidney Res. Clin. Pract.,
34, 194 (2015);
https://doi.org/10.1016/j.krcp.2015.10.004
8. J.R. Vane, Nat. New Biol., 231, 232 (1971);
https://doi.org/10.1038/newbio231232a0
9. A. Zarghi and S. Arfaei, Iran. J. Pharm. Res., 10, 655 (2011).
10. A.A. Farag, E.M. Khalifa, N.A. Sadik, S.Y. Abbas, A.G. Al-Sehemi
and Y.A. Ammar, Med. Chem. Res., 22, 440 (2013);
https://doi.org/10.1007/s00044-012-0046-6
11. M. Lindner, W. Sippl and A.A. Radwan, Sci. Pharm., 78, 195 (2010);
https://doi.org/10.3797/scipharm.0912-19
12. M.F. Zayed and M.H. Hassan, Saudi Pharm. J., 22, 157 (2014);
https://doi.org/10.1016/j.jsps.2013.03.004
13. C.S. Rajput and S. Singhal, J. Pharm., 2013, 907525 (2013);
https://doi.org/10.1155/2013/907525
14. A.M. Alafeefy, A.A. Kadi, O.A. Al-Deeb, K.E. El-Tahir and N.A. Al-
Jaber, Eur. J. Med. Chem., 45, 4947 (2010);
https://doi.org/10.1016/j.ejmech.2010.07.067
15. J. Wang, P.A. Kollman and I.D. Kuntz, Proteins, 36, 1 (1999).
16. H.A. Abuelizz, R. Al-Salahi, J. Al-Asri, J. Mortier, M. Marzouk, E.
Ezzeldin, A.A. Ali, M.G. Khalil, G. Wolber, H.A. Ghabbour, A.A.
Almehizia and G.A. Abdel Jaleel, Chem. Cent. J., 11, 103 (2017);
https://doi.org/10.1186/s13065-017-0321-1
17. B. Ahmed, P.K. Pandey, H. Khan, M. Bala and J. Prasad, Pharmacogn.
Mag., 15, 313 (2019);
https://doi.org/10.4103/pm.pm_625_18
18. E. Harder, W. Damm, J. Maple, C. Wu, M. Reboul, J.Y. Xiang, L. Wang,
D. Lupyan, M.K. Dahlgren, J.L. Knight, J.W. Kaus, D.S. Cerutti, G.
Krilov, W.L. Jorgensen, R. Abel and R.A. Friesner, J. Chem. Theory
Comput., 12, 281 (2016);
https://doi.org/10.1021/acs.jctc.5b00864
19. T.A. Halgren, R.B. Murphy, R.A. Friesner, H.S. Beard, L.L. Frye, W.T.
Pollard and J.L. Banks, J. Med. Chem., 47, 1750 (2004);
https://doi.org/10.1021/jm030644s
20. R.A. Friesner, R.B. Murphy, M.P. Repasky, L.L. Frye, J.R. Greenwood,
T.A. Halgren, P.C. Sanschagrin and D.T. Mainz, J. Med. Chem., 49,
6177 (2006);
https://doi.org/10.1021/jm051256o
21. H. Alogheli, G. Olanders, W. Schaal, P. Brandt and A. Karlén, J. Chem.
Inf. Model., 57, 190 (2017);
https://doi.org/10.1021/acs.jcim.6b00443
22. M. Kontoyianni, L.M. McClellan and G.S. Sokol, J. Med. Chem., 47,
558 (2004);
https://doi.org/10.1021/jm0302997
23. E. Kellenberger, J. Rodrigo, P. Muller and D. Rognan, Proteins, 57,
225 (2004);
https://doi.org/10.1002/prot.20149
24. C.A. Winter, E.A. Risley and R.H. Silber, J. Pharmacol. Exp. Ther.,
162, 196 (1968).
25. N.B. Eddy and D. Leimbach, J. Pharmacol. Exp. Ther., 107, 385 (1953).
26. A.P. Sithara, M. Ravi, S. Mallya, Sudhakara, S. Bairy, P. Srikanth and
Ravishankar, Int. J. Pharmacol. Clin. Sci., 2, 105 (2013).
1062 Kanth et al. Asian J. Chem.