Cytotoxic oxindole derivatives: in vitro EGFR inhibition, pharmacophore modeling, 3D-QSAR and molecular dynamics studies

Article abstract of DOI:10.1080/10799893.2019.1683865

Epidermal growth factor receptor (EGFR) is one of the vital protein targets in many solid tumors and is an attractive target for developing new drugs. In this study we have focused on elucidation of the mechanistic insights of cytotoxic potentials of oxin

Full text of DOI:10.1080/10799893.2019.1683865

Journal of Receptors and Signal Transduction
ISSN: 1079-9893 (Print) 1532-4281 (Online) Journal homepage: https://www.tandfonline.com/loi/irst20
Cytotoxic oxindole derivatives: in vitro EGFR
inhibition, pharmacophore modeling, 3D-QSAR
and molecular dynamics studies
Sasikala Maadwar & Rajitha Galla
To cite this article: Sasikala Maadwar & Rajitha Galla (2019): Cytotoxic oxindole derivatives:
in vitro EGFR inhibition, pharmacophore modeling, 3D-QSAR and molecular dynamics studies,
Journal of Receptors and Signal Transduction, DOI: 10.1080/10799893.2019.1683865
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JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION
https://doi.org/10.1080/10799893.2019.1683865
RESEARCH ARTICLE
Cytotoxic oxindole derivatives: in vitro EGFR inhibition, pharmacophore
modeling, 3D-QSAR and molecular dynamics studies
Sasikala Maadwara,b and Rajitha Gallaa
b
aInstitute of Pharmaceutical Technology, Sri Padmavati Mahila Visvavidyalayam, Tirupati, India; Medicinal Chemistry and Pharmacology
Division, Indian Institute of Chemical Technology, Hyderabad, India
ABSTRACT
ARTICLE HISTORY
Received 5 September 2019
Accepted 7 October 2019
Epidermal growth factor receptor (EGFR) is one of the vital protein targets in many solid tumors and is
an attractive target for developing new drugs. In this study we have focused on elucidation of the
mechanistic insights of cytotoxic potentials of oxindole derivatives using various in vitro and molecular
modeling techniques. In vitro EGFR inhibition assay is performed with the potent cytotoxic oxindole
compounds which are previously proved for their cytotoxic activity against breast cancer (MCF7) and
ovarian cancer (SKVO3) cell lines. Molecular docking studies against kinase domain of EGFR protein
revealed the probable interactions of oxindole derivatives. Pharmacophore modeling studies had iden-
tified a pharmacophore model with three hydrogen bond acceptors and three aromatic rings
(AAARRR.1003) as a potential model for cytotoxic activity against MCF7 cell lines and validated
through 3D QSAR studies resulting in superior regression scores (r2 ¼ 0.92, q2 ¼ 0.80 and Pearson
R ¼ 0.95). Molecular dynamic studies have revealed the conformational changes in the EGFR-com-
pound 2complex during the 25 ns simulation time frame.
KEYWORDS
Oxindoles; EGFR; docking;
pharmacophore modeling;
3D QSAR;
molecular dynamics
Introduction
cell carcinoma [12]. Substituted isatins have been proved for
their EGFR inhibitory activity [13].
Cancer is considered as a major cause for increased death
rate in humans. Among women, breast cancer is the most
prevalent and ovarian cancer is genital system related can-
cers with an estimate of 255,180 and 22,440 new cases,
respectively [1]. Stanley Cohen, Nobel Prize Laureate in
Physiology/Medicine, discovered epidermal growth factor
(EGFR) 25 years ago and elucidated its role in cell growth.
This furthered knowledge on signaling events in cancer biol-
ogy and enabled to face challenges posed by the abnormal
cellular events resulting in cancer. Epidermal growth factor
receptors (EGFRs) are a large family of receptor tyrosine kin-
ases (TK) expressed in several types of cancer, including
breast, lung, esophageal, and head and neck. EGFR and its
family members are the major contributors of a complex sig-
naling cascade that modulates growth, signaling, differenti-
ation, adhesion, migration and survival of cancer cells. [2].
Isatin is an oxindole containing heterocyclic moiety, an
endogenous compound isolated in 1988 and reported to pos-
sess a wide range of biological activities including anticancer
[3], antidepressant [4], anticonvulsant [5], antifungal [6], anti-
HIV [7], anti-inflammatory [8]. During past decades, researchers
In the present work, in order to elucidate the mechanistic
insights of the disubstituted oxindole molecules we have
evaluated them for their in vitro EGFR inhibition which we
have previously proved their cytotoxic potentials against
human breast and ovarian cancer cell lines. Synthesis of
these compounds is shown in the Scheme 1 [14–15]. Further
we have performed docking studies against EGFR protein.
Additionally, pharmacophore modeling studies were per-
formed for dataset of oxindole derivatives to identify the
potential model for cytotoxic activity against MCF7 cell lines
and validated through 3D QSAR studies. Additionally,
molecular dynamics simulations are conducted to elucidate
the conformational changes of the EGFR-oxindole complex.
Materials and methods
In vitro EGFR inhibition
The compounds with higher cytotoxic activity were selected for
evaluation of EGFR inhibition using Kinase-Glo Plus lumines-
cence kinase assay kit and IC50 values were calculated by meas-
have embarked on the development of new isatin based anti- uring the protein kinase generated ADP [16]. Test compounds
cancer agents [9–11]. Owing to these properties, derivatives of selected for the assay were diluted to 100 mM in 10% DMSO
isatin have been developed for therapeutic applications. from which, 5 mL was added to a 50 mL reaction mixture
Recently, a 5-fluoro-3-substituted-2-oxoindole derivative com- (10 mM MgCl2, 40 mM Tris, pH 7.4, 0.1 mg/mL BSA, 0.2 mg/mL
pound SU11248 [Sutent] received FDA approval for the treat- Poly [Glu, Tyr] substrate, 10 mM ATP and EGFR) and reactions
ment of gastrointestinal stromal tumors and advanced renal were conducted at 30 ꢀC for 40 min. Following, 50 mL of assay
CONTACT Sasikala Maadwar
maadwarsasikala@gmail.com
Institute of Pharmaceutical Technology, Sri Padmavati Mahila Visvavidyalayam, Tirupati, India.
ß 2019 Informa UK Limited, trading as Taylor & Francis Group

2
S. MAADWAR AND R. GALLA
O
O
O
O
O
O
Y
X
O
O
O
(1-12)
O
N
R1
R2
O
O
O
O
O
Y
X
Y
(13-24)
O
O
O
N
X
BF3O(C2H5)2
R1
R2
O
O
RT/DCM/
5-10 MIN
N
O
O
R1
R2
O
O
Y
X
(25-36)
O
O
O
O
N
R1
O
R2
O
O
Y
(37-48)
X
O
O
N
R1
R1: H, CH3, C2H5, CH2=CH-CH2, CH3CO,C6H5CH2
R2: H, CH3
R2
X: H, Cl, Br, OCH3, CH3
Y: H, Cl
Scheme 1. Synthesis of data set compounds.
solution was added to each reaction and incubated at room minimized by using the Ligprep tool of Schrodinger suite
and applying Optimized Potentials for Liquid Simulations
(OPLS - 2005) Force Field. Molecular properties and ADME
profiling was performed using Qikprop module of
Schrodinger Suite to selected compounds which were geo-
metrically optimized.
temperature for 5 min. The reaction mixture was incubated in a
96-well plate at 30 ꢀC for 30 min and the reaction was termi-
nated by adding 25 mL of ADP-Glo reagent. The 96-well plate
was shaken and then incubated for 30 min at ambient tem-
perature. ADP was generated from the protein kinase reaction
by adding 50 mL of kinase detection reagent and resulted in
an increase in luminescence signal in the presence of ADP-Glo
assay kit. Blank control was set up and the corrected activity
for each protein kinase target was determined by removing the
blank control value. Additionally, regression analysis was per-
formed to determine the correlation between MCF7 cytotoxicity
and EGFR inhibition of the tested compounds.
Molecular docking studies
Initially, digital structure of the target kinase domain of EGFR
was retrieved from the Protein data bank website with PDB
Id: 1M17. Protein structure was optimized using Protein prep-
aration wizard. Protein structure was corrected by deleting
all non-interacted water molecules and added hydrogen
atoms to satisfy the valences and optimized by using OPLS-
2005 force field. Glide XP docking protocol was used for
molecular docking studies [17]. Glide XP modules include
two basic steps, Receptor Grid generation and ligand dock-
ing. A grid was generated considering the volume of the
active binding pocket of the protein and all ligands were
Molecular modelling and 3D QSAR studies
Dataset ligands
Substitutions of 48 newly synthesized di- and tri- methoxy
oxindole derivatives were shown in Table 1. 2D structures
were geometrically optimized to 3D and energy was docked into the active site of the target protein. Glide score

JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION
3
Table 1. Experimental and predicted cytotoxicity against MCF7/Wt cell lines.
Atom-based QSAR
Compound no.
R1
R2
X
Y
pIC50 experimental
Pharm set
pIC50 predicted
Data set
1
2
3
4
5
6
7
8
H
H
H
CH3
H
C6H5–CH2
H
H
H
H
H
H
H
H
CH3
H
H
H
H
H
H
H
H
H
H
Cl
H
H
4.731
5.143
5.114
4.87
Inactive
Active
Active
4.72
5.11
5.12
4.80
4.78
4.72
4.67
4.57
4.86
4.73
Training
Training
Training
Training
Training
Test
Training
Training
Training
Training
Cl
Br
H
OCH3
H
CH3
H
H
Active
4.74
Inactive
Inactive
Inactive
Inactive
Active
4.545
4.635
4.618
4.833
4.735
11
12
CH2–CH–CH2
H
H
CH3
Inactive
13
14
15
16
17
18
19
20
21
22
23
24
H
H
H
CH3
H
H
H
H
H
H
CH3
H
H
H
Cl
Br
H
OCH3
H
CH3
H
H
H
H
H
H
H
H
H
H
Cl
H
H
H
H
4.73
4.53
4.67
4.458
4.389
5.036
4.728
5.097
4.813
4.86
Inactive
Inactive
Inactive
Inactive
Inactive
Active
Inactive
Active
Active
4.70
4.57
4.67
4.42
4.42
5.04
4.74
5.09
4.83
4.85
4.93
4.90
Test
Training
Training
Training
Test
Training
Training
Training
Training
Training
Training
Training
H
C6H5–CH2
H
H
C2H5
CH3–CO
CH2–CH–CH2
H
H
H
H
Active
Active
Active
H
CH3
4.914
4.907
25
26
27
28
29
30
31
32
33
34
35
36
H
H
H
CH3
H
H
H
H
H
H
CH3
H
H
H
H
H
H
Cl
Br
H
OCH3
H
CH3
H
H
H
H
H
H
H
H
H
Cl
H
H
H
H
4.807
4.955
4.955
4.767
4.742
4.747
4.775
4.917
4.857
4.967
4.925
4.943
Active
Active
Active
Active
Inactive
Inactive
Active
Active
Active
Active
Active
Active
4.74
4.87
4.93
4.81
4.66
4.77
4.80
4.91
4.86
4.99
4.89
4.89
Test
Test
Training
Training
Test
Training
Training
Training
Training
Training
Test
H
C6H5–CH2
H
H
C2H5
CH3–CO
CH2–CH–CH2
H
H
H
CH3
Training
(continued)

4
S. MAADWAR AND R. GALLA
Table 1. Continued.
Atom-based QSAR
pIC50 predicted
Compound no.
R1
R2
X
Y
pIC50 experimental
Pharm set
Data set
37
38
39
40
41
42
43
44
45
46
47
48
H
H
H
CH3
H
C6H5–CH2
H
H
C2H5
H
H
H
H
H
H
CH3
H
H
H
H
H
H
H
H
H
H
Cl
H
H
H
H
4.839
4.914
4.791
4.821
4.815
4.833
5.036
4.955
4.914
4.839
4.742
4.943
Active
Active
Active
Active
Active
Active
Active
Active
Active
Active
Inactive
Active
4.83
4.79
4.80
4.82
4.84
4.86
5.04
4.98
4.84
4.81
4.79
4.92
Training
Test
Training
Test
Training
Training
Training
Training
Training
Training
Training
Training
Cl
Br
H
OCH3
H
CH3
H
H
H
H
CH3–CO
CH2–CH–CH2
H
H
H
H
CH3
includes a steric-clash term, adds polar terms featured by validation provided the predictive value of the models. The
Schrodinger to correct electrostatic mismatches. Glide score cross validated coefficient, r2cv, was calculated using the fol-
is a combination of hydrophilic, hydrophobic, metal binding
groups, Van der Waals energy, freezing rotatable bonds and
polar interactions with receptor.
lowing equation:
X
2
ðYpredicted ꢁ Yobserved
Þ
rc2v ¼ 1ꢁ
(1)
X
2
ðYobserved ꢁ Ymean
Þ
where Ypredicted, Yobserved and Ymean are the predicted,
observed and the mean values of the target property
Pharmacophore modelling and 3D-QSAR studies
By the allocation of activity threshold range whole data set
was fractioned into active (pIC50>4.75) and inactive
(pIC50<4.75). These ligands were internally validated through
PHASE module to generate/identify series of common
pharmacophore hypotheses [18,19]. A 3D pharmacophore
model was developed using a set of pharmacophore features
to generate sites for all the compounds, concurring with vari-
ous chemical features that may make easy non-covalent
binding among the ligand and its binding pocket. Distinctive
six pharmacophore features are hydrogen bond acceptor (A),
hydrogen bond donor (D), hydrophobic group (H), negatively
ionizable (N), positively ionizable (P) and aromatic ring (R).
Maximum of six and a minimum of five sites were selected
in order to obtain an efficient pharmacophore model.
Hypotheses were generated by a systematic variation of
number of sites (nsites) and the number of matching active
compounds (nact). With nact ¼ nact – tot. Initially (nact - tot) is
the total number of active compounds in the training set,
2
(pIC50), respectively. (YobservedꢁYmean
)
is the predictive
residual sum of squares (PRESS). The predictive correlation
coefficient (r2pred), based on molecules of the test set, is
defined as follows:
SDꢁPRESS
r2
¼
(2)
pred
SD
where SD is the sum of the squared deviation between the
biological activities of the test set and mean activities of the
training set molecules, PRESS is the sum of squared deviation
between predicted and actual activity values for every mol-
ecule in the test set. According to the literature, 3D-QSAR
models were accepted if
r2>0:6; r2cvðq2Þ > 0:5
(3)
Molecular dynamics
The docked complexes of EGFR protein and compound 2
was selected for molecular dynamics studies using Desmond
by D.E. Shaw Research [20,21] as discussed by Gade et al.,
2018). Molecular dynamic simulations were performed for
25 ns for each complex using TIP4PEW water solvent model,
and neutralized by adding sodium ions. The system was
maintained at 0.15 M salt concentration (Naþ Cl–) simulating
the physiological condition and dynamics simulation were
n
sites. The scoring protocol provides a ranking of different
hypotheses to choose the most appropriate for further
investigation.
Pharmacophore-based QSAR studies were performed by
choosing best common pharmacophore hypothesis. 80% of
dataset ligands were randomly selected as training set and
remaining as test set. Different QSAR models were gener-
ated, each containing upto 7 PLS factors through regression
analysis. Leave one-out (LOO) and leave-half-out (LHO) cross- performed using OPLS 2005 force field and NPT

JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION
5
ensemble(constant number of atoms, at constant pressure Among the compounds with halo-substitution, chloro sub-
stituted compounds were more active when compared to
bromo substituted compounds, whereas, among the com-
pounds with alkyl substitutions methyl and allyl substituted
derivatives were more active when compared to ethyl sub-
stituted compound. Overall, oxindole derivatives with halo
(chloro and bromo substitution at fourth and fifth position)
and alkyl (methyl at fifth position and allyl at first position)
were reported with relatively higher activity.
[1.01352 bar], and constant temperature [300K] for 25 ns. For
system ensemble, Nose–Hoover chain thermostat with a
relaxation time of 1 ps and Martyna–Tobias–Klein barostat
with 2 ps relaxation time and isocratic cooling style and
Coulombic interactions were cut off at 9 Ao radius were used.
Results and discussions
In vitro EGFR inhibition
Molecular docking studies
Out of 48 compounds which are previously synthesized in
our laboratory, a total of 15 compounds with relatively
higher cytotoxic activity against MCF7 cell lines were
chosen for in vitro EGFR inhibition assay. Inhibitory concen-
trations (mM) were ranged between 3.09 1.48 (compound
2) and 30.20 2.18 (compound 23) (Table 2). The chloro-
substitution at fifth position on oxindole nucleus of com-
pound 2, resulting in the resonance effect, could be the
Molecular docking studies were performed to determine the
protein-ligand interactions and also to understand the con-
formational changes in the protein-ligand complex. Docking
studies were performed with the compounds which were
evaluated for in vitro EGFR inhibition. Docking scores of
these compounds were ranged between –2.086 (compound
18) and –5.206 (compound 45). Most commonly interacted
reason for the relatively higher EGFR inhibitory activity. protein residues through hydrogen bonds include ARG 817
Table 2. In vitro and in silico EGFR inhibition studies of Novel Istain derivatives.
EGFR inhibition in terms
No. of
Interacting
H bond
Compound
of IC50 value (mM)
Dock score
H-bonds
amino acids
distance (Å)
Binding energy
2
3.09 1.48
4.11 0.86
5.28 1.47
–2.86
2
2
2
LYS 851
GLY 833
ARG 817
CYS 773
ARG 817
ASN 818
ARG 817
ARG 817
LYS 721
CYS 773
LYS 721
ARG 817
–
ARG 817
CYS 773
ARG 817
–
LEU 694
LYS 721
–
2.01
2.40
2.14
2.04
2.05
2.63
2.45
2.17
2.05
2.09
2.30, 2.08
1.95
–
1.80
2.04
1.97
–
1.72
1.98
–
–41.224458
3
–2.186
–2.988
–34.509403
–41.271329
20
43
18
5.63 0.92
8.92 2.18
–3.757
–2.086
1
2
–27.1737818
–36.063729
34
9.27 1.29
–3.822
3
–51.462836
26
27
44
11.71 1.83
12.85 1.68
13.26 1.39
–3.62
–5.088
–3.305
1
0
2
–48.169181
–57.645734
–43.294269
48
35
36
32
45
23
Gefitinib
14.09 0.97
23.61 2.28
24.50 1.00
27.53 2.47
28.57 2.81
30.20 2.18
0.062 0.009
–2.967
–5.079
–5.095
–3.971
–5.206
–4.605
–
1
0
1
1
0
0
–
–32.542379
–60.1305874
–54.604515
–40.899004
–56.013714
–59.013236
–
–
–
–
–
Figure 1. Ligand binding mode of compound 2 with kinase domain of EGFR protein.

6
S. MAADWAR AND R. GALLA
and CYS 773. Hydrogen bonding was also identified between 851, LEU 694 and CYS 773. However, for compounds such as
protein residues such as LYS 721, ASN 818, GLY 833, LYS 23, 27, 35 and 45 no contribution of H-bonding in the bind-
ing interaction was observed. Binding interaction profile of
the most active EGFR inhibitor (compound 2) among the ser-
ies was shown in Figure 1. Protein-ligand binding energies of
the docked compounds were determined through post dock-
ing calculations and revealed that compound 35 was having
highest binding energy (–60.13 kcal) among the series. Dock
scores, binding profile and binding energies for the eval-
uated compounds were shown in Table 2.
Pharmacophore modelling and 3D QSAR studies
A total of 70 hypotheses with 6 pharmacophore features
were obtained. Of the 70 common pharmacophore hypothe-
ses, a six feature pharmacophoric model, AAARRR.1003 con-
stituting three hydrogen bond acceptors and three aromatic
ring systems was identified as most suitable pharmacophore
model. The 3D spatial arrangement of the 6 pharmacophoric
features of AAARRR.1003 and overlapping of data set ligands
over the obtained pharmacophore model were shown in
Figure 2. A potential QSAR model for the dataset ligands
over AAARRRR.1003 hypothesis was built by employing par-
tial least squares (PLS) regression analysis and the results
were tabulated in Table 3. Regression analysis of total 7 fac-
tors were given of which result of PLS-7 was considered to
be the best as the regression coefficients r2 is 0.96 (for train-
ing set), q2 is 0.76 (for test set) and Pearson-R is 0.90.
Predicted activity of all the dataset ligands obtained from
QSAR studies considering PLS 7 were listed in Table 1. The
plots of experimental activity versus predicted activity of all
Figure 2. 3D spatial arrangement of the common pharmaco-
phore AAARRR.1003.
Table 3. Regression scores of AAARRR.1003.
Hypothesis
Factors
F
R2
Q2
Pearson R
AAARRR.1003
1
2
3
4
5
6
7
15.9
17.4
24.1
33.4
37.8
51.5
91.8
0.313
0.2458
0.5863
0.5718
0.7107
0.7109
0.7989
0.7617
0.6732
0.8607
0.8059
0.8752
0.8875
0.9494
0.8948
0.5056
0.6867
0.8067
0.8591
0.9115
0.9568
Figure 3. Fitness of predicted vs actual pIC50 for all data set ligands and training set ligands (inset).

JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION
7
dataset ligands and training set ligands were shown in Mean Square Deviation (RMSD) is used to measure the aver-
age change in displacement of a selection of atoms for a
particular frame with respect to a reference frame. EGFR-
compound 2 complex was in stable confirmation until 15 ns.
However, there is a sudden spike in the RMSD values of Ca
and side chain amino acids immediately 15 ns and larger
deviation was observed at 20 ns, later in the last few ns of
the simulation, complex seems to be stable at higher
RMSD value. Similar to the deviations observed in the pro-
tein structure, larger deviations were recorded from 15 ns
(Figure 5).
Over the simulation period, total number of protein-ligand
contacts ranged between one and more than nine contacts.
During the simulation time frame ARG 817 was the most
interacted amino acid, followed by LEU 820 and MET 769.
Interactions with amino acids such as ALA 698, ALA 719, GLU
738, MET 742, LEU 764, PRO 770, PHE 771, CYC 773, ASP 776,
ASN 818 and VAL 821 had negligible impact on the overall
Figure 3.
Molecular dynamics
Molecular dynamics simulations performed in the solvent
environment at constant temperature and pressure has
revealed the confirmation changes of the EGFR-compound 2
complex. Stability of the complex is determined in terms of
the RMSD and RMSF of the protein components along with
the ligand. Root mean square fluctuation (RMSF) is useful for
characterizing local changes along the protein chain. During
the simulation, fluctuations in the N- and C-terminals of the
protein were more than any other part of the protein.
Particularly, the Ca and side chain residues in the loop
region of the C-terminal has reported greater fluctuations
(RMSF) beyond 22.5 A0. Majority of the interactions were
observed in the beta-strands of the protein (Figure 4). Root interaction profile (Figure 6). In this simulation model, hydro-
Figure 4. Root mean square fluctuations (RMSF) for the C-alphas and side chain residues of EGFRprotein.
Figure 5. Root mean square deviation (RMSD) of simulated EGFR-compound 2 complex.

8
S. MAADWAR AND R. GALLA
Figure 6. Protein–ligand interaction diagram of protein residues during molecular dynamics simulation.
Figure 7. Interaction fractions of various EGFR residues during molecular dynamic simulations.
gen bonds were recorded with MET 769 and THR 830, how- (most prominent) and minor impact with LYS 721 and ASP
ever, the impact is only transient. Hydrophobic interactions 831 (Figure 7).
were prominent with LEU 694, LYS 721, VAL 702 and LEU
During the 25 ns simulation, deviation in the molecular
820 amino acids. Water bridges were recorded with ARG 871 properties such as ligand RMSD, radius of gyration [rGyr],

JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION
9
Figure 8. Fluctuations in the compound 2 properties during 25 ns simulation.
Figure 9. Extent of torsion at each rotational bond.

10
S. MAADWAR AND R. GALLA
[5]
[6]
Verma M, Pandeya SN, Singh KN, et al. Anticonvulsant activity of
Schiff bases of isatin derivatives. Acta pharm. 2004;54(1):49–56.
Pandeya SN, Yogeeshwari P. Sri Ram Nath DG. Synthesis, antibac-
terial and antifungal activities of N-mannich bases of 3-[N2-pyri-
methaminylimino] isatin. Indian J Pharm Sci. 2002;64:209–212.
Selvam P, Murugesh N, Chandra Mohan M, et al. Synthesis and
antiHIV activity of Novel Isatin-Sulphonamides. Indian J Pharm
Sci. 2008;70(6):779–782.
molecular surface area [MolSA], solvent accessible surface
area [SASA], and polar surface area [PSA] of compound 2
was minimal with no intramolecular hydrogen bonds (Figure
8). However, ligand torsion was higher and radially outward
with the terminal methoxy groups of both the trimethoxy
benzenes attached to the core nucleus (Figure 9).
[7]
[8]
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Conclusion
In the current investigation, a series of novel oxindole deriva-
tives were taken which are previously proved for their cyto-
toxic potentials. Enzyme inhibitory studies suggested that
the inhibitory activity of these compounds against EGFR pro-
tein could be the reason for the higher cytotoxicity.
Molecular docking studies further supported the elucidation
of binding patterns of the molecules in EGFR protein envir-
onment. Pharmacophore modeling studies identified
AAARRR.1003 common pharmacophore model as a valid
model, which was also supported by the employed regres-
sion analysis. Results from molecular dynamics simulations
elucidated the mechanistic insight of EGFR inhibition by
oxindole compounds and their binding phenomenon.
Further simulations with longer time period may provide
deeper insights of ligand interactions in the protein environ-
ment. It is noteworthy to use compound 2 as a new scaffold
for further development of multifunctional compounds.
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Acknowledgements
The authors thank Dr Sunil Misra (senior scientist, Biology Division, CSIR-
IICT, Hyderabad) for providing biological laboratory facilities to perform
biological evaluation. The authors also thank Mrs Swetha and Mr
Devender (Discovery Labs, CSIR-IICT, Hyderabad), for providing facilities
to perform chemical reactions. We would like to acknowledge Mr Reddy
G (ddlabs.in, Hyderabad), for writing assistance and editorial support.
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2 inhibitors: pharmacophore modeling and ligand based 3D-
QSAR studies of pyrido-indole derivatives.
J Recept Signal
Transduct. 2014;35(2):189–201.
Disclosure statement
Rajendra Prasad VVS, Deepak Reddy G, Appaji D, et al.
Chemosensitizing acrodones: in vitro calmouldine dependent
cAMP phosphodiesterase inhibition, docking, pharmacophore
modelling and 3D QSAR studies. J Mol Graph Model. 2013;40:
116–124.
No potential conflict of interest was reported by the authors.
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