Quantitative structure activity relationship and biological activity studies of 4-methyl-2-(4-substituted phenyl)quinoline derivatives

Article abstract of DOI:10.14233/ajchem.2018.20811

Quantitative structure activity relationship (QSAR) studies of some 4-methyl-2-(4-substituted phenyl)quinoline derivatives were carried out to determine their predicted biological activities. Numbers of descriptors were tested to adjudge a quantitative correlation between activity and structural features using training set and test set. Significant correlation was observed between activities and descriptors. The results were interpreted on the basis of linear regression analysis. Experimental antibacterial activities of the test set compounds were determined. The predicted biological activities generated by QSAR model were compared with the experimental antibacterial activities. The concurrence between the predicted and experimental biological activities validates the QSAR model. Thus, it can be concluded that the model under the present investigation can be applied for predicting the unknown biological activities of structurally similar molecules.

Full text of DOI:10.14233/ajchem.2018.20811

Asian Journal of Chemistry; Vol. 30, No. 3 (2018), 479-482
A
SIAN
J
OURNAL OF HEMISTRY
C
https://doi.org/10.14233/ajchem.2018.20811
Quantitative Structure Activity Relationship and Biological Activity Studies of
-Methyl-2-(4-substituted phenyl)quinoline Derivatives
4
1,*
2
3
P.K. RAHANGDALE , F. INAM and S.S. CHOURASIA
1
2
3
Department of Chemistry, Bhawabhuti College, Amgaon-441 902, India
Department of Chemistry, Government Vidarbha Institute of Science and Humanities, Amravati-441 604, India
Department of Chemistry, M.B. Patel College, Deori-441 901, India
*
Corresponding author: E-mail: pralhadrahangdale@gmail.com
Received: 25 May 2017; Accepted: 28 July 2017;
Published online: 31 January 2018;
AJC-18727
Quantitative structure activity relationship (QSAR) studies of some 4-methyl-2-(4-substituted phenyl)quinoline derivatives were carried
out to determine their predicted biological activities. Numbers of descriptors were tested to adjudge a quantitative correlation between
activity and structural features using training set and test set. Significant correlation was observed between activities and descriptors. The
results were interpreted on the basis of linear regression analysis. Experimental antibacterial activities of the test set compounds were
determined. The predicted biological activities generated by QSAR model were compared with the experimental antibacterial activities.
The concurrence between the predicted and experimental biological activities validates the QSAR model. Thus, it can be concluded that
the model under the present investigation can be applied for predicting the unknown biological activities of structurally similar molecules.
Keywords: Quantitative structure activity relationship, Quinoline derivatives, Biological activity.
Quantitative structure activity relationships (QSARs) signifies
computerized statistical method which correlates the activity of the
compound with changes in the structure. The biological activity of
compounds is considered as a function of various physico-chemical
parameters. The biological activity can be optimized by choosing
such substituents whichwould enhance desired physico-chemical
properties. Agrawal et al. [19-28] have reported QSAR studies
on different organic drug compounds.
QSAR deals the relationship of magnitude of various structural
properties with the biological activity. Compounds which have
similar structures to a pharmacologically active drug themselves
are often biologically active. Even though the activity of these
compounds may be either similar to that of the original compound
but may differ in potency and may have unwanted side effects.
The activity may completely differ from the original compound.
These structurally related activities are commonly referred to as
structure-active relationship (SAR) [29]. The mathematical and
statistical analysis of QSAR data finally helps to reduce the number
of molecules for study with respect to potent biological activity.
INTRODUCTION
Quinoline and its derivatives are the important scaffold of
biologically active compounds present in nature [1-6]. Many
heterocyclic units contain quinoline scaffold as an integral part
and show antiviral [7], antimalarial [8-10], antibacterial [8-13]
andanticanceractivities[14]. Conventionalmethodsforthesynthesis
of quinoline and its derivatives reported in the literature include
Skraup [15], Doebner-Von Miller [16], Friedlander [17] and
Combes synthesis. Newer synthetic methods like microwave
assisted organicsynthesis[18]havecomeintoplaysincetheconven-
tional methods suffer from drawbacks such as drastic conditions,
use of hazardous chemicals and poor yield. The test set quinoline
molecules under the present investigation have been synthesized
by one pot microwave assisted organic synthesis approach which
offer advantages like quick reaction time and improved yields.
Even after the synthesis of the potential drug molecule, one
needs to test the biological efficacy of the molecule. The most
difficult problem in the development of molecules as drug is the
time and expenses involved in the development of the drug. It is
reported that a single successful drug molecule takes nearly 14 to
EXPERIMENTAL
1
6 years of research and clinical trial and that to at tremendous
cost. The cost of more than 800 million dollars is expected to
prove a molecule as the drug, which itself is a great problem for
developing countries.
All the chemicals/reagents used in this investigation were
of chemically pure or analytical reagent grade. A number of
quinoline derivatives were synthesized in accordance with the

480 Rahangdale et al.
Asian J. Chem.
Scheme-I. The structures of the derivatives were confirmed with
H NMR, IR and mass spectroscopy. The synthesised quinoline
TABLE-3
COMPOSITION OF TRAINING SET MOLECULES FROM 11-30
1
derivatives were used as test set for the calculation of antibacterial
activities.Thephysico-chemical data ofthesynthesizedcompounds
are given in Table-1.
N
N
CH3
R
R
OCH
2
CONHN=CH
R
2
OCH CONHN=C
1
1-20
21-30
HCl, Acetone
Compound No.
R-CHO
Compound No.
R-COCH3
MW, 2-3 min.
1
1
1
1
1
1
1
1
1
2
1
2
3
4
5
6
7
8
9
0
C H5
21
22
23
24
25
26
27
28
29
30
C H
p-Cl
6
6
5
N
p-Cl
p-OCH3
p-CH3
NH2
CHO
p-OCH3
p-CH3
m-NO2
p-NO2
p-NH2
p-Br
Q -Q
1
8
R
Scheme-I: Synthesis of test set quinoline derivatives
p-(CH ) N
3
2
o-Cl
m-Br
p-OH
m-OH
p-Br
TABLE-1
SYNTHESIZED QUINOLINE DERIVATIVES WITH
THEIR YIELDS AND MELTING POINTS
p-OH
o-OH
Compound
R
H
m.f.
Yield (%)
m.p. (°C)
177-179
183-185
224-227
288-290
237-239
218-221
248-252
360-362
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
C H N
79
74
73
77
72
86
87
76
16
13
Descriptor calculation: Datawarrier software [32] was
used to calculate the various physico-chemical descriptors like
constitutional, molecular, steric and electronic. The details of
descriptors used to develop the model are listed Table-4 and the
descriptors calculated for the training set compounds are given
in Table-5.
CH3
OCH3
OH
C H N
17 15
C H NO
17
15
C H NO
16
13
NO2
Cl
C H N O
16 12 2 2
C H ClN
16
12
Br
C H BrN
16 12
COOH
C H NO
17 13 2
TABLE-4
DESCRIPTORS USED FOR TRAINING
AND TEST SET MOLECULES
QSAR studies
Generation of trainingset: The training set was constructed
using a series of substituted quinoline based compounds as per
literature method [30,31]. A total 30 molecules with different
substitution on the quinoline scaffold were selected as training
set with their known biological activities against bacteria and
fungi. The biological activity data were in the form of reported
zone of inhibition (ZOI) in millimetres. The details of the struc-
ture are given in Tables 2 and 3.
Descriptor used
Symbol
TMW
c log P
TSA
Total molecular weight in g/mol; natural abundance
c log P; P; conc. (octanol)/conc. (water)
Total surface area (from polar and non-polar SAS
approximation)
Drug likeness
Drug
LLE
LELP
MSI
Lipophilic ligand efficiency (LLE)
Ligand efficiency lipophilic price (LELP)
Molecular shape index
Rotatable bond count
RBC
TABLE-2
COMPOSITION OF TRAINING DATA SET
MOLECULES FROM 1-10
Regression analysis: The linear regression was performed
by using the software SPSS by stepwise method [33]. The best
model derived from the regression analysis was used to predict
the biological activity of the synthesised compounds. No outliers
have been determined and the equations were derived using the
entire training data set (n = 30). The resulting models are depicted
in eqns. 1-3 and the derived equations as generated by the QSAR
model were then used to calculate the predicted antibacterial
activities.
HN
N
XH
R
AH
BH
R
1
N
Cl
1
-10
R
2
1
2
Compound No.
R
H
R /R
Cl
QSAR model for E. coli
1
2
3
4
5
6
7
8
9
6-CH3
7-CH3
8-CH3
Cl
Cl
Cl
Cl
Biological activity = (-9.462) + (-0.001*TMW) + (6.281*
c log P) + (0.06*TSA) + (-0.068*Drug) + (-4.581*MSI) +
(0.872*RBC)
(1)
6-OCH3
H
6-CH3
7-CH3
8-CH3
6-OCH3
OCH3
OCH3
OCH3
OCH3
OCH3
QSAR model for S. aureus
Biological activity = (2.96) + (0.001* TMW) + (-3.636*
c log P) + (-0.037*TSA) + (0.012*Drug) + (19.186* MSI)
10
+ (-1.303* RBC)
(2)

Vol. 30, No. 3 (2018)
QSAR and Biological Activity Studies of 4-Methyl-2-(4-substituted phenyl)quinoline Derivatives 481
TABLE-5
VALUE OF DESCRIPTORS CALCULATED FOR THE TRAINING SET COMPOUNDS
Compound No.
TMW
c log P
5.3854
5.7293
5.7293
5.7293
5.3154
4.0334
4.3773
4.3773
4.3773
3.9634
3.0925
3.6985
3.0225
3.4364
2.9889
3.6985
3.8177
2.7468
2.7468
3.8177
3.0304
3.6364
2.9604
3.3743
2.1088
2.1088
2.3531
3.7556
2.6847
2.6847
TSA
Drug
1.8974
1.8000
1.8000
1.8000
1.7961
1.7961
1.7352
1.7352
1.7352
1.7961
4.1966
4.2278
4.2013
4.1258
4.9362
4.2278
2.4066
4.1816
4.1816
2.4066
3.1347
3.1884
3.1842
3.1347
-1.9440
-1.9440
3.1166
1.3447
3.1430
3.1430
LLE
LELP
10.4680
12.0020
12.2500
12.4320
12.1360
9.2973
10.5640
10.6400
10.7070
10.1120
6.5145
8.1687
6.9844
7.6545
7.2401
8.2996
8.5961
6.2046
6.2235
8.6749
6.9049
8.6537
7.3453
8.0698
5.4595
5.4717
5.6656
9.0613
6.4906
6.5033
MSI
RBC
2
2
2
2
3
4
4
4
4
5
5
5
6
5
6
5
5
5
5
5
5
5
6
5
6
6
5
5
5
5
1
2
3
4
5
6
7
8
9
376.673
390.700
390.700
390.700
406.699
367.835
381.862
381.862
381.862
397.861
305.336
339.781
335.362
319.363
348.405
339.781
384.232
321.335
321.335
384.232
319.363
353.808
349.389
333.390
364.360
364.360
334.378
398.259
335.362
335.362
259.83
272.09
272.09
272.09
282.09
273.51
285.77
285.77
285.77
295.77
244.11
259.53
266.37
256.37
278.93
259.53
262.74
250.46
250.46
262.74
254.58
270.00
276.84
266.84
278.25
278.25
263.10
273.21
260.93
260.93
3.6146
2.9697
2.7936
2.6686
2.9856
4.1884
3.7776
3.7196
3.6685
4.0366
4.8661
4.2223
4.8636
4.4175
4.8350
4.0974
3.9519
4.9979
4.9744
3.8813
4.6474
4.0212
4.6779
4.2455
5.4933
5.4762
5.2155
3.7972
4.8529
4.8382
0.58333
0.56000
0.60000
0.56000
0.57692
0.57692
0.55556
0.59259
0.55556
0.57143
0.65217
0.66667
0.68000
0.66667
0.65385
0.62500
0.62500
0.66667
0.62500
0.66667
0.62500
0.64000
0.65385
0.64000
0.59259
0.62963
0.64000
0.64000
0.64000
0.60000
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
QSAR model for P. aeruginosa
RESULTS AND DISCUSSION
A series of 4-methyl-2-(4-substituted phenyl)quinoline
Biological activity = (28.883) + (-0.03* TMW) + (2.502*
c log P) + (0.028*TSA) + (-0.236*Drug) + (-7.08*MSI) +
derivatives (Q1-Q8) have been successfully synthesized. The
purity of the synthesized compounds was established by TLC
and determination of the melting points. The structures of the
(0.991*RBC)
(3)
The predicted and experimental antibacterial activities are
depicted in Table-5 and represented graphically in Fig. 1.
in vitro Antimicrobial activity: The in vitro antibacterial
activities of 2,4-substituted quinoline derivatives have been
investigated against three strains of bacteria. Nutrient agar media
was employed for the bacterial growth. Bacterial plate was
incubated at 37 ºC for 24 h. Three microbial strains viz. E. coli,
S. aureus and P. aeruginosa were used in antimicrobial assay.
Streptomycin was used as standard. Most of the synthesised
compounds were tested for their antimicrobial potency as compared
to reference drug within a MIC range of 25-50 µg/mL. The
screening results are tabulated in Table-6.
1
synthesised compounds were elucidated by IR, H NMR and mass
spectral data. The synthesized compounds were screened for their
antibacterial activities by cup method against various strains of
gram positive and gram negative bacteria.All the derivatives were
significantly active against E. coli. None of the derivative has
shown profound activity against S. aureus whereas all quinoline
derivatives have shown significant activity against P. aeruginosa.
Conclusion
4-Methyl-2-(4-substituted phenyl)quinoline derivatives
have shown remarkable anti biological activities. QSAR studies
3
3
3
6.0
5.5
5.0
17
Predicted Experimental
Predicted
Experimental
2
9
1
2
7
2
3
27
25
y = 0.1759x + 25.151
34.5
2
R
= 0.0188
34.0
33.5
33.0
23
-
Predicted
y = -0.1453x - 8.1859
y = -0.0898x + 34.971
2
Experimental
Linear (Predicted)
2
R = 0.0512
21
R = 0.077
-8
19
32.5
-13
a
b
c
d
e
f
g
h
a
b
c
d
e
f
g
h
a
b
c
d
e
f
g
h
Test set quinoline derivtive number
Test set quinoline derivtive number
Test set quinoline derivtive number
Fig. 1. Predicted and experimental biological activities of test set quinoline compounds (a) E. coli, (b) P. aeruginosa, (c) S. aureus

482 Rahangdale et al.
Asian J. Chem.
TABLE-6
ANTIBACTERIAL SCREENING OF THE SYNTHESIZED QUINOLINE DERIVATIVES
Diameter of zone of inhibition (mm)
Compound
E. coli
S. aureus
Experimental
P. aeruginosa
Predicted
24.90
27.68
26.43
22.98
21.66
29.48
30.51
23.87
Experimental
Predicted
-8.27
-9.52
-9.28
-6.79
Predicted
34.76
35.38
34.94
33.42
33.91
35.47
34.99
33.66
Experimental
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
25
25
25
20
20
28
26
24
11
9
34
35
35
33
34
35
35
34
10
10
10
11
8
-6.89
-10.57
-11.10
-8.32
10
Streptomycin
25
24
28
1
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The concordance between QSAR predicted and experimental
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For S. aureus, some different descriptors seems to plays important
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