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2D AND 3D QSAR STUDY OF QUINOLINE DERIVATIVES AS
ANTITUBERCULAR AGENTS
Sarita Ahirwar1*, Priyadarshini Agarwal
2 and
Pradeep Patra
3
1Assistant Professor Sri Satya Sai School of Pharmacy, Sehore (M.P.) 466001.
2Department of Pharmacy, Barkatullah University, Bhopal (M.P.) 462026.
3Department of Pharmacy, Kanpur Institute of Technology, Kanpur (U.P.).
ABSTRACT
Tuberculosis (TB) remains the leading cause of mortality due to
bacterial pathogen Mycobacterium Tuberculosis. There is also a
considerable effort to discover and develop newer substituted
quinoline. In this research article the 2D QSAR of quinoline
derivatives is done with the help of Vlife MDS 3.5 Software and 2D
QSAR of 35 compound were done by using PLS Method and statistical
values of the best model is r2( 0.9182),q
2( 0.8160).and 3D statistical
values of the model is r2(0.6422), Pred_r
2 (0.6198) Thus above stated
work can be further used for the designing of new potent antitubercular
drugs.
KEYWORDS: Antitubercular activity, Antimycobacterial activity,
Fluoroquinolines, Quinoline Derivatives, DNA Gyrase Inhibitor, Partial Least Square.
INTRODUCTION
Tuberculosis (TB) remains a health problem of enormous dimension throughout the world. It
is estimated that nearly 1 billion people will become newly infected, over 150 million will
become sick, and 36 million will die worldwide between now and 2020 if control is not
further strengthened.[1]
The response of patients with MDR-TB to treatment with expensive
and toxic second-line drugs is poor and the mortality rate is about 50%. Recent advances such
as the availability of the TB genome sequence have provided a wide range of novel targets
for drug design.[2, 3]
WWOORRLLDD JJOOUURRNNAALL OOFF PPHHAARRMMAACCYY AANNDD PPHHAARRMMAACCEEUUTTIICCAALL SSCCIIEENNCCEESS
SSJJIIFF IImmppaacctt FFaaccttoorr 55..221100
VVoolluummee 44,, IIssssuuee 0066,, 667711--668844.. RReesseeaarrcchh AArrttiiccllee IISSSSNN 2278 – 4357
Article Received on
31 March 2015,
Revised on 20 April 2015,
Accepted on 12 May 2015
*Correspondence for
Author
Sarita Ahirwar
Assistant Professor, Sri
Satya Sai School of
Pharmacy, Pachama,
Sehore (M.P.) 466001.
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Fluoroquinolonic nucleus led to the development of new derivatives with better solubility,
higher antimicrobial activity, prolonged serum half-life, Fluoroquinolones exhibit potent in
vitro and in vivo antimycobacterial activity.[4, 5, 6]
Fluoroquinolones are used for the clinical
control of multidrug resistant[7]
.The bactericidal activity generated by Fluoroquinoline has
good antitubercular activity against Mycobacterium tuberculosis. Fluoroquinoline inhibit the
enzyme bacterial DNA gyrase, which nicks double stranded DNA, introduces negative
supercoils and then reseals the nicked ends. This is necessary to prevent excessive positive
supercoiling of strands when they separate to permit replication or transcription. The DNA
Gyrase consists of two A and two B subunit. A subunit carries out nicking of DNA, B
subunit introduces negative supercoils and then A subunit reseals the strands. Quinolines bind
to A subunit with high affinity and interfere with its strand cutting and resealing function.
Recent evidence indicates that in gram positive bacteria, the major target of quinoline action
is a similar enzyme topoisomerase IV which nicks and separates daughter DNA strand after
DNA replication. Greater affinity for topoisomerase IV may confer higher potency gram
positive bacteria. To date few studies have been undertaken to optimize the fluoroquinolones
against M. tuberculosis.[8, 9, 10,11]
MATERIAL AND METHODS
Two series combine were selected for QSAR from the journal Bioorganic & Medicinal
Chemistry Letters. Only 35 structures were selected from combine series. Structure of
compound with substitution of R and R1 position and their biological activity is given in
table 1 and table 2.
Fig. 1: Basic Moiety used for Substitution from Series -1
N
O O
OHF
OCH3
R
R1
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Table 1: List of Compound Selected from Series -1
[12]
Sr. No. R R1 MIC -logP
1. H
CH3N
NO
O
13.72 -1.1373
2. NO2
CH3N
NO
O
6.25 -0.7958
3. H
CH3N
NO
O
1.57 -0.1958
4. NO2
CH3N
NO
O
0.35 0.4559
5. NH2
CH3N
NO
O
3.06 -0.4857
6. H
CH3N
S
2.06 -0.3138
7. NO2
CH3N
S
1.84 -0.2648
8. H
CH3N
N
7.06 -0.8488
9. NO2
CH3N
N
6.41 -0.8068
10. H
CH3N
Cl
OH
6.43 -0.8082
11. NO2
CH3N
Cl
OH
5.88 -0.7693
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12. H
CH3N
N
NH O
Cl
5.94 -0.7737
13. NO2
CH3N
N
NH O
Cl
5.47 -0.7379
14. H
CH3N
N
O
CH3
CH3
1.69 -0.2278
15. NO2
CH3N
N
O
CH3
CH3
1.55 -0.1903
16. H
CH3N
O
O
0.93 0.0315
17. NO2
CH3N
O
O
0.84 0.0757
18. H
CH3N
N
N
O
OH
3.53 -0.5477
19. NO2
CH3N
N
N
O
OH
3.120 -0.4941
20. H CH3
N
O
CH3
CH3
4.14 -0.6170
21. NO2 CH3
N
O
CH3
CH3
1.85 -0.2671
Fig. 2: Basic Moiety used for substitution from series -2
N S
O O
OHR
R1
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Table 2: List of Compound Selected from Series-2[13]
Sr. No. R R1 MIC -logP
22 F
CH3N
NO
O
1.47 -0.1673
23 NO2
CH3N
NO
O
5.60 -0.7481
24 F
CH3
N
N
O
N
O
CH3
F
F
2.52 -0.4014
25 NO2
CH3
N
N
O
N
O
CH3
F
F
4.85 -0.6857
26 F
CH3N
S
3.76 -0.5751
27 NO2
CH3N
S
7.09 -0.8506
28 F
CH3N
N
0.39 0.4089
29 NO2
CH3N
N
3.08 -0.4885
30 F
CH3N
Cl
OH
0.36 0.4436
31 NO2
CH3N
Cl
OH
2.84
-0.4533
32 F
CH3N
N
NH O
Cl
2.77 -0.4424
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33 NO2
CH3N
N
NH O
Cl
5.31 -0.7250
34 F
CH3N
O
O
0.86 +0.0655
35 NO2
CH3N
O
O
1.62 -0.2095
2D QSAR Study
Partial least square regression method is used to generate 2D QSAR equation. For variable
selection, stepwise forward-backward method was used.
Criteria for Selection of Model
n = number of molecules (> 20 molecules)
k = number of descriptors in a model (statistically n/5 descriptors in a model)
df = degree of freedom (n-k-1) (higher is better)
r2 = coefficient of determination (> 0.7)
q2 = cross-validated r
2 (>0.5)
pred_r2 = r
2 for external test set (>0.5)
SEE = standard error of estimate (smaller is better)
F-test = F-test for statistical significance of the model (higher is better, for same set of
descriptors and compounds).
Selected Models
About 20 QSAR models were generated by using partial least square regression method
coupled with stepwise forward-backward method. Among the various models two significant
QSAR models were finally selected. Model summary of two best models are given below.
RESULTS AND DISCUSSION
For QSAR analysis regression was performed using MIC (Minimum Inhibitory
Concentration) values as dependent variables (Biological Activity) and calculated parameters
as independent variables (Descriptor). In any thorough investigation of the effects of
molecular properties, it is essential to prove that the results are both statistically valid and
make chemical sense.
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Table 3: Uni-Column Statistics
Model
No. Set
Column
Name Average Maximum Minimum
Std
Deviation Sum
1 Training pMIC -0.3619 0.4559 -1.1373 0.4033 -7.5999
Test pMIC -0.4391 -0.1673 -0.8082 0.2902 -3.0735
2 Training pMIC -0.5131 0.4559 -1.1373 0.4319 -9.2358
Test pMIC -0.2885 0.0655 -0.5751 0.2161 -2.0192
Table 4: Statistics of Model-1 and Model-2
Model No. r2 q
2 r
2 se q
2 se pred_r
2 pred_r
2se oc n df f-test
1 0.7771 0.5177 0.2065 0.3038 0.6873 0.1688 1 21 17 19.7514
2 0.9182 0.8160 0.1361 0.2042 0.7662 0.1571 3 18 14 52.3941
Equation:
Model 1: pMIC = - 0.4126 T_N_O_2- 0.0683 SsOHE-index- 0.5273 SaaOcount+ 0.0144
T_T_T_7-0.5286 SsNH2count+ 0.0110
Model 2: pMIC = - 0.1825 T_C_O_8+ 0.1704 T_C_O_2+ 0.1045 chiV0- 0.5277 SaaOcount-
0.3968T_O_S_14 -2.5839
Table 5: Correlation Matrix of Model -1
Model-1 T_N_O_2 SsOHE-index SaaOcount T_T_T_7 SsNH2count Score
T_N_O_2 1 -0.29862 0.244851 0.339478 -0.14752 5
SsOHE-index -0.29862 1 -0.18471 0.230371 -0.11265 5
SaaOcount 0.244851 -0.18471 1 0.315507 -0.09129 5
T_T_T_7 0.339478 0.230371 0.315507 1 -0.10427 5
SsNH2count -0.14752 -0.11265 -0.09129 -0.10427 1 5
Table 6: Correlation Matrix of Model -2
Model-2 T_C_O_8 T_C_O_2 chiV0 SaaOcount T_O_S_14 Score
T_C_O_8 1 0.441522 0.261899 0.2928 -0.04402 5
T_C_O_2 0.441522 1 -0.01371 0.379136 0.083592 5
chiV0 0.261899 -0.01371 1 0.280158 0.194184 5
SaaOcount 0.2928 0.379136 0.280158 1 -0.18898 5
T_O_S_14 -0.04402 0.083592 0.194184 -0.18898 1 5
Fig. 3: Contribution Chart of Model-1 Fig. 4: Contribution Chart of Model-2
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Table 7: Contribution Values of Model -1 and Model -2
Table 8: Actual Predicted Activity Table 03
Compounds Model -1 Model -2
Code A B C A B C
SA01 -1.1373 -1.01325 -0.12406 -1.1373 -1.17791 0.040614
SA02 -0.7958 -0.80125 0.005453 -0.7958 -0.71309 -0.08272
SA03 -0.1958 0.008999 -0.2048 -0.1958 -0.08527 -0.11053
SA04 0.4559 0.22099 0.23491 0.4559 0.37956 0.07634
SA05 -0.4857 -0.50862 0.02292 -0.4857 -0.03303 -0.45267
SA06 -0.3138 -0.23985 -0.07395 -0.3138 -0.70909 0.39529
SA07 -0.2648 -0.02786 -0.23694 -0.2648 -0.24426 -0.02054
SA08 -0.8488 -0.08976 -0.75904 -0.8488 -0.72559 -0.12321
SA09 -0.8068 0.122229 -0.92903 -0.8068 -0.62571 -0.18109
SA10 -0.8082 -0.77386 -0.03434 -0.8082 -0.49246 -0.31574
SA11 -0.7693 -0.56423 -0.20507 -0.7693 -0.39258 -0.37672
SA12 -0.7737 -0.81766 0.043962 -0.7737 -0.85667 0.082967
SA13 -0.7379 -0.61301 -0.1249 -0.7379 -0.78584 0.047943
SA14 -0.2278 -0.48695 0.259146 -0.2278 -0.47294 0.245137
SA15 -0.1903 -0.27496 0.084655 -0.1903 -0.37306 0.182758
SA16 0.0315 -0.07187 0.103368 0.0315 -0.62395 0.655446
SA17 0.0757 -0.04393 0.119634 0.0757 -0.62395 0.699646
SA18 -0.5477 -0.65713 0.109431 -0.5477 -1.08256 0.534863
SA19 -0.4941 -0.43227 -0.06183 -0.4941 -0.61774 0.123635
SA20 -0.617 -0.61027 -0.00674 -0.617 -1.24041 0.623413
SA21 -0.2671 -0.35506 0.087961 -0.2671 -0.77558 0.508484
SA24 -0.1673 0.128766 -0.29607 -0.1673 -0.15703 -0.01027
SA25 -0.7481 0.239284 -0.98738 -0.7481 -0.81846 0.070355
SA26 -0.4014 -0.65447 0.253067 -0.4014 -0.17906 -0.22235
SA27 -0.6857 -0.54395 -0.14175 -0.6857 -0.84048 0.154782
SA28 -0.5751 -0.10568 -0.46942 -0.5751 -0.56652 -0.00858
SA29 -0.8506 0.004841 -0.85544 -0.8506 -0.863 0.0124
SA30 0.4089 0.030005 0.378895 0.4089 -0.21808 0.626975
SA31 -0.4885 0.140523 -0.62902 -0.4885 -0.51455 0.026052
SA32 0.4436 -0.67005 1.113649 0.4436 0.197536 0.246064
SA33 -0.4533 -0.56425 0.110949 -0.4533 -0.46389 0.010591
SA34 -0.4424 -0.69777 0.255366 -0.4424 -0.34915 -0.09325
SA35 -0.725 -0.58725 -0.13775 -0.725 -0.64563 -0.07937
SA38 0.0655 0.033495 0.032005 0.0655 0.248521 -0.18302
SA39 -0.2095 0.144012 -0.35351 -0.2095 -0.04796 -0.16154
A: Actal Activity, B: Predicted Activity, C: Residual Activity.
Descriptor Contribution Descriptor Contribution
T_N_O_2 -30.90% T_C_O_8 -30.40%
SsOHE-index -26.09% T_C_O_2 26.92%
SaaOcount -17.40% chiV0 16.98%
T_T_T_7 14.99% SaaOcount -16.98
SsNH2count -10.62% T_O_S_14 -9.31%
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Fig. 5: Fitness Plot of Model-1 Fig. 6: Fitness Plot of Model-2
Descriptors Contribution
T_N_O_2: This descriptor signifies distance between nitrogen and oxygen by two bonds.
SsOHE-index: This descriptor signifies number of –OH group connected with one single
bond.
SaaOcount: This descriptor signifies total number of oxygen connected with two aromatic
bonds.
T_T_T_7:- This descriptor signifies connected of any atom by seven bonds.
SsNH2count: This descriptor signifies number of –NH2 group connected with one single
bond.
T_C_O_8: This descriptor signifies distance between carbon and oxygen by eight bonds.
T_C_O_2: This descriptor signifies distance between carbon and oxygen by two bonds.
ChiV0: this descriptor signifies atomic valence connectivity index from and this is
calculated as sum of 1/ sqrt over all heay atom i with vi >0.
SaaOcount: This descriptor signifies total number of oxygen connected with two aromatic
bonds.
T_O_S_14: This descriptor signifies distance between oxygen and sulphur by fourteen
bonds.
3D QSAR Model
Statistics
Statistics value of 3D Model
Terms q2 q
2se Pred_r
2 Pred_r
2 se K Nearest Neighbour n Degree of Freedom
Values 0.6422 0.2401 0.6198 0.2518 2 23 19
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Uni-Column Statistics
From the uni column statistics of the 3D model, it was seen that the maximum of the training
set was higher than test set and minimum of the test set was higher than training set.
Table 9: Uni-Column Statistics of 3D QSAR Model
Model Set Column Name Average Maximum Minimum Std Deviation Sum
3D
Model
Train-ing pMIC -0.3949 0.4559 -1.1373 0.4014 -9.0817
Test pMIC -0.4779 0.4436 -0.8506 0.3990 -5.2565
Correlation Matrix
From the correlation of 3D model shown in table 10, it was seen that descriptor ware not
correlated with each other.
Table 10: Correlation Matrix of 3D Model
S_1046 S_517 S_926 Score
S_1046 1 0.421463 -0.44634 3
S_517 0.421463 1 -0.27119 3
S_926 -0.44634 -0.27119 1 3
Fitness Plot Graph
From the fitness plot it was seen that all the test set structures were near to the best fit line but
some structures of the training deviated from the best fit line and they were far from the best
fit line.
Fig. 7: Fitness Graph of 3D QSAR Model
Actual Predicted Activity
The actual activity along with predicted activity and residual is given in table11. In the 3D
model structure SA35 was deleted from the training set. Since it was not giving satisfactory
values if included in test set.
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Table 11: Actual Predicted Activity of 3D Model
Show Points
The show point provides the information regarding the site where the structural modification
has to be done. Figure 08 shows the descriptors that have been generated on the lead (SA01).
Code pMIC Predicted Residual
SA01 -1.1373 -0.82125 -0.31605
SA02 -0.7958 -0.54597 -0.24983
SA03 -0.1958 -0.44365 0.247854
SA04 0.4559 -0.1869 0.642796
SA05 -0.4857 -0.4218 -0.0639
SA06 -0.3138 -0.33415 0.020348
SA07 -0.2648 -0.36699 0.102191
SA08 -0.8488 -0.78238 -0.06642
SA09 -0.8068 -0.80902 0.002223
SA10 -0.8082 -0.52204 -0.28616
SA11 -0.7693 -0.39661 -0.37269
SA12 -0.7737 -0.4436 -0.3301
SA13 -0.7379 -0.4219 -0.316
SA14 -0.2278 -0.0794 -0.14841
SA15 -0.1903 0.053109 -0.24341
SA16 0.0315 -0.06043 0.091928
SA17 0.0757 -0.07955 0.155245
SA18 -0.5477 -0.44245 -0.10525
SA19 -0.4941 -0.47667 -0.01743
SA20 -0.617 -0.38569 -0.23131
SA21 -0.2671 -0.5857 0.3186
SA22 -0.1673 -0.4219 0.254597
SA23 -0.7481 -0.70517 -0.04293
SA24 -0.4014 -0.19012 -0.21128
SA25 -0.6857 -0.5855 -0.10021
SA26 -0.5751 -0.96647 0.391367
SA27 -0.8506 -0.58941 -0.2612
SA28 0.4089 0.261926 0.146974
SA29 -0.4885 -0.70744 0.218938
SA30 0.4436 0.432392 0.011208
SA31 -0.4533 -0.5871 0.1338
SA32 -0.4424 -0.16838 -0.27402
SA33 -0.725 -0.55943 -0.16557
SA34 0.0655 0.006934 0.058566
SA35 -0.2095 - -0.2095
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Fig. 8: Site of alteration on SA01 (Lead)
CONCLUSION
From 2D and 3D QSAR study it is concluded that in 2D QSAR, T_T_T_7, ChiV0, T_C_O_2
descriptor were highly correlated with activity and have positive contribution in the model.
From the 2D models, it was seen that on increasing the steric hindrance, there was an
increasing in the antitubercular activity. In 3D QSAR model S-1046, S-517, S-926 descriptor
were generated which shows that steric hindrance was important parameter for antitubercular
activity. The descriptors showed by QSAR study can be used further for study and designing
of new compounds. Consequently this study may prove to be helpful in development and
optimization of existing antitubercular activity of this class of compounds.
ACKNOWLEGEMENT
Authors wishes to thank V-Life technical staff for their time to time support. Authors are also
thankful to Department of Pharmacy, Barkatullah University, Bhopal for providing molecular
modeling facilities.
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