2d and 3d qsar study of quinoline derivatives as

www.wjpps.com Vol 4, Issue 06, 2015.

671

Sarita et al. World Journal of Pharmacy and Pharmaceutical Sciences

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]

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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.

http://www.wjpps.com/


672


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



674


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.



681


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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2d and 3d qsar study of quinoline derivatives as

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