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CHAPTER 3

QSAR Studies of Synthesized Quinazoline Derivatives

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Introduction

In computational drug design, QSAR is well established screening tool

used in medicinal chemistry. A quantitative relationship was established

between chemical structure and biological activities using statistical

methods {1}.

Previous reports suggest the well understanding between biological

activity and structure of synthesized drugs by QSAR with multi-linear

regression method {2-13}. A relationship between Biological activity

physicochemical parameters was effortfully studied by QSAR.

3.1 Molecular Modelling

The structures were sketched in the ISIS draw 2.4 (Standlone Software).

The compounds were modeled into 3-D with the help of HYPERCHEM

(Hypercube Inc.) and Corina modules. 3-D structure helps in the

computation of 3-D descriptors. Files were saved as .hin file and .mol file

as extension file and used as input file for the other software.

3.2 Generation of Molecular Descriptors

The extension files .hin file and .mol files were imported into the

Molecular Modelling Pro (ChemSW, San Francisco). These files were used

as input file to calculate the molecular descriptors. The descriptors

which were calculated: Molecular weight (MW), molecular surface area.

(SA), molar volume (MV), Kier Chi (χ), Kappa shape (κ) and verloop

parameters were calculated using a semi-emperical package of Mopac

software with a time limit of 30 min. Finally, lipophillic parameters (log P)

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were estimated by Hyperchem. For evaluation purposes, estimated log P

values were compared with experimental value. All the descriptors were

cross checked by computing in other software (Codessa and Dragon).

3.3 Statistical analysis

The data was analyzed by using software Instat (Graphpad software Inc.).

Stepwise regression analysis was used to determine the most significant

descriptors. The regression coefficient was obtained by multi-linear

regression analysis. For each regression analysis following descriptors

information was obtained: Number of observations used in the analysis

(n), Square of Correlation (r2), Correlation coefficient, standard error of

estimate (S) and Fisher’s criterion (F).

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Table 3.1 List of descriptors obtained from molecular modeling pro, Codessa and Dragon

ID molecular_weight molecular_volume Log_P H_bond_acceptor H_bond_donor dipole_moment

QD-1 401.4673 204.785 3.929334 0.55 0.71 2.13

QD-2 339.3961 183.7485 2.450334 0.56 0.53 2.28

QD-3 355.3958 181.1044 1.783334 0.99 0.89 3.96

QD-4 384.3941 197.7232 2.193334 0.56 0.5 4.64

QD-5 354.4113 182.0343 1.223334 1.09 0.97 1.8

QD-6 437.4482 218.9772 4.215334 0.56 0.65 3.28

QD-7 435.912 214.0956 4.642334 0.55 0.68 3.28

QD-8 369.4225 197.6281 2.369334 0.69 0.49 3.27

QD-9 354.4107 190.4607 1.223334 1.09 0.97 1.53

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Table 3.2 List of descriptors obtained from molecular modeling pro, Codessa and Dragon

MR parachor connectivity_0 connectivity_1 connectivity_2 connectivity_3 valence_0 valence_1 valence_2

122.912 881.5006 40.0454 15.30966 13.22475 11.75374 35.71955 10.08156 7.116197

103.423 746.9003 34.93252 12.73718 11.14009 9.797268 31.3328 8.420875 5.986976

104.948 761.5004 35.80275 13.13103 11.76195 10.20795 31.70266 8.555163 6.167824

113.051 818.0004 36.3801 14.04172 12.67278 10.84558 31.44991 8.829145 6.348595

106.845 774.0005 36.80275 13.13103 11.77385 10.1244 32.83279 8.620234 6.246413

122.612 901.7004 39.78589 16.09736 14.46847 12.57511 34.32078 10.28089 7.397931

127.779 921.1006 39.91564 15.70351 13.84661 12.16442 35.77448 10.55838 7.692567

109.684 806.9004 38.50987 13.66904 11.93105 10.6162 34.6637 8.943931 6.349452

106.845 774.0003 36.80276 13.13103 11.76195 10.20795 32.83279 8.620233 6.242958

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Table 3.3 List of descriptors obtained from molecular modeling pro, Codessa and Dragon

valence_3 kappa_2 connectivity_index_4 valence_index_4 L1 B1 B2 B3 B4

5.080245 10.95052 10.15549 3.564728 6.287826 5.640179 5.640179 6.788778 6.788778

4.227356 9 8.271731 2.877637 10.52871 5.52398 5.52398 5.640624 5.640624

4.320871 9.212018 8.423429 2.900914 2.084063 1.5 1.7 1.5 1.77

4.456359 10.08 8.957654 3.007789 3.463127 1.55 2.576403 2.439515 1.55

4.340194 9.212018 8.566384 2.962002 8.435451 1.727584 4.378659 3.290881 2.378622

5.221111 11.37278 10.45888 3.5882 6.357246 1.7 6.313941 6.313544 1.700029

5.402116 11.16 10.30718 3.702183 3.470095 1.77 1.77 1.77 1.77

4.543586 9.871282 8.734046 3.030503 6.357519 2.067487 3.01426 8.070215 2.082071

4.364251 9.212018 8.423428 2.922604 8.46167 1.770107 6.83426 4.204411 1.770202

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3.4 Results and Discussion

In this study several substituents related parameters were

optimized against log (1/c) i.e. anti-bacterial activity. The values of the

selected descriptors for each of the compounds were shown in the Table

3.1-3.3. This data clearly explains the substituent related constitutional

parameters as well as connectivity and shape parameters. Among the

Verloop’s steric parameters, only the length parameter L and the breadth

parameter B1 were considered, since the other breadth parameters B2,

B3 and B4 were found to be highly correlated to B1. Sets of non-

correlated descriptors were chosen for regression analyses {14-20}. In

each of the sets log P were kept in common. The regression analysis was

done separately for each bacterial strain taken under this study. The

results are shown in Table 3.4. In equations A-D the right hand side

descriptor values were derived for the substituents and the left hand side

denoted the property of the whole molecule. The antibacterial activity of

substituted Quinazoline derivatives against B.Subtilis is best described

using the molecular descriptors (r2 = 0.99, Eq. A). Value of the

descriptors for different compounds put in the equation to predict drug

activity for different bacteria. The comparisons of observed and predicted

values are shown in Tables 3.5 - 3.6. There is no significant difference

observed between predicted and experimental values.

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Table 3.4 regression EquationsB.subtilis (A) log(1/c)=-27.134-0.6784*[Log_P]+0.7143*[MR]-

0.2273*[parachor]+0.06112*[valence_0]+5.300*

[kappa_2]-0.02180*[L1]-0.01031*[B1]+

8.536*[valence_1]

Sum-of-squares 1.580E-06

SD of residuals 0.001257

R squared 0.9999

Adjusted R squared 1.0000

Multiple R 1.0000

F 31233.73

E.coli(B) log(1/c)=1.367-0.2895*[Log_P]

+0.06847*[parachor]-3.975*[kappa_2]-

0.08254*[L1]-1.864*[х_1]-2.947* [valence_1] +

3.556*[connectivity_1] -1.774[connectivity_2]

Sum-of-squares 0.0001634

SD of residuals 0.01278

R squared 0.9994

Adjusted R squared 0.9945

Multiple R 0.9997

F 205.8681

S.aureus(C) [log(1/c)=27.453-0.1987*[Log_P]-0.6347*

[MR]+0.2058*[parachor]+0.03065*[valence_0]-

5.305*[kappa_2]+0.03973*[L1]-0.07073*[B1]-

7.974* [valence_1]

Sum-of-squares 0.0007182

SD of residuals 0.02680

R squared 0.9974

Adjusted R squared 0.9764

Multiple R 0.9987

F 47.5625

S.dysentrae log(1/c)=3.977-0.1194*[Log_P]+0.02197* [MR]-

1.641*[connectivity_1]+0.7730*[kappa_2]-

0.01404*[L1]-0.05564*[B1]+

0.6911*[connectivity_2]

Sum-of-squares 1.035E-05

SD of residuals 0.003218

R squared 0.9999

Adjusted R squared 0.9989

Multiple R 0.9999

F 1012.8279

Negative correlation between hydrophobicity and drug activity explained

by the equations A-D and it was observed that drug activity increases

with decrease in hydrophobicity. Earlier reported studies on Schiff base

suggest the positive relationship between hydrphobicity and anti-

bacterial activity whereas our study contradicted these previous findings.

Perhaps there may be alterations in active sites of currently studied

72

compounds. An equation outcome gives an idea for the relationship

between connectivity parameters and antibacterial activity. Moreover this

eqation values explains the binding efficacy of substituents to the

receptors and the electron withdrawing groups in the metaposition of the

phenyl group resulted in an increase in activity.

Table 3.5 Compound-wise diagnostics values for Gram Positive bacteria

log(1/c)B.S(pre) log1/cB.S(obs) residual log(1/c)S.A(pre) log(1/c)SA(obs) Residual

1.206 0.934 -0.272 0.9065 0.976 0.0695

1.136 1.045 -0.091 1.136 1.1366 0.006

0.852 0.789 -0.063 0.852 0.873 0.021

0.885 0.812 -0.073 1.186 1.034 -0.152

0.85 0.879 0.029 1.1515 1.243 0.091

1.6409 1.79 0.149 1.242 1.585 0.343

1.242 0.934 -0.308 1.545 1.243 -0.302

0.868 0.85 -0.068 1.169 1.078 -0.091

0.8505 0.82 -0.03 0.8505 0.949 0.99

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Table 3.6 Compound-wise diagnostics values for Gram negative bacteria

3.5 Conclusions

Conclusively, the QSAR studied relationship results in the increase in

antibacterial activity with decrease in hydrophobicity which can be

further explained the dramatically decline in growth of micro-organisms.

Now this judgement is beneficial in illucudating the novel approach in

drug designing and development for the future prospective.

log(1/c)E.C(pre) log(1/c)E.C(obs) residual log(1/c)S.D(pre) log(1/c)S.D(obs) residual

1.206 1.1038 -0.1022 1.206 1.104 -0.102

1.136 0.832 -0.304 1.136 1.032 -0.104

0.852 0.917 0.065 0.852 0.97 0.118

1.186 1.153 -0.033 0.885 0.853 -0.032

0.674 0.553 -0.121 0.674 0.753 0.079

1.242 1.186 -0.056 1.64 1.586 -0.054

1.545 1.243 -0.302 1.545 1.443 -0.102

0.868 0.943 0.075 0.868 0.943 0.075

0.8505 0.8549 0.044 1.151 1.1518 0.008

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