Vol 5, No 12 (2017)

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Sarla Biloniya et al IJSRE Volume 05 Issue 12 December 2017 Page 7754

Volume||5||Issue||12||December-2017||Pages-7754-7768||ISSN(e):2321-7545 Website: http://ijsae.in Index Copernicus Value- 76.10 DOI: http://dx.doi.org/10.18535/ijsre/v5i12.02

Modeling of Carbonic Anhydrase Inhibitor-I (Ca-I) With Log Ki (Nanomolar Affinity)

Authors

Sarla Biloniya1, Dr. R.G. Verma2, Dr. Aruna Solanki3, 1

Govt. Science College, Dewas 2

BLP Govt. College, Mhow

E-mail: [email protected], [email protected], ABSTRACT:

This paper deals with a QSAR study on a large set of carbonic anhydrase inhibitor-I using a combination of topological descriptor. The regression analysis has been carried out assuming linear relationship between LogKi nd topological descriptor. The analysis of the data has indicated that an excellent model is obtained. The obtained model is further supported through cross validation.

Keywords: QSAR/ topological descriptor/ carbonic anhydrase inhibitor-I/LogKi

INTRODUCTION

Quantitative Structure Activity (QSAR) is the method of choice. The method, using multivariate statistics, was developed by Hansch and Fujita[1], and it has been successfully applied to many drug and agrochemical design problems. In its simplest form the biological response is assumed to be a function of a number of molecular parameters which correlate with molecular size, lipid solubility or electronic properties [e.g. pI50 = f (physicochemical parameters)].

Quantitative Structure Activity (QSAR) technique represents an effort to link structural or property descriptors of compound with activities. These physico-chemical descriptors which obtain in parameters to accounts for hydrophobicity, topology, electronic parameters and steric effects, are determined empirically or more recently by computational methods. Quantitative Structure Activity Relationship (QSAR) is currently being applied in many disciplines, with many pertaining to drug design and environmental risk assessments.

In QSAR molecular structure are represented in the form of various descriptors, for the modeling of the properties or activities under consideration, the QSAR pipeline is represented below.

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Sulfonamides as a target molecule

The sulfonamides represent an important class of biologically active compounds with at least five different classes of pharmacological agents that have obtained from the sulfonamide structure as lead. The antibacterial sulfonamides continue to play an important role in chemotherapy, alone or in combination with other drugs. Furthermore, the sulfonamides that inhibit the zinc enzyme carbonic anhydrase pouses many applications as diuretic, antiglucoma or antiepileptic drugs among others[2]. Some antithyroid drugs have also been developed starting from the sulfonamide structure as lead molecule[3].

Carbonic anhydrase (CA) catalyzes the inter-conversion of carbon dioxide to biocarbonate. There are three classes α, β, and γ of CA, divided into three genetically unrelated families, viz: animal, plant and bacterial CAs, respectively{4,5}.

Carbonic anhydrase – I is a zinc dependent enzyme. It catalyzes the reversible hydration of carbon dioxide through a two step zinc hydroxide mechanism.[5,6]

CA catalyses the interconversion of CO2 to HCO3-in a two-stage reaction. Human CA catalyzes the reversible hydration of CO2 in two distinct half reactions.[7,8]

Topological Indices :

The descriptors tested has been divided into three separate category viz., Topological indices, Balaban and Balaban type descriptors and Quantum Theoretical descriptors.

In the fields of chemical graph theory, molecular topology, and mathematical chemistry, a topological index also known as a connectivity index is a type of a molecular descriptor that is calculated based on the molecular graph of a chemical compound[9]. Topological indices are numerical parameters of a graph which characterize its topology and are usually graph invariant. Topological indices are used for example in the development of quantitative structure-activity relationships (QSARs) in which the biological activity or other properties of molecules are correlated with their chemical structure[10].

Balaban indices:

Balaban Distance Connectivity Index, J

Quantum Theoretical Descriptors:

Quantum chemical calculations, in principle, can express all of the electronic and geometric properties of a molecule as the electronic structure is fundamental to other molecular observables. The quantum chemical methods used throughout this work will be presented in section 1.4 given below. Chapter 3 gives an overview of quantum chemical descriptors used in present work.

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Sarla Biloniya et al IJSRE Volume 05 Issue 12 December 2017 Page 7756 CF3SO2Cl

(A)

n-C4F9SO2Cl

(B)

O

Cl

F

(D)

F

S

O

Cl

(C)

Figure 1

RESULT & DISCUSSION 3.1 Biological activities

The first step in developing QSAR equations is to compile a list of compounds for which the experimentally determined inhibitory activity is known. This can be obtained from various sources such as from electronic database, directly from experimental method and most commonly from the literature.

This list consists of 95 substituted aromatic sulfonamides collected from the literature.[11-13]

The inhibition data are expressed in terms of nanomolar affinity (KI) as log Ki for the investigated isozyme and are presented in the Table 1. This biological activity can also be expressed as an inhibition potency of the compounds i.e., its inhibition potency to reduce the activity of Carbonic anhydrase enzyme, participating in many biological actions.

The type of tail attached to the different substituted aromatic sulphonamides is indicated by A, B, C, & D and the substituted sulphonamides are represented by 1, 2, 3, 4 ………

Table 1 : The Inhibition activity logKi (hCAI) of the substituted sulphonamides used in the present study.

S.No Compound logKi

1. _A1 _4.311

2. _A2 _4.272

3. _A3 _4.037

4.

A4 4.17

5.

A5 3.699

6. _A6 _2.778

7. _A7 _2.699

8.

A8 2.863

9.

A9 3.017

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Sarla Biloniya et al IJSRE Volume 05 Issue 12 December 2017 Page 7757 11.

A11 1.954

12.

A12 2

13. _A13 _1.38

14. _A14 _1.114

15.

A15 0.477

16.

A16 0.699

17. _A17 _1.322

18. _A18 _1.362

19. _A19 _1.398

20.

A20 -0.046

21. _A21 _-0.046

22. _A23 ₀

23.

A24 3.708

24.

A25 2.74

25. _B1 _2.398

26. _B2 _2.23

27.

B3 2.204

28.

B4 2.255

29. _B5 _2.176

30. _B6 _2.176

31.

B7 1.991

32.

B8 2.628

33. _B9 _2.686

34. _B10 _1.708

35.

B11 0.903

36.

B12 0.301

37. _B13 _0.301

38. _B14 _0.477

39.

B15 0.301

40.

B16 0.602

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B18 1.301

43.

B19 1.301

44. _B20 _-0.301

45. _B21 _-0.301

46.

B23 0

47.

B24 2.663

48. _B25 _2.585

49. _C1 _1.38

50. _C2 ₁

51.

C3 1

52. _C4 _1.204

53. _C5 _1.176

54.

C6 1.176

55.

C7 0.954

56. _C8 _2.041

57. _C9 _2.097

58.

C10 1.176

59.

C11 0.699

60. _C12 _-0.523

61. _C13 _-0.523

62.

C14 -0.398

63.

C15 0

64. _C16 _0.176

65. _C17 _0.903

66.

C18 0.903

67.

C19 1.041

68. _C20 _-0.699

69. _C21 _-0.523

70.

C23 -0.301

71.

C24 1.602

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D1 1.544

74.

D2 1.279

75. _D3 _1.23

76. _D4 _1.362

77.

D5 1.301

78.

D6 1.23

79. _D7 _1.176

80. _D8 _2.097

81. _D9 _2.193

82.

D10 1.58

83. _D11 _1.079

84. _D12 _0.301

85.

D13 0.176

86.

D15 0.301

87. _D16 _0.903

88. _D17 _1.255

89.

D18 1.556

90.

D19 1.431

91. _D20 _-0.301

92. _D21 _-0.222

93.

D23 -0.155

94.

D24 1.732

95. _D25 _1.699

* A,B,C,D represents respective tails attached to the mentioned aromatic sulphonamides. 3.2 Calculation of Topological Descriptors

In order to deduce the correlation of observed activity, in terms of logKi of reported compounds with different structural parameters, systematic QSAR investigations have been carried out using the QSAR model proposed.

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3.3 Regression analysis

Correlation analysis was carried out with the response variable and independent parameters of the 95 substituted aromatic sulphonamides set. Multiple linear regression analysis is the method of choice in the present investigation.

From this large set of descriptors, the useful or relevant descriptors are selected using the methodology of variable selection in multiple regression analysis.

The intercorrelatedness of all variables i.e., dependent and independent variables is demonstrated in Table 5 in the form of correlation matrix. There are two types of stepwise multiple linear regression methods : 1. Stepdown multiple linear regression : In this method, initially combination of all possible descriptors are taken in consideration and in each step one descriptor shall be removed from the large pool of descriptor. In this method number of independent parameter in the mathematical equation decreases with each step and finally reaches to the significant model. i.e., model starts from multivariate combination and ends at heptavariate, hexavariate, pentavariate, tetravariate and so on.

2. Stepup multiple linear regression method :

On the other hand this starts from univariate, bivariate, trivariate and so on, that means each descriptor has been added in a successive step. In the present investigation/study step – up multiple linear regression has been adopted. The results of various combination has been demonstrated in the Table 6, showing all the relevant statistical parameters of each combination.

Table 5 : Correlation matrix of all variable used in present study.

logKi QC QN QO QH χ

1

W J

logKi 1

QC -0.67493 1

QN -0.20769 0.12886 1

QO 0.28088 -0.11932 -0.07994 1

QH 0.20214 -0.03315 -0.06005 0.17591 1

χ1 _-0.54915 _0.27714 _0.16595 _-0.15716 _0.05504 ₁

W -0.43311 0.23576 0.12365 -0.06998 0.07639 0.96293 1

J 0.44907 -0.17748 -0.03184 0.47246 0.19162 -0.40249 -0.40218 1 JHETZ 0.39223 -0.09191 -0.02464 0.56484 0.16569 -0.48654 -0.45644 0.89262

JHETM 0.3769 -0.06569 -0.02646 0.5534 0.15478 -0.48656 -0.4519 0.86103

JHETV 0.48854 -0.23053 -0.05894 0.449 0.33701 -0.41053 -0.41798 0.94058

JHETE 0.45809 -0.19571 -0.03682 0.46631 0.23098 -0.45249 -0.46091 0.97007

JHETP 0.44793 -0.18465 -0.05824 0.52153 0.27841 -0.42503 -0.41019 0.90188

BAC -0.12988 0.112 0.10284 0.29954 0.17709 0.54993 0.53185 0.50643 HOMO -0.01427 -0.00625 -0.04238 0.22694 0.05542 0.19671 0.13716 0.23405 LUMO -0.00929 0.03949 0.00707 0.06318 0.06617 -0.00269 -0.03665 0.00777 I1 0.42204 -0.10437 -0.1739 0.23027 0.03675 -0.6599 -0.5243 0.18096

I2 0.02899 0.05606 0.06083 0.34616 0.03525 0.32199 0.34092 0.57999

I3 -0.3196 0.07157 0.06145 0.21874 0.00676 0.21723 0.12249 -0.33843

I4 -0.13332 -0.0236 0.05236 -0.80662 -0.07989 0.12242 0.06177 -0.42859

JHETZ JHETM JHETV JHETE JHETP BAC HOMO LUMO

JHETZ 1

JHETM 0.97986 1

JHETV 0.88601 0.88436 1

JHETE 0.92184 0.89538 0.95754 1

JHETP 0.94867 0.92958 0.95402 0.93273 1

BAC 0.3469 0.33147 0.45011 0.42974 0.40284 1

HOMO 0.08314 0.06466 0.14486 0.17215 0.10106 0.38668 1

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Sarla Biloniya et al IJSRE Volume 05 Issue 12 December 2017 Page 7761 I2 0.3482 0.31968 0.40533 0.45938 0.34408 0.83932 0.47406 -0.01027

I3 -0.17153 -0.16168 -0.2419 -0.27705 -0.17815 -0.12652 0.17704 0.19921

I4 -0.58546 -0.57773 -0.39337 -0.43051 -0.49053 -0.29711 -0.22353 -0.08752

I1 I2 I3 I4

I1 1

I2 -0.33803 1

I3 -0.33803 -0.33803 1

I4 -0.3286 -0.3286 -0.3286 1

The correlation matrix for the aforementioned descriptors and logKi is presented in Table 5 and shows that there is significant correlation among the logKi and QC (Mulliken charge on Carbon atom of sulphonamide group) and χ1

(First order connectivity index) descriptors with -0.67493 and -0.54915 regression coefficient, respectively, which is greater than 0.5 value.

From the correlation matrix it can also conclude that none of the aforementioned indices is highly correlated with the activity and none of the individual parameters is sufficient to explore quantitative structure activity relationship (QSAR). This means that it is not possible to obtain a statistically significant mono-parametric model. Based on the correlation matrix, it is concluded that only multiparametric regressions involving combinations of the indices mentioned before may result in a statistically significant regression expression. For achieving this goal we have carried out step-wise regression analysis following the multiple linear regression method. The different results obtained in step wise method, ie., starting from monoparametric to multiparametric models are given in Table 6. Although thousands of permutation combinations are tested, but only few combinations are listed below.

3.4 QSAR Models Monoparametric Model

The resulting mono parametric models are depicted in model 1-12, along with statistical parameters of the regression. The results are taken with the entire dataset of 95 compounds (n = 95).

Out of the 12 significant monoparametric results model 1 shows best statistical results and the model can be expressed in the form of Eq 1 as below :

logKi = -7.82 (±0.886) QC + 1.6511 --- (1)

n = 95, r = -0.6749, Se 0.8632, F = 77.808, Q = 0.782

The regression coefficient of Mulliken charge of Carbon atom of sulphonamide group is -7.82, the negative coefficient indicate that inhibition potency decreases with increase in value of QC and vice versa. The standard error of mulliken charge QC is mentioned within parentheses.

Therefore, any factor which reduces mulliken charge on the Carbon atom increase inhibition activity. The r value of -0.6749 is the maximum r value shown by the single descriptor, rest all descriptors shows lesser r value and this can be clearly observed in correlation matrix and correlation Table (i.e., Table 6). However χ1

is second descriptor showing significant r value i.e., -0.5492. In order to determine more descriptive model, biparametric combinations were tested. Relatively more emphasis has been given to the biparametric combinations containing either QC or χ1.

The stanadard error of estimation of Eq (1) is 0.8632, which is greater than the value of r, therefore this also illustrate the requirement of biparametric model. The higer value of Se and comparatively lower value of r result in very low quality factor i.e., Q = 0.782

Biparametric Model

In order to improve above result upto the more significant level, biparametric combinations were tested and some biparametric models are shown in Table 6, the most appropriate result has been obtained by the combination of QC and χ1 and model is expressed in the form of Eq 2.

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Sarla Biloniya et al IJSRE Volume 05 Issue 12 December 2017 Page 7762 n = 95, r = 0.773, Se = 0.7462, F = 68.303, Q = 1.04

In Eq (2) logKi (inhibition constant) shows similar dependence on QC as shown by the Eq (1), another parameter introduced in the model is the Randic first order connectivity index. The negative correlation coefficient of the connectivity index indicate that inhibition potency decreases with increase in the value of the χ1

. Therefore, lower values of both QC and χ1 is favorable for the inhibition activity of the given set of substituted aromatic sulphonamides.

The significant raise in the value of r from -0.6749 to 0.773 has been observed, and reduction in the value of standard error of estimation, from 0.8632 to 0.7462, but there is slight reduction in F ratio from 77.808 to 68.303, but this can be justify by the significant raise in the value of Q (Quality factor), from 0.782 to 1.04, therefore Eq (2) is statistically justifying the incorporation of χ1 in the equation.

Biparametric model clearly demonstrating the substitution which reduce mulliken charge on Carbon atom of sulphonamides as well as reducing χ1

of the compound is favorable and enhancing the inhibition potency of the sulphonamides.

Triparametric Model

In the step wise systematic multiple linear regression procedure, after obtaining biparamteric model, triparametric combinations has been tested to find out the third descriptor participating in regulating inhibition activity of the compounds.

Again several tri combinations has been tested, few are presented in Table 6. The more importance has been given to the triparametric combinations containing parameters of Eq (2) i.e., QC and χ1 . Also, it has been observed that the best results has been shown by the combination which carries QC and χ1 .

The Triparametric with its statistical parameters is as below :

logKi = -6.1321 (±0.717) QC - 0.3162 (±0.0486) χ1 + 0.0012 (±0.00024) W + 7.763

---(3) n = 95, r = 0.8262, Se = 0.6664, F = 65.216, Q = 1.24

In triparametric combination, Wiener index is introduced as a third parameter. However rest of the two parameters i.e., QC and χ1 are playing same role as in Eq (1) and Eq (2) with their negative correlation coefficient.

On the other hand newly introduced Wiener index showing positive correlation coefficient, leads to the increase in the value of logKi with raise in the value of W. Therefore, sulphonamides having substitution, which reduces Mulliken charge at Carbon atom of sulphonamide group, possessing lower connectivity index and higher value of Wiener index results in efficient inhibitory potency.

Statistical parameters also justifying the incorporation of W in the model. There is a significant raise in the value of r from 0.773 to 0.8262, standard error of estimation reduced from 0.7462 to 0.6664, F ratio however reduced from 68.303 to 65.216 but this can be ignored due to considerable increase in Quality factor Q from 1.04 to 1.24. All statistical parameters supporting the fitness of model.

Therefore, from Eq (3) it has been clearly demonstrated that higher value of W and lower value of QC and χ1

has been favorable for the inhibitory activity of the substituted aromatic sulphonamides. Tetraparametric model

Another step of step wise multiple linear regression method introduce one more parameter to improve statistical results of the model. This helps to find out the fourth important parameters, participating in the inhibition activity of the sulphonamides under consideration.

As in the previous step, several tetraparametric combinations has been tested, out of which few are presented in the Table 6 in the tetraparametric models. It has been observed that tetraparametric combination, which contain all the three parameters of triparametric models are the most promising among all the other tetraparametric combinations.

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Sarla Biloniya et al IJSRE Volume 05 Issue 12 December 2017 Page 7763 logKi = -5.6814 (±0.6521) QC - 0.3153 (±0.0437)χ1 + 0.0013 (± 0.00022) W + 0.7233 (±0.1528) JHETV + 6.0029 --- (4)

n = 95, r = 0.8636, Se = 0.5996, F = 66.018, Q = 1.44

The positive coefficient of newly introduce parameter JHETV, i.e., Balaban type index for van der waal weighted distance matrix, indicate that inhibition activity increases with increase in the value of JHETV. Rest three parameters are following the same trends as they are in Eq (1), (2)and (3).

The inclusion of JHETV is justified by observing statistical parameters of Eq (4), the r value increased from 0.8262 to 0.8636, standard error of estimation reduced from 0.664 to 0.5996 and the most remarkable achievement in this model is the raise in the value of F ratio from 65.216 to 66.018, Also, there is increase in the value of Q from 1.24 to 1.44.

Eq (4) suggests that the lower value of QC and χ1 and higher values of W and JHETV is favorable for the inhibitory action of the sulphonamides.

Pentaparametric model

In order to improve results upto its atmost level, pentaparametric combination has been tested and recorded in Table 6. Out of all result only few important ones are listed in the Table 6.

The Pentaparametric combination is shown below in the form of Eq (5)

logKi = -4.5733 (± 0.6876) QC - 0.3919 (± 0.0464) χ1 + 0.0016 (± 0.00022) W - 0.5327 (± 0.1494) JHETM + 1.7727 (±0.3275) JHETV + 7.23 ---(5)

n = 95, r = 0.8818, Se = 0.564, F = 62.237, Q = 1.56

The incorporation of JHETM has been justified by the relative trends in statistical parameters, from Eq(4) to Eq (5) which can be described as : Raise in r from 0.8636 to 0.8818, decrease in Se from 0.5996 to 0.564 and increased Q value from 1.44 to 1.56.

The logKi values has been calculated for all the 95 compounds with their residual values (difference of Observed logKi and Calculated logKi). The fitness of the model can also observed by ploting a linear graph between observed and calculated logKi. The observed, calculated logKi with their residual are given in Table 7, and graph as Figure 1

Figure 1: Graphical representation of Obs. logKi and Calc. logKi from Eq (5)

-2 -1 0 1 2 3 4

-1 0 1 2 3 4 5

Obs logKi

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Table 7: Observed logKi, Calculated logKi from Eq (5) with their residual values S.No Obs logKi Calc. logKi Residual

1. _4.311 _3.298 _1.0127

2. _4.272 _3.471 _0.8013

3. _4.037 _3.562 _0.4747

4. _4.17 _2.975 _1.195

5. _3.699 _3.565 _0.134

6. _2.778 _3.383 _-0.6046

7. _2.699 _3.328 _-0.629

8. _2.863 _3.42 _-0.557

9. _3.017 _3.277 _-0.26

10. _2.633 _3.436 _-0.8029

11. _1.954 _1.869 _0.0846

12. ₂ _2.766 _-0.7657

13. _1.38 _1.507 _-0.1272

14. _1.114 _1.278 _-0.164

15. _0.477 _1.519 _-1.0422

16. _0.699 _0.706 _-0.0072

17. _1.322 _0.66 _0.6619

18. _1.362 _1.047 _0.3153

19. _1.398 _0.936 _0.4623

20. _-0.046 _-0.196 _0.1503

21. _-0.046 _0.363 _-0.4091

22. ₀ _0.559 _-0.5588

23. _3.708 _2.75 _0.9578

24. _2.74 _2.969 _-0.229

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26. _2.23 _1.546 _0.684

27. _2.204 _1.657 _0.5471

28. _2.255 _1.412 _0.8432

29. _2.176 _2.148 _0.0276

30. _2.176 _2.446 _-0.2704

31. _1.991 _1.497 _0.4943

32. _2.628 _2.29 _0.3382

33. _2.686 _2.295 _0.3911

34. _1.708 _2.304 _-0.5959

35. _0.903 _1.161 _-0.2579

36. _0.301 _1.389 _-1.0883

37. _0.301 _0.608 _-0.3073

38. _0.477 _0.553 _-0.0755

39. _0.301 _1.17 _-0.8688

40. _0.602 _1.184 _-0.582

41. _1.176 _1.329 _-0.1526

42. _1.301 _1.67 _-0.3687

43. _1.301 _1.502 _-0.2005

44. _-0.301 _-0.411 _0.1097

45. _-0.301 _-0.541 _0.2399

46. ₀ _0.685 _-0.6849

47. _2.663 _1.464 _1.1992

48. _2.585 _2.059 _0.5256

49. _1.38 _1.526 _-0.1464

50. ₁ _1.628 _-0.6285

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52. _1.204 _0.795 _0.4093

53. _1.176 _1.478 _-0.3021

54. _1.176 _1.693 _-0.5168

55. _0.954 _0.873 _0.0811

56. _2.041 _0.99 _1.0506

57. _2.097 _1.648 _0.4487

58. _1.176 _0.811 _0.3647

59. _0.699 _0.335 _0.3637

60. _-0.523 _0.472 _-0.9953

61. _-0.523 _0.117 _-0.6401

62. _-0.398 _0.015 _-0.4133

63. ₀ _0.351 _-0.3507

64. _0.176 _0.382 _-0.2056

65. _0.903 _0.471 _0.4316

66. _0.903 _0.789 _0.1142

67. _1.041 _0.633 _0.4078

68. _-0.699 _-1.054 _0.355

69. _-0.523 _-0.898 _0.3754

70. _-0.301 _0.06 _-0.3612

71. _1.602 _1.071 _0.5309

72. _1.544 _1.365 _0.1788

73. _1.544 _1.659 _-0.1153

74. _1.279 _1.521 _-0.2418

75. _1.23 _1.583 _-0.3534

76. _1.362 _1.182 _0.1799

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78. _1.23 _1.951 _-0.7209

79. _1.176 _1.377 _-0.2015

80. _2.097 _1.343 _0.7541

81. _2.193 _1.368 _0.8247

82. _1.58 _1.39 _0.1897

83. _1.079 _0.919 _0.1602

84. _0.301 _0.984 _-0.6827

85. _0.176 _0.749 _-0.5727

86. _0.301 _0.582 _-0.2811

87. _0.903 _0.651 _0.2521

88. _1.255 _0.732 _0.5226

89. _1.556 _0.924 _0.6321

90. _1.431 _0.772 _0.6586

91. _-0.301 _-0.758 _0.4566

92. _-0.222 _0.733 _-0.9555

93. _-0.155 _0.827 _-0.9819

94. _1.732 _1.448 _0.2841

95. _1.699 _1.639 _0.0598

Usually on the basis of maximum residual value the compound are removed from the dataset, but in present case the improvement in result is only observed by removing compounds no 93 from above dataset.

In a stepwise manner few outliers has been removed one by one, first compounds no 93 was removed. After removing compounds no 93 the same combination i.e., Eq (5) has been tested for the set of 94 substituted aromatic sulphonamides, and the model.

According to both the Eq’s the QC possessed the highest correlation coefficient among all the five descriptors and W showing least correlation coefficient among all the five parameters. Therefore, the sequence of priority on the basis on their correlation coefficient shall be arranged as :

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Figure 7 : Relative contributions of the all the five parameters participating in the Eq (5) to model inbitory activity of the sulphonamides.

CONCLUSION:

From above mention result and discussion it is conclude that models which obtained by the combination of topological indices and structural description have better predictivity and quality.

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