Predicting the Anti-Inflammatory Activity of Novel 5-Phenylsulfamoyl-2-(2-Nitroxy) (Acetoxy) Benzoic acid derivatives using 2D and 3D-QSAR (kNN-MFA) Analysis

 

Kiran Madhawai*, Dr. Dinesh Rishipathak, Dr. Santosh Chhajed, Dr. Sanjay Kshirsagar

Department of Pharmaceutical Chemistry, Bhujbal Knowledge City, MET’S IOP, Adgaon, Nashik-422003, India.

*Corresponding Author E-mail: kiranmadhawai777@gmail.com

 

ABSTRACT:

The quantitative structure–activity relationship (QSAR) analyses were carried out for a series of new side chain modified 5-phenylsulfamoyl-2-(2-nitroxy) (acetoxy) benzoic acid derivatives to find out the structural requirements of their anti-inflammatory activities. The statistically significant best 2D QSAR models for anti-inflammatory activity having correlation coefficient (r2) = 0.897 and cross validated squared correlation coefficient (q2) = 0.701 with external predictive ability (pred_r2) = 0.390 were developed by multiple linear regression coupled with genetic algorithm (GA–MLR) and stepwise (SW–MLR) forward algorithm, respectively. The results of the present study may be useful on the designing of more potent analogues as antimalarial agents.

 

KEYWORDS: QSAR, 5-phenylsulfamoyl-2-(2-nitroxy) (acetoxy) benzoic acid, Anti-inflammatory, MLR, Genetic algorithm.

 

 


1. INTRODUCTION:

Most NSAIDs are weak organic acids. Once absorbed, they get bound to serum albumin .Due to increased vascular permeability in localized sites of inflammation; this high degree of protein binding may result in the delivery of higher levels of NSAIDs. They are a chemically diverse group.

 

The primary effect of NSAIDs is to inhibit COX enzyme, thereby blocking the transformation of arachidonic acid to prostaglandins, prostacyclin and thromboxanes. These result in complex effects on vascular permeability and platelet aggregation, undoubtfully contributing to the overall clinical effects of these compounds (Abadi at all 2005).

COX 1, or prostaglandin synthase H, is a house keeping enzyme that regulates normal cellular functions and is stimulated by hormones and growth factors. It is constitutively expressed in most tissues and is inhibited by NSAIDs in varying degrees. COX 1 is important in maintaining the integrity of the gastric and duodenal mucosa and many of the side effects of NSAIDs on the gastrointestinal tract are attributed to its inhibition (Furst and Ulrich, 2007).

 

COX 2 or prostaglandin synthase H2 is an inducible enzyme and is usually not detectable in most tissues. Its expression is increased during states of inflammation or experimentally in response to mitogenic stimuli. Its expression is inhibited by glucocorticoids. COX-2 is also inhibited by all of the presently used NSAIDs, to a lesser or greater degree. Thus, differences in the effectiveness with which a particular NSAID inhibits an isoform of COX may affect with its activity and its potential toxicity. It has been proposed that the ideal NSAID would inhibit the inducible COX-2 alone, without having any effect on COX-1.

 

Our interest is in developing new effective medications as anti-inflammatory agent because of less toxicity in GIT. Pharmacological evaluation shows the NO2 release of compound will decrease the GIT toxicity (Bandarage, et al, 2000).

 

The purpose of the present study is to investigate the physicochemical parameters responsible for the anti-inflammatory effect of 5-phenylsulfamoyl-2-(2-(nitrooxy) (acetoxy) benzoic acid derivatives, explore the correlation between them and to obtain more information for designing novel substituted 5-phenylsulfamoyl-2-(2-(nitrooxy) (acetoxy) benzoic acid derivatives with potent protective activity. In the present investigation, three widely used techniques, viz. stepwise forward variable selection method, Genetic algorithm and simulated annealing have been applied for descriptor optimization and multiple linear regression analysis, principal component regression and partial least square has been applied for two and three-dimensional QSAR models development. The generated models provide insight into the influence of various interactive fields on the activity and, thus, can help in designing and forecasting the protecting effect of novel 5-phenylsulfamoyl-2-(2-(nitrooxy) (acetoxy)benzoic acid molecules (Bhandari, et al, 2009).

 

A series of 5-phenylsulfamoyl-2-(2-(nitrooxy) (acetoxy)benzoic acid analogues which were reported are chosen for QSAR study in order to establish quantitative relationship between physiochemical properties and biological activities of the compounds using MDS software (VlifeScience)

 

2. MATERIALS AND METHODS:

All molecular modelling studies (2D and 3D) were performed using the Molecular Design Suite (VLife MDS software package, version 4.6; from VLife Sciences, Pune, India), on a HP computer with a Pentium IV processor and a Windows 7 operating system. Structures were sketched using the 2D draw application and converted to 3D structure

 

 

Fig 1: Parent Chemical structure of 5-phenylsulfamoyl-2-(2-(nitrooxy) (acetoxy) benzoic acid drivatives

 

 

Table 1: Substituent and Biological activity

Sr. No.

Substitution

%NO Release

logIC50

1

2-methoxy

20.86

1.319

2

3-methoxy

11.23

1.050

3

4-methoxy

14.31

1.155

4

2-nitro

11.26

1.651

5

3-nitro

13.02

1.114

6

4-nitro

11.51

1.060

7

2-fluro

18.20

1.260

8

4-fluro

17.82

1.250

9

2,4-dichloro

16.34

1.213

10

-H

15.74

1.197

11

4-bromo

12.14

1.084

12

4-chloro

11.25

1.051

13

6-methyl

9.00

0.954

14

5-methyl

14.40

1.158

15

4-methyl

15.95

1.202

16

Naphthalene

19.90

1.298

17

Hydrazine

14.80

1.170

18

4-carboxyl

12.30

1.089

19

2-carboxyl

11.31

1.053

 

2.1 Biological data:

The anti-inflammatory activity of 5-phenylsulfamoyl-2-(2-(nitrooxy) (acetoxy) benzoic acid derivative were taken from the reported work. The total set of compounds was divided into a training set for 2d and 3D QSAR models and a test for validating the quality of the model. Selection of training and test set was done on the basis of structural diversity and wide range of activity such that the test set molecules represent a range of biological activity similar to that of the training set; thus, the test set is truly representative of the training set.  The biological activity values [IC50 (µM)] reported in micromolar units were converted to their molar units and then further to negative logarithmic scale and subsequently used as the dependent variable for the QSAR analysis. The values of IC50   along with the structure of the compounds in the series are listed in Table 1.

 

2.2. Molecular modeling for 2D QSARS:

In 2D QSAR analysis, significant methods Multiple linear regression, principle component regression and partial least square were applied to generate the2D-QSAR model. The 2D structures were converted to 3D structures by sending them to MDS software. Each compound was energy minimized and batch optimized by using Merck Molecular Force Field and charges followed by Austin Model-1. Hamiltonian method was available in MOPAC module with the convergence criterion 0.001 kcal/mol A fixing Root Mean Square Gradients (RMS) to 0.001 kcal/mol.2D descriptors (physicochemical and alignment independent) were calculated for the optimized compounds on QSAR pus worksheet. The invariable descriptors (the descriptors that are constant for all the molecules) were removed, as they do not contribute to QSAR .Most stable structure for each compound was generated after energy minimization and used for calculating various physicochemical descriptors like thermodynamic, steric and electronic. The energy-minimized geometry was used for the calculation of the various 2D descriptors (individual, Chi, ChiV, path-count, chi Chain, ChiV Chain, Chain path count, cluster, path cluster, kappa, Element count, Estate contributions, semi-empirical, Hydrophilic-hydrophobic, polar surface area and Alignment independent) and was considered as independent variables in the present study. 

 

QSAR analysis was performed after removal of all the invariable columns, as they do not contribute to the QSAR. The optimal test and training data set were generated using the manual as well as random data selection method. Sphere exclusion method is used for creating training and test set from the data. All 19 molecules were subjected to regression analysis using Multiple linear regression analysis, as model building methods coupled with step-ward forward backward variable selection method. Regression analysis was carried out for treatment of drug abuse disorders and the best model was cross-validated. Best two dimensional QSAR results obtained by multiple linear regression analysis (using random and manual data selection method), partial least squares regression are obtained by the following table.

 

Table 2; 2D QSAR model optimization by Multiple Linear Regression analysis

Trials

r2

q2

r2se

q2se

Pred_r2

Pred_r2se

F test

1(Model-13)

0.897

0.701

0.047

0.019

0.309

0.1262

14.19

2(Model-14)

0.878

0.764

0.040

0.085

0.129

0.114

18.84

3(Model-15)

0.799

0.741

0.094

0.147

0.292

0.106

20.94

 

The selection of the best model is based on the values of r2 (squared correlation coefficient), q2 (cross-validated correlation coefficient), pred_r2 (predicted correlation coefficient for the external test set), F (Fisher ratio) value. High values of the F-test indicated that the model was statistically significant. r2se, q2se and pred_r2se are the standard errors terms for r2, q2 and pred_r2 respectively.

 

2.3. Three dimensional (3-D) QSAR studies:

In the kNN-MFA method three models were generated for the selected members of training and test sets, and the corresponding best two models are reported here in. VLife Molecular Design Suite 4.6 allows user to choose probe, grid size and grid interval for the generation of descriptors. The variable selection methods along with the corresponding parameters are allow to be choosen, and optimum models are generated by maximizing q2.. K-Nearest neighbor molecular field analysis requires suitable alignment of given set of molecules. To derive the kNN-MFA descriptor fields, a 3D cubic lattice with grid spacing of 2A0 and a charge of + 1.0 with default but energy 30Kcal/mole to generate stearic field, electrostatic, hydrophobic fields. The 3D QSAR studies were performed by kNN-MFA using stepwise forward backward, simulated annealing selection method and genetic algorithm method. The software produced more than 1568 descriptors are prior to module development descriptors having zero values or same values were removed which result in more than total 2500 descriptors for all the compounds in separate columns. This algorithm allows constructing training sets covering all descriptors space areas occupied by representative points. KNN-MFA with simulated annealing and stepwise variable selection was employed for selection of variables to obtain the QSAR models.

 

2.4. k-Nearest neighbor molecular field analysis (kNN-MFA):

The kNN methodology relies on a simple distance learning approach whereby an unknown member is classified according to the majority of its kNN in training set. The nearness is measured by an appropriate distance metric. The 3D QSAR studies were performed by kNN-MFA using stepwise forward backward variable selection method. In this method the cross-correlation limit set to 0.5 and term selection criterion as q2. F-test ‘in’ was set to 4.0, and F test ‘out’ to 3.99. As some additional parameters, variance cutoff was set at 0 kcal/mol and scaling to auto scaling; additionally, kNN parameter setting was done within the range of 2-5 and prediction method was selected as the distance-based weighted average.

 

The model was derived by clicking OK, after all the parameters have been set. Once the significant model is obtained, its fitness plot and contour plot is saved.

 

Table 3 : Best results of 3D-QSAR models obtained by kNN-MFA method

Trials

kNN

DOF

q2

q2_se

pred_r2

pred_r2se

1(Model-16)

3

10

0.8170

0.2667

0.7773

0.2815

2(Model-17)

2

9

0.7121

0.2951

0.6754

0.3759

3(Model-18)

2

10

0.7979

0.2638

0.5119

0.4780


2.5. Development and validation of QSAR models:

The developed QSAR models are evaluated using the following statistical measures: n, (the number of compounds in regression); k, (number of variables); DF, (degree of freedom); optimum component, (number of optimum PLS components in the model); r2, (the squared correlation coefficient); r2se, (standard error of squared correlation coefficient); F test, (Fischer’s value) for statistical significance; q2, (cross-validated correlation coefficient); q2_se, (standard error of cross-validated square correlation co-efficient); pred_r2, (r2 for external test set); pred_r2se, (standard error of predicted squared regression); Z score, (Z score calculated by the randomization test); best_ran_q2, (highest q2 value in the randomization test); best_ran_r2, (highest r2 value in the randomization test). The regression coefficient r2 is a relative measure of fit by the regression equation. It represents the part of the variation in the observed data that is explained by the regression. However, a QSAR model is considered to be predictive, if the following conditions are satisfied: r2 > 0.6, q2 > 0.6 and pred_r2 > 0. The F-test reflects the ratio of the variance explained by the model and the variance due to the error in the regression. High values of the F-test indicate that the model is statistically significant. The low standard error of r2 (r2_se), q2 (q2_se) and pred_r2 (Pred_r2se) shows absolute quality of fitness of the model. Internal validation was carried out using ‘leave-one-out’ (q2, LOO) method. The cross-validated coefficient, q2, was calculated using the following equation:

 

http://www.sciencedirect.com/sd/blank.gifWhere yi, and View the MathML source are the actual and predicted activity of the ith molecule in the training set, respectively, and ymean is the average activity of all molecules in the training set.

 

However, a high q2 value does not necessarily give a suitable representation of the real predictive power of the model for antimalarial ligands. So, an external validation was also carried out in the present study. The external predictive power of the model was assessed by predicting pIC50 value of the nine test set molecules, which were not included in the QSAR model development. The predictive ability of the selected model was also confirmed by pred_r2.

 

 

Where yi and ŷi are the actual and predicted activity of the ith molecule in the test set, respectively, and ymean is the average activity of all molecules in the training set.

To evaluate the statistical significance of the QSAR model or an actual data set, we have employed a one-tail hypothesis testing.

 

 

3. RESULTS AND DISCUSSION:

The importance and utility of the new 2D and 3D QSAR method discussed has been established by applying it to known sets of molecules as described above. The importance and utility of the new 2D and 3D QSAR method discussed has been established by applying it to known sets of molecules as described above.

 

All the calculated descriptors were considered as independent variable and biological activity as dependent variable. In 2D QSAR analysis, significant methods like Multiple linear regression analysis, Partial Least Square (PLS) and Principal Component Regression (PCR) were applied to generate the model having good q2 and pred_r2  values, one of which was selected having good internal and external predictivity. Selection of training and test set was by Manual data selection and random data selection method. Training and test set were selected if they follow the unicolumn statistics, i.e. maximum of the test is less than maximum of training set and minimum of the test set is greater than of training set, which is prerequisite for further QSAR analysis. This result shows that the test is interpolative i.e., derived from the min-max range of training set. The mean and standard deviation of the training and test set provides insight to the relative difference of mean and point density distribution of the two sets the QSAR models developed by kNN-MFA include both the electrostatic, steric descriptors along with their range to indicate their importance for interaction in molecular field. QSAR investigations of the substituted 5-N-phenylsulfamoyl-2-(2-(nitrooxy)   (acetoxy) benzoic   acid   derivatives   series resulted in several QSAR equations.   

 

3.1 2D-QSAR model:

Model 13:

By Random data selection method;

Model-13 (Test set: 2, 3, 4, 8, 16, and 19)

pIC50 = -0.1801 (SaasC count) - 0.2101 (T_N_N_3) + 0.2741(T_N_N_7) + 1.906

 

Statistics:            

[n= 13; Degree of freedom= 9; r2 =0.897; q2=0.701; F test=14.19; r2se=0.047; q2se= 0.019; pred_r2= 0.309; pred_r2se = 0.126]

 

From model 13 explains 89.7% (r2 = 0.897) of the total variance in the training set as well as it has internal (q2) and external (pred_r2) predictive ability of 70.16 % and 12.6 % respectively. The F test shows the statistical significance of 99.99 % of the model which means that probability of failure of the model is 1 in 10000. In addition, the randomization test shows confidence of 99.9999 (Alpha Rand Pred R^2 = 0.00000) that the generated model is not random and hence chosen as the QSAR model. The F-test=14.19 which is greater than the tabulated value 2.93766 (Bolton 2004).

 

From QSAR model 13 it is revealed that,

1.      Negative coefficient value of T_N_N_3 [This is the count of number of Nitrogen atoms (single double or triple bonded) separated from any other Nitrogen atom (single double or triple bonded) by 3 bonds in a molecule.] on the biological activity indicated that lower values leads to good inhibitory activity while higher value leads to reduced inhibitory activity.

2.      Positive coefficient value of T_N_N_7 [This is the count of number of nitrogen atoms (single double or triple bonded) separated from any nitrogen atom (single or double bonded) by 7 bond distance in a molecule.] on the activity indicated that higher value leads to better inhibitory activity whereas lower value leads to decrease inhibitory activity.

3.      Negative coefficient value of SaasC count [This descriptor signifies the total no of carbon connected with one single bond along with two aromatic bonds.]On the biological activity indicated that lower values leads to good activity while higher value leads to reduced activity.

 

Contribution chart Data fitness plot and activity of training and test set for model 13 is represented in Fig.2, 3, 4 and 5 respectively.

 

Fig. 2 contribution plot

 

Fig.3 Data fitness plot

 

Fig.4 Training set activity

 

 

Fig.5 Test set activity (2, 3, 4, 8, 16, 19)

 

3.2.3D-QSAR model:

Model-16 (Test set: 11, 12, 14, 18, and 19)

pIC50= E_397 (-2.4827,-0.4193) + S_469 (0.0643, 0.1358)                                                

Statistics: [kNN= 3; n = 14; DOF= 10; q2= 0.8170; q2_se = 0.2667; pred_r2 = 0.7773; pred_r2se = 0.2815]

 

From QSAR model 16 it is revealed that,

The model 16 explains values of k (3), q2 (0.8170), pred_r2 (0.7773), q2_se (0.2667), and pred_r2 se (0.2815) prove that QSAR equation so obtained is statistically significant and shows the predictive power of the model is 81.70% (internal validation).

 

The data fitness plot for model 16 is shown in Fig.7.The plot of observed vs. predicted activity Fig. 8,9 provides an idea about how well the model was trained and how well it predicts the activity of the external test set.

 

Fig. 6 Plot of contribution chart

 

 

Fig. 7 Data fitness plot

                                                                                                                                         

 

Fig. 8 Training Set

 

Fig. 9 Test Set (11, 12, 14, 18, 19)

 

Steric field, S_469 (0.0643, 0.1358) positive steric potential is present around the field which indicates the favorability of bulky groups on the salicylic acid ring to increase the activity.

 

Electrostatic field, E_937 (-2.4827,-0.4193) negative coefficient suggested that electronegative substituent may be favorable on the position of salicylic acid ring for better activity.

 

4. CONCLUSION:

In the present investigation, all proposed QSAR models were statistically significant, thus, from above  QSAR  investigations  it  could  be  concluded  that  2D/3D  descriptors  properties  of substituted 5-phenylsulfamoyl-2-(2-(nitrooxy) (acetoxy)benzoic acid derivatives are mainly involved in treatment of drug abuse disorders. The good correlation between experimental and predicted biological activity for compounds in the test set further highlights the reliability of the constructed QSAR model. The requirements for the more potent biological activity are explored with 2D, 3D and group based QSAR studies. The 2D technique indicates the importance of XlogP, SssCH3-Eindex, SssCH3-count, and SlogP of the compounds on the activity. The 3D QSAR analysis makes it possible to relate chemical structures of ligands and their binding affinity with respect to different bio targets by using the kNN-MFA techniques. Thus it provides a direct view of factors expressed in terms of molecular fields (electrostatic, steric) affecting the binding affinity. This in turn could give the reasonably good prediction of binding affinity. The location and range of function values at the field points selected by the models provide clues for the design of new molecules. Hence, this method is expected to provide a good alternative for the drug design. The 3D-QSAR model showed that electrostatic effects dominantly determine the binding affinities and these QSAR models developed in this study would be useful for the development of new drugs as a medicament for the drug abuse disorder. The 2D-QSAR studies revealed that alignment independent descriptors were the major contributing descriptors. The descriptor values obtained in this study helped in quantification of the structural features of 5-phenylsulfamoyl-2- (2-(nitrooxy) (acetoxy) benzoic acid derivative.

 

 

After successful QSAR studies, attempts were made to predict the activities of the newly designed analogues of these reported compounds. Above series of 5-phenylsulfamoyl-2-(2-nitroxy) (acetoxy) benzoic acid derivatives and some newer compounds were suggested for synthesis on the basis of selected model.

 

In future we can synthesize these designed compounds using the selected scheme and confirm their activity by carrying out in vivo evaluation.

 


 

 

 

Table 4: Newly Designed 5-phenylsulafamoyl-2-(2-nitroxy) (acetoxy) benzoic acid molecules with predicted activities

Sr. No.

Newly Designed Molecules

logIC50

1

 

1.15715

 

2

 

1.15714

 

3

 

1.5715

 

4

 

1.1221

 


 

5. ACKNOWLEDGMENT:

The author wishes to express gratitude to V-life sciences Technologies Pvt. Ltd. for providing the software for the study. Also the authors are thankful to the trustee, Bhujbal Knowledge City for providing the necessary facilities to carry out the research work.

 

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Received on 08.06.2017       Accepted on 12.08.2017     

© Asian Pharma Press All Right Reserved

Asian J. Res. Pharm. Sci. 2017; 7(4): 227-234.

DOI:  10.5958/2231-5659.2017.00036.4