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IJRDPL - Vol 7, Issue 1 (2018), January - February

Pages: 2931-2940
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A QSAR study of some Phenoxyacetamide derivatives as a MAO-A inhibitor

Author: Kanhiya Lal Bais*, Lakhan Lal, Rajmani Mafidar, Anjita Singh, Rahul Mathur, M. Bhowmick, Jagdish C Rathi

Category: Pharmaceutical Sciences

Abstract:

Antidepressants are the most prescribed therapy for depression. The prevailing theory is that antidepressants increase the concentration of one or more brain chemicals (neurotransmitters) that nerves in the brain use to communicate with one another. The neurotransmitters affected by antidepressants are norepinephrine, serotonin, and dopamine. In order to address the need for new MAO inhibitors with less side effects, we can aim compounds previously discovered for their potential as MAOIs. Among them, safinamide was reported to be a potent anti-MAO B agent, and milacemide, which was found to be a potent MAO inhibitor and a prodrug for glycine. The present work deals with the aim because Currentely available MAO inhibitors {Isocarboxazid (Marplan), Phenelzine (Nardil), Selegiline (Emsam), Tranylcypromine (Parnate) etc} develop side effects because they do not selectively for MAO-A and MAO-B. So, the present study is focused to develop potent selective MAO-A inhibitors, to treat depression, that may be of better pharmacological activity with less adverse effect.

Keywords: Antipyretic syrup, Stability, Assay of paracetamol syrup

DOI: 10.21276/IJRDPL

DOI URL: http://dx.doi.org/10.21276/IJRDPL.2278-0238.2018.7(1).2931-2940

Full Text:

INTRODUCTION

Now a day, the entire pharmaceutical industry is faced with the challenge of increasing productivity and innovation. The major hurdles are the increasing costs of research and development and a simultaneous stagnating number of new chemical entities (NCEs).

For several thousand years, man has used herbs and potions as medicines, but it is only since the mid-nineteenth century that serious efforts were made to isolate and purify the active principles of these remedies and Medicinal chemistry received further boost in 1940 as pharmacology, which until then had been dominated by physiology, became increasingly biochemical in character with new understanding of the role of enzymes and cell receptors. Successful drug synthesis depends upon the ability to identify new chemical entities that have potential to treat diseases in a safe and efficient manner.

In order to address the need for new MAO inhibitors with less side effects, we can aim compounds previously discovered for their potential as MAOIs. Among them, safinamide was reported to be a potent anti-MAO B agent, and milacemide, which was found to be a potent MAO inhibitor and a prodrug for glycine [1-5, 32, 33].

2-Phenoxyacetamide

According to Wei et al., several substitutions are possible at R1- R5 and which can affect the MAO inhibitory activity of enzyme and gives a variety of compounds with satisfactory MAO activity. Results of this study show that most of the synthesized compounds are potent and selective inhibitors of MAO-A rather than of MAO-B.

Selection of series of Phenoxyacetamide [6-9, 34]

For the QSAR study to target MAO enzyme selection of series is based on IC50 value. The ratio of Maximum and minimum IC50 value should be ? 1000. The following series of Phenoxyacetamide is selected on the same basis.

ľ First eighteen compounds

2-Phenoxyacetamide

The discovery of a lead compound is assumed to be the most complicated aspect of the drug scheming process. Once a lead compound for a novel therapeutically vigorous drug has been revealed, it is additionally subjected to effectual toxicological studies so that its worth and protection can be thoroughly evaluated before the instigation of its clinical trials [35].

MATERIALS AND METHODS

A series of phenoxyacetamide derivatives was selected from a reported article which presented the synthesis of novel derivatives of this compound and evaluated their MAO A and MAO B inhibitory activity. Structure build-up, physico-chemical property determination, and sequential multiple regression analysis was performed on the reported series. In QSAR study all computational work was performed using ChemDraw2D Ultra8.0 and Chem3D Ultra 8.0 software core i3 Duo processor and a windows7 Operating system. The regression analysis was carried out using VALSTAT software.

EXPERIMENTAL WORK

QSAR Study of Selected Series of Compounds

The QSAR paradigm is based on the assumption that there is an underlying relationship between the molecular structure and biological activity. On this assumption QSAR attempts to establish a correlation between various molecular properties of a set of molecules with their experimentally known biological activity. Determination of QSAR generally proceeds as follows:

Biological Activity Calculation

Several substitutions were carried out on Phenoxyacetamide ring and 28 compounds were synthesized and their inhibitory potency towards monoamine oxidases A (MAO-A) and B (MAO-B) were evaluated using enzyme and cancer cell lysate. 2-(4 Methoxyphenoxy) acetamide and (2-(4-((prop-2ynylimino) methyl)phenoxy) acetamide were successfully identified as the most specific MAO-A inhibitor, and the most potent MAO-A/-B inhibitor, respectively [10-14]. The MAO A inhibitory activity data IC50 values were determined against MAO A subunit of synthesized compounds of series were converted to pIC50. The MAO A inhibitory activity of synthesized compounds was determined in terms of MIC against isoenzyme. The MIC is expressed as micro Molar concentration(C) and converted into ľlog C values (called as biological activity). These dependent data are tabulated in the Tables 2 and 3.

Table 2: Dependent data used for QSAR study for MAO A inhibitory activity (1-20)

Compounds

R1

R2

R3

R4

R5

X

IC50 (ÁM)

Biological Activity

1

H

H

H

H

H

O

69

4.1611

2

O

149

3.8268

3

H

H

F

H

H

O

92

4.0362

4

H

H

Cl

H

H

O

490

3.3098

5

Cl

H

H

H

H

O

98

4.0087

6

H

H

CHO

H

H

O

89

4.0506

7

CHO

H

H

H

H

O

142

3.8477

8

H

CH3

CH3

H

H

O

113

3.9469

9

CH3

H

CH3

H

H

O

26

4.5850

10

H

H

CH3

H

H

O

03

5.5228

11

O CH3

H

H

H

H

O

96

4.0177

12

H

H

O CH3

H

H

O

04

5.3979

13

COOH

H

H

H

H

O

217

3.66355

14

COOCH3

H

H

H

H

O

108

3.9665

15

H

H

NHCOOC(CH3)3

H

H

O

196

3.7077

16

H

H

NHCOCH3

H

H

O

61

4.2146

17

H

H

H

H

H

S

166

3.7798

18

H

H

CH3

H

H

S

292

3.5346

19.

61

4.2146

20.

186

3.7304

Table 3: Dependent data used for QSAR study for MAO A inhibitory activity (20-28)

Compound

Structure

IC50 (ÁM)

Biological Activity

21

0.018

7.7447

22

0.094

7.0268

23

96

4.0177

24

37

4.4317

25

0.068

7.1674

26

0.168

6.7746

27

147

3.8326

28

107

3.9706

Determination of Molecular Descriptors

The structures of the remaining twenty-four compounds were fabricated by means of Chemdraw Ultra 7.0.1 of Chemoffice Ultra 7.0.1 suite software, which is a product of Cambridge soft corporation, U.S.A. These structures were then saved in MDL (mol) format which is followed by energy minimization using Chem3D ultra 7.0.1 by the means of MM2 (Molecular Mechanics) force fields and followed by MOPAC-Closed shell (AM-1) pro force fields using 0.100 as root mean square gradient.

The minimization was executed until the Root Mean Square (RMS) gradient value reaches a value smaller than 0.1kcal / mol. The minimized molecules were subjected to reoptimization via Austin model-1 method until RMS gradient attains a value smaller than 0.0001 kcal / mol A┬░ using MOPAC. The geometry optimization of the lowest energy structure was carried out using Eigenvector Following (EF) routine. The descriptor values for all the molecules were calculated using ôcompute propertiesö module of program [15-21].

The properties of all these compounds were simultaneously computed using Chem3D ultra. Subsequently, all these calculated properties were arranged in Microsoft Excel 2007 sheet and subjected to the statistical software VALSTAT.

Selection of Training and Test Set:

The compounds were divided into training and test sets by random selection. The training set was used for the model development and the test set was used for cross validation of QSAR model developed by the training set. The data of 28 molecules was randomly into training set of 22 compounds (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 19, 22, 23, 24, 25, 26, 27, 28) and test set of 6 compounds (1, 16, 17, 18, 20, 21) for multiple linear regression model using log1/Ic50 activity as dependent variable and various 2D descriptors as independent variables [22-28].

QSAR Models Development

A set of 28 compounds were selected from the report obtained from the MAO-A inhibitory activity and divided as training set and test set each consisting of 22 and 6 compounds respectively by random selection method. The descriptors were used in this study are given along with values in the Table 4.

QSAR Model 1

BA= [3.73764(┬▒1.82156)] +pc [-1.12149(┬▒0.335012)] +be [-0.096964(┬▒0.0405414)] +sc [2.9265(┬▒1.88014)]

n=22, r=0.692907, r2=0.48012, variance=0.77052, std= 0.877793, F=5.54112, Q2= 0.25059, Spress = 1.0539, r2pred = 0.278751

QSAR Model 2

BA= [5.3987(┬▒0.917169)] +caa [0.00264657(┬▒0.00185923)] +pc [-1.03793(┬▒0.327816)] +be [-0.108387(┬▒0.0393591)]

n=22, r=0.685439, r2=0.469826, variance=0.785776, std= 0.88644, F=5.31704, Q2 = 0.208232, Spress = 1.08328, r2pred= 0.256099

QSAR Model 3

BA= [5.1416(┬▒1.27483)] +cma [0.00679232(┬▒0.00597503)] +pc [-1.15213(┬▒0.365385)] +be [-0.106365(┬▒0.0406552)]

n=22, r=0.670563, r2=0.449655, variance=0.815672, std= 0.903146, F=4.90225, Q2 = 0.162244, Spress = 1.11429, r2pred= 0.301534

QSAR Model 4

BA= [5.45628(┬▒0.946519)] +caa [0.00480994(┬▒0.00217723)] +se [-0.338721(┬▒0.124616)] +1,4ve [-0.33031(┬▒0.117859)]

n=22, r=0.661066, r2=0.437009, variance=0.834415, std= 0.913463, F=4.65736, Q2 =0.211758, Spress = 1.08086, r2pred = -0.548193

QSAR Model 5

BA= [2.85869(┬▒1.76879)] +pc [-0.969545(┬▒0.343327)] +se[-0.239747(┬▒0.121902)] +sc [3.45088(┬▒1.91372)]

n=22, r=0.660365, r2=0.586081, variance=0.835789, std= 0.914215, F=4.63983, Q2 = 0.0692081, Spress = 1.17454, r2pred= 0.623923

Validation of Model and Prediction of Biological Activity: Validation of model was done in two steps:

  1. External validation
  2. Internal validation
  1. External validation: the model was validated by VALSTAT software, randomly making 22 compounds (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 19, 22, 23, 24, 25, 26, 27, 28) of training set and 6 compounds (1, 16, 17, 18, 20, 21) of test set.
  2. Internal validation: The VALSTAT software automatically performed leave one out methods to get best model to increase biological activity. QSAR model was developed and this model was used to predict the biological activity of test set of compounds.

RESULT AND DISCUSSION

Statistical data of 5 models given in table 6. For all models training set (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 19, 22, 23, 24, 25, 26, 27, 28) of compounds and test set (1, 16, 17, 18, 20, 21) of compounds were taken randomly by VALSTAT software computer program and intercorrelation limit was used at 0.5. Out of all 5 models, model 5 was selected based on high value of stastical data Q2, r2 and r2 prediction.

Table 4 Descriptors of Compounds of Selected Series Calculated for QSAR Study

Table 5 Intercorrelation Matrix of Descriptors

Table 6 Statistical Data for Developed QSAR Models

Model No.

Number of Data point (n)

Correlation Coefficient (r)

Coefficient

of determination (r2)

Cross validated r2 (Q2)

r2pred

Standard deviation (Std)

Spress (Standard error for prediction)

1.

22

0.692907

0.48012

0.25059

0.278751

0.877793

1.0539

2.

22

0.685439

0.469826

0.208232

0.256099

0.88644

1.08328

3.

22

0.670563

0.449655

0.162244

0.301534

0.903146

1.11429

4.

22

0.661066

0.437009

0.211758

-0.548193

0.913463

1.08086

5*.

22

0.660365

0.586081

0.692081

0.623923

0.914215

1.17454

Model no. 5 shows descriptors partition coefficient, stretch energy and shape coefficient are positively correlated with biological activity of compounds. When increased the value of both descriptors it will be increased biological activity of compounds.

Final correlation matrix is given in table 9. Observed, calculated, residual, predicted residual values for training set of compounds and observed, predicted, predicted residual values for test sat of compounds is given in table 7 and 8 respectively.

Graph between observed values and calculated values, observed values and predicted values for training set of compounds is given in figure 1 and 2 respectively. Graph for test set of compounds plotted between observed values and predicted values is given in figure 3.

Fig. 1: Graph between Observed and Calculated Values for Training Set of Compounds

Fig. 2: Graph between Observed and Predicted Values for Training Set of Compounds

Fig. 3: Graph between Observed and Predicted Values for Test Set of Compounds

Table 7: Observed, Calculated, Predicted, Calculated Residual and Predicted Residual Values for Training set of Compounds

S. No.

Compounds

Observed

values

Calculated values

Calculated

Residual values

Predicted values

Predicted

Residual values

2

3.8268

3.4004

0.42639

3.5672

0.2596

3

4.0362

4.301

-0.26483

4.1833

-0.14714

4

3.3098

4.0341

-0.72433

3.4015

-0.0917

5

4.0087

3.6131

0.39561

3.9226

0.08608

6

4.0506

4.2155

-0.1649

4.1107

-0.06009

7

3.8477

4.1086

-0.26088

3.6394

0.20834

8

3.9469

4.1553

-0.20841

4.0792

-0.13225

9

4.5850

3.9605

0.62452

4.3987

0.18635

10

5.5228

5.0959

0.4269

4.9819

0.54094

11

4.0177

4.1552

-0.13747

4.1222

-0.10451

12

5.3979

4.9923

0.40561

5.181

0.21695

13

3.66355

4.03

-0.36645

3.8556

-0.19204

14

3.9665

4.0947

-0.12818

4.0825

-0.11599

15

3.7077

3.0485

0.65922

2.9277

0.78005

19

4.2146

4.1329

0.0817

4.6452

-0.43055

22

7.0268

6.9662

0.06064

6.8031

0.22375

23

4.0177

3.9622

0.05547

3.9402

0.07752

24

4.4317

4.5865

-0.15475

4.7624

-0.3307

25

7.1674

6.952

0.21537

6.8956

0.27176

26

6.7746

6.2994

0.47516

6.6376

0.13696

27

3.8326

3.9198

-0.08722

3.953

-0.12043

28

3.9706

4.0507

-0.08012

4.067

-0.09636

Table 9: Observed, Predicted and Predicted Residual Values for Test set of Compounds

Compounds

Observed value

Predicted value

Residual values

1

4.1611

4.2201

-0.05897

16

4.2146

4.4159

-0.20131

17

3.7798

4.0469

-0.26706

18

3.5346

4.0925

-0.55788

20

3.7304

3.6286

0.10177

21

7.7447

6.6598

1.0849

Table 10: Correlation Matrix of Model

pc

se

sc

Pc

1

Se

0.073053

1.000000

Sc

0.355831

0.238467

1.000000

pc = partition coefficient; se = stretch energy; sc = shape coefficient

CONCLUSION

QSAR study was performed on all compounds against MAO- A inhibitors using CHEM DRAW software version 7.0. For all compounds the descriptor/parameter values were calculated by ôcomputeö properties program. The Regression Analysis was carried out using VALSTAT software.

The best model was selected on the basis of Statical Parameters like r, r2, q2, pred. r2, standard deviation, spress. From the QSAR study concluded that partition coefficient and 1,4vander wall energy both is positively correlated to activity. It should be attached to the molecules to increase the Biological Activity. Based on best model, we can design new compound to improve activity.

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