Rationalization of Physicochemical and Structural Requirement of Some Substituted 5-(Biphenyl-4-ylmethyl)Pyrazole as Angiotensin II Receptor Antagonist: A QSAR Approach

Document Type: Research Paper

Authors

1 B.R. Nahata College of Pharmacy, Mandsaur, India

2 Shree Aurobindo Institute of Pharmacy, India

Abstract

      A series of angiotensin II (A II) receptor antagonist of some substituted 5-(biphenyl-4-ylmethyl) pyrazole were subjected to QSAR analysis using Hansch and Fujita-Ban model, by using combination of thermodynamic, electronic, spatial descriptor and presence or absence of substituent respectively. Several QSAR model were obtained using stepwise regression analysis. Two models from both the method were selected on the basis of the statistical value that shows good significance with AII antagonistic activity. The best QSAR models further validated by leave one out cross validation method. The studies have help to ascertain the role of different substituent in explaining the observed antagonistic activity of this analogue. From Fujita-Ban model, it is predicted that butane and propane at position 1, COOH at position 4 are essential for activity. Group like CH2CF3 at position 1 and COOH in place of tetrazole at R3 position contribute negative to the biological activity. In Hansch model it is predicted that molar refractivity at the 1 and 3 position shows positive contribution to the biological activity. Field effect at position 4 also shows positives contribution to the biological activity. Hydrogen donar at position R3 and field effect at position 1 contributes negatively to the biological activity.

Keywords


1. Introduction

 

      The vasoactive hormone angiotensin II (AII) produced by the rennin-angiotensin system (RAS) is a potent regulator of blood pressure, homeostasis, fluid volume and electrolyte balance in mammals [1]. The clinical success achieved by angiotensin converting enzyme (ACE) inhibitors in the treatment of the hypertension and congestive heart failure has made the RAS a major focus for the discovery of novel hypertensive agents. However, ACE also has kinase activity, and this lack of specificity has been implicated in the occasional side effect of ACE inhibitors such as dry cough and angiodema [2]. With the development of A II receptor antagonist, a specific attempt to inhibit the activity of RAS has become the main pharmacological approach.

 

 

Figure 1. A plot of observed vs. calculated angiotensin II antagonistic activity with residual presentation using Fuzita- ban QSAR model.

     There are at least two distinct AII receptor subtype, designated as AT1 and AT2 [3]. The AT1 receptor is G-protein coupled and mediates most of the known physiological effect of AII, including the maintenance of blood pressure [4]. The AT2 receptor is thought to be involved in fetal growth and adult tissue repair and remodeling, especially in cardiovascular system [5]. Losartan, the most advanced nonpeptide AII antagonist, mediates its effect by blocking the AII AT1 receptor subtype [6]. Due to our interest in various structural and new potential treatments for hypertensive disorders, we subjected a series of some substituted methyl pyrazole to QSAR analysis. QSAR is an important tool in drug designing technique [7, 8] to achieve different objective like diagnosis of mechanism of action of drug, quantitative prediction of biological activity of compound, classification of compound into various classes, optimization of lead compound and refinement of synthetic target. To achieve this target, various QSAR model have been used such as Hansch, Free-Wilson and Fujita-Ban model [9]. To gain insight into the structural and molecular requirement influencing the AII antagonistic activity, we herein describe QSAR analysis of substituted pyrazole derivatives. A QSAR Model has been obtained for AII antagonistic activity. The relevance of the model for the design of novel derivatives should be assessed only in terms of predictivity, internal or external, but also in terms of their ability to provide a chemical and structural explanation of their binding interaction. Here, we propose general models from two methods for the antagonist and present minimal structural requirement for an AII antagonist. These results should serve as a guideline in design of more potent and selective AII antagonist.

 

 

Figure 2. A plot of observed vs. calculated angiotensin II antagonistic activity with residual presentation using Hansch QSAR model.

 

Table 1. Analogs of 5-(biphenyl-4- ylmethyl) pyrazoles and their biological activity.

S. No.

R(4)

R1(3)

R2(1)

R3

IC50a

LogIC50b

1

COOH

H

Bu

tetrazolyl

9.1

8.041

2

COOH

Me

Bu

tetrazolyl

2.2

8.567

3

COOH

Me

Pr

tetrazolyl

9.3

8.031

4

COOH

Me

Et

tetrazolyl

120

6.920

5

COOH

Me

CH2CF3

tetrazolyl

770

6.113

6

COOH

Me

Ph

tetrazolyl

78

7.102

7

COOH

Et

Bu

tetrazolyl

8.7

8.060

8

COOH

I-Pr

Bu

tetrazolyl

7.3

8.136

9

COOH

Pr

Bu

tetrazolyl

10

8

10

COOH

Ph

Bu

tetrazolyl

9

8.045

11

COOH

Et

Pr

tetrazolyl

3.7

8.431

12

COOH

I-Pr

Pr

tetrazolyl

6.7

8.173

13

COOH

C-Pr

Pr

tetrazolyl

4.4

8.356

14

COOH

Butyl

Pr

tetrazolyl

3

8.522

15

COOH

CH2Ph

Pr

tetrazolyl

4.6

8.337

16

COOH

Me

Bu

tetrazolyl

290

6.537

17

COOH

Et

Bu

tetrazolyl

110

6.958

18

COOH

I-Pr

Pr

tetrazolyl

490

6.309

19

COOCH3

Me

Bu

tetrazolyl

55

7.259

20

COOCH3

t-Bu

Pr

tetrazolyl

19

7.721

21

CONH2

Me

Bu

tetrazolyl

70

7.154

22

CONH2

C-Pr

Pr

tetrazolyl

9.9

8.004

23

CH2OH

Me

Bu

tetrazolyl

350

6.456

24

CHO

Me

Bu

tetrazolyl

74

7.130

25

COCH3

Me

Bu

tetrazolyl

65

7.187

26

H

t-Bu

Pr

tetrazolyl

94

7.026

27

COOH

Me

Bu

SO2NHPh

12

7.920

28

COOH

Me

Bu

SO2NHCOOBu

13

7.886

29

COOH

Me

Bu

SO2NHCOOBu

30

7.522

30

COOH

C-Pr

Pr

SO2NHPh

18

7.744

31

CONH2

C-Pr

Pr

SO2NHPh

7.4

8.130

32

COOH

I-Pr

Pr

COOH

130

6.886

33

COOH

I-Pr

Pr

NHSO2CF3

74

8.041

aConcentration of 50 percent  antihypertensive  activity data against  against A II receptor; bNegative logarithm of IC50 activity data.

Table 2. Substituent constants, calculated, and predicted value with residual and Z-score data analogs of 5-(biphenyl-4- ylmethyl) pyrazoles and their data used in Fuzita-ban analysis.

S. No.

CH2CF3(N1)

Bu2(N1)  Pr2(N1)  COOH

COOH3 exp.

Calculated

Cal.Res.

Z-score

1

0

1

0

1

0

8.041

7.945

0.095

0.311

2

0

1

0

1

0

8.567

7.945

0.622

2.020

3

0

0

1

1

0

8.031

8.324

-0.293

-0.952

4

0

0

0

1

0

6.920

6.765

0.154

0.502

5

1

0

0

1

0

6.113

6.113

0

0.403

6

0

0

0

1

0

7.102

6.765

0.336

1.093

7

0

1

0

1

0

8.060

7.945

0.115

0.374

8

0

1

0

1

0

8.136

7.945

0.191

0.621

9

0

1

0

1

0

8.00

7.945

0.054

0.177

10

0

1

0

1

0

8.045

7.945

0.100

0.326

11

0

0

1

1

0

8.431

8.324

0.107

0.347

12

0

0

1

1

0

8.173

8.324

-0.150

-0.489

13

0

0

1

1

0

8.356

8.324

0.031

0.103

14

0

0

1

1

0

8.522

8.324

0.198

0.643

15

0

0

1

1

0

8.337

8.324

0.012

0.040

16

0

0

0

1

0

6.537

6.765

-0.228

-0.74

17

0

0

0

1

0

6.958

6.765

0.192

0.625

18

0

0

0

1

0

6.309

6.765

-0.456

-1.48

19

0

1

0

0

0

7.259

7.172

0.086

0.282

20

0

0

1

0

0

7.721

7.552

0.169

0.548

21

0

1

0

0

0

7.154

7.172

-0.017

-0.057

22

0

0

1

0

0

8.004

7.552

0.452

1.468

23

0

1

0

0

0

6.456

7.172

-0.767

-2.326

24

0

1

0

0

0

7.130

7.172

-0.041

-0.135

25

0

1

0

0

0

7.187

7.172

0.014

0.047

26

0

0

1

0

0

7.026

7.552

-0.525

-1.705

27

0

1

0

1

0

7.920

7.945

-0.024

-0.079

28

0

1

0

1

0

7.886

7.945

-0.059

-0.191

29

0

0

0

1

0

7.522

7.945

-0.422

-1.370

30

0

0

1

1

0

7.744

8.324

-0.580

-1.883

31

0

0

1

1

1

8.130

7.552

0.578

1.878

32

0

0

1

0

1

6.886

6.886

0

0.346


2. Materials and methods

 

     The AII antagonistic activity data of 5-(Biphenyl-4-ylmethyl) pyrazoles analogs (Figure 1) were taken from the reported work of Carmen Almansa et al. [10]. The antihyper-tensive activity data against AII receptor (IC50 in nm) was converted to negative logarithmic mole dose (-logIC50) in order to reduce the skewness of the data set, for quantitative structure activity relationship analysis (Table 1). Initially, series was subjected to Fujita-Ban analysis using regression technique in order to estimate the de novo contribution of substituents to the activity of the molecules. Further Hansch approach was carried out to established correlations between AII antagonistic activity and various substituents constants at position R1, R2, R3, R4 and R'of the molecule. Values of the substituents constants like hydrophobic (π), steric (Molar refractivity or MR), hydrogen acceptor (HA), hydrogen donor (HD) and electronic (field effect or F, resonance effect or R and Hammett's constant or σ), taken from the reported work of Hansch et al. [11] were selected as independent variable and biological activity as dependant variable. Stepwise multiple regression analysis [12, 13] was performed to derive QSAR model and in addition to advance statistical validation procedure to select best QSAR model from high populated QSAR model by software Valstat [14]. Resulting QSAR model assessed through a number of statistics obtained in conjunction with such calculation: Correlation coefficient (r), standard deviation (s), F-test, Bootstrapping (r2), Cross validation (Q2), chance statistics (evaluated as the ratio of the equivalent regression equations to the total number of randomized sets; a chance value of 0.001 corresponds to 0.1% chance of fortuitous correlation), outliers (on the basis of Z-score value) and leave one out method (Loo) was employed for cross validation of the best equation.

 

Table 3. Substituent constants, calculated, and predicted value with residual and Z-score data analogs of 5-(biphenyl-4- ylmethyl) pyrazoles and their data used in Hansch analysis.

S. No

F2(R2)

F(4)

MR1(R2)

MR3(R1)

Hdon 3(R3)

Exp.

Calculated

Cal.Res.

Z-score

1

-0.06

0.33

1.03

18.33

0

8.041

7.437

0.603

1.385

2

-0.06

0.33

5.65

18.33

0

8.567

7.603

0.964

2.210

3

-0.06

0.33

5.65

18.33

0

8.031

7.603

0.427

0.041

4

-0.05

0.33

5.65

18.33

0

6.920

7.568

-0.647

-1.485

5

0.37

0.33

5.65

18.33

0

6.113

6.095

0.018

0.981

6

0.08

0.33

5.65

18.33

0

7.102

7.111

-0.208

-0.021

7

-0.06

0.33

10.30

18.33

0

8.060

7.771

0.289

0.663

8

-0.06

0.33

14.96

18.33

0

8.136

7.939

0.197

-0.615

9

-0.06

0.33

14.96

18.33

0

8

7.939

0.060

0.139

10

-0.06

0.33

25.36

18.33

0

8.045

8.313

-0.268

0.453

11

-0.06

0.33

10.30

18.33

0

8.431

7.771

0.660

1.510

12

-0.06

0.33

14.96

18.33

0

8.173

7.939

0.234

-0.331

13

-0.06

0.33

13.53

18.33

0

8.356

7.887

0.468

1.075

14

-0.06

0.33

19.61

18.33

0

8.522

8.106

0.416

0.954

15

-0.06

0.33

30.01

18.33

0

8.337

8.481

-0.144

0.538

16

-0.06

0.33

5.65

18.33

0

6.537

7.603

-1.065

-1.863

17

-0.06

0.33

10.30

18.33

0

6.958

7.771

-0.812

-2.444

19

-0.06

0.33

5.65

18.33

0

7.259

7.603

-0.343

-0.788

20

-0.06

0.33

19.62

18.33

0

7.721

8.107

-0.385

-0.884

21

-0.06

0.24

5.65

18.33

0

7.154

7.315

-0.160

-0.368

22

-0.06

0.24

13.53

18.33

0

8.004

7.599

0.404

0.386

23

-0.06

0

5.65

18.33

0

6.456

6.547

-0.091

-0.209

24

-0.06

0.31

5.65

18.33

0

7.130

7.539

-0.408

-0.937

25

-0.06

0.32

5.65

18.33

0

7.187

7.571

-0.384

-0.881

26

-0.06

0

19.62

18.33

0

7.026

7.050

-0.024

-0.055

27

-0.06

0.33

5.65

42.85

1

7.920

7.822

0.098

0.225

28

-0.06

0.33

5.65

40.03

1

7.886

7.717

0.168

0.928

29

-0.06

0.33

5.65

40.03

1

7.522

7.717

-0.194

-0.446

30

-0.06

0.33

13.53

42.85

1

7.744

8.106

-0.361

-0.198

31

-0.06

0.24

13.53

42.85

1

8.130

7.818

0.312

0.716

32

-0.06

0.33

14.96

6.93

1

6.886

6.823

0.063

0.144

33

-0.06

0.33

14.96

17.54

1

7.130

7.217

-0.086

-0.829


3. Results and discussion

 

     Fujita-Ban analysis gave significant tri-variant regression expression (Equation 1) which account for more than 84% variance in activity with de novo contribution of substituents to the activity of the molecules.

 

BA=[6.06(±0.24)]+(CH2CF3)2(N1)[-0.65(±0.43)]+Pr2 (N1)[1.43(±0.21)]+Bu2 (N1)[1.15(±0.21)]+COOH[0.70(±0.16)]+ COOH3[-1.31(±0.41)] (Equation 1)

 

n=33, r=0.84, r^2=0.718, variance=0.156, std=0.395, F=13.7911, FIT=118.88

 

      The equation showed moderate corelation coefficient value 0.84 with one outlier. The fitness of model can be improved by removing outlier, hence remaining 32 compounds were considered for the QSAR analysis.

 

The optimized model showed equation have statistically significant corelation and significant for antagonistic activity.

 

BA=[5.99(±0.20)]+(CH2CF3)2(N1)[-0.65(±0.36)]+Pr2(N1)[1.5(±0.18)]+Bu2(N1) [1.17(±0.18)]+COOH[0.77(±0.14)]+COOH3 [-1.43(±0.35)] (Equation 2)

 

n=32, r=0.895, r^2=0.801, variance=0.113, std=0.336, F=20.96, FIT=187.726

 

     Fujita-Ban analysis (Table 2) of AII antagonistic data of 5-(Biphenyl-4-ylmethyl) pyrazole inferred that the 1st position of pyrazole ring is favorable for the butane, propane and the 4th position of ring is favorable for COOH group, respectively. Group like CH2CF3 at position 1and COOH in place of tetrazole at 2' position contribute negative to the biological activity. De novo contribution of groups also help in understanding of binding of molecule with AII receptor by means of possible hydrogen bond interaction in between COOH group at position 4 on the ring and polar positive charge region of AII receptor active site, second possible hydrophobic interaction of 1 position group of hydrocarbon and lipophillic pocket of AII receptor active site.

 

       Hansch analysis 33 compounds was subjected to stepwise multiple linear regression analysis, in order to develop QSAR between antagonistic activity at A II as dependent variables and substituents constants as independent variables, several significant models were obtained. Amongst them best model No. 3 selected on the basis of statistical parameter as follows

 

BA=[5.54(±0.53)]+F2 [-3.35(±1.30)]+F [2.91(±1.21)]+MR1[0.031(±0.015)]+MR3 [0.036(±0.015)]+Hdon3[-0.60(±0.33)]

 

(Equation 3) n=33, r=0.66, r^2=0.43, variance=0.31, std=0.55, F=4.24, FIT=36.5

 

      This equation showed moderate correlation coefficient value 0.66 with one outlier (Comp. No. 18). The fitness of the model can be improved by removing outlier. Hence remaining 32 compounds were considered for the QSAR analysis of AII antagonistic activity. The optimized model gave statistically significant correlation for antagonistic activity.

 

BA=[5.4(±0.46)]+F2[-3.50(±1.11)]+F [3.20(±1.04)]+MR1[0.03(±0.013)]+MR3 [0.037(±0.013)]+Hdon3[-0.69(±0.28)]

 

(Equation 4)

 

n=32, r=0.747, r^2=0.558, variance=0.226, std=0.476, F=6.58, FIT=58.98

 

     Only high correlation coefficient is not enough to select the equation as a model and hence various statistical approaches were used to confirm the robustness and practical applicability of equations. The equations 3 tested for presence of outliers one outlier was present suggested that although equation having good correlation coefficient but they are unable to explain the deviation of prediction of activity of a compound which involved in generation of expression. Equations 4 in randomize biological activity test showed probability of chance correlation were less than 0.1%.

 

      The equation was has better correlation coefficient (r=0.74), which accounts for more than 74.0% of the variance in the activity, the equation shows, that in multi-variant model, dependent variable can be predicted from a linear combination of the independent variables. The p value is less than 0.003 for each physiochemical parameters involved in model generation. The data showed overall internal statistical significance level better than 99.9% as it exceeded the tabulated F(4, 27)α 0.001=6.51 The model was further tested for outlier by Z-score method and one compound was found to be an outlier (Table 3) which suggested that the model is able to explain the structurally diverse analogs that is helpful in designing of more potent compounds using physiochemical parameters. Leave one out cross validation method was employed for prediction of the activity (Figure 1 & 2 and Table 3), cross-validated squared correlation co-efficient (Q2=0.710), predictive residual sum of square (SPRESS=0.426) and standard error of prediction (SDEP=0.376) suggested good internal consistency as well as predictive ability of the biological activity with low SDEP. Randomized biological activity test (Chance < 0.001) revealed that the results were not based on chance correlation. In general, the model fulfills the statistical validation criteria in a significant echelon to achieve theoretical base for proposing more active compounds which is helpful for rationalizing the interaction between molecule and receptor. Molar refractivity at the 1 and 3 positions shows positive contribution to the biological activity. Field effect at position 4 also shows positives contribution to the biological activity. Hydrogen donor at position 2' and field effect at position 2 contributes negatively to the biological activity.

 

 

Acknowledgement

 

Authors are very thankful to the head, school of Pharmacy, DAVV, Indore for giving facility to complete the project.

 

 

 

 

[1]      Ferrario CM. The renin-angiotensin system: Importance in physiology. J Cardiovasc Pharm 1990; 15: S1-5.

 

[2]      Cockcroft JR, Sciberras DG, Goldberg MR, Ritter MJ. Comparision of angiotensin converting enzyme inhibition with angiotensin II receptor antagonism in the human forearm. J Cardiovasc Pharm 1993; 22: 579-84.

 

[3]      De Gaspro M, Jhussain A, Alexander W, Cattr KJ, Chiu AT, Drew M, Good Freiend T, Harding JW, Inagani T, Timmermans PBMWM. Proposed update of angiotensin receptor nomenclature. hypertension 1995; 25: 924-39.

 

[4]      Timmermans PBMWM, Benfield P, Chiu AT, Herblin BF, Wong PC, Smith RD. Am J Hypertension 1992; 5: 2215-355.

 

[5]      Nahmias C, Strosberg AD. The angiotensin AT2 receptor: Searching for signal transduction pathway and physiological function. Trends Pharmacol Sci 1995; 16: 223-5.

 

[6]      Carini DJ, Dunica JV, Aldrich PE, Chiu AT, Johnson AL, Pieerce ME, Price WA, Santella JB, Wells GJ, Wexler RR, Wong PC, Yoo S, Timmermans, PBMWM. Nonpeptide angiotensin II receptor antagonist: The discovery of series of n-(biphenylmethyl) imidazoles as potent, orally-active antihypertensives. J Med Chem 1991; 34: 2525-7.

 

[7]      Tute M.S. History and objectives of quantitative drug design. In: Hansch C, (editor). Comprehensive medicinal chemistry. Vol 2, 4. New York: Pergamon Press, 1990. p. 579.

 

[8]      Kubinyi  H.  In: Wolff  ME,  (editor),  Berger's medicinal chemistry and drug discovery. 5th ed. Vol. 1. New York: John Wiley and Sons Inc., 1995; p. 91.

 

[9]      Sarathy KP, Girdhar R, Yadav MR. QSAR study by Fujita- Ban model of some substituted α, β-diaryl five membered hetrocycles as Cox-1/ Cox-2 inhibitors. Indian Drugs 2003; 40: 9-18.

 

[10]   Almansa C, Gomez LA, Cavalcanti FL, de Arriba AF, Rafanell JG, Forn J. Synthesis and structure-activitry relationship of a new series of potent AT1 selective angiotensin II receptor antagonists: 5-(Biphenyl-4-ylmethyl) pyrazoles. J Med Chem 1997; 40: 547-58.

 

[11]   Hansch C, Leo A. Substituent constants for correlation analysis in chemistry and biology. New York: John Wiley and Sons, 1979; p. 114.

 

[12]    Draper NR, Smith H. Applied regression analysis. New York: John Wiley and Sons, 1996; p. 193.

[13]   Chakarvarti SK, Ajmani S, Chaturvedi SC. SMRAILS-A lotus 1-2-3 spreadsheet for stepwise

multiple parameter regression analysis applied to QSAR analysis. Indian J Pharm Sci 1998; 60: 371-4.

 

[14]  Gupta AK, Arokia BM, Khakhedikar SG. Valstat: Valiation program for quantitative structure acyivity relationship studies. Indian J Pharm Sci 2004; 66: 396-402.