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

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.

^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.

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.

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 1^st position of pyrazole ring is favorable for the butane, propane and the 4^th 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 (S_PRESS=0.426) and standard error of prediction (S_DEP=0.376) suggested good internal consistency as well as predictive ability of the biological activity with low S_DEP. 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.