Insight into the Structural Requirement of 2-Alkyl-4-( biphenylmethoxy ) quinolines as Nonpeptide Angiotensin II Receptor Antagonists : A QSAR Approach

In the current study a quantitative structure activity relationship approach using sequential multiple linear regression analysis was applied to a series of 2-alkyl4-(biphenylylmethoxy)quinolines as angiotensin II (Ang II) receptor antagonists by using Chem 3D and Dragon Software. The studies, carried out on 33 analogs, give statistically significant correlations of selective Ang II antagonistic activity with physical properties concerning size, symmetry, shape and distribution of molecule atoms. Among several 2D quantitative structure activity relationship models, one model gave good statistical significance (r > 0.81, Ftest = 10.47, S < 0.30, chance correlation < 0.01). 3D QSAR studies show that Hennery’s law constant, Dipole and VDWE play a significant role in Ang II antagonistic activity. These QSAR studies help us in the design and prediction of novel substituted benzimidazole Ang II receptor antagonists.


Introduction
The renin-angiotensin-aldosterone system (RAAS) is known to play an important role in electrolyte homeostasis and in the regulation of blood pressure and congestive heart failure [1].The octapeptide angiotensin II (Ang II) is produced by the renin angiotensin system (RAS) and is a potent vasoconstrictor and thus plays an important role in the pathophysiology of hypertension [2,3] .This directed many researchers toward the designing of drugs to block the effect of Ang II either by inhibiting the angiotensin converting enzyme (ACE) or renin or by blocking the Ang II receptor [4,5].Two distinct subtypes of Ang II receptors [type 1(AT1) and type 2 (AT2)] have been identified, and belong to the G-protein-coupled receptor family (GPCRs).AT1 and AT2 are seventransmembrane-spanning receptors, comprising an extra cellular glycosylated region connected to the seven transmembrane-alfa-helices, which are linked by three intracellular and three extracellular loops.The carboxy-terminal domain of the protein is cytoplasmic and is a regulatory site.AT1 is a 359-amino acids protein, while AT2 is made up of 363 amino acids and is 30% homologous with AT1.Both receptors are N-linked glycosylated post-translationally [6,7].To gain insight into the structural and molecular requirement influencing the AII antagonistic activities, we herein describe the QSAR analysis of 2-alkyl-4-(biphenylylmethoxy)quinolines and a QSAR Model has been obtained for Ang II antagonistic activity.The relevance of the model for the design of novel derivatives should be assessed not only in terms of predictivity, but also in terms of their ability to provide a chemical and structural explanation of their binding interaction.Here we propose a general model for the antagonist and present minimal structural requirement for an Angiotensin II antagonist.These results should serve as a guideline in designing more potent and selective Ang II antagonist.

Methodology
The Ang II receptor antagonistic activity data of 2-alkyl 4-(biphenylylmethoxy)quinolines were taken from the reported work of Bradbury et al [8] (Table 1).The biological activity data (IC 50 in µM) was converted to negative logarithmic mole dose (pIC 50 ) for quantitative structure activity relationship (QSAR) analysis.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.Then Hansch approach was carried out to establish the correlations between Ang II antagonistic activity and various substituents constants at position R 1 , R 2 , R 3 , A, X and Y-Z of the key 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), were taken from the reported work of Hansch et al [9].The series was further subjected to molecular modeling studies using CS Chem-Office Software version 8.0 (Cambridge Soft) [10] and DRAGON [11] running on P-IV processor and regression analysis program VALSTAT [12].

Tab. 1.
Structure and activities of 2-alkyl-4-(biphenylmethoxy)quinolines used in QSAR.The structure of the corresponding 2-alkyl-4-(biphenylmethoxy)quinoline was drawn in Chem draw ultra 8 and was copied to Chem 3D ultra to create 3D model, which served as a template model.For every compound the template compound was suitably modified considering its structural feature keeping the same sequence of atoms for every compound.These structures were then subjected to energy minimization using molecular mechanism (MM2) until the root mean square (RMS) gradients value became less than 0.1 kcal/mol Å.The minimized molecules are then subjected to re-optimization via AM1 method using closed shell (restricted) wave function of MOPAC module until the RMS Sci Pharm.2009; 77; 33-45.
gradient attained a value less than 0.0001 kcal/mol Å.The geometric optimization of the lowest energy structure was carried out eigenvector (EF) routine.The energy-minimized geometry was used for the calculation of descriptor and extended Hückel charges of different atoms.The descriptor values were calculated using the "computed properties" module of the program.The descriptor values used in model generation are shown in table 1.The data was transferred to a statistical program in order to establish a correlation between physicochemical parameters as an independent variable and the Ang II antagonistic activity as a dependent variable using a sequential multiple linear regression analysis method (in sequential multiple regression the program searched for all permutation and combination sequentially for the data set).The ± data within the parentheses represent the standard deviation, associated with the coefficient of descriptors in regression equations.The best model was selected from the various statistically significant equations on the basis of observed squared correlation coefficient (r 2 ), standard error of estimate (SE), sequential Fischer test (F), bootstrapping squared correlation coefficient (r 2 bs ), bootstrapping standard deviation (S bs ), cross validated squared correlation coefficient using leave one out procedure (r 2 cv ), 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).

Results and Discussion
In order to develop 2D QSAR, the data set was subjected to a stepwise multiple linear regression analysis.This resulted into several correlation equations between the pIC50 values as a dependent variable and several quantifying parameters as an independent variable.Equation 1 was considered as model for antagonistic activity on Ang II.This model shows a better correlation coefficient (r = 0.813) with low standard error of estimation.Eqn. 1 accounts for 66% variance in the biological activity.The eqn. 1 shows overall internal statistical significance level better than 99.9% as it exceeds the tabulated F (5.20) 27,5 = 10.47.The Inter-correlation among the parameter (ICPA) is significantly low which suggests the non-dependency of the parameters.Equation 1 suggested that all the parameters in the quinoline ring contributed positively and linearly to the antagonistic activity.
The series was further subjected to Hansch approach in order to develop 2D-QSAR between inhibitions of Ang II receptor against hypertension, which account for more than 66% variance in activity.This model has a coefficient of correlation (r=0.72) that explains 52% variance in the activity.The model showed an overall internal statistical significance level better than 99.5 % as it exceeded the tabulated F (5.11) 4,28 = 7.60.The intercorrelation among the parameter is less than 0.2 suggesting the absence of interdependency of substituent constants.The model was further tested for outliers by Z-score method and no compound was found to be such an outlier.Eqn. 2 shows that Molar refractivity (MR_R 2 , MR_R 3 ), which is representative of bulkiness or molar volume of substituents, Hydrogen acceptor effect of substituents contributed positively to the equation and they are conducive for Ang II antagonistic activity.Followed by 2D-QSAR analysis, the series was further subjected to molecular modeling studies in order to explore the three dimensional properties of the molecules which are responsible for the interaction of molecules with Ang II inhibitory activity.All the descriptor values were calculated from the program (Chem 3D 8, DRAGON) were considered as independent variable.Stepwise linear regression analysis method was used to develop multi-variant QSAR equation.Plot between observed pIC 50 and predicted (LOO) pIC 50 with residual presentation using model-4 A high correlation coefficient is not enough to select the equation as model.The equation screened out on the basis of the validation technique.Internal statistical significance level of equation was confirmed using sequential Fisher test for the equation having significance level more than more than 99.9% as it exceeded the tabulated F (6.12) 4,26 = 9.963 which suggest that equation are applicable for more than 999 out of 1000 times.
This equation was analyzed for search of outlier and two outliers namely compound No. 8 and 9 were identified from their Z score values.The interdependency of physiochemical properties for equation 4 was checked in order to confirm inimitable contribution of the properties to the expression.Bootstrapping technique (r 2 bsp = 0.652) was used to confirm the independent measure for the stability of regression equation.On the basis statistical criteria Eqn. 4 was selected as Model.Model has better correlation coefficient (r = 0.778), which accounts for more than 60% variance in the activity.The q 2 value (In the biological activity data of leave one compound) depicted confidence limit grater than 95%, which minimizes the risk of finding significant explanatory equation for the biological activity just by the chance.The crossvalidated squared correlation coefficient (q 2 = 0.367) and standard error of prediction, (S DEP = 0.631) further support good internal consistency of the model.

Tab. 5.
Calculated The 3D model suggests that functional group descriptor like nRORPh, which is the number of ethers (aromatic) and Mor03v (3D-MoRSE-signal 03 /weighted by atomic Van der waals volume), Mor18u (3D-MoRSE-signal 18/ unweighted) which were the Morse code (3D molecular representation of structure based on electron diffraction code) [13][14][15][16]  Where, s is scattering angle r ij is interatomic distance of i th and j th atom A i and A j are atomic properties of i th and j th atom respectively including atomic number, atomic mass, partial atomic charge, residual electro-negativities, and atom polarizability.
The contribution of MoRSE code suggested that the Vander Waals volume is decisive in the interaction with receptor.Functional group descriptors and MoRSE code of AT1 receptor angiotensin II antagonist activity of quinoline derivative bearing acidic heterocycles can be modeled excellently.Some geometric descriptors like FDI (folding degree index) and SPAM (Average Span R) contributed negatively to the equation.QSAR study revels that all models gave insight to some common important structural feature.For the angiotensin II antagonistic activity Electronic, thermodynamic and steric parameters are important.

Eqn. 4 .
Fig. 4.Plot between observed pIC 50 and predicted (LOO) pIC 50 with residual presentation using model-4 was calculated by summing atom weights viewed by a different angular scattering function.The values of these code functions were calculated at 32 evenly distributed values of scattering angle(s) in the range of 0-31 Å −1 from the three dimensional atomic co-ordinates of a molecule.The 3D-MoRSE code calculated by using following expression; and is reported as a vector in three dimensions were contributed positively to the equation.Steric descriptor like PMI_Y that describes mass distribution over the molecule on Y-component in spatial arrangement, contributed negatively to the activity suggesting that the increase in bulkiness on Y-component of molecule is favourable for the Ang II antagonistic activity.The property values of descriptor were given inTable-1.The reliability of the equation has been further confirmed by internal validation using leave-oneout (LOO) cross validation method to ensure the robustness of the equation.Although equation shows moderate internal consistency (q 2 = 0.358).Bootstrapping technique was used to confirm the independent measure for the stability of the regression equation, r2bsp > 0.63, and standard error of prediction (S DEP = 0.412).Parameters were also calculated with DRAGON.Sequential multiple linear regression analysis was carried out for development of the QSAR equations.Conformational and geometrical related physiochemical properties are helpful in understanding the probable binding site of drug with receptor.Correlation were established between physicochemical parameters and angiotensin antagonistic activity using sequential multiple linear regression technique.Best Eq. were selected as a model (Eqn.4) Eqn. 3 explains for more than 57% variance in activity.The equation 4 was considered as 3D-model for the data set.The model is used for internal predictivity, the value of leave one out cross validation squared correlation coefficient (q 2 = 0.358) suggested goodness of the prediction.Waals interaction energy term for atom separated by exactly 3 chemical bonds, the electronic descriptor like (D_2) Dipole energy which is the first derivative of the energy with respect to an applied electric field.It measures the asymmetry in the molecular charge Sci Pharm.2009; 77; 33-45.distribution of randomized biological activity test, the value of chance statistic (Chance <0.002) revel that results were not based on the chance correlation.