MODELING ANGIOTENSIN II ANTAGONIST ACTIVITY OF 4H-1, 2, 4-TRIAZOLES

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1 Singh et.al/journal of Engineering, Science and Management Education/Vol., 010/57-66 MODELING ANGIOTENSIN II ANTAGONIST ACTIVITY OF 4H-1,, 4-TRIAZOLES Jyoti Singh*, Santosh Kumar Shukla**, Basheerulla Shaik***, Vijay K. Agrawal*** Recieved Sept. 14, 010, Revised Sept. 6, 010 ; Accepted Sept. 30, 010 Abstract The antihypertensive activity ( ) for a set of 38 derivatives of 4H-1,, 4-triazoles is modeled using 1 topological indices. The MLR analysis reveals that Jhetm,, 0 v 1 v,, topological indices are the best descriptors to be used for modeling the activity. The results have shown that models proposed by us are better than reported earlier, Keywords: Antihypertensive Activity, MLR Analysis, Topological indices, Triazoles INTRODUCTION Hypertension which causes heart failure is very common now-a-days. Consequently, several efforts have been made in search of potent anti-hypertensive drugs. Of late it has been known that the vasoactive hormone angiotensin II produced by the renin-angiotensin system (RAS) plays a major role in the treatment of cardiovascular disease. It regulates fluid volume, electrolyte balance and blood volume in mammals [1-]. Rennin inhibitors are highly specific enzymes which are mainly responsible in conversion of angiotensen to angiotensin I. It has been found experimentally that these inhibitors exert potent antihypertensive effects; however, their poor oral bioavailability is a hurdle in its being developed as anti-hypertensive drug[3]. Studies also reveal that angiotensin converting enzyme (ACE) inhibitors [4] are very useful in the treatment of hypertension and congestive heart failures as ACE can inactivate the hypertensive non-apeptide bradykinin [5]. Till date several papers dealing with quantitative structure activity relationship (QSAR) studies of angiotensin II antagonists have been reported [6-9]. But search is still going on for new potent antihypertensive drugs. Zhou et al [10]. using partial least square (RBPLS) proposed a new method for modeling the antagonisms of angiotensin II antagonists. The feature RBPLS method is to train a sequence of robust PLS models on various weighted versions of the original training set and then combine the predictions from the constructed PLS models to obtain integrative results. Using 17 variables they proposed a model as given in the following Table. R (correlation coefficient) PLS BPLS RBPLS Training set Test set The maximum allowed latent variable number AIM OF THE PRESENT INVESTIGATION: The aim of our investigation is to investigate whether or not we can use lesser no of topological indices for modeling antihypertensive activity ( ) of 4H-1,, 4-triazoles which possess angiotensin II antagonistic properties. In doing so we will use least square method for proposing significant models. The molecular structure of 4H-1,, 4-triazoles is shown below: Fig. 1. Molecular structure of 4H-1,, 4-triazoles as angiotensin II antagonists. Data Set Used In order to model the anti-hypertensive activity of 38 derivatives of 4H-1,, 4-triazoles as angiotensin II antagonists were used as a data set [1]. Ashton et al [13]. have synthesized 4H-1,, 4-triazoles and modeled for their antagonism against angiotensin II (IC ). The structural details of the compounds along with their antihypertensive activity are presented in Table 1. Topological Indices Used For modeling the antihypertensive activity following topological indices are used. W, J, JhetZ, Jhetm, Jhetv, Jhete, Jhetp. The method for their calculation has been discussed elsewhere [14]. These topological indices are calculated using Dragon software [15] and are reported in Table. The Randic [16] and Kier and Hall indices [17-18] are presented in Table 3. A correlation matrix is given in Table 4 the close look which yielded information: 1. All the branching indices are better for modeling the activity of present set of compounds. * Deptt. of SHM, Amrapali Instt. of Tech. & Science, Haldwani, Uttarakhand. ** Quality Assurance Deptt., Jubilant organasys Ltd., Bhagwanpur, Roorkee. *** Director, National Institute of Technical Teachers Training and Research, Shamla Hills, Bhopal, India., apsvka@yahoo.co.in3 57

2 . Strong co-linearity exists between following pair of v 1 v parameters: W, W, W, W, W, W, v 3 v W, W, JhetZ-J, Jhetm-J, Jhetv-J, Jhete-J, Jhetp- J, Jhetm- Jhetv, Jhetm- Jhetv, Jhete- Jhetp, Jhetv- JhetZ, Jhete- JhetZ, Jhetp- JhetZ, Jhete- Jhetv, Jhetp- Jhetv, Jhetp- Jhete. Modeling of Activity Using Mono-parametric Models Fifteen Mono-parametric Models (Table 5) are proposed in 0 v that one with as a correlating parameter gave the best mono-variable model: 0 v = (±0.0464) n = 38, Se = , R = 0.763, R A = , f-ratio = 95.86, Q = Modeling of activity using bi-parametric models Nine bi-parametric models with better statistical quality as compared to mono-parametric models are obtained and the 0 v 3 v one with and as the correlating parameters is found be the best. For this model the R value comes out to be indicating that this model explains 74% variance of the 19, 0 activity. The Q value (Pogliani's Quality factor ) also suggests that this model is better than the mono-parametric model discussed above: 0 v 3 v = (±0.1535) (±0.360) n = 38, Se = , R = , R A = 0.768, f-ratio =.199, Q = Modeling of Activity Using Tri-parametric Models Five three-parametric models with still better statistics than the two-parametric models discussed are obtained. A model 1 0 v 3 v with,, as the correlating parameters shows a better statistics (R = ) : 1 0 v = (±0.746) (±0.305) - 3 v 0.713(±0.3860) n = 38, Se = , R = , R A = 0.734, f-ratio = , Q = Modeling of Activity Using Tetra-parametric Models To have still better results higher order correlation models have been tried. During this process seven four-parametric models have been resulted. Here Balaban type (Jhete) 1, index has the best statistical values among all the models. 3 0 v 3 v This model contains Jhete,, and as correlating parameter. The model resulted is as below: 3 = -3.71(±1.3606) Jhete-0.86(±0.388) 0 v 3 v (±0.4330) (±0.5453) n = 38, Se = 0.06, R = , R A = , f-ratio = , Q = Table 1: Structural Details and their Values Of The Compounds Used In The Present Study. Compd. X Y No. 1. C 4 H 9 C 6 H SC H 5 C 6 H SC 3 H 7 C 6 H C 4 H 9 4-Pyridyl C 4 H 9 3- Pyridyl C 4 H 9 -Furyl SC H 5 CH C 6 H SC 3 H 7 CH C 6 H SC 3 H 7 CH CH C 6 H SC 3 H 7 (CH ) 3 C 6 H SC 3 H 7 CH SC 6 H SC H 5 CH OMe SC H 5 CF SC 3 H 7 CF C 4 H 9 SCH COOMe C 4 H 9 SCH CONHMe C 4 H 9 SCH CH OH C 4 H 9 SCHEtCOOMe C 4 H 9 SCH COC 6 H C 4 H 9 SC 6 H C 4 H 9 SCH C 6 H C 4 H 9 SCH CH C 6 H C 4 H 9 SCH C 6 H C 4 H 9 SCH (-Me-C 6 H 4 ) C 4 H 9 SCH (3-Me-C 6 H 4 ) C 4 H 9 SCH (4-Me-C 6 H 4 ) C 4 H 9 SCH (-Cl-C 6 H 4 ) C 4 H 9 SCH (3-Cl-C 6 H 4 ) C 4 H 9 SCH (4-Cl-C 6 H 4 ) C 4 H 9 SCH (3-OMe-C 6 H 4 ) C 4 H 9 SCH (4-OMe-C 6 H 4 ) C 4 H 9 SOCH (4-OMe-C 6 H 4 ) C 4 H 9 SCH (-CN-C 6 H 4 ) C 4 H 9 SCH (4-CF 3 -C 6 H 4 ) C 4 H 9 SCH (-Naphtyl) C 4 H 9 SCH(CO Me)C 6 H C 4 H 9 SCH (-CO Me-C 6 H 4 ) C 4 H 9 SCH (3-CO Me-C 6 H 4 ) 7. 58

3 Table : Value of Topological Indices For The Compounds Used In The Present Study. Compd. No. W J JhetZ Jhetm Jhetv Jhete Jhetp

4 Table 3. Values of Randic and Kier and Hall Connectivity Indices For The Compounds Used In The Present Study. Compd. No v 1 v v 3 v

5 1 Journal of Engineering, Science and Management Education W J JhetZ Jhetm Jhetv Jhete Jhetp W Table 4. Correlation Matrix Showing Inter-Correlation Among All The Topological Indices And Also Correlation With Log1/ic. J JhetZ Jhetm Jhetv Jhete Jhetp ? ? ? v v ??? v v 0? ? ? v v

6 Table 5 : Regression Parameters And Quality of Correlation For The Various Models. Model. No Parameters used Ai = (i=1 to 5) B Se R R A F-ratio Q = R/Se W (± ) ? (±0.049) (±0.0681) (±0.0938) (±0.0464) V (±0.0783) ? V 0.855(±0.1084) (±0.883) (±1.47) (±0.1636) (± ) (±1.184) (±0.434) (±0.63) (±0.581) (±0.1535) (±0.485) (±0.1846) (±0.1765) 8. V (±0.1311) Jhete (±0.0636) 10. Jhetp (±0.0) 11. 0? (±0.176) 1. W (±0.1793) 13. Jhetv (±0.058) (±0.1419) (±0.1981) (±0.3967) v (±0.360) v (±0.3766) (±0.38) 19. 0? (±0.4579) 1.01(±0.437) 0. 0? v (±0.3991) (±0.935) W (± ) (±0.308) v (±0.4010). 3. J (±0.746) 1? v (±0.3860) (±0.305) (±1.4300) (±0.3463) (±0.4738) (±0.15)

7 4. Jhetv 5. JhetZ Jhete 6. Jhetv Jhetp 7. J 8. Jhete 9. Jhete v 30. J JhetZ Jhete 31. Jhetm Jhete v 3. Jhetm Jhete Model. No v Parameters used (±1.6087) (±0.3108) 1.410(±0.3709) (±0.4495) 8.667(±4.1441) (±5.033) (±0.699) (±0.5135) (± ) (± ) (±0.) (±0.4607) (±1.5413) (±0.3466) 1.680(±0.4371) (±0.6) (±1.969) (±0.3375) 1.887(±0.4763) (±0.596) -3.71(±1.3606) -0.86(±0.388) (±0.4330) (±0.5453) (±7.5479) 9.565(±3.9937) -5.93(±8.8806) 1.073(±0.800) -1.96(±0.5911) 7.047(±4.0177) (±4.9530) -0.67(±0.3340) (±0.469) (±0.53) 7.983(±3.8400) (±4.8570) (±0.3141) (±0.4135) -.074(±0.5656) Journal of Engineering, Science and Management Education Table 6 : Regression Parameters and Quality of Correlation For The Few Best Models After Deletion of 3 Compounds (17, 1, And 31) B Se R R A F-ratio Q = R/Se (±0.0437) v (±0.59) (±0.368) (±0.1403) v (±0.39) (±0.766) Jhete (±1.1455) (±0.856).0345(±0.4197) Jhete? Ai = (i=1 to 5) (±0.4769) (±1.1076) (±0.3586) (±0.1931).3046(±0.4069) (±0.4549)

8 Table 7: Cross-Validated Parameters for the Proposed Models (Table 6) after Deletion of 3 Compounds (17, 1, 31) Model. No 1. Parameters PRESS/SSY R CV S Used PRESS PSE Jhete 4. Jhete 5. Jhete? 1 v? Table 8. : Comparison of Observed ad Estimated Log1/IC Values Using The Best Model-5 (Table 6.) Compd. Obs. log Est. log No. 1/IC 1/IC Residual Compd. Obs. log Est. log No. 1/IC 1/IC Residual

9 Modeling of Activity Using Penta-parametric Models Finally, three five-parametric models have been obtained in which the R value approaches to The model containing 3 0 v 3 v Jhetm, Jhete,,, as the correlating parameters is found to be the best: =7.983(±3.8400)Jhetm (±4.8570)Jhete- 3 0 v (±0.3141) (±0.4135).074(±0.5656) 3 v n = 38, Se = 0.060, R = 0.818, R A = , f-ratio = , Q = It has been observed that compounds 17, 1, 31 are serious outliers hence they were deleted from the data set and the process of obtaining better models has been repeated. A notable change in statistics has been observed and the new models obtained are presented in Table v 1 v The best five-parametric model contains Jhetm,,,, as correlating parameters. The R value comes out to be and the Q value also gives a significant rise. The model is reported below: 1 =-4.887(±1.1076) Jhete (±0.3586) 0 v (±0.1931) (±0.4069) (±0.4549) 1 v n = 35, Se , R = , R A = , f-ratio = , Q = v A perusal of above model indicates that Jhete, and have negative coefficients indicating that decrease in their magnitude is faourable for the exhibition of the activity, 1 v whereas the coefficients of and are positive signs suggesting their positive role towards the exhibition of the activity. Cross-Validation The essential feature of multiple regression analysis is crossvalidation which assesses the productivity of the computed model[11]. Cross-validation provides the values of PRESS, SSY, S PRESS, R CV and PSE from which we can investigate the predictive power of the proposed model. It is argued that PRESS is a good estimate of the real prediction error of the model and if it is smaller than SSY the model predicts better than chance and can be considered statistically significant. Furthermore, the ratio PRESS/SSY can be used to calculate approximate confidence intervals of prediction of new compound. To be a reasonable QSAR model PRESS/SSY should be smaller than 0.4 and the value of this ratio smaller than 0.1 indicates an excellent model. Also, if PRESS value is transformed in a dimension less term by relating it to the initial sum of squares, we obtain R CV (Q ) i.e. the complement to the traces on of unexplained variance over the total variance. Thus, PRESS and R CV have good properties. However, for practical purposes of end users the use of square-root of PRESS/N, which is called predictive square error (PSE) is more directly related to the uncertainty of the predictions. This parameter, namely PSE is much more useful when S PRESS (uncertainty of prediction) comes out to be the same as Se (standard error of estimation). All crossvalidated parameters given in Table 7 are in accordance with the aforementioned findings. It is interesting to mention that in the present case S PRESS is found the same as that of Se. thus, the uncertainty in prediction is decided by PSE. The PSE values also support our results discussed above. The validity of the models have been tested using cross 11 validation method and the five parametric model discussed above has been found to be the best. The R CV comes out to be also the lowest value of PSE for this model comes out to be 0.310confirms our finding. Randic Recommendation: It is therefore, necessary to examine the existence of colinearity defect, if any, in these models. If such defect exists 1 then the recommendation of Randic for explaining it will be 3 used. We have applied Randic recommendations for explaining collinearity defect. According Randic under certain situations even highly correlated descriptors could be retained in the model. Randic stated that if a descriptor strongly correlates with another descriptor already used in a regression, such a descriptor in most studies should not be discarded. In practice, 1 the two descriptors which are highly correlated are and. In many structure-property-activity studies is being 1 discarded if is used in the correlation. This is not theoretically justified and despite the widespread practice the same should be stopped. Although two highly correlated descriptors overall depict the same features of molecular structure, it is important to recognize that even highly interrelated descriptors differ in some other structural traits. The difference between them may be relatively small but nevertheless very important for structure-property regression. Finally, the antihypertensive activity for the compounds used in the present study has been predicted using the best fiveparametric model. The values calculated are very close to the observed ones (Table 8) indicating that the used model is the best for predicting the 1/IC activity. A comparison between observed and estimated activity has also been demonstrated in fig.. CONCLUSIONS: 1. The antihypertensive activity of 4H-1,, 4-triazoles acting as angiotensin II antagonists can be modeled using topological descriptors. 1 0 v 1 v. Topological indices Jhetm,,,, are observed to be the best descriptors to be used in this case. 3. The predictive power of the model is 86.6% meaning thereby, that it could explain 86% variance of the data. 4. Results obtained (R = ) in this study is better than the results reported by Zhou et al. 65

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