A 3D-QSAR of Angiotensin II (AT1) Receptor Antagonists Based on Receptor Surface Analysis

Molecular Modeling Studies of Substituted 2,4,5-Trisubstituted Triazolinones Aryl and Nonaryl Derivatives as Angiotensin II AT1Receptor Antagonists

The development of new therapies to treat hypertension and cardiovascular diseases. A series of 2,4,5-trisubstituted triazolinones aryl and nonaryl derivatives were subjected toGroup-based QSAR,k-nearest neighbourmolecular field analysis, and pharmacophore mapping. Multiple linear regression (MLR) methodology coupled with feature selection method namely simulated annealing, was applied to derive Group based QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The best physicochemical descriptors, namely, R1chiV1, R2T_N_O_3, R2chlorines count, R2T_C_N_4, and R2SssNHE index, contribute significantly to the biological activity. The statistically significant best Group-based QSAR model hasr2=0.8357andq2=0.7266with pred_r2=0.8138. The 3D-QSAR studies were performed using the simulated annealing selectionk-nearest neighbormolecular field analysis approach; a leave-one-out cross-validated correlation coefficientq2=0.7461and predicate activity pred_r2=0.7790were obtained. Contour maps using this approach showed that steric, electrostatic, and hydrophobic effects dominantly determine binding affinities. Pharmacophore hypotheses were generated by the mol sign module and found to contain common features like hydrogen bond donor acceptor, donor, positive, negative ionizable, and hydrophobic features. This model can be used for preliminary screening of large number of substituted 3H-1,-2,-4 triazolinone aryl and nonaryl derivatives. The information rendered by 3D-QSAR models may lead to a better understanding of structural requirements of triazolinone aryl and nonaryl derivatives and also aid in designing novel potent antihypertensive molecules.

Design, synthesis, and evaluation of 5-sulfamoyl benzimidazole derivatives as novel angiotensin II receptor antagonists

A series of 5-alkylsulfamoyl benzimidazole derivatives have been designed and synthesized as novel angiotensin II (Ang II) receptor antagonists. The compounds have been evaluated for in vitro Ang II antagonism and for in vivo antihypertensive activity on isolated rat aortic ring and desoxycortisone acetate induced hypertensive rats, respectively. The activity is found related to size of alkyl group. The maximum activity is observed with a compact and bulky alkyl group like tert-butyl and cyclohexyl. The compounds 4g and 4h have shown promising both in vitro and in vivo activities. A receptor binding model is also proposed on the basis on the basis of structure-activity relationship in this study.

Development of predictive in silico model for cyclosporine- and aureobasidin-based P-glycoprotein inhibitors employing receptor surface analysis

P-glycoprotein (Pgp) is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents. This problem prompted several research groups to search for effective P-gp inhibitors. Cyclosporine A (CsA), aureobasidin A (AbA) and related analogues were reported to possess potent inhibitory actions against Pgp. In this work we employed receptor surface analysis (RSA) to construct two satisfactory receptor surface models (RSMs) for cyclosporine- and aureobasidin-based Pgp inhibitors. These pseudoreceptors were combined to achieve satisfactory three-dimensional quantitative structure activity relationship (3D-QSAR) for 68 different cyclosporine and aureobasidin derivatives. Upon validation against an external set of 16 randomly selected Pgp inhibitors, the optimal 3D-QSAR was found to be self-consistent and predictive (r(LOO)(2)=0.673, r(PRESS)(2)=0.600). The resulting 3D-QSAR was employed to probe the structural factors that control the inhibitory activities of cyclosporine and aureobasidin analogues against Pgp.

QSAR study of angiotensin II antagonists using robust boosting partial least squares regression

In the current study, robust boosting partial least squares (RBPLS) regression has been proposed to model the activities of a series of 4H-1,2,4-triazoles as angiotensin II antagonists. RBPLS works by sequentially employing PLS method to the robustly reweighted versions of the training compounds, and then combing these resulting predictors through weighted median. In PLS modeling, an F-statistic has been introduced to automatically determine the number of PLS components. The results obtained by RBPLS have been compared to those by boosting partial least squares (BPLS) repression and partial least squares (PLS) regression, showing the good performance of RBPLS in improving the QSAR modeling. In addition, the interaction of angiotensin II antagonists is a complex one, including topological, spatial, thermodynamic and electronic effects.

Proposal of a New Binding Orientation for Non-Peptide AT1 Antagonists: Homology Modeling, Docking and Three-Dimensional Quantitative Structure−Activity Relationship Analysis

A three-dimensional model of the AT1 receptor was constructed by means of a homology modeling procedure, using the X-ray structure of bovine rhodopsin as the initial template and taking into account the available site-directed mutagenesis data. The docking of losartan and its active metabolite EXP3174, followed by 1 ns of molecular dynamics (MD) simulation inserted into the phospholipid bilayer, suggested a different binding orientation for these antagonists from those previously proposed. Furthermore, the docking of several non-peptide antagonists was used as an alignment tool for the development of a three-dimensional quantitative structure-activity relationship (3D-QSAR) model, and the good results confirmed our binding hypothesis and the reliability of the model.

QSAR modeling of AT1 receptor antagonists using ANN

Multiple linear regression (MLR) and artificial neural networks (ANN) have been used for structure-activity relationship analysis for a set of 113 AT1 receptor antagonists. The ANN model showed better performance with a 6-6-1 architecture than MLR. The results obtained from this study indicate that three descriptors, hydration energy (EH), n-octanol/water partition (LOGP), and energy of the lowest unoccupied molecular orbital (LUMO), play an important role on the activity of AT1 receptor antagonists with biphenyltetrazole structures. This information is pertinent to the further design of new AT1 receptor antagonists. 1 A plot of observed versus predicted PIC50 produced from the best nonlinear model using 6-6-1 architecture.