3-D QSAR studies of triazolinone based balanced AT 1 /AT 2 receptor antagonists

Pandya, Trupti; Pandey, Suresh K; Tiwari, Mayank; Chaturvedi, S. C.; Saxena, Anil Kumar

doi:10.1016/s0968-0896(00)00243-1

Cited by 17 publications

(14 citation statements)

References 28 publications

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“…Out of the total set of 51 molecules of the two papers, a collection of 34 congeneric b 3 -AR agonists was chosen as the training set using the picking rules for pharmacophore hypothesis generation as described in CATALYST [35,54]. The 3D-QSAR models were developed on the pharmacophoric hypothesis alignments using APEX-3D [55][56][57][58][59][60][61][62] while the rest 17 compounds were used as an external test set to select the best model. To further validate the pharmacophoric hypothesis and 3D-QSAR models in terms of their predictive ability, another test set (testset-2) ( Table 2) belonging to completely different structural class [13] was used.…”

Section: B 3 -Ar Agonists and Experimental Datamentioning

confidence: 99%

Characterization of β3-adrenergic receptor: determination of pharmacophore and 3D QSAR model for β3 adrenergic receptor agonism

Prathipati

Saxena

2005

J Comput Aided Mol Des

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The beta3-adrenoreceptor (beta3-AR) has been shown to mediate various pharmacological and physiological effects such as lipolysis, thermogenesis, and intestinal smooth muscle relaxation. It also plays an important role in glucose homeostasis and energy balance. Molecular modeling studies were undertaken to develop predictive pharmacophoric hypothesis and 3D-QSAR model, which may explain variations in beta3-AR agonistic activity in terms of chemical features and physicochemical properties. The two softwares, CATALYST for pharmacophoric alignment and APEX-3D for 3D-QSAR modeling were used to establish the structure activity relationships for beta3-AR agonistic activity. Among the several statistically significant models, the selection of the best pharmacophore and 3D-QSAR model was based on its ability to estimate the activity of external test sets of similar and different structural types along with the reasonable consistency of the model with the limited information of the active site of beta3-AR. The final 3D-QSAR model was derived using the pharmacophoric alignments from the hypothesis which consisted of four chemical features: basic or positive ionizable feature on the nitrogen of the aryloxypropylamino group, two ring aromatic features corresponding to the phenyl ring of the phenoxide and the benzenesulphonamido groups and a hydrogen-bond donor (HBD) in the vicinity of the nitrogen atom of the benzenesulphonamido group with the most active molecule mapping in an energetically favorable extended conformation. This hypothesis was in agreement with the site directed mutagenesis studies on human beta3-AR and correlated well the observed and estimated activity both in, training and both the external test sets. It also mapped reasonably well to six beta3-AR agonists of different structural classes under clinical development and thus this hypothesis may have a universal applicability in providing a powerful template for virtual screening and also for designing new chemical entities (NCEs) as beta3-AR agonists.

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Section: B 3 -Ar Agonists and Experimental Datamentioning

confidence: 99%

Characterization of β3-adrenergic receptor: determination of pharmacophore and 3D QSAR model for β3 adrenergic receptor agonism

Prathipati

Saxena

2005

J Comput Aided Mol Des

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“…The SAR analysis of losartan and other sartan suggests the key insights in drug receptor interactions: a bulky substituents which occupies a large hydrophobic cavity in AT 1 receptors, an alkyl chain which could fit into a second lipophilic pocket in the AT 1 receptor, a diaryl structure able to fit into the third hydrophobic pocket in the receptor, an acidic group able to interact with the basic residue of the AT 1 receptor, a moiety which is capable of hydrogen bonding interaction with the AT 1 receptor, a heterocyclic nitrogen acting as a hydrogen bond acceptor (Almansa et al, 1997). Numerous data sets which are reported in the literature were subjected to QSAR analysis in order to design novel angiotensin II receptor antagonists (Balasubramanian et al, 2007;Belvisi et al, 1996;Datar et al, 2004a, b;Chaturvedi, 2008, 2010;Kurup et al, 2001;Chaturvedi, 2004, 2005;Pandya et al, 2001;Parate and Chaturvedi, 2010;Yan et al, 2007;Yoo et al, 1999). Development of quantitative structure-activity relationship by aid of various physicochemical parameters has been an important task in lead optimization and has further facilitated in design novel therapeutic agents.…”

Section: Introductionmentioning

confidence: 99%

Predicting 3H-1,2,4-triazolinones as angiotensin II receptor antagonists: 2D and 3D QSAR by kNN-molecular field analysis approach

Parate

Chaturvedi

2011

Med Chem Res

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A series of forty two 3H-1,2,4-triazolinones compounds displaying variable inhibition of angiotensin II receptor AT 1 activity were selected to develop models for establishing 2D QSAR by multiple regression, partial least square analysis and principle component analysis and 3D QSAR Analysis by kNN-molecular field analysis (kNN-MFA) approach developed based on principles of the k-nearest neighbor method combined with various variable selection procedures and by genetic algorithm approach. The QSAR analysis of 3H-1,2,4-triazolinones with substituents at N 2 position is aimed at identification of structural patterns responsible for manifestation of the biological activity as well as drug receptor interactions based on certain physicochemical alignment independent parameters via 2D QSAR and steric and electrostatic descriptors in 3D kNN-MFA approach. QSAR model validation becomes an essential part in the development of a statistically valid and predictive model, because the real utility of a QSAR model was to design and predict accurately the modeled properties of the newly synthesized compounds as antihypertensive.

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“…In addition, since AII receptor antagonist does not affect the metabolism of bradykinin so they may not have the side effect of ACE inhibitors, such as dry cough and angiodema. Recently, the QSAR analysis is a highly interested area for designing the compound before synthesis [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

QSAR Study on 6-Substituted Benzimidazoles: An Insight into the Structural Requirement for the Angiotensin II AT1 Receptor Antagonist

Jain

2009

Sci. Pharm.

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With an aim to identify the structural requirements for selective AT1 angiotensin antagonistic activity, a quantitative structure activity relationship (QSAR) analysis was carried out on a series of 6-substituted benzimidazole derivatives. The QSAR expressions were generated using 28 compounds and the predictive ability of the resulting model was evaluated against a test set of 12 compounds. The internal (cross validated squared correlation coefficient) and external consistency (predictive correlation coefficient) of the QSAR model was 0.78 and 0.40 respectively. In the present work QSAR analysis reveals that geometrical, structural, and shape descriptors govern the angiotensin II AT1 antagonistic activity.

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3-D QSAR studies of triazolinone based balanced AT 1 /AT 2 receptor antagonists

Cited by 17 publications

References 28 publications

Characterization of β3-adrenergic receptor: determination of pharmacophore and 3D QSAR model for β3 adrenergic receptor agonism

Characterization of β3-adrenergic receptor: determination of pharmacophore and 3D QSAR model for β3 adrenergic receptor agonism

Predicting 3H-1,2,4-triazolinones as angiotensin II receptor antagonists: 2D and 3D QSAR by kNN-molecular field analysis approach

QSAR Study on 6-Substituted Benzimidazoles: An Insight into the Structural Requirement for the Angiotensin II AT1 Receptor Antagonist

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