Three-dimensional QSAR and pharmacophore mapping of biphenyl benzoic acid derivatives as selective human β3-adrenergic receptor agonists

Abstract

Molecular modeling studies were performed to develop a predictive common pharmacophore hypothesis (CPH) and use it for alignment in threedimensional (3D) quantitative structure-activity relationship (QSAR) studies using CoMFA and CoMSIA, with a diverse set of 80 b 3 -adrenergic receptor (b 3 -AR) agonists. Using PHASE (Pharmacophore Alignment and Scoring Engine) six-point CPH with one acceptor, one negative charge, one positive charge, and three rings, features were derived for pharmacophore-based alignment of molecules. CPH was selected by correlating the observed and estimated activity for the training set and test set of molecules using partial least squares analysis. The validated pharmacophore hypothesis was used for alignment of molecules in CoMFA and CoMSIA model development. The models so generated showed a good ''predictive'' r 2 value of 0.6635 and 0.8665 for CoMFA and CoMSIA, respectively. The 3D contour CoMFA/CoMSIA maps provided an interpretable explanation of SAR for the compounds and also permitted interesting conclusions about the substituent effects at different positions of the biphenyl benzoic acid derivatives. CPH can also provide a powerful template for virtual screening and design of new b 3 -AR agonists.

Pharmacophore‐guided Virtual Screening to Identify New β3‐adrenergic Receptor Agonists

“…They were examined in SciFinder for potentially known agonist activities on the hβ 3 -AR prior to testing (the summary of search results is provided in Supporting Information Table S2). Pharmacophore features [a] Software Prathipati-Saxena model [27] 51 (34,17) RA, PI, HBD, HBA, H Catalyst v4.6 Shakya model [28] 4 HBA, HBD, HBA, HpAr, HpAl, RA Catalyst v4.7 Telvekar model [29] 80 (56,24) RA, HBA, PI, RA, RA, NI PHASE v3.0…”

“…However, none of those studies involved experimental in vitro validation of predicted hits. [27][28][29][30][31] Therefore, this study aimed to perform pharmacophore-based virtual screening and also confirm the biological activity of selected hit compounds.…”

Structure-Activity Relationships Based on 3D-QSAR CoMFA/CoMSIA and Design of Aryloxypropanol-Amine Agonists with Selectivity for the Human β3-Adrenergic Receptor and Anti-Obesity and Anti-Diabetic Profiles

“…In the last 10 years, there have been only two reports of quantitative structure-activity relationship (QSAR) studies on selective compounds for the β3-AR [ 27 , 28 ], one of which was conducted by our research group [ 28 ]. In both cases, the studies were carried out on phenylethanolamine-type compounds.…”

2D-QSAR and 3D-QSAR/CoMSIA Studies on a Series of (R)-2-((2-(1H-Indol-2-yl)ethyl)amino)-1-Phenylethan-1-ol with Human β3-Adrenergic Activity

“…In this context, the 3D-QSAR techniques become useful as tools to rationally design and direct the synthesis of potentially active derivatives. Few QSAR studies on this receptor have been reported in the literature [ 28 , 29 , 30 ]. Some of the limitations presented by these works include low predictability of the test set compounds [ 28 ], poor data distribution along the line y = x, a narrow range of studied biological activity [ 29 ], and the low potency of compounds used in the formulation of the model.…”

“…Some of the limitations presented by these works include low predictability of the test set compounds [ 28 ], poor data distribution along the line y = x, a narrow range of studied biological activity [ 29 ], and the low potency of compounds used in the formulation of the model. Other studies do not demonstrate that the combination of the considered descriptors is optimal, which can lead to explanations of the structure–activity correlation based on nonsignificant information [ 30 ].…”