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

Abstract

The wide tissue distribution of the adrenergic β3 receptor makes it a potential target for the treatment of multiple pathologies such as diabetes, obesity, depression, overactive bladder (OAB), and cancer. Currently, there is only one drug on the market, mirabegron, approved for the treatment of OAB. In the present study, we have carried out an extensive structure-activity relationship analysis of a series of 41 aryloxypropanolamine compounds based on three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. This is the first combined comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) study in a series of selective aryloxypropanolamines displaying anti-diabetes and anti-obesity pharmacological profiles. The best CoMFA and CoMSIA models presented values of r2ncv = 0.993 and 0.984 and values of r2test = 0.865 and 0.918, respectively. The results obtained were subjected to extensive external validation (q2, r2, r2m, etc.) and a final series of compounds was designed and their biological activity was predicted (best pEC50 = 8.561).

Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) on a Series of Piperazine-Carboxamides Fatty Acid Amide Hydrolase (FAAH) Inhibitors as a Useful Tool for the Design of New Cannabinoid Ligands

“…Furthermore, the Y-randomization test [23] (Table 3) was applied to assess the robustness of the model (see Table S1 of the Supplementary Material) as previously described by Lorca et al [24]. The obtained QSAR models show low q 2 and r 2 ncv values (Table 3).…”

“…In this work we used the method developed by Roy et al [25] for determination of applicability domain (AD) as previously described by Lorca et al [24].…”

Molecular Modeling and Design Studies of Purine Derivatives as Novel CDK2 Inhibitors

“…The training set contains structurally diverse compounds representing all those chemical classes in the whole dataset and the activities of the training set compounds span the entire pIC 50 value range of the dataset, which can help the QSAR models portray more accurately what structural modifications are favorable or detrimental to activity. The test set compounds were selected randomly, but it is ensured that they represent the whole range of both structure diversity and activity of the training set compounds [ 29 ]. The training set was used to build 3D-QSAR models and the test set was used to evaluate and validate the predictive quality and reliability of the 3D-QSAR models obtained.…”

In silico molecular investigations of pyridine N-Oxide compounds as potential inhibitors of SARS-CoV-2: 3D QSAR, molecular docking modeling, and ADMET screening

“…The best developed model is the one with high values of cross-validation Q 2 , external validation r 2 ext , and with small standard error of estimate (SEE). CoMSIA models were obtained with the association between five fields steric (S), electrostatic (E), hydrophobic (H), donor hydrogen bond (D), and acceptor hydrogen bond (A) (Lorca et al, 2018).…”