On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity. Issue 16 (10th December 2018)

• Record Type: Journal Article
• Authors: Kronenberger, Thales; Windshügel, Björn; Wrenger, Carsten; Honorio, Kathia M.; Maltarollo, Vinicius G.
• Is Part Of: Journal of biomolecular structure & dynamics. Volume 36:Issue 16(2018)
• Page Start: 4378
• Page End: 4391
• Publication Date: 2018-12-10
• Subjects: Farnesoid X receptor -- anthranilic acid derivatives -- CoMFA -- CoMSIA -- HQSAR -- drug design; FXR, Farnesoid X receptor -- BA, Bile acid -- LBP, Ligand binding pocket -- LBD, Ligand binding domain -- NR, Nuclear receptor -- AAD, Anthranilic acid derivative -- QSAR, Quantitative structure-activity relationship -- HQSAR, Hologram quantitative structure-activity relationship -- HL, Hologram length -- CoMSIA, Comparative molecular similarity indices analysis -- CoMFA, Comparative molecular fields analysis -- ρc, Concordance correlation coefficient -- cpd. or CPD., Compound -- MAE, Mean absolute error -- ANOVA, Analysis of variance -- MIF, Molecular interaction field -- PDB, Protein data bank; Biomolecules -- Periodicals; Molecular structure -- Periodicals; Molecular Biology -- Periodicals; Biomechanics -- Periodicals; 572
• DOI: 10.1080/07391102.2017.1417161
• Languages: English
• ISSNs: 0739-1102

Abstract

Abstract : Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC50 values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality ( Q 2 LOO, Q 2 (F2), and Q 2 (F3) ), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.