DESIGN OF NEW PEROXISOME PROLIFERATORS GAMMA ACTIVATED RECEPTOR AGONISTS (PPARγ) VIA QSAR BASED MODELING

RB Tripathi, J Jain, AW Siddiqui

Abstract


"Introduction: The Peroxisome proliferators-activated receptors (PPARs) are one of the nuclear fatty acid receptors, which contain a type II zinc finger DNA binding pattern and a hydrophobic ligand binding pocket. These receptors are thought to play an essential role in metabolic diseases such as obesity, insulin resistance, and coronary artery disease. Therefore Peroxisome Proliferators-Activated Receptor (PPARγ) activators have drawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize new PPARγ activators. Objective: The aim of the study was to finding new selective human PPARγ (PPARγ) modulators that are able to improve glucose homeostasis with reduced side effects compared with TZDs and identify the specific molecular descriptor and structural constraint to improve the agonist activity of PPARγ analogs. Material and Method: Software’s that was used for this study include S.P. Gupta QSAR software (QSAR analysis), Valstat (Comparative QSAR analysis and calculation of L-O-O, Q2, r2, Spress), BILIN (Comparative QSAR analysis and calculation of Q2, r, S, Spress, and F), etc., allowing directly performing statistical analysis. Then multiple linear regression based QSAR software (received from BITS-Pilani, India) generates QSAR equations. Result and Discussion: In this study, we explored the quantitative structure–activity relationship (QSAR) study of a series of meta-substituted Phenyl-propanoic acids as Peroxisome Proliferators Gamma activated receptor agonists (PPARγ).
The activities of meta-substituted Phenyl-propanoic acids derivatives correlated with various physicochemical, electronic and steric parameters. Conclusion: The identified QSAR models highlighted the significance of molar refractivity and hydrophobicity to the biological activity.
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Keywords


PPARγ agonist, Diabetes, QSAR, TZDs, Multiple regressions.

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DOI: https://doi.org/10.31069/japsr.v1i01.13059

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