Ligand Based Pharmacophore Modeling and Virtual Screening for Novel Antidiabetics Targeting PPAR-gamma
Partha Sarathi Bairy1, Prashant Gahtori2, Abhilasha Mishra3, Veerma Ram4

1Partha Sarathi Bairy, School of Pharmacy, Graphic Era Hill University, Dehradun, Uttarakhand, India.

2Prashant Gahtori, School of Pharmacy, Graphic Era Hill University, Dehradun, Uttarakhand, India.

3Abhilasha Mishra, Department of Allied Sciences Chemistry, Graphic Era Deemed University, Dehradun, Uttarakhand, India.

4Veerma Ram, School of Pharmaceutical Science and Technology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India.

Manuscript received on 01 June 2019 | Revised Manuscript received on 07 June 2019 | Manuscript Published on 04 July 2020 | PP: 17-24 | Volume-8 Issue- 4S3 March 2019 | Retrieval Number: D10050384S319/2019©BEIESP | DOI: 10.35940/ijitee.D1005.0384S319

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Abstract: A modern sedentary lifestyle with a more calorigenic fast-food diet increasing the prevalence of metabolic syndrome in middle and high-income countries. Peroxisome proliferator-activated receptors (PPARs) are a group of the nuclear receptor, which regulates the metabolic process in physiological systems via influencing gene expressions of cell proliferation, glucose, lipid metabolism, and inflammation. Later one PPAR-γ agonist is a well-established class of pharmacological agents for diabetic control with some promising molecule in the clinical stages. Herein, we have chosen a hybrid indole and azaindole class for developing an effective pharmacophore model. A series of compounds with indole carboxylic acid and hydroxyazaindole core along with their tested biological activity were selected for generating a valid pharmacophore model using Hip-Hop and HypoGen algorithm of Discovery Studio v3.1. A total of 38 numbers of ligand were considered for pharmacophore generation and mapping including test set and training set. Depending upon proper calculative measures the best-validated hypothesis with two hydrophobic, one hydrogen bond acceptor, and one ring aromatic features are set forth for further shortlisting of compounds. A similarity search tool in PubChem structure database with a 70% similarity of best active compounds yields more than four lakhs compounds. The screened drug-like compounds were further shortlisted using ‘rules of five’ and TOPKAT module. The best-validated Hypo Gen pharmacophore was utilized for further screening to get the best structures for future in-silico consideration and identifying potential hits for effective diabetes drug discovery research.

Keywords: PPAR-γ, Diabetes Mellitus, Pharmacophore, Ligand, Metabolism.
Scope of the Article: Social Sciences