Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches

Saudi Pharmaceutical Journal

J.V. Manwar¹  Abdul Samad¹  Nobendu Mukerjee¹  Vijay H. Masand²  Sami Al-Hussain²  Magdi E.A. Zaki³  Praveen Sharma⁴  Arabinda Ghosh⁵  Minal S. Jaiswal⁵  Ravindra L. Bakal⁶  Rahul D. Jawarkar⁶  Israa Lewaa⁷  Syed Nasir Abbas Bukhari⁸  Ajaykumar Gandhi⁹ 

[1] Corresponding authors.;Department of Chemistry, Faculty of Science, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia;Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra 431 004, India;Department of Chemistry, Vidyabharti Mahavidyalaya, Camp Road, Amravati, Maharashtra, India;Department of Medicinal Chemistry and Pharmacognosy, Dr. Rajendra Gode College of Pharmacy, University-Mardi Road, Amravati, Maharashtra, India;Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati, Maharashtra, India;Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, West Bengal 700118, Kolkata, India;Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq;Microbiology Division, Department of Botany, Gauhati University, Guwahati, Assam 781014, India;

关键词: QSAR;    Antidiabetic;    Aldose reductase;    GA-MLR;    Molecular Docking;    MDS;   

DOI  :  

来源: DOAJ

【 摘 要 】

The aldose reductase (AR) enzyme is an important target enzyme in the development of therapeutics against hyperglycaemia induced health complications such as retinopathy, etc. In the present study, a quantitative structure activity relationship (QSAR) evaluation of a dataset of 226 reported AR inhibitor (ARi) molecules is performed using a genetic algorithm – multi linear regression (GA-MLR) technique. Multi-criteria decision making (MCDM) analysis furnished two five variables based QSAR models with acceptably high performance reflected in various statistical parameters such as, R2 = 0.79–0.80, Q2LOO = 0.78–0.79, Q2LMO = 0.78–0.79. The QSAR model analysis revealed some of the molecular features that play crucial role in deciding inhibitory potency of the molecule against AR such as; hydrophobic Nitrogen within 2 Å of the center of mass of the molecule, non-ring Carbon separated by three and four bonds from hydrogen bond donor atoms, number of sp2 hybridized Oxygen separated by four bonds from sp2 hybridized Carbon atoms, etc. 14 in silico generated hits, using a compound 18 (a most potent ARi from present dataset with pIC50 = 8.04 M) as a template, on QSAR based virtual screening (QSAR-VS) furnished a scaffold 5 with better ARi activity (pIC50 = 8.05 M) than template compound 18. Furthermore, molecular docking of compound 18 (Docking Score = –7.91 kcal/mol) and scaffold 5 (Docking Score = –8.08 kcal/mol) against AR, divulged that they both occupy the specific pocket(s) in AR receptor binding sites through hydrogen bonding and hydrophobic interactions. Molecular dynamic simulation (MDS) and MMGBSA studies right back the docking results by revealing the fact that binding site residues interact with scaffold 5 and compound 18 to produce a stable complex similar to co-crystallized ligand's conformation. The QSAR analysis, molecular docking, and MDS results are all in agreement and complementary. QSAR-VS successfully identified a more potent novel ARi and can be used in the development of therapeutic agents to treat diabetes.

【 授权许可】

Unknown