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

Ravindra L Bakal; Rahul D Jawarkar; J V Manwar; Minal S Jaiswal; Arabinda Ghosh; Ajaykumar Gandhi; Magdi E A Zaki; Sami Al-Hussain; Abdul Samad; Vijay H Masand; Nobendu Mukerjee; Syed Nasir Abbas Bukhari; Praveen Sharma; Israa Lewaa

doi:10.1016/j.jsps.2022.04.003

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

Saudi Pharm J. 2022 Jun;30(6):693-710. doi: 10.1016/j.jsps.2022.04.003. Epub 2022 Apr 7.

Authors

Affiliations

¹ Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati, Maharashtra, India.
² Department of Medicinal Chemistry and Pharmacognosy, Dr. Rajendra Gode College of Pharmacy, University-Mardi Road, Amravati, Maharashtra, India.
³ Microbiology Division, Department of Botany, Gauhati University, Guwahati, Assam 781014, India.
⁴ Department of Chemistry, Government College of Arts and Science, Aurangabad, Maharashtra 431 004, India.
⁵ Department of Chemistry, Faculty of Science, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia.
⁶ Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq.
⁷ Department of Chemistry, Vidyabharti Mahavidyalaya, Camp Road, Amravati, Maharashtra, India.
⁸ Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, West Bengal 700118, Kolkata, India.
⁹ Department of Health Sciences, Novel Global Community Educational Foundation, Australia.
¹⁰ Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Aljouf, Sakaka, 2014, Saudi Arabia.
¹¹ Department of Pharmaceutics, Vinayaka College of Pharmacy, Hathod, Indore, Madhyapradesh, India.
¹² Department of Business Administration, Faculty of Business Administration, Economics and Political Science, British University in Egypt, Elsherouk City, Cairo 11837, Egypt.

Abstract

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, Q² _LOO = 0.78-0.79, Q² _LMO = 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.

Keywords: Aldose reductase; Antidiabetic; Ari, Aldose Reductase Inhibitors; CADD, Computer Aided Drug Designing; CCC, Concordance Correlation Coefficient; GA, Genetic Algorithm; GA-MLR; H_ringN_2B, H_ringN_2B represents a combination of ring nitrogen and hydrogen separated by within 2 bonds; MD, Molecular Dynamic; MDS; MLR, Multiple Linear Regression; MMGBSA, Molecular mechanics generalized born surface area; Minus_don_3B, Occurrence of a donor within three bonds from a negatively charged atom; Molecular Docking; OECD, Organization for Economic Co-operation and Development; OLS, Ordinary Least Square; QSAR; QSAR, Quantitative Structure-Activity Relationship; QSAR, Quantitative structure activity Relationship; QSARINS, QSAR Insubria; SMILES, Simplified Molecular, Input Line-Entry System; Virtual screening; allminus_SASA, Solvent Accessible surface area of the all negatively charged atoms; com_Nhyd_2A, presence of hydrophobic nitrogen within 2Å of the center of mass; com_ringCminus_2A, Encodes information on the number of negatively charged ring Carbon atoms within 2Å from the center of mass of the molecule; don_notringC_4B, Occurrence of the non-ring carbon atom within four bonds from the donor; don_ringC_6Ac, Occurrence of the partially charged ring carbon atoms within 6Å of the donor; fsp2Osp2C4B, the frequency of occurrence of a sp2 hybridized carbon atom exactly four bonds from a sp2 hybridized oxygen atom.