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

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.

SUBMITTER: Bakal RL

PROVIDER: S-EPMC9257878 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Alzheimer's disease (AD) leads to gradual memory loss including other compromised cognitive abilities. Acetylcholinesterase (AChE), an important biochemical enzyme from the cholinesterase (ChE) family, is recognized as primary pharmacological target for treating AD. Currently marketed drugs for AD treatment are primarily AChE inhibitors and coumarin derivatives comprising a wide variety of pharmacological activities have proved their efficacy towards AChE inhibition. Ensaculin (KA-672 HCl), a compound that belong to the coumarin family, is a clinical trial candidate for AD treatment. Therefore, a ligand library was prepared with 60 reported coumarin derivatives for field-based 3D-QSAR and pharmacophore modelling. The field-based 3D-QSAR model obtained at partial least square (PLS) factor 7, was the best validated model that predicted activity closer to original activity for each ligand introduced. The contour maps demonstrated spatial distribution of favourable and unfavorable steric, hydrophobic, electrostatic and H-bond donor and acceptor contours around coumarin nucleus. The best pharmacophore model, ADHRR_1 exhibited five essential pharmacophoric features of four different traits for optimum AChE inhibition. Virtual screening through ADHRR_1 accompanied with molecular docking and MM/GBSA identified 10 HITs from a 4,00,000 coumarin derivatives from PubChem database. HITs comprised docking scores ranging from -12.096 kcal/mol to -8.271 kcal/mol and compared with the reference drug Donepezil (-8.271 kcal/mol). ADME properties analysis led into detecting two leads (HIT 1 and HIT 2) among these 10 HITs. Molecular Dynamics Simulation indicated thermodynamic stability of the complex of lead compounds with AChE protein. Finally, thorough survey of the experimental results from 3D-QSAR modelling, pharmacophore modelling and molecular docking interactions led us to develop the lead formula I for future advancements in treating AD through AChE inhibitors.

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

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