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2000
Volume 32, Issue 5
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

Introduction

Aldose reductase-2 (ALR2) is a key enzyme in the polyol pathway whose overexpression is implicated in several diabetic complications, including neuropathy, nephropathy, retinopathy, and atherosclerotic plaque formation. Under hyperglycemic conditions, the intracellular accumulation of sorbitol and the depletion of NADPH lead to osmotic imbalance and oxidative stress, driven by the formation of reactive oxygen species and advanced glycation end products. Although various ALR2 inhibitors have been developed, their clinical application has been hampered by nonselective inhibition of both ALR2 and the homologous enzyme ALR1.

Methods

In this study, we employed a comprehensive approach to evaluate the inhibitory potential of natural compounds from against ALR2. Our workflow integrated with ADMET, molecular docking with scoring function and glide XP, molecular dynamics (MD) simulations, PCA, FEL, and MM/GBSA. Through this analysis, four natural compounds of (Compound Name: p-Coumaric acid, Vanillic acid, 4-Oxoniobenzoate, and Phloroglucinol) displayed significant bonds formation including hydrogen and hydrophobic bonds with the target protein.

Results

These bonds exhibited the ligand stability. Further, the MD simulation analysis, followed by post-simulation analysis, verified the dynamic stability of these four natural compounds and compared them with the native ligand of the target protein. These natural compounds exhibit particularly stable binding within the ALR2 selectivity pocket, demonstrating an inhibitory effect over ALR1 when compared with the reference inhibitor, Epalrestat.

Conclusion

These promising findings suggest that CID: 8468 and CID: 135 merit further evaluation through , , and clinical studies as potential selective inhibitors for the treatment of diabetic complications.

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References

  1. TomicD. ShawJ.E. MaglianoD.J. The burden and risks of emerging complications of diabetes mellitus.Nat. Rev. Endocrinol.202218952553910.1038/s41574‑022‑00690‑735668219
    [Google Scholar]
  2. LinX. XuY. PanX. Global, regional, and national burden and trend of diabetes in 195 countries and territories: An analysis from 1990 to 2025.Sci. Rep.20201011479010.1038/s41598‑020‑71908‑932901098
    [Google Scholar]
  3. BandayM.Z. SameerA.S. NissarS. Pathophysiology of diabetes: An overview.Avicenna J. Med.202010417418810.4103/ajm.ajm_53_2033437689
    [Google Scholar]
  4. PapachristoforouE. LambadiariV. MaratouE. MakrilakisK. Association of glycemic indices (Hyperglycemia, Glucose Variability, and Hypoglycemia) with oxidative stress and diabetic complications.J. Diabetes Res.2020202011710.1155/2020/748979533123598
    [Google Scholar]
  5. KumarM. ChoudharyS. SinghP.K. SilakariO. Addressing selectivity issues of aldose reductase 2 inhibitors for the management of diabetic complications.Future Med. Chem.202012141327135810.4155/fmc‑2020‑003232602375
    [Google Scholar]
  6. KumariP. KohalR. Bhavana, Gupta GD, Verma SK. Selectivity challenges for aldose reductase inhibitors: A review on comparative SAR and interaction studies.J. Mol. Struct.2024131813920710.1016/j.molstruc.2024.139207
    [Google Scholar]
  7. KousaxidisA. PetrouA. LavrentakiV. FesatidouM. NicolaouI. GeronikakiA. Aldose reductase and protein tyrosine phosphatase 1B inhibitors as a promising therapeutic approach for diabetes mellitus.Eur. J. Med. Chem.202020711274210.1016/j.ejmech.2020.11274232871344
    [Google Scholar]
  8. KadorP.F. KinoshitaJ.H. SharplessN.E. Aldose reductase inhibitors: A potential new class of agents for the pharmacological control of certain diabetic complications.J. Med. Chem.198528784184910.1021/jm00145a0013925146
    [Google Scholar]
  9. KumarM. SinghP.K. ChoudharyS. SilakariO. Hydantoin based dual inhibitors of ALR2 and PARP-1: Design, synthesis, in-vitro and in-vivo evaluation.Bioorg. Chem.202212910610810.1016/j.bioorg.2022.10610836063781
    [Google Scholar]
  10. SinghM. KapoorA. BhatnagarA. Physiological and pathological roles of aldose reductase.Metabolites2021111065510.3390/metabo1110065534677370
    [Google Scholar]
  11. ShehzadM.T. ImranA. HameedA. Exploring synthetic and therapeutic prospects of new thiazoline derivatives as aldose reductase (ALR2) inhibitors.RSC Advances20211128172591728210.1039/D1RA01716K35479726
    [Google Scholar]
  12. BernardoniB.L. D’AgostinoI. ScianòF. La MottaC. The challenging inhibition of Aldose Reductase for the treatment of diabetic complications: A 2019-2023 update of the patent literature.Expert Opin. Ther. Pat.202434111085110310.1080/13543776.2024.241257339365044
    [Google Scholar]
  13. AlshaghdaliK. AlharaziT. RezguiR. Identification and evaluation of putative type 2 diabetes mellitus inhibitors derived from Cichorium intybus.J. Mol. Struct.2024130613762910.1016/j.molstruc.2024.137629
    [Google Scholar]
  14. ZhangJ. ChenX. HanL. Research progress in traditional applications, phytochemistry, pharmacology, and safety evaluation of Cynomorium songaricum.Molecules202429594110.3390/molecules2905094138474452
    [Google Scholar]
  15. CuiJ.L. VijayakumarV. ZhangG. Partitioning of fungal endophyte assemblages in root-parasitic plant Cynomorium songaricum and its host Nitraria tangutorum.Front. Microbiol.2018966610.3389/fmicb.2018.0066629686655
    [Google Scholar]
  16. LiuZ. LiQ. ZhaoF. ChenJ. A decade review on phytochemistry and pharmacological activities of Cynomorium songaricum Rupr.: Insights into metabolic syndrome.Phytomedicine202514015660210.1016/j.phymed.2025.15660240058318
    [Google Scholar]
  17. CuiJ-L GongY XueX-Z ZhangY-Y WangM-L WangJ-H A phytochemical and pharmacological review on Cynomorium Songaricum as functional and medicinal food.Nat Product Commun20181341934578X180130042810.1177/1934578X1801300428
    [Google Scholar]
  18. KimS. ChenJ. ChengT. PubChem 2019 update: Improved access to chemical data.Nucleic Acids Res.201947D1D1102D110910.1093/nar/gky103330371825
    [Google Scholar]
  19. PrivalM.J. Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals.Environ. Mol. Mutagen.2001371556910.1002/1098‑2280(2001)37:1<55:AID‑EM1006>3.0.CO;2‑511170242
    [Google Scholar]
  20. BermanH.M. WestbrookJ. FengZ. The protein data bank.Nucleic Acids Res.200028123524210.1093/nar/28.1.23510592235
    [Google Scholar]
  21. ZhangL. ZhangH. ZhaoY. Inhibitor selectivity between aldo-keto reductase superfamily members AKR1B10 and AKR1B1: Role of Trp112 (Trp111).FEBS Lett.2013587223681368610.1016/j.febslet.2013.09.03124100137
    [Google Scholar]
  22. WuG. RobertsonD.H. BrooksC.L. ViethM. Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm.J. Comput. Chem.200324131549156210.1002/jcc.1030612925999
    [Google Scholar]
  23. GustafsenC. OlsenD. VilstrupJ. Heparan sulfate proteoglycans present PCSK9 to the LDL receptor.Nat. Commun.20178150310.1038/s41467‑017‑00568‑728894089
    [Google Scholar]
  24. PuratchikodyA. IrfanN. BalasubramaniyanS. Conceptual design of hybrid PCSK9 lead inhibitors against coronary artery disease.Biocatal. Agric. Biotechnol.20191742744010.1016/j.bcab.2018.12.014
    [Google Scholar]
  25. WangS. JiangJ.H. LiR.Y. DengP. Docking-based virtual screening of TβR1 inhibitors: Evaluation of pose prediction and scoring functions.BMC Chem.20201415210.1186/s13065‑020‑00704‑332818203
    [Google Scholar]
  26. FriesnerR.A. BanksJ.L. MurphyR.B. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.J. Med. Chem.20044771739174910.1021/jm030643015027865
    [Google Scholar]
  27. Schrödinger Release Maestro. 2023-1: Glide.New York, NYSchrödinger, LLC2023
    [Google Scholar]
  28. Schrödinger Release Maestro.New York, NYSchrödinger, LLC2021
    [Google Scholar]
  29. BowersK.J. ChowE. XuH. Scalable algorithms for molecular dynamics simulations on commodity clusters.Proceedings of the 2006 ACM/IEEE Conference on SupercomputingTampa, FL, USA11-17 November. 2006434310.1109/SC.2006.54
    [Google Scholar]
  30. ShawD.E. Desmond Molecular Dynamics System. 2023-1: Glide.New York, NYSchrödinger, LLC2023
    [Google Scholar]
  31. ShivakumarD. WilliamsJ. WuY. DammW. ShelleyJ. ShermanW. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field.J. Chem. Theory Comput.2010651509151910.1021/ct900587b26615687
    [Google Scholar]
  32. GrantB.J. RodriguesA.P.C. ElSawyK.M. McCammonJ.A. CavesL.S.D. Bio3d: An R package for the comparative analysis of protein structures.Bioinformatics200622212695269610.1093/bioinformatics/btl46116940322
    [Google Scholar]
  33. KagamiL.P. das Neves GM, Timmers LFSM, Caceres RA, Eifler-Lima VL. Geo-Measures: A PyMOL plugin for protein structure ensembles analysis.Comput. Biol. Chem.20208710732210.1016/j.compbiolchem.2020.10732232604028
    [Google Scholar]
  34. DeLanoW.L. Pymol: An open-source molecular graphics tool.Protein Crystallogr2002408292
    [Google Scholar]
  35. PettersenE.F. GoddardT.D. HuangC.C. UCSF Chimera: A visualization system for exploratory research and analysis.J. Comput. Chem.200425131605161210.1002/jcc.2008415264254
    [Google Scholar]
  36. LiJ. YanagisawaK. YoshikawaY. OhueM. AkiyamaY. Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning.Bioinformatics20223841110111710.1093/bioinformatics/btab72634849593
    [Google Scholar]
  37. NairB.R. PillaiL.S. Molecular docking studies using Sinigrin and Tamoxifen.J. Pharmacogn. Phytochem.20187232173221
    [Google Scholar]
  38. BhardwajV.K. SinghR. DasP. PurohitR. Evaluation of acridinedione analogs as potential SARS-CoV-2 main protease inhibitors and their comparison with repurposed anti-viral drugs.Comput. Biol. Med.202112810411710.1016/j.compbiomed.2020.10411733217661
    [Google Scholar]
  39. LombardoF. DesaiP.V. ArimotoR. In silico absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK): Utility and best practices. An industry perspective from the international consortium for innovation through quality in pharmaceutical development.J. Med. Chem.201760229097911310.1021/acs.jmedchem.7b0048728609624
    [Google Scholar]
  40. KrammerA. KirchhoffP.D. JiangX. VenkatachalamC.M. WaldmanM. LigScore: A novel scoring function for predicting binding affinities.J. Mol. Graph. Model.200523539540710.1016/j.jmgm.2004.11.00715781182
    [Google Scholar]
  41. AhmedH. BergmannF. ZeitlingerM. Protein binding in translational antimicrobial development-focus on interspecies differences.Antibiotics202211792310.3390/antibiotics1107092335884177
    [Google Scholar]
  42. DiL. An update on the importance of plasma protein binding in drug discovery and development.Expert Opin. Drug Discov.202116121453146510.1080/17460441.2021.196174134403271
    [Google Scholar]
  43. WhaleG. ParsonsJ. van GinkelK. Improving our understanding of the environmental persistence of chemicals.Integr. Environ. Assess. Manag.20211761123113510.1002/ieam.443833913596
    [Google Scholar]
  44. PrathipatiP. SaxenaA.K. Evaluation of binary QSAR models derived from LUDI and MOE scoring functions for structure based virtual screening.J. Chem. Inf. Model.20064613951[PMID: 16426038
    [Google Scholar]
  45. QuattriniL. La MottaC. Aldose reductase inhibitors: 2013-present.Expert Opin. Ther. Pat.201929319921310.1080/13543776.2019.158264630760060
    [Google Scholar]
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