Adenocarpine, Marmesin, and Lycocernuine from Ficus Benjamina As Promising Inhibitors of Aldose Reductase in Diabetes: A Bioinformatics-Guided Approach

Overview

Journal Appl Biochem Biotechnol

Date 2025 Jan 21

PMID 39836299

Authors

M Oliur Rahman

Sheikh Sunzid Ahmed

Ali S Alqahtani

Kaiser Hamid

Maria Sultana

Mohammad Ajmal Ali

Affiliations

Soon will be listed here.

Abstract

Diabetes affects approximately 422 million people worldwide, leading to 1.5 million deaths annually and causing severe complications such as kidney failure, neuropathy, and cardiovascular disease. Aldose reductase (AR), a key enzyme in the polyol pathway, is an important therapeutic target for managing these complications. The high cost, severe side effects, and rising drug resistance in traditional diabetes treatments underscore the urgent need for novel AR-targeting antidiabetic agents. Ficus benjamina used in traditional medicine demonstrates promising potential for diabetes management. This study investigated the antidiabetic potential of F. benjamina phytocompounds targeting AR receptor employing a structure-based drug design approach to identify potential antidiabetic drug agents. Using molecular docking, ADMET analysis, molecular dynamics (MD) simulation, MM/GBSA, MM/PBSA, and DFT calculations, we identified three promising lead compounds: adenocarpine (- 9.2 kcal/mol), marmesin (- 8.8 kcal/mol), and lycocernuine (- 8.4 kcal/mol). These compounds presented favorable pharmacokinetic, pharmacodynamic, and toxicity profiles, with a 500-ns MD simulation confirming their stability, supported by PCA and Gibbs FEL analysis. MM/GBSA study identified adenocarpine (- 72.53 kcal/mol) as the best compound, outperforming marmesin (- 70 kcal/mol) and lycocernuine (- 61.95 kcal/mol). DFT analysis revealed that adenocarpine exhibited the highest molecular reactivity (3.914 eV), while lycocernuine demonstrated the greatest kinetic stability (6.377 eV). Marmesin and lycocernuine showed increased reactivity upon transitioning from the free states (4.441 eV and 6.377 eV, respectively) to the bound states (4.359 eV and 6.231 eV, respectively). These results could lead to the development of adenocarpine, marmesin, and lycocernuine as novel drug candidates for diabetes, warranting further in vitro and in vivo validation.

References

Eizirik D, Pasquali L, Cnop M . Pancreatic β-cells in type 1 and type 2 diabetes mellitus: different pathways to failure. Nat Rev Endocrinol. 2020; 16(7):349-362. DOI: 10.1038/s41574-020-0355-7. View

Hex N, Bartlett C, Wright D, Taylor M, Varley D . Estimating the current and future costs of Type 1 and Type 2 diabetes in the UK, including direct health costs and indirect societal and productivity costs. Diabet Med. 2012; 29(7):855-62. DOI: 10.1111/j.1464-5491.2012.03698.x. View

Alberti K, Zimmet P . Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998; 15(7):539-53. DOI: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S. View

Kumar Verma S, Thareja S . Structure based comprehensive modelling, spatial fingerprints mapping and ADME screening of curcumin analogues as novel ALR2 inhibitors. PLoS One. 2017; 12(4):e0175318. PMC: 5388491. DOI: 10.1371/journal.pone.0175318. View

Srivastava S, Ramana K, Bhatnagar A . Role of aldose reductase and oxidative damage in diabetes and the consequent potential for therapeutic options. Endocr Rev. 2005; 26(3):380-92. DOI: 10.1210/er.2004-0028. View

Gupta J . The Role of Aldose Reductase in Polyol Pathway: An Emerging Pharmacological Target in Diabetic Complications and Associated Morbidities. Curr Pharm Biotechnol. 2023; 25(9):1073-1081. DOI: 10.2174/1389201025666230830125147. View

Singh Grewal A, Bhardwaj S, Pandita D, Lather V, Sekhon B . Updates on Aldose Reductase Inhibitors for Management of Diabetic Complications and Non-diabetic Diseases. Mini Rev Med Chem. 2015; 16(2):120-62. DOI: 10.2174/1389557515666150909143737. View

Bhatnagar A, Srivastava S . Aldose reductase: congenial and injurious profiles of an enigmatic enzyme. Biochem Med Metab Biol. 1992; 48(2):91-121. DOI: 10.1016/0885-4505(92)90055-4. View

Chen Y, Liu Q, Meng X, Zhao L, Zheng X, Feng W . Catalpol ameliorates fructose-induced renal inflammation by inhibiting TLR4/MyD88 signaling and uric acid reabsorption. Eur J Pharmacol. 2024; 967:176356. DOI: 10.1016/j.ejphar.2024.176356. View

10.

Newman D, Cragg G . Natural Products as Sources of New Drugs from 1981 to 2014. J Nat Prod. 2016; 79(3):629-61. DOI: 10.1021/acs.jnatprod.5b01055. View

11.

Ahmed S, Rahman M . From Flora to Pharmaceuticals: 100 new additions to angiosperms of Gafargaon subdistrict in Bangladesh and unraveling antidiabetic drug candidates targeting DPP4 through in silico approach. PLoS One. 2024; 19(3):e0301348. PMC: 10980240. DOI: 10.1371/journal.pone.0301348. View

12.

Guex N, Peitsch M . SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis. 1998; 18(15):2714-23. DOI: 10.1002/elps.1150181505. View

13.

Yarmolinsky L, Huleihel M, Zaccai M, Ben-Shabat S . Potent antiviral flavone glycosides from Ficus benjamina leaves. Fitoterapia. 2011; 83(2):362-7. DOI: 10.1016/j.fitote.2011.11.014. View

14.

Tian W, Chen C, Lei X, Zhao J, Liang J . CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res. 2018; 46(W1):W363-W367. PMC: 6031066. DOI: 10.1093/nar/gky473. View

15.

Minibaeva G, Ivanova A, Polishchuk P . EasyDock: customizable and scalable docking tool. J Cheminform. 2023; 15(1):102. PMC: 10619229. DOI: 10.1186/s13321-023-00772-2. View

16.

Daina A, Michielin O, Zoete V . SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017; 7:42717. PMC: 5335600. DOI: 10.1038/srep42717. View

17.

Wang Z, Pan H, Sun H, Kang Y, Liu H, Cao D . fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation. Brief Bioinform. 2022; 23(5). DOI: 10.1093/bib/bbac201. View

18.

Zoete V, Daina A, Bovigny C, Michielin O . SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening. J Chem Inf Model. 2016; 56(8):1399-404. DOI: 10.1021/acs.jcim.6b00174. View

19.

Islam S, Ahmed S, Habib N, Ferdous M, Sanjida S, Mou M . High-throughput virtual screening of marine algae metabolites as high-affinity inhibitors of ISKNV major capsid protein: An analysis of models and DFT calculation to find novel drug molecules for fighting infectious spleen and kidney necrosis virus.... Heliyon. 2023; 9(6):e16383. PMC: 10245175. DOI: 10.1016/j.heliyon.2023.e16383. View

20.

Ballester P . Machine Learning for Molecular Modelling in Drug Design. Biomolecules. 2019; 9(6). PMC: 6627644. DOI: 10.3390/biom9060216. View