AccScience Publishing / EJMO / Online First / DOI: 10.36922/EJMO025150114
Submit to EJMO
Cite this article
8
Download
115
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ORIGINAL RESEARCH ARTICLE

Targeting the FN3K–Nrf2 axis: Discovery and pre-clinical evaluation of novel inhibitors for breast cancer therapy

Erica Alves1 ,  Gurupadayya Bannimath1* ,  Prabitha Prabhakaran1
Show Less
1 Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysore, Karnataka, India
EJMO, 025150114 https://doi.org/10.36922/EJMO025150114
Received: 13 April 2025 | Revised: 17 May 2025 | Accepted: 4 June 2025 | Published online: 28 July 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
Cite
XML
Abstract

Introduction: Fructosamine-3-kinase (FN3K), a deglycation enzyme implicated in redox regulation through the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, has emerged as a novel therapeutic target in breast cancer. Elevated FN3K activity enhances antioxidant defenses, promoting cancer cell survival and resistance to therapies. Pharmacological inhibition of FN3K may sensitize tumors to oxidative stress.

Objective: This study aimed to identify and validate potent FN3K inhibitors through structure-based virtual screening (SBVS) and in vitro evaluation in breast cancer models.

Methods: A homology-modeled FN3K structure was generated and validated using SWISS-MODEL, PROCHECK, and QMEAN. Compound libraries, including the Food and Drug Administration (FDA)-approved kinase inhibitors, World Health Organization (WHO) essential medicines, and anti-breast cancer agents, were screened using Schrödinger’s Glide module. Top-ranked compounds were prioritized based on binding affinity, molecular interactions, absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling. In vitro validation in MCF-7, BT-474, T-47D, and Vero cell lines included MTT cytotoxicity assay and evaluation of FN3K and Nrf2 expression through quantitative PCR (qPCR) and Western blotting. Statistical analyses were performed to assess the significance of observed effects.

Results: Oxaliplatin, lansoprazole, and capivasertib exhibited strong binding affinities (Glide scores: −9.2 to −8.1 kcal/mol) and selective cytotoxicity in breast cancer cell lines (IC50: 90 – 110 μg/mL). qPCR analysis revealed >99% downregulation of FN3K, accompanied by significant suppression of Nrf2 in cancer cells. Minimal modulation was observed in Vero cells, indicating tumor selectivity. Western blotting further corroborated the downregulation of FN3K and Nrf2 at the protein level. Molecular dynamics (MD) simulations validated the binding stability of the lead small molecules, reinforcing their potential as effective inhibitors.

Conclusion: The integrated in silico and in vitro analysis supports FN3K as a viable therapeutic target in breast cancer. Oxaliplatin, lansoprazole, and capivasertib demonstrated strong FN3K inhibition and modulation of tumor redox homeostasis, suggesting their potential for further pre-clinical development as novel anti-cancer agents targeting metabolic adaptability.

Graphical abstract
Keywords
Breast neoplasms
Fructosamine-3-kinase
Nuclear factor erythroid 2-related factor 2 protein
Oxidative stress
Antineoplastic agents
Funding
The work was supported by the Council of Scientific and Industrial Research, Human Resource Development Group (CSIR-HRDG), New Delhi, India (Grant No.111- 5634- 11759/2023/1 dated February 19th, 2024).
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
References
  1. Tufail M, Jiang CH, Li N. Altered metabolism in cancer: Insights into energy pathways and therapeutic targets. Mol Cancer. 2024;23(1):203. doi: 10.1186/s12943-024-02119-3

 

  1. Yousefi T, Pasha ARG, Kamrani G, et al. Evaluation of fructosamine 3-kinase and glyoxalase 1 activity in normal and breast cancer tissues. Biomedicine (Taipei). 2021;11(3):15-22. doi: 10.37796/2211-8039.1130

 

  1. Alves E, Bannimath G, Prabhakaran P, Beeraka NM. The FN3K-Nrf2 axis: A novel therapeutic target in cancer metabolism. Eurasian J Med Oncol. 2024;8(4):338-339. doi: 10.14744/ejmo.2024.20665

 

  1. Sanghvi VR, Leibold J, Mina M, et al. The oncogenic action of NRF2 depends on de-glycation by fructosamine-3-kinase. Cell. 2019;178(4):807-819.e21. doi: 10.1016/j.cell.2019.07.031

 

  1. Beeraka NM, Zhang J, Mandal S, et al. Screening fructosamine-3-kinase (FN3K) inhibitors, a deglycating enzyme of oncogenic Nrf2: Human FN3K homology modelling, docking and molecular dynamics simulations. PLoS One. 2023;18(11):e0283705. doi: 10.1371/journal.pone.0283705

 

  1. Beeraka NM, Zhang J, Uthaiah CA, et al. Expression patterns and relevance of FN3K, Nrf2, and NQO1 in breast cancers. Eurasian J Med Oncol. 2024;8(1):88-105. doi: 10.14744/ejmo.2024.61033

 

  1. Delpierre G, Collard F, Fortpied J, Van Schaftingen E. Fructosamine 3-kinase, an enzyme involved in protein deglycation. Biochem Soc Trans. 2004;32(6):813-816. doi: 10.1042/bst0320813

 

  1. Lionta E, Spyrou G, Vassilatis DK, Cournia Z. Structure-based virtual screening for drug discovery: Principles, applications and recent advances. Curr Top Med Chem. 2014;14(16):1923-1938. doi: 10.2174/1568026614666140929124445

 

  1. Roskoski R Jr. Properties of FDA-approved small molecule protein kinase inhibitors: A 2023 update. Pharmacol Res. 2023;187:106552. doi: 10.1016/j.phrs.2023.106552

 

  1. Beeraka NM, Bovilla VR, Doreswamy SH, Puttalingaiah S, Srinivasan A, Madhunapantula SV. The taming of nuclear factor erythroid-2-related factor-2 (Nrf2) deglycation by fructosamine-3-kinase (FN3K)-inhibitors-a novel strategy to combat cancers. Cancers (Basel). 2021;13(2):281. doi: 10.3390/cancers13020281

 

  1. Beeraka NM, Zhang J, Zhao D, et al. Combinatorial implications of Nrf2 inhibitors with FN3K inhibitor: In vitro breast cancer study. Curr Pharm Des. 2023;29(30):2408-2425. doi: 10.2174/0113816128261466231011114600

 

  1. Carles F, Bourg S, Meyer C, Bonnet P. PKIDB: A curated, annotated and updated database of protein kinase inhibitors in clinical trials. Molecules. 2018;23(4):908. doi: 10.3390/molecules23040908

 

  1. World Health Organization. WHO Model Lists of Essential Medicines. 23rd List; 2023. Available from: https://www.who. int/publications/i/item/WHO-MHP-HPS-EML-2023.01 [Last accessed on 2025 Mar 14].

 

  1. National Cancer Institute. Breast Cancer Treatment Drugs. Bethesda, MD: National Cancer Institute. Available from: https://www.cancer.gov/about-cancer/treatment/drugs/ breast [Last accessed on 2025 Mar 14].

 

  1. Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: A problem-centric review. AAPS J. 2012;14(1):133-141. doi: 10.1208/s12248-012-9322-0

 

  1. Hevener KE, Pesavento R, Ren J, Lee H, Ratia K, Johnson ME. Hit-to-lead: Hit validation and assessment. Methods Enzymol. 2018;610:265-309. doi: 10.1016/bs.mie.2018.09.022

 

  1. Wang XJ, Sun Z, Villeneuve NF, et al. Nrf2 enhances resistance of cancer cells to chemotherapeutic drugs, the dark side of Nrf2. Carcinogenesis. 2008;29(6):1235-1243. doi: 10.1093/carcin/bgn095

 

  1. Schrödinger LLC. Maestro: Schrödinger Release 2024-1. New York, NY: Schrödinger, LLC; 2024. Available from: https://www.schrodinger.com/maestro [Last accessed on 2025 Feb 21].

 

  1. Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem. 2006;49(21):6177-6196. doi: 10.1021/jm051256o

 

  1. Liu T, Lu D, Zhang H, et al. Applying high-performance computing in drug discovery and molecular simulation. Natl Sci Rev. 2016;3(1):49-63. doi: 10.1093/nsr/nww003

 

  1. UniProt Consortium. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 2019;47(D1):D506-D515. doi: 10.1093/nar/gky1049

 

  1. UniProt. FN3K Fructosamine-3-Kinase Homo Sapiens (Human). UniProtKB; 2025. Available from: https://www. uniprot.org/uniprotkb/q9H479/entry [Last accessed on 2025 Mar 15].

 

  1. Sievers F, Higgins DG. Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 2018;27(1):135-145. doi: 10.1002/pro.3290

 

  1. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Res. 2003;31(13):3381-3385. doi: 10.1093/nar/gkg520

 

  1. Ko J, Park H, Heo L, Seok C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res. 2012;40(Web Server issue):W294-W297. doi: 10.1093/nar/gks493

 

  1. Kleywegt GJ, Jones TA. Phi/Psi-chology: Ramachandran revisited. Structure. 1996;4(12):1395-1400. doi: 10.1016/S0969-2126(96)00147-5

 

  1. University of California, Los Angeles (UCLA). SAVES - Structure Analysis and Verification Server. Available from: https://saves.mbi.ucla.edu [Last accessed on 2025 Mar 15].

 

  1. Biasini M, Bienert S, Waterhouse A, et al. SWISS-MODEL: Modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014;42(Web Server issue):W252-W258. doi: 10.1093/nar/gku340

 

  1. Benkert P, Tosatto SC, Schomburg D. QMEAN: A comprehensive scoring function for model quality assessment. Proteins. 2008;71(1):261-277. doi: 10.1002/prot.21715

 

  1. Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics. 2011;27(3):343-350. doi: 10.1093/bioinformatics/btq662

 

  1. Schrödinger LLC. Protein Preparation Workflow. Schrödinger. Available from: https://www.schrodinger. com/life-science/learn/white-papers/protein-preparation-workflow [Last accessed on 2025 Mar 15].

 

  1. National Center for Biotechnology Information (NCBI). PubChem. Available from: https://pubchem.ncbi.nlm.nih. gov [Last accessed on 2025 Mar 15].

 

  1. Knox C, Wilson M, Klinger CM, et al. DrugBank 6.0: The drugbank knowledgebase for 2024. Nucleic Acids Res. 2024;52(D1):D1265-D1275. doi: 10.1093/nar/gkad976

 

  1. Sahayarayan JJ, Rajan KS, Vidhyavathi R, et al. In-silico protein-ligand docking studies against the estrogen protein of breast cancer using pharmacophore based virtual screening approaches. Saudi J Biol Sci. 2021;28(1):400-407. doi: 10.1016/j.sjbs.2020.10.023

 

  1. Ge Y, Pande V, Seierstad MJ, Damm-Ganamet KL. Exploring the application of sitemap and site finder for focused cryptic pocket identification. J Phys Chem B. 2024;128(26):6233- 6245. doi: 10.1021/acs.jpcb.4c00664

 

  1. Elekofehinti OO, Iwaloye O, Josiah SS, Lawal AO, Akinjiyan MO, Ariyo EO. Molecular docking studies, molecular dynamics and ADME/tox reveal therapeutic potentials of STOCK1N-69160 against papain-like protease of SARS-CoV-2. Mol Divers. 2021;25:1761-1773. doi: 10.1007/s11030-020-10151-w

 

  1. Park MS, Dessal AL, Smrcka AV, Stern HA. Evaluating docking methods for prediction of binding affinities of small molecules to the G protein betagamma subunits. J Chem Inf Model. 2010;49(2):437-443.doi: 10.1021/ci800412z

 

  1. 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. doi: 10.1038/srep42717

 

  1. Li W, Zhou J, Xu Y. Study of the in vitro cytotoxicity testing of medical devices. Biomed Rep. 2015;3(5):617-620. doi: 10.3892/br.2015.481

 

  1. International Organization for Standardization. ISO 10993- 5:2009. Biological Evaluation of Medical Devices-Part 5: Tests for In Vitro Cytotoxicity. 3rd ed. Geneva: ISO; 2009.

 

  1. Senthilraja P, Kathiresan K. In vitro cytotoxicity MTT assay in vero, HepG2 and MCF-7 cell lines study of marine yeast. J Appl Pharm Sci. 2015;5(3):80-84. doi: 10.7324/JAPS.2015.50313

 

  1. Freshney RI. Culture of Animal Cells: A Manual of Basic Technique and Specialized Applications. 6th ed. Hoboken, NJ: Wiley-Blackwell; 2010.

 

  1. Chomczynski P, Sacchi N. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem. 1987;162(1):156-159. doi: 10.1016/0003-2697(87)90021-2

 

  1. Rio DC, Ares M Jr., Hannon GJ, Nilsen TW. Determining the yield and quality of purified RNA. Cold Spring Harb Protoc. 2010;2010(6). doi: 10.1101/pdb.top82

 

  1. Taylor SC, Laperriere G, Germain H. Droplet digital PCR versus qPCR for gene expression analysis with low abundant targets: From variable nonsense to publication quality data. Sci Rep. 2017;7(1):2409. doi: 10.1038/s41598-017-02217-x

 

  1. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCt method. Methods. 2001;25(4):402-408. doi: 10.1006/meth.2001.1262

 

  1. Pfaffl MW. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001;29(9):e45. doi: 10.1093/nar/29.9.e45

 

  1. Mahmood T, Yang PC. Western blot: Technique, theory, and trouble shooting. N Am J Med Sci. 2012;4(9):429-434. doi: 10.4103/1947-2714.100998

 

  1. Taylor SC, Posch A. The design of a quantitative western blot experiment. Biomed Res Int. 2014;2014:361590. doi: 10.1155/2014/361590

 

  1. Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: Fundamental algorithms for scientific computing in python. Nat Methods. 2020;17(3):261-272. doi: 10.1038/s41592-019-0686-2

 

  1. Ahad NA, Syed Yahaya SS. Sensitivity analysis of Welch’s t-test. AIP Conf Proc. 2014;1605:888-893. doi: 10.1063/1.4887707

 

  1. Lee S, Lee DK. What is the proper way to apply the multiple comparison test. Korean J Anesthesiol. 2018;71(5):353-360. doi: 10.4097/kja.d.18.00242

 

  1. Kasi A. Oxaliplatin. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2025. Available from: https://www. ncbi.nlm.nih.gov/books/nbk557690 [Last accessed on 2023 May 16].

 

  1. Fernandez-Teruel C, Cullberg M, González-García I, Schiavon G, Zhou D. An exposure-safety analysis to support the dosage of the novel AKT inhibitor capivasertib. Cancer Chemother Pharmacol. 2025;95:48. doi: 10.1007/s00280-025-04775-8

 

  1. Ahmed A, Clarke JO. Proton Pump Inhibitors (PPI). In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2025. Available from: https://www.ncbi.nlm.nih.gov/books/ nbk557385 [Last accessed on 2023 May 01].

 

  1. Lau A, Villeneuve NF, Sun Z, Wong PK, Zhang DD. Dual roles of Nrf2 in cancer. Pharmacol Res. 2008;58(5-6):262- 270. doi: 10.1016/j.phrs.2008.09.003

 

  1. Kumar H, Kumar RM, Bhattacharjee D, Somanna P, Jain V. Role of Nrf2 signaling cascade in breast cancer: Strategies and treatment. Front Pharmacol. 2022;13:720076. doi: 10.3389/fphar.2022.720076

 

  1. Bogliolo S, Cassani C, Gardella B, et al. Oxaliplatin for the treatment of ovarian cancer. Expert Opin Investig Drugs. 2015;24(9):1275-1286. doi: 10.1517/13543784.2015.1062874

 

  1. Innominato PF, Karaboué A, Focan C, et al. Efficacy and safety of chronomodulated irinotecan, oxaliplatin, 5-fluorouracil and leucovorin combination as first- or second-line treatment against metastatic colorectal cancer: Results from the international EORTC 05011 trial. Int J Cancer. 2021;148(10):2512-2521. doi: 10.1002/ijc.33422

 

  1. Rugo HS, Oliveira M, Howell SJ, et al. Capivasertib and fulvestrant for patients with hormone receptor-positive advanced breast cancer: Characterization, time course, and management of frequent adverse events from the phase III CAPItello-291 study. ESMO Open. 2024;9(9):103697. doi: 10.1016/j.esmoop.2024.103697

 

  1. Khadempar S, Lotfi M, Haghiralsadat F, Saidijam M, Ghasemi N, Afshar S. Lansoprazole as a potent HDAC2 inhibitor for treatment of colorectal cancer: An in-silico analysis and experimental validation. Comput Biol Med. 2023;166:107518. doi: 10.1016/j.compbiomed.2023.107518

 

  1. Spugnini EP, Buglioni S, Carocci F, et al. High dose lansoprazole combined with metronomic chemotherapy: A phase I/II study in companion animals with spontaneously occurring tumors. J Transl Med. 2014;12:225. doi: 10.1186/s12967-014-0225-y

 

  1. Fais S, De Milito A, You H, Qin W. Targeting vacuolar H⁺-ATPases as a new strategy against cancer. Cancer Res. 2007;67(22):10627-10630. doi: 10.1158/0008-5472.CAN-07-1805

 

  1. Kao HW, Tsai KW, Lin WC. Synergistic effect of metformin and lansoprazole against gastric cancer through growth inhibition. Int J Med Sci. 2023;20(6):717-724. doi: 10.7150/ijms.82407

 

  1. Salo-Ahen OMH, Alanko I, Bhadane R, et al. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes. 2021;9(1):71. doi: 10.3390/pr9010071
Share
Back to top
Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept