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Volume 20, Issue 2
  • ISSN: 1574-8855
  • E-ISSN: 2212-3903

Abstract

Introduction and Objective

Breast cancer ranks as the second-most prevalent cause of death among women worldwide, with particularly elevated mortality rates in India. Breast cancer’s origin involves biochemical pathway alterations influenced by tumor-inducing proteins. Research has highlighted glycogen synthase kinase-3 beta (GSK-3β) as a crucial protein that regulates the expression of various genes in the cell cycle. Mutations in this protein have a significant impact on cellular development. As a consequence, it triggers aggressive subtypes of breast cancer, such as triple-negative breast cancer. So, the primary aim of this study is to identify novel chemicals targeting GSK-3β using machine learning methods, molecular modeling, and dynamic techniques.

Materials and Methods

To achieve the study's objective, small molecules were screened using a Machine Learning (ML) approach, and subsequently, molecular docking and dynamic modelling investigations were conducted to explore interactions between drugs and GSK-3β.

Results

The research findings highlighted a specific compound, piperidine, 4-(3,4-dichlorophenyl)-4-[4-(1H-pyrazol-4-yl) phenyl], which exhibited a superior docking score of -9.6 kcal/mol. Piperidine also formed conventional hydrogen bonds with the target protein. Furthermore, the calculated binding free energy of -12.46 kcal/mol suggested that this compound exhibited greater stability compared to commercially available drugs.

Conclusion

These promising findings highlight the potential of piperidine and similar small molecules as promising candidates for targeting the tumor-inducing protein GSK-3β. Subsequent investigations, both and , will be essential to assess their effectiveness in combating breast cancer.

© 2025 Bentham Science Publishers
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2025-04-10

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References

  1. MathurP. SathishkumarK. ChaturvediM. DasP. StephenS. Cancer incidence estimates for 2022 & projection for 2025: Result from National Cancer Registry Programme, India.Indian J. Med. Res.2022156459860710.4103/ijmr.ijmr_1821_2236510887
     [Google Scholar]
  2. GiaquintoA.N. SungH. MillerK.D. KramerJ.L. NewmanL.A. MinihanA. JemalA. SiegelR.L. Breast cancer statistics, 2022.CA Cancer J. Clin.202272652454110.3322/caac.2175436190501
     [Google Scholar]
  3. JussawallaD.J. Breast cancer in India.Gann Monographs on Cancer Research.19761818719310.1016/j.hoc.2023.05.014
     [Google Scholar]
  4. Becerra-TomásN. BalducciK. AbarL. AuneD. CariolouM. GreenwoodD.C. MarkozannesG. NanuN. VieiraR. GiovannucciE.L. GunterM.J. JacksonA.A. KampmanE. LundV. AllenK. BrocktonN.T. CrokerH. KatsikiotiD. McGinley-GieserD. MitrouP. WisemanM. CrossA.J. RiboliE. ClintonS.K. McTiernanA. NoratT. TsilidisK.K. ChanD.S.M. Postdiagnosis dietary factors, supplement use and breast cancer prognosis: Global Cancer Update Programme (CUP Global) systematic literature review and meta-analysis.Int. J. Cancer2023152461663410.1002/ijc.3432136279902
     [Google Scholar]
  5. BeerakaN.M. ZhangJ. ZhaoD. LiuJ. A UC. Vikram PRH. ShivaprakashP. BannimathN. ManogaranP. SinelnikovM.Y. BannimathG. FanR. Combinatorial implications of Nrf2 inhibitors with FN3K inhibitor: In vitro breast cancer study.Curr. Pharm. Des.202329302408242510.2174/011381612826146623101111460037861038
     [Google Scholar]
  6. HeR. DuS. LeiT. XieX. WangY. Glycogen synthase kinase 3β in tumorigenesis and oncotherapy (Review).Oncol. Rep.20204462373238510.3892/or.2020.781733125126
     [Google Scholar]
  7. TungsukruthaiS. PetpiroonN. ChanvorachoteP. Molecular mechanisms of breast cancer metastasis and potential anti-metastatic compounds.Anticancer Res.20183852607261810.21873/anticanres.1250229715080
     [Google Scholar]
  8. TossA. CristofanilliM. Molecular characterization and targeted therapeutic approaches in breast cancer.Breast Cancer Res.20151716010.1186/s13058‑015‑0560‑925902832
     [Google Scholar]
  9. WuR. ZhaoB. RenX. WuS. LiuM. WangZ. LiuW. Mir-27a-3p targeting gsk3β promotes triple-negative breast cancer proliferation and migration through wnt/β-catenin pathway.Cancer Manag. Res.2020126241624910.2147/CMAR.S25541932801869
     [Google Scholar]
  10. GuoC. LiS. LiangA. CuiM. LouY. WangH. PPA1 promotes breast cancer proliferation and metastasis through PI3K/AKT/GSK3β Signaling pathway.Front. Cell Dev. Biol.2021973055810.3389/fcell.2021.73055834595179
     [Google Scholar]
  11. SongN. ZhongJ. HuQ. GuT. YangB. ZhangJ. YuJ. MaX. ChenQ. QiJ. LiuY. SuW. FengZ. WangX. WangH. FGF18 enhances migration and the epithelial-mesenchymal transition in breast cancer by regulating Akt/GSK3β/B-catenin signaling.Cell. Physiol. Biochem.20184931060107310.1159/00049328630196303
     [Google Scholar]
  12. GaoC. YuanX. JiangZ. GanD. DingL. SunY. ZhouJ. XuL. LiuY. WangG. Regulation of AKT phosphorylation by GSK3β and PTEN to control chemoresistance in breast cancer.Breast Cancer Res. Treat.2019176229130110.1007/s10549‑019‑05239‑331006103
     [Google Scholar]
  13. DudaP. AkulaS.M. AbramsS.L. SteelmanL.S. MartelliA.M. CoccoL. RattiS. CandidoS. LibraM. MontaltoG. CervelloM. GizakA. RakusD. McCubreyJ.A. Targeting GSK3 and associated signaling pathways involved in cancer.Cells202095111010.3390/cells905111032365809
     [Google Scholar]
  14. HeY. SunM.M. ZhangG.G. YangJ. ChenK.S. XuW.W. LiB. Targeting PI3K/Akt signal transduction for cancer therapy.Signal Transduct. Target. Ther.20216142510.1038/s41392‑021‑00828‑534916492
     [Google Scholar]
  15. OzmanZ. Ozbek IptecB. SahinE. Guney EskilerG. Deveci OzkanA. KaleliS. Regulation of valproic acid induced EMT by AKT/GSK3β/β-catenin signaling pathway in triple negative breast cancer.Mol. Biol. Rep.20214821335134310.1007/s11033‑021‑06173‑833515347
     [Google Scholar]
  16. ZhuL. ShenX.B. YuanP.C. ShaoT.L. WangG.D. LiuX.P. Arctigenin inhibits proliferation of ER-positive breast cancer cells through cell cycle arrest mediated by GSK3-dependent cyclin D1 degradation.Life Sci.202025611798310.1016/j.lfs.2020.11798332565252
     [Google Scholar]
  17. AlvesM. BorgesD.P. KimberlyA. Martins NetoF. OliveiraA.C. SousaJ.C. NogueiraC.D. CarneiroB.A. TavoraF. Glycogen synthase kinase-3 beta expression correlates with worse overall survival in non-small cell lung cancer—a clinicopathological series.Front. Oncol.20211162105010.3389/fonc.2021.62105033767989
     [Google Scholar]
  18. WangY. MaX. ZhouW. LiuC. ZhangH. Reregulated mitochondrial dysfunction reverses cisplatin resistance microenvironment in colorectal cancer.Smart Medicine202211e2022001310.1002/SMMD.20220013
     [Google Scholar]
  19. GuoZ. LiangE. LiW. JiangL. ZhiF. Essential meiotic structure-specific endonuclease1 ( EME1 ) promotes malignant features in gastric cancer cells via the Akt/GSK3B/CCND1 pathway.Bioengineered20211229869988410.1080/21655979.2021.199937134719326
     [Google Scholar]
  20. SunA. LiC. ChenR. HuangY. ChenQ. CuiX. LiuH. ThrasherJ.B. LiB. GSK-3β controls autophagy by modulating LKB1-AMPK pathway in prostate cancer cells.Prostate201676217218310.1002/pros.2310626440826
     [Google Scholar]
  21. ZhangS. GaoW. TangJ. ZhangH. ZhouY. LiuJ. ChenK. LiuF. LiW. ToS.K.Y. WongA.S.T. ZhangX. ZhouH. ZengJ.Z. The roles of GSK-3β in regulation of retinoid signaling and sorafenib treatment response in hepatocellular carcinoma.Theranostics20201031230124410.7150/thno.3871131938062
     [Google Scholar]
  22. ThapaR. GuptaG. BhatA.A. AlmalkiW.H. AlzareaS.I. KazmiI. SaleemS. KhanR. AltwaijryN. DurejaH. SinghS.K. DuaK. A review of glycogen synthase kinase-3 (GSK3) inhibitors for cancers therapies.Int. J. Biol. Macromol.2023253Pt 712737510.1016/j.ijbiomac.2023.12737537839597
     [Google Scholar]
  23. XuJ. MaoC. HouY. LuoY. BinderJ.L. ZhouY. BekrisL.M. ShinJ. HuM. WangF. EngC. OpreaT.I. FlanaganM.E. PieperA.A. CummingsJ. LeverenzJ.B. ChengF. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer’s disease.Cell Rep.202241911171710.1016/j.celrep.2022.11171736450252
     [Google Scholar]
  24. AkazawaM. HashimotoK. Artificial intelligence in ovarian cancer diagnosis.Anticancer Res.20204084795480010.21873/anticanres.1448232727807
     [Google Scholar]
  25. JungJ. DaiJ. LiuB. WuQ. Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis.PLOS Digital Health202431e000043810.1371/journal.pdig.000043838289965
     [Google Scholar]
  26. WuQ. DaiJ. Enhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.J. Bone Miner. Res.202439446247210.1093/jbmr/zjae02538477741
     [Google Scholar]
  27. LaiosA. KatsenouA. TanY.S. JohnsonR. OtifyM. KaufmannA. MunotS. ThangaveluA. HutsonR. BroadheadT. TheophilouG. NugentD. De JongD. Feature selection is critical for 2-year prognosis in advanced stage high grade serous ovarian cancer by using machine learning.Cancer Contr.20212810.1177/1073274821104467834693730
     [Google Scholar]
  28. DaiJ. LatifiS. A deep learning framework for prediction of the mechanism of action.Int. J. Comput. Appl.2021183121710.5120/ijca2021921383
     [Google Scholar]
  29. IssaN.T. StathiasV. SchürerS. DakshanamurthyS. Machine and deep learning approaches for cancer drug repurposing.Semin. Cancer Biol.20216813214210.1016/j.semcancer.2019.12.01131904426
     [Google Scholar]
  30. UrbinaF. PuhlA.C. EkinsS. Recent advances in drug repurposing using machine learning.Curr. Opin. Chem. Biol.202165748410.1016/j.cbpa.2021.06.00134274565
     [Google Scholar]
  31. VignauxP.A. MineraliE. FoilD.H. PuhlA.C. EkinsS. Machine learning for discovery of GSK3β inhibitors.ACS Omega2020541265512656110.1021/acsomega.0c0330233110983
     [Google Scholar]
  32. VatanseverS. SchlessingerA. WackerD. KaniskanH.Ü. JinJ. ZhouM.M. ZhangB. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Med. Res. Rev.20214131427147310.1002/med.2176433295676
     [Google Scholar]
  33. NassarH. SipplW. DahabR.A. TahaM. Molecular docking, molecular dynamics simulations and in vitro screening reveal cefixime and ceftriaxone as GSK3β covalent inhibitors.RSC Advances20231317112781129010.1039/D3RA01145C37057264
     [Google Scholar]
  34. TrottO. OlsonAJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J Comput Chem.20103124556110.1002/jcc.21334
     [Google Scholar]
  35. MouL. MaZ. MengX. LiW. LiangS. ChenX. Exploration of the selective binding mechanism of GSK3β via molecular modeling and molecular dynamics simulation studies.Med. Chem. Res.202029469069810.1007/s00044‑020‑02514‑7
     [Google Scholar]
  36. SaravananK. HundayG. KumaradhasP. Binding and stability of indirubin-3-monoxime in the GSK3β enzyme: A molecular dynamics simulation and binding free energy study.J. Biomol. Struct. Dyn.202038495797410.1080/07391102.2019.159130130963817
     [Google Scholar]
  37. ZhuJ. WuY. XuL. JinJ. Theoretical studies on the selectivity mechanisms of glycogen synthase kinase 3β (GSK3β) with pyrazine ATP-competitive Inhibitors by 3DQSAR, molecular docking, molecular dynamics simulation and free energy calculations.Curr. Computeraided Drug Des.2020161173010.2174/18756697OTk0jNDkgTcVY31284868
     [Google Scholar]
  38. GopikrishnanM. George Priya DossC. Molecular docking and dynamic approach to screen the drug candidate against the Imipenem-resistant CarO porin in Acinetobacter baumannii .Microb. Pathog.202317710604910.1016/j.micpath.2023.10604936858184
     [Google Scholar]
  39. KimS. ChenJ. ChengT. GindulyteA. HeJ. HeS. LiQ. ShoemakerB.A. ThiessenP.A. YuB. ZaslavskyL. ZhangJ. BoltonE.E. PubChem 2023 update.Nucleic Acids Res.202351D1D1373D138010.1093/nar/gkac95636305812
     [Google Scholar]
  40. WishartD.S. KnoxC. GuoA.C. ShrivastavaS. HassanaliM. StothardP. ChangZ. WoolseyJ. DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res.20063490001D668D67210.1093/nar/gkj06716381955
     [Google Scholar]
  41. YapC.W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints.J. Comput. Chem.20113271466147410.1002/jcc.2170721425294
     [Google Scholar]
  42. HallM. HolmesG. PfahringerB. ReutemannP. FrankE. WittenI.H. The WEKA data mining software.An update.2014
     [Google Scholar]
  43. ShirbhateS.V. SherekarS.S. ThakareV.M. Performance evaluation of PCA filter in clustered based intrusion detection system.International Conference on Electronic Systems, Signal Processing and Computing TechnologiesNagpur, India201421722110.1109/ICESC.2014.100
     [Google Scholar]
  44. NakraA. DuhanM. Comparative analysis of bayes net classifier, naive bayes classifier and combination of both classifiers using WEKA.Int. J. Comp. Sci. Info Tech2019113384510.5815/ijitcs.2019.03.04
     [Google Scholar]
  45. AlpanK. IlgiG.S. Classification of diabetes dataset with data mining techniques by using WEKA approach.4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)Turkey202010.1109/ISMSIT50672.2020.9254720
     [Google Scholar]
  46. Salih HasanB.M. AbdulazeezA.M. A review of principal component analysis algorithm for dimensionality reduction.J. Soft Comput. Data. Min.20212110.30880/jscdm.2021.02.01.003
     [Google Scholar]
  47. TrevethanR. Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice.Front. Public Health2017530710.3389/fpubh.2017.0030729209603
     [Google Scholar]
  48. AmaralB. CapacciA. AndersonT. TezerC. BajramiB. LullaM. LucasB. ChodaparambilJ.V. MarcotteD. KumarP.R. MuruganP. SpilkerK. CullivanM. WangT. PetersonA.C. EnyedyI. MaB. ChenT. YousafZ. CalhounM. GolonzhkaO. DillonG.M. KoiralaS. Elucidation of the GSK3α structure informs the design of novel, paralog-selective inhibitors.ACS Chem. Neurosci.20231461080109410.1021/acschemneuro.2c0047636812145
     [Google Scholar]
  49. LipinskiC.A. LombardoF. DominyB.W. FeeneyP.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.Adv. Drug Deliv. Rev.2001461-332610.1016/S0169‑409X(00)00129‑011259830
     [Google Scholar]
  50. MorrisG.M. HueyR. LindstromW. SannerM.F. BelewR.K. GoodsellD.S. OlsonA.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.J. Comput. Chem.200930162785279110.1002/jcc.2125619399780
     [Google Scholar]
  51. TanchukV. TaninV. VovkA. PodaG. A new scoring function for molecular docking based on AutoDock and AutoDock Vina.Curr. Drug Discov. Technol.201512317017810.2174/157016381266615082511020826302746
     [Google Scholar]
  52. EberhardtJ. Santos-MartinsD. TillackA.F. ForliS. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings.J. Chem. Inf. Model.20216183891389810.1021/acs.jcim.1c0020334278794
     [Google Scholar]
  53. LemkulJ. From proteins to perturbed hamiltonians: a suite of tutorials for the GROMACS-2018 molecular simulation package.Living J. Comput. Mol. Sci.20191110.33011/livecoms.1.1.5068
     [Google Scholar]
  54. DeepasreeK. SubhashreeV. Molecular docking and dynamic simulation studies of terpenoid compounds against phosphatidylinositol-specific phospholipase C from Listeria monocytogenes .Inform. Med. Unlock.20233910125210.1016/j.imu.2023.101252
     [Google Scholar]
  55. DomotoT. UeharaM. BolidongD. MinamotoT. Glycogen synthase kinase 3β in cancer biology and treatment.Cells202096138810.3390/cells906138832503133
     [Google Scholar]
  56. ZhouW. MaX. WangJ. XuX. KoivistoO. FengJ. ViitalaT. ZhangH. Co-delivery CPT and PTX prodrug with a photo/thermo-responsive nanoplatform for triple-negative breast cancer therapy.Smart Medicine202211e2022003610.1002/SMMD.20220036
     [Google Scholar]
  57. DebelaD.T. MuzazuS.G.Y. HeraroK.D. NdalamaM.T. MeseleB.W. HaileD.C. KituiS.K. ManyazewalT. New approaches and procedures for cancer treatment: Current perspectives.SAGE Open Med.2021910.1177/2050312121103436634408877
     [Google Scholar]
  58. QiR. ZouQ. Trends and potential of machine learning and deep learning in drug study at single-cell level.Research20236005010.34133/research.005036930772
     [Google Scholar]
  59. LaiosA. De Oliveira SilvaR.V. Dantas De FreitasD.L. TanY.S. SaalminkG. ZubayraevaA. JohnsonR. KaufmannA. OtifyM. HutsonR. ThangaveluA. BroadheadT. NugentD. TheophilouG. Gomes de LimaK.M. De JongD. Machine learning-based risk prediction of critical care unit admission for advanced stage high grade serous ovarian cancer patients undergoing cytoreductive surgery: The Leeds-Natal score.J. Clin. Med.20211118710.3390/jcm1101008735011828
     [Google Scholar]
  60. PaikE.S. LeeJ.W. ParkJ.Y. KimJ.H. KimM. KimT.J. ChoiC.H. KimB.G. BaeD.S. SeoS.W. Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.J. Gynecol. Oncol.2019304e6510.3802/jgo.2019.30.e6531074247
     [Google Scholar]
  61. BahiaM.S. KaspiO. TouitouM. BinayevI. DhailS. SpiegelJ. KhazanovN. YosipofA. SenderowitzH. A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations.Mol. Inform.2023424220018610.1002/minf.20220018636617991
     [Google Scholar]
  62. PirzadaR.H. AhmadB. QayyumN. ChoiS. Modeling structure–activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics.Front. Endocrinol. (Lausanne)202314108432710.3389/fendo.2023.108432736950681
     [Google Scholar]
  63. DouB. ZhuZ. MerkurjevE. KeL. ChenL. JiangJ. ZhuY. LiuJ. ZhangB. WeiG.W. Machine learning methods for small data challenges in molecular science.Chem. Rev.2023123138736878010.1021/acs.chemrev.3c0018937384816
     [Google Scholar]
  64. ZhuJ. WuY. WangM. LiK. XuL. ChenY. CaiY. JinJ. Integrating machine learning-based virtual screening with multiple protein structures and bio-assay evaluation for discovery of novel GSK3β inhibitors.Front. Pharmacol.20201156605810.3389/fphar.2020.56605833041806
     [Google Scholar]
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Machine Learning Prediction and Simulation of Drugs Targeting GSK-3β in Breast Cancer
Curr Drug Ther 20, 196 (2025); https://doi.org/10.2174/0115748855333541240819111638
/content/journals/cdth/10.2174/0115748855333541240819111638
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  • Article Type:     Research Article
Keyword(s): breast cancer; data mining; Glycogen synthase kinase 3 beta (GSK-3β); machine learning; molecular docking; molecular dynamic simulations (MDS)

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