Skip to content
2000
Volume 31, Issue 24
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

Background

Anaplastic Lymphoma Kinase (ALK) is implicated in several cancers, including anaplastic large cell lymphoma, non-small cell lung cancer, and neuroblastoma. Targeted inhibition of ALK represents a promising therapeutic strategy.

Aims

This study aimed to identify and evaluate potential ALK inhibitors using virtual screening and computational analyses to determine their binding stability, affinity, and dynamic behavior, ultimately assessing their potential as therapeutic agents for ALK-driven cancers.

Objective

The objective of this study was to identify potential ALK inhibitors using virtual screening techniques and to evaluate their binding affinities and stability through computational analyses.

Methods

This study utilized virtual screening to identify potential ALK inhibitors from the MTiOpen Screen Diverse library and selected three compounds (24331480, 26536128, and 24353407) based on their binding affinities. These compounds underwent optimization using Density Functional Theory (DFT) and were re- docked to confirm binding stability. Molecular dynamics simulations, hydrogen bond analysis, MM/PBSA calculations, and PCA-based free energy landscape analysis were also carried out.

Results

The re-docking experiments confirmed the stable and strong binding affinities of the selected compounds to the ALK active site. Molecular dynamics simulations revealed stable interactions throughout the 200 ns simulation period. Hydrogen bond analysis demonstrated consistent hydrogen bonds between key residues and the inhibitors. MM/PBSA calculations indicated favorable binding free energies, suggesting strong binding affinities. Finally, PCA-based free energy landscape analysis highlighted energetically favorable binding modes.

Conclusion

The identified compounds (24331480, 26536128, and 24353407) exhibited promising inhibitory potential against ALK. These findings warrant further experimental validation to confirm their potential as therapeutic agents for ALK-driven cancers.

Loading

Article metrics loading...

/content/journals/cpd/10.2174/0113816128342778250218105338
2025-03-11
2025-08-16
Loading full text...

Full text loading...

References

  1. WebbT.R. SlavishJ. GeorgeR.E. LookA.T. XueL. JiangQ. CuiX. RentropW.B. MorrisS.W. Anaplastic lymphoma kinase: Role in cancer pathogenesis and small-molecule inhibitor development for therapy.Expert Rev. Anticancer Ther.20099333135610.1586/14737140.9.3.33119275511
    [Google Scholar]
  2. ArbourK.C. RielyG.J. Diagnosis and treatment of anaplastic lymphoma kinase–Positive Non–small cell lung cancer.Hematol. Oncol. Clin. North Am.201731110111110.1016/j.hoc.2016.08.01227912826
    [Google Scholar]
  3. AubryA. GaliacyS. AlloucheM. Targeting ALK in cancer: Therapeutic potential of proapoptotic peptides.Cancers (Basel)201911327510.3390/cancers1103027530813562
    [Google Scholar]
  4. Della CorteC.M. ViscardiG. Di LielloR. FasanoM. MartinelliE. TroianiT. CiardielloF. MorgilloF. Role and targeting of anaplastic lymphoma kinase in cancer.Mol. Cancer20181713010.1186/s12943‑018‑0776‑229455642
    [Google Scholar]
  5. GristinaV. La MantiaM. IaconoF. GalvanoA. RussoA. BazanV. The emerging therapeutic landscape of ALK inhibitors in non-small cell lung cancer.Pharmaceuticals (Basel)2020131247410.3390/ph1312047433352844
    [Google Scholar]
  6. LabbéC.M. ReyJ. LagorceD. VavrušaM. BecotJ. SperandioO. VilloutreixB.O. TufféryP. MitevaM.A. MTiOpenScreen: A web server for structure-based virtual screening.Nucleic Acids Res.201543W1W448W45410.1093/nar/gkv30625855812
    [Google Scholar]
  7. BermanH.M. WestbrookJ. FengZ. GillilandG. BhatT.N. WeissigH. ShindyalovI.N. BourneP.E. The protein data bank.Nucleic Acids Res.200028123524210.1093/nar/28.1.23510592235
    [Google Scholar]
  8. MichellysP.Y. ChenB. JiangT. JinY. LuW. MarsiljeT.H. PeiW. UnoT. ZhuX. WuB. NguyenT.N. BursulayaB. LeeC. LiN. KimS. TuntlandT. LiuB. SunF. SteffyA. HoodT. Design and synthesis of novel selective anaplastic lymphoma kinase inhibitors.Bioorg. Med. Chem. Lett.20162631090109610.1016/j.bmcl.2015.11.04926750252
    [Google Scholar]
  9. PettersenE.F. GoddardT.D. HuangC.C. CouchG.S. GreenblattD.M. MengE.C. FerrinT.E. UCSF Chimera—A visualization system for exploratory research and analysis.J. Comput. Chem.200425131605161210.1002/jcc.2008415264254
    [Google Scholar]
  10. TrottO. OlsonA.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.201031245546110.1002/jcc.2133419499576
    [Google Scholar]
  11. LipinskiC.A. Lead- and drug-like compounds: The rule-of-five revolution.Drug Discov. Today. Technol.20041433734110.1016/j.ddtec.2004.11.00724981612
    [Google Scholar]
  12. Lipinski’s Rule of Five - an Overview | ScienceDirect Topics.Available from: https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/lipinskis-rule-of-five (Accessed on 2 January 2024).
  13. YadavaU. GuptaH. RoychoudhuryM. A comparison of crystallographic and DFT optimized geometries on two taxane diterpenoids and docking studies with phospholipase A2.Med. Chem. Res.20122192162216810.1007/s00044‑011‑9724‑z
    [Google Scholar]
  14. FrischM.J. TrucksG.W. SchlegelH.B. ScuseriaG.E. RobbM.A. CheesemanJ.R. ScalmaniG. BaroneV. PeterssonG.A. NakatsujiH. CaricatoM. LiX. HratchianH.P. IzmaylovA.F. BloinoJ. ZhengG. SonnenbergJ.L. HadaM. EharaM. ToyotaK. FukudaR. HasegawaJ. IshidaM. NakajimaT. HondaY. KitaoO. NakaiH. VrevenT. MontgomeryJ.A.Jr PeraltaJ.E. OgliaroF. BearparkM. HeydJ.J. BrothersE. KudinK.N. StaroverovV.N. KeithT. KobayashiR. NormandJ. RaghavachariK. RendellA. BurantJ.C. IyengarS.S. TomasiJ. CossiM. MillamJ.M. KleneM. AdamoC. CammiR. OchterskiJ.W. MartinR.L. MorokumaK. FarkasO. ForesmanJ.B. FoxD.J. Gaussian 09, Revision D.01Gaussian, Inc.Wallingford CT2016Available from: https: //gaussian.com/
    [Google Scholar]
  15. BeckeA.D. Density-functional exchange-energy approximation with correct asymptotic behavior.Phys. Rev. A Gen. Phys.19883863098310010.1103/PhysRevA.38.30989900728
    [Google Scholar]
  16. LeeC. YangW. ParrR.G. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density.Phys. Rev. B Condens. Matter198837278578910.1103/PhysRevB.37.7859944570
    [Google Scholar]
  17. SamadA. HuqM.A. RahmanM.S. Bioinformatics approaches identified dasatinib and bortezomib inhibit the activity of MCM7 protein as a potential treatment against human cancer.Sci. Rep.2022121153910.1038/s41598‑022‑05621‑035087187
    [Google Scholar]
  18. WangJ. WolfR.M. CaldwellJ.W. KollmanP.A. CaseD.A. Development and testing of a general amber force field.J. Comput. Chem.20042591157117410.1002/jcc.2003515116359
    [Google Scholar]
  19. CaseD.A. CheathamT.E.III DardenT. GohlkeH. LuoR. MerzK.M.Jr OnufrievA. SimmerlingC. WangB. WoodsR.J. The Amber biomolecular simulation programs.J. Comput. Chem.200526161668168810.1002/jcc.2029016200636
    [Google Scholar]
  20. MarkP. NilssonL. Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K.J. Phys. Chem. A2001105439954996010.1021/jp003020w
    [Google Scholar]
  21. MillerB.R.III McGeeT.D.Jr SwailsJ.M. HomeyerN. GohlkeH. RoitbergA.E. MMPBSA.py: An efficient program for end-state free energy calculations.J. Chem. Theory Comput.2012893314332110.1021/ct300418h26605738
    [Google Scholar]
  22. GenhedenS. RydeU. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities.Expert Opin. Drug Discov.201510544946110.1517/17460441.2015.103293625835573
    [Google Scholar]
  23. KagamiL.P. das NevesG.M. TimmersL.F.S.M. CaceresR.A. Eifler-LimaV.L. Geo-Measures: A PyMOL plugin for protein structure ensembles analysis.Comput. Biol. Chem.20208710732210.1016/j.compbiolchem.2020.10732232604028
    [Google Scholar]
  24. DeLanoW.L. Pymol: An open-source molecular graphics tool. CCP4 Newsl.Protein Crystallogr2002408292
    [Google Scholar]
  25. BIOVIADassault Systèmes Discovery Studio Visualizer.San Diego, CA, USA2020Available from: https://discover.3ds.com/ discovery-studio-visualizer-download
    [Google Scholar]
  26. JamesN. SuranaR. ThigaleI. PreethiB. ShanthiV. RamanathanK. Exploring novel ALK inhibitors using energy based pharmacophore mapping and high-throughput virtual screening.Indian J Pharm Edu Res20185270771710.5530/ijper.52.4.82
    [Google Scholar]
  27. AlshammariS.O. AlshammariQ.A. Natural product-derived ALK inhibitors for treating ALK-driven lung cancers: An in silico study.Mol. Divers.202411410.1007/s11030‑024‑10953‑239115579
    [Google Scholar]
  28. KumarV. JalwalP. SoniA. KhatriR. In silico screening of phytochemicals for anaplastic lymphoma kinase positive oncogenicity.J. Pharm. Negat. Results20221450146310.47750/pnr.2022.13.S10.170
    [Google Scholar]
  29. LinJ.J. RielyG.J. ShawA.T. Targeting ALK: Precision medicine takes on drug resistance.Cancer Discov.20177213715510.1158/2159‑8290.CD‑16‑112328122866
    [Google Scholar]
  30. ZhongL. LiY. XiongL. WangW. WuM. YuanT. YangW. TianC. MiaoZ. WangT. YangS. Small molecules in targeted cancer therapy: Advances, challenges, and future perspectives.Signal Transduct. Target. Ther.20216120110.1038/s41392‑021‑00572‑w34054126
    [Google Scholar]
  31. KongX. PanP. SunH. XiaH. WangX. LiY. HouT. Drug discovery targeting Anaplastic Lymphoma Kinase (ALK).J. Med. Chem.20196224109271095410.1021/acs.jmedchem.9b0044631419130
    [Google Scholar]
  32. SharmaG.G. RedaelliS. CecconM. ZappaM. MauriM. NigoghossianM. MassiminoL. CordaniN. FarinaF. PiazzaR. Gambacorti-PasseriniC. MologniL. In vitro and in vivo characterization of resistance to lorlatinib treatment in ALK mutated cancers.Proceedings of the American Association for Cancer Research Annual Meeting 2018Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018; 78(13 Suppl): 902.
    [Google Scholar]
  33. HouH. SunD. LiuK. JiangM. LiuD. ZhuJ. ZhouN. CongJ. ZhangX. The safety and serious adverse events of approved ALK inhibitors in malignancies: A meta-analysis.Cancer Manag. Res.2019114109411810.2147/CMAR.S19009831190983
    [Google Scholar]
  34. RothensteinJ.M. LetarteN. Managing treatment-related adverse events associated with Alk inhibitors.Curr. Oncol.2014211192610.3747/co.21.174024523601
    [Google Scholar]
  35. ChiaP.L. JohnT. DobrovicA. MitchellP. Prevalence and natural history of ALK positive non-small-cell lung cancer and the clinical impact of targeted therapy with ALK inhibitors.Clin. Epidemiol.2014642343210.2147/CLEP.S6971825429239
    [Google Scholar]
/content/journals/cpd/10.2174/0113816128342778250218105338
Loading
/content/journals/cpd/10.2174/0113816128342778250218105338
Loading

Data & Media loading...

Supplements

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test