Skip to content
2000
Volume 21, Issue 15
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Background

CDK4/6 plays a crucial role in regulating cell proliferation, and inhibiting this kinase can effectively prevent the initiation of cell growth and division. However, current FDA-approved CDK4/6 inhibitors have limitations such as poor bioavailability, adverse effects, high cost, and limited accessibility. Thus, this research aimed to discover novel CDK4/6 inhibitors to overcome the challenges associated with FDA-approved inhibitors.

Methods

To identify potential CDK4/6 inhibitors, we have performed structure-based virtual screening. Chem-space and Mcule databases have been screened, followed by a series of filtering steps. These steps included assessing drug-likeness, PAINS alert, synthetic accessibility scores, ADMET properties, consensus molecular docking, and performing molecular dynamics simulations.

Results

Four new compounds (CSC089414133, CSC091186116, CSC096023304, CSC101755872) have been identified as potential CDK4/6 inhibitors. These compounds exhibited strong binding affinity with CDK4/6, possessed drug-like features, showed no PAINS alert, had a low synthetic accessibility score, demonstrated effective ADMET properties, were non-toxic, and exhibited high stability.

Conclusion

Inhibiting CDK4/6 with the identified compounds may lead to reduced cell proliferation and the promotion of cancer cell death.

Loading

Article metrics loading...

/content/journals/lddd/10.2174/0115701808273043231130100833
2024-01-08
2024-12-29
Loading full text...

Full text loading...

References

  1. SiegelR.L. MillerK.D. JemalA. Cancer statistics, 2020.CA Cancer J. Clin.202070173010.3322/caac.21590 31912902
    [Google Scholar]
  2. SungH. FerlayJ. SiegelR.L. LaversanneM. SoerjomataramI. JemalA. BrayF. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.CA Cancer J. Clin.202171320924910.3322/caac.21660 33538338
    [Google Scholar]
  3. NebenfuehrS. KollmannK. SexlV. The role of CDK6 in cancer.Int. J. Cancer2020147112988299510.1002/ijc.33054 32406095
    [Google Scholar]
  4. TadesseS. YuM. KumarasiriM. LeB.T. WangS. Targeting CDK6 in cancer: State of the art and new insights.Cell Cycle201514203220323010.1080/15384101.2015.1084445 26315616
    [Google Scholar]
  5. WoodD.J. EndicottJ.A. Structural insights into the functional diversity of the CDK–cyclin family.Open Biol.20188918011210.1098/rsob.180112 30185601
    [Google Scholar]
  6. WhittakerS.R. MallingerA. WorkmanP. ClarkeP.A. Inhibitors of cyclin-dependent kinases as cancer therapeutics.Pharmacol. Ther.20171738310510.1016/j.pharmthera.2017.02.008 28174091
    [Google Scholar]
  7. BockstaeleL. BisteauX. PaternotS. RogerP.P. Differential regulation of cyclin-dependent kinase 4 (CDK4) and CDK6, evidence that CDK4 might not be activated by CDK7, and design of a CDK6 activating mutation.Mol. Cell. Biol.200929154188420010.1128/MCB.01823‑08 19487459
    [Google Scholar]
  8. RanaS. BendjennatM. KourS. KingH.M. KizhakeS. ZahidM. NatarajanA. Selective degradation of CDK6 by a palbociclib based PROTAC.Bioorg. Med. Chem. Lett.201929111375137910.1016/j.bmcl.2019.03.035 30935795
    [Google Scholar]
  9. FinnR.S. AleshinA. SlamonD.J. Targeting the cyclin-dependent kinases (CDK) 4/6 in estrogen receptor-positive breast cancers.Breast Cancer Res.20161811710.1186/s13058‑015‑0661‑5 26857361
    [Google Scholar]
  10. HaiY. ChristiansonD.W. Histone deacetylase 6 structure and molecular basis of catalysis and inhibition.Nat. Chem. Biol.201612974174710.1038/nchembio.2134 27454933
    [Google Scholar]
  11. 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.20084 15264254
    [Google Scholar]
  12. VerbaK.A. WangR.Y-R. ArakawaA. LiuY. ShirouzuM. YokoyamaS. Atomic structure of Hsp90-Cdc37-Cdk4 reveals that Hsp90 traps and stabilizes an unfolded kinase.Sci.201635262931542154710.1126/science.aaf5023
    [Google Scholar]
  13. Jaime-GarzaM. NowotnyC.A. CoutandinD. WangF. TabiosM. AgardD.A. Hsp90 provides a platform for kinase dephosphorylation by PP5.Nat. Commun.2023141219710.1038/s41467‑023‑37659‑7 37069154
    [Google Scholar]
  14. SinghT. BiswasD. JayaramB. AADS-an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors.J. Chem. Inf. Model.201151102515252710.1021/ci200193z 21877713
    [Google Scholar]
  15. TianW. ChenC. LeiX. ZhaoJ. LiangJ. CASTp 3.0: Computed atlas of surface topography of proteins.Nucleic Acids Res.201846W1W363W36710.1093/nar/gky473 29860391
    [Google Scholar]
  16. IrwinJ.J. TangK.G. YoungJ. DandarchuluunC. WongB.R. KhurelbaatarM. MorozY.S. MayfieldJ. SayleR.A. ZINC20-a free ultralarge-scale chemical database for ligand discovery.J. Chem. Inf. Model.202060126065607310.1021/acs.jcim.0c00675 33118813
    [Google Scholar]
  17. GaultonA. BellisL.J. BentoA.P. ChambersJ. DaviesM. HerseyA. LightY. McGlincheyS. MichalovichD. Al-LazikaniB. OveringtonJ.P. ChEMBL: A large-scale bioactivity database for drug discovery.Nucleic Acids Res.201240D1D1100D110710.1093/nar/gkr777 21948594
    [Google Scholar]
  18. O’BoyleN.M. BanckM. JamesC.A. MorleyC. VandermeerschT. HutchisonG.R. Open Babel: An open chemical toolbox.J. Cheminform.2011313310.1186/1758‑2946‑3‑33 21982300
    [Google Scholar]
  19. LiH. LeungK.S. WongM.H. Idock: A multithreaded virtual screening tool for flexible ligand docking.2012 IEEE Symp Comput Intell Comput Biol CIBCB 20122012778410.1109/CIBCB.2012.6217214
    [Google Scholar]
  20. FeinsteinW.P. BrylinskiM. Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets.J. Cheminform.2015711810.1186/s13321‑015‑0067‑5 26082804
    [Google Scholar]
  21. LandrumG. Landrum G. RDKit: Open-Source Cheminformatics Software.Available from: https://github.com/rdkit/rdkit/releases/tag/Release_2016_09_4 2016
  22. McKinneyW. others. Data structures for statistical computing in python.Proceedings of the 9th Python in Science Conference2010516
    [Google Scholar]
  23. 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.461-3326
    [Google Scholar]
  24. GhoseA.K. ViswanadhanV.N. WendoloskiJ.J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases.J. Comb. Chem.1999115568
    [Google Scholar]
  25. VeberD.F. JohnsonS.R. ChengH.Y. SmithB.R. WardK.W. KoppleK.D. Molecular properties that influence the oral bioavailability of drug candidates.J. Med. Chem.200245122615262310.1021/jm020017n 12036371
    [Google Scholar]
  26. MueggeI. HealdS.L. BrittelliD. Simple selection criteria for drug-like chemical matter.J. Med. Chem.200144121841184610.1021/jm015507e 11384230
    [Google Scholar]
  27. BaellJ.B. HollowayG.A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays.J. Med. Chem.20105372719274010.1021/jm901137j 20131845
    [Google Scholar]
  28. ErtlP. SchuffenhauerA. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions.J. Cheminform.200911810.1186/1758‑2946‑1‑8 20298526
    [Google Scholar]
  29. FukunishiY. KurosawaT. MikamiY. NakamuraH. Prediction of synthetic accessibility based on commercially available compound databases.J. Chem. Inf. Model.201454123259326710.1021/ci500568d 25420000
    [Google Scholar]
  30. SunD. GaoW. HuH. ZhouS. Why 90% of clinical drug development fails and how to improve it?Acta Pharm. Sin. B20221273049306210.1016/j.apsb.2022.02.002 35865092
    [Google Scholar]
  31. SeyhanA.A. Lost in translation: the valley of death across preclinical and clinical divide-identification of problems and overcoming obstacles.Transl. Med. Commun.2019411810.1186/s41231‑019‑0050‑7
    [Google Scholar]
  32. DainaA. MichielinO. ZoeteV. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules.Sci. Rep.2017714271710.1038/srep42717 28256516
    [Google Scholar]
  33. SanderT. FreyssJ. von KorffM. RufenerC. DataWarrior: An open-source program for chemistry aware data visualization and analysis.J. Chem. Inf. Model.201555246047310.1021/ci500588j 25558886
    [Google Scholar]
  34. AllenW.J. BaliusT.E. MukherjeeS. BrozellS.R. MoustakasD.T. LangP.T. CaseD.A. KuntzI.D. RizzoR.C. DOCK 6: Impact of new features and current docking performance.J. Comput. Chem.201536151132115610.1002/jcc.23905 25914306
    [Google Scholar]
  35. GrosdidierA. ZoeteV. MichielinO. SwissDock, a protein-small molecule docking web service based on EADock DSS.Nucleic Acids Res.201139Web Server issue)(Suppl.W270W27710.1093/nar/gkr36621624888
    [Google Scholar]
  36. TrottO. OlsonA.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.Available from: https://onlinelibrary.wiley.com/doi/10.1002/jcc.21334 2009
  37. 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.1c00203 34278794
    [Google Scholar]
  38. KoesD.R. BaumgartnerM.P. CamachoC.J. Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise.J. Chem. Inf. Model.20135381893190410.1021/ci300604z 23379370
    [Google Scholar]
  39. 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.21256 19399780
    [Google Scholar]
  40. DebnathA. MazumderR. MazumderA. SinghR. SrivastavaS. In silico identification of HDAC inhibitors for multiple myeloma: A structure-based virtual screening, drug likeness, ADMET profiling, molecular docking, and molecular dynamics simulation study.Lett. Drug Des. Discov.202320110.2174/1570180820666230125102954
    [Google Scholar]
  41. Palacio-RodríguezK. LansI. CavasottoC.N. CossioP. Exponential consensus ranking improves the outcome in docking and receptor ensemble docking.Sci. Rep.201991514210.1038/s41598‑019‑41594‑3 30914702
    [Google Scholar]
  42. HoustonD.R. WalkinshawM.D. Consensus docking: improving the reliability of docking in a virtual screening context.J. Chem. Inf. Model.201353238439010.1021/ci300399w 23351099
    [Google Scholar]
  43. Van Der SpoelD. LindahlE. HessB. GroenhofG. MarkA.E. BerendsenH.J.C. GROMACS: Fast, flexible, and free.J. Comput. Chem.200526161701171810.1002/jcc.20291 16211538
    [Google Scholar]
  44. BrooksB.R. BrooksC. MackerellA.D. NilssonL. PetrellaR.J. RouxB. CHARMM: Molecular dynamics simulation package.J. Comput. Chem.200930101545161410.1002/jcc.21287 19444816
    [Google Scholar]
  45. BerendsenH.J.C. van der SpoelD. van DrunenR. GROMACS: A message-passing parallel molecular dynamics implementation.Comput. Phys. Commun.1995911-3435610.1016/0010‑4655(95)00042‑E
    [Google Scholar]
  46. PillaiG.G. Jupyter Notebook for MD using Gromacs. zenodo2020
    [Google Scholar]
  47. JorgensenW.L. ChandrasekharJ. MaduraJ.D. ImpeyR.W. KleinM.L. Comparison of simple potential functions for simulating liquid water.J. Chem. Phys.198379292693510.1063/1.445869
    [Google Scholar]
  48. Bondi, A van der Waals Volumes and Radii.J. Phys. Chem.1964683441451
    [Google Scholar]
  49. BerendsenH.J.C. PostmaJ.P.M. van GunsterenW.F. DiNolaA. HaakJ.R. Molecular dynamics with coupling to an external bath.J. Chem. Phys.19848183684369010.1063/1.448118
    [Google Scholar]
  50. BaellJ. WaltersM.A. Chemistry: Chemical con artists foil drug discovery.Nature2014513751948148310.1038/513481a 25254460
    [Google Scholar]
  51. PoliG. TuccinardiT. Consensus docking in drug discovery.Curr. Bioact. Compd.202016318219010.2174/1573407214666181023114820
    [Google Scholar]
  52. MarshJ.A. TeichmannS.A. Relative solvent accessible surface area predicts protein conformational changes upon binding.Structure201119685986710.1016/j.str.2011.03.010 21645856
    [Google Scholar]
  53. KumariR. KumarR. LynnA. g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations.J. Chem. Inf. Model.20145471951196210.1021/ci500020m 24850022
    [Google Scholar]
  54. BakerN.A. SeptD. JosephS. HolstM.J. McCammonJ.A. Electrostatics of nanosystems: Application to microtubules and the ribosome.Proc. Natl. Acad. Sci. USA20019818100371004110.1073/pnas.181342398 11517324
    [Google Scholar]
  55. WagonerJ.A. BakerN.A. Assessing implicit models for nonpolar mean solvation forces: The importance of dispersion and volume terms.Proc. Natl. Acad. Sci. USA2006103228331833610.1073/pnas.0600118103 16709675
    [Google Scholar]
  56. EisenhaberF. LijnzaadP. ArgosP. SanderC. ScharfM. The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies.J. Comput. Chem.199516327328410.1002/jcc.540160303
    [Google Scholar]
  57. PilleronS. Soto-Perez-de-CelisE. VignatJ. FerlayJ. SoerjomataramI. BrayF. SarfatiD. Estimated global cancer incidence in the oldest adults in 2018 and projections to 2050.Int. J. Cancer2021148360160810.1002/ijc.33232 32706917
    [Google Scholar]
  58. BhattacharyaA. Sen GuhaP. ChowdhuryN. BagchiA. GuhaD. Virtual screening and molecular docking of flavone derivatives as a potential anticancer drug in the presence of Dexamethasone.Biointerface Res. Appl. Chem.2023133119
    [Google Scholar]
  59. BaigM.H. YousufM. KhanM.I. KhanI. AhmadI. AlshahraniM.Y. HassanM.I. DongJ.J. Investigating the mechanism of inhibition of cyclin-dependent kinase 6 inhibitory potential by Selonsertib: Newer insights into drug repurposing.Front. Oncol.202212May86545410.3389/fonc.2022.865454 35720007
    [Google Scholar]
  60. ChukwuemekaP.O. UmarH.I. IwaloyeO. OretadeO.M. OlowosokeC.B. OretadeO.J. ElabiyiM.O. Predictive hybrid paradigm for cytotoxic activity of 1,3,4-thiadiazole derivatives as CDK6 inhibitors against human (MCF-7) breast cancer cell line and its structural modifications: rational for novel cancer therapeutics.J. Biomol. Struct. Dyn.202240188518853710.1080/07391102.2021.1913231 33890551
    [Google Scholar]
  61. NusantoroY.R. FadlanA. In silico studies of isatinyl-2-aminobenzoylhydrazone transition metal complexes against cyclin-dependent kinase 6 (CDK6).Pharm Reports2021114
    [Google Scholar]
  62. LuoX. ZhaoY. TangP. DuX. LiF. WangQ. LiR. HeJ. Discovery of new small-molecule cyclin-dependent kinase 6 inhibitors through computational approaches.Mol. Divers.202125136738210.1007/s11030‑020‑10120‑3 32770459
    [Google Scholar]
  63. SusantiN.M.P. DamayantiS. KartasasmitaR.E. TjahjonoD.H. A search for cyclin-dependent kinase 4/6 Inhibitors by pharmacophore-based virtual screening, molecular docking, and molecular dynamic simulations.Int. J. Mol. Sci.202122241342310.3390/ijms222413423 34948218
    [Google Scholar]
  64. GurungA.B. AliM.A. LeeJ. FarahM.A. Al-AnaziK.M. Molecular docking and dynamics simulation study of bioactive compounds from Ficus carica L. with important anticancer drug targets.PLoS One2021167e025403510.1371/journal.pone.0254035
    [Google Scholar]
  65. PinantiH.N. NafisahW. ChristinaY.I. Rifa’iM. Widodo DjatiM.S. Biflavonoid compounds from Selaginella doederleinii Hieron as anticancer agents of hormone receptor-positive (HR+) breast cancer based on in silico study.IOP Conf. Ser. Earth Environ. Sci.2021743101202810.1088/1755‑1315/743/1/012028
    [Google Scholar]
  66. YousufM. KhanP. ShamsiA. ShahbaazM. HasanG.M. HaqueQ.M.R. ChristoffelsA. IslamA. HassanM.I. Inhibiting CDK6 activity by quercetin is an attractive strategy for cancer therapy.ACS Omega2020542274802749110.1021/acsomega.0c03975 33134711
    [Google Scholar]
  67. YousufM. ShamsiA. KhanP. ShahbaazM. AlAjmiM.F. HussainA. HassanG.M. IslamA. Rizwanul HaqueQ.M. HassanM.I. Ellagic acid controls cell proliferation and induces apoptosis in breast cancer cells via inhibition of cyclin-dependent kinase 6.Int. J. Mol. Sci.20202110352610.3390/ijms21103526 32429317
    [Google Scholar]
  68. UllahA. ProttoyN.I. ArafY. HossainS. SarkarB. SahaA. Molecular docking and pharmacological property analysis of phytochemicals from <i>Clitoria ternatea</i> as potent inhibitors of cell cycle checkpoint proteins in the Cyclin/CDK pathway in cancer cells.Comput. Mol. Biosci.201993819410.4236/cmb.2019.93007
    [Google Scholar]
  69. BasatiG. Saffari-ChaleshtoriJ. AbbaszadehS. Asadi-SamaniM. Ashrafi-DehkordiK. Molecular dynamics mechanisms of the inhibitory effects of abemaciclib, hymenialdisine, and indirubin on CDK-6.Curr. Drug Res. Rev.201911213514110.2174/2589977511666191018180001 31875784
    [Google Scholar]
  70. SharmaV. SharmaP.C. KumarV. In silico molecular docking analysis of natural pyridoacridines as anticancer agents.Adv Chem201619
    [Google Scholar]
  71. ChoY.S. AngoveH. BrainC. ChenC.H. ChengH. ChengR. Fragment-based discovery of 7-azabenzimidazoles as potent, highly selective, and orally active CDK4/6 inhibitors.ACS Med. Chem. Lett.201236445449
    [Google Scholar]
  72. StorkC. KirchmairJ. PAIN(S) relievers for medicinal chemists: How computational methods can assist in hit evaluation.Future Med. Chem.201810131533153510.4155/fmc‑2018‑0116 29956552
    [Google Scholar]
  73. SkoraczyńskiG. KitlasM. MiasojedowB. GambinA. Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning.J. Cheminform.2023151610.1186/s13321‑023‑00678‑z 36641473
    [Google Scholar]
  74. YuJ. WangJ. ZhaoH. GaoJ. KangY. CaoD. WangZ. HouT. Organic compound synthetic accessibility prediction based on the graph attention mechanism.J. Chem. Inf. Model.202262122973298610.1021/acs.jcim.2c00038 35675668
    [Google Scholar]
  75. ThakkarA. ChadimováV. BjerrumE.J. EngkvistO. ReymondJ.L. Retrosynthetic accessibility score (RAscore)-rapid machine learned synthesizability classification from AI driven retrosynthetic planning.Chem. Sci.20211293339334910.1039/D0SC05401A 34164104
    [Google Scholar]
  76. AhmedS. ZhouZ. ZhouJ. ChenS.Q. Pharmacogenomics of drug metabolizing enzymes and transporters: Relevance to precision medicine.Genomics Proteomics Bioinformatics201614529831310.1016/j.gpb.2016.03.008 27729266
    [Google Scholar]
  77. FogelD.B. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review.Contemp. Clin. Trials Commun.20181115616410.1016/j.conctc.2018.08.001 30112460
    [Google Scholar]
/content/journals/lddd/10.2174/0115701808273043231130100833
Loading
/content/journals/lddd/10.2174/0115701808273043231130100833
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.

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