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2000
Volume 1, Issue 1
  • ISSN: 2210-299X
  • E-ISSN: 2210-3007
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Abstract

Background

Cancer is caused by dysregulation of the cell cycle, which results in abnormal proliferation and the inability of cells to differentiate or die. Cyclins and cyclin-dependent kinases (CDK4) inhibitors are drugs that target a specific enzyme, CDK4 that controls cell cycle progression in cancer.

Aim & Objective

The aim of this study is to obtain an optimized pharmacophore of pyrrolo[2,3-] pyrimidine as a CDK4 inhibitor using QSAR studies. This aids in determining the link between structure and activity in newly developed chemical entities (NCE’s). To perform molecular docking and ADMET analysis to determine the binding affinity and drug-likeness of NCE’s.

Materials and Methods

The Multiple linear regression approach (MLR) method was utilised to generate the QSAR Model using the programme QSARINS v.2.2.4. For molecular docking, the Autodock vina software was employed. While the Swiss ADME and ToxiM online tools were used to predict toxicity.

Results and Discussion

The best models generated for 2D QSAR had correlation coefficients of R2= 0.9247 & Q2= 0.924 and for 3D QSAR, coefficients were R2 = 0.9297 and Q2 = 0.876. A novel series of 68 derivatives was designed based on QSAR investigations. Molecule C-58 has shown maximum binding affinity in molecular docking as compared to the standard Ribociclib.

Conclusion

Fifteen compounds have shown potential as CDK4 inhibitors based on docking studies, pharmacokinetic behavior and toxicity profile. The maximum binding affinity was demonstrated by molecule C-58.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2023-01-01
2025-12-08
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References

  1. VijayaraghavanS. MoulderS. KeyomarsiK. LaymanR.M. Inhibiting CDK in cancer therapy: Current evidence and future directions.Target. Oncol.2018131213810.1007/s11523‑017‑0541‑229218622
    [Google Scholar]
  2. FerlayJ. ColombetM. SoerjomataramI. ParkinD.M. PiñerosM. ZnaorA. BrayF. Cancer statistics for the year 2020: An overview.Int. J. Cancer2021149477878910.1002/ijc.3358833818764
    [Google Scholar]
  3. MalumbresM. Protein family review- Cyclin-dependent kinases.Genome Biol.20141512218
    [Google Scholar]
  4. PeyressatreM. PrévelC. PelleranoM. MorrisM. Targeting cyclin-dependent kinases in human cancers: From small molecules to Peptide inhibitors.Cancers20157117923710.3390/cancers701017925625291
    [Google Scholar]
  5. WhittakerS.R. MallingerA. WorkmanP. ClarkeP.A. Inhibitors of cyclin-dependent kinases as cancer therapeutics.Pharmacol. Ther.20171738310510.1016/j.pharmthera.2017.02.00828174091
    [Google Scholar]
  6. HeptinstallA.B. AdiyasaI.W.S. CanoC. HardcastleI.R. Recent advances in CDK inhibitors for cancer therapy.Future Med. Chem.201810111369138810.4155/fmc‑2017‑024629846081
    [Google Scholar]
  7. ThangavelC. BoopathiE. LiuY. McNairC. HaberA. PerepelyukM. BhardwajA. AddyaS. ErtelA. ShoyeleS. BirbeR. SalvinoJ.M. DickerA.P. KnudsenK.E. DenR.B. Therapeutic challenge with a CDK 4/6 inhibitor induces an RB-dependent SMAC-mediated apoptotic response in non–small cell lung cancer.Clin. Cancer Res.20182461402141410.1158/1078‑0432.CCR‑17‑207429311118
    [Google Scholar]
  8. SpringL.M. WanderS.A. ZangardiM. BardiaA. CDK 4/6 inhibitors in breast cancer: Current controversies and future directions.Curr. Oncol. Rep.20192132510.1007/s11912‑019‑0769‑330806829
    [Google Scholar]
  9. MariauleG. BelmontP. Cyclin-dependent kinase inhibitors as marketed anticancer drugs: Where are we now? A short survey.Molecules2014199143661438210.3390/molecules19091436625215591
    [Google Scholar]
  10. ShapiroG.I. Cyclin-dependent kinase pathways as targets for cancer treatment.J. Clin. Oncol.200624111770178310.1200/JCO.2005.03.768916603719
    [Google Scholar]
  11. QinA. ReddyH.G. WeinbergF.D. KalemkerianG.P. Cyclin-dependent kinase inhibitors for the treatment of lung cancer.Expert Opin. Pharmacother.202021894195210.1080/14656566.2020.173838532164461
    [Google Scholar]
  12. LaderianB. FojoT. CDK4/6 Inhibition as a therapeutic strategy in breast cancer: Palbociclib, ribociclib, and abemaciclib.Semin. Oncol.201744639540310.1053/j.seminoncol.2018.03.00629935901
    [Google Scholar]
  13. LiY. DuR. NieY. WangT. MaY. FanY. Design, synthesis and biological assessment of novel CDK4 inhibitor with potent anticancer activity.Bioorg. Chem.202110910471710.1016/j.bioorg.2021.10471733647744
    [Google Scholar]
  14. LiJ. LeiB. LiuH. LiS. YaoX. LiuM. GramaticaP. QSAR study of malonyl-CoA decarboxylase inhibitors using GA-MLR and a new strategy of consensus modeling.J. Comput. Chem.200829162636264710.1002/jcc.2100218484640
    [Google Scholar]
  15. GramaticaP. CassaniS. RoyP.P. KovarichS. YapC.W. PapaE. QSAR modeling is not “push a button and find a correlation”: A case study of toxicity of (Benzo‐)triazoles on algae.Mol. Inform.20123111-1281783510.1002/minf.20120007527476736
    [Google Scholar]
  16. GramaticaP. ChiricoN. PapaE. CassaniS. KovarichS. QSARINS: A new software for the development, analysis, and validation of QSAR MLR models.J. Comput. Chem.201334242121213210.1002/jcc.23361
    [Google Scholar]
  17. DRAGON for Windows (Software for molecular Descriptor Calculation), Talete srl.Available from: http://www.talete.mi 2022
  18. Anand MariadossA.V. Krishnan DhanabalanA. MunusamyH. GunasekaranK. DavidE. In silico studies towards enhancing the anticancer activity of phytochemical phloretin against cancer drug targets.Curr. Drug Ther.201813217418810.2174/1574885513666180402134054
    [Google Scholar]
  19. HassanS.S. AbbasS.Q. AliF. IshaqM. BanoI. HassanM. JinH.Z. BungauS.G. A comprehensive in silico exploration of pharmacological properties, bioactivities, molecular docking, and anticancer potential of vieloplain f from Xylopia vielana targeting b-raf kinase.Molecules202227391710.3390/molecules2703091735164181
    [Google Scholar]
  20. Available from: https://portal.vlifesciences.com 2022
  21. Talete srl, DRAGON for Windows (Software for Molecular Descriptor Calculations). Version 5.2—2005, version 5.3—2005, version 5.4—2006, version 5.5—2007.Available from: http://www.talete.mi 2022
  22. OECD Principles.Available from: http://www.oecd.org/dataoecd/33/37/37849783. pdf (Accessed on: 2022).2022
  23. AptulaA.O. JeliazkovaN.G. SchultzT.W. CroninM.T.D. The better predictive model: High q2 for the training set or low root mean square error of prediction for the test set?QSAR Comb. Sci.200524338539610.1002/qsar.200430909
    [Google Scholar]
  24. ShiL.M. FangH. TongW. WuJ. PerkinsR. BlairR.M. BranhamW.S. DialS.L. MolandC.L. SheehanD.M. QSAR models using a large diverse set of estrogens.J. Chem. Inf. Comput. Sci.200141118619510.1021/ci000066d11206373
    [Google Scholar]
  25. SchüürmannG. EbertR.U. ChenJ. WangB. KühneR. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean.J. Chem. Inf. Model.200848112140214510.1021/ci800253u18954136
    [Google Scholar]
  26. ConsonniV. BallabioD. TodeschiniR. Evaluation of model predictive ability by external validation techniques.J. Chemometr.2010243-419420110.1002/cem.1290
    [Google Scholar]
  27. LinL.I.K. A concordance correlation coefficient to evaluate reproducibility.Biometrics198945125526810.2307/25320512720055
    [Google Scholar]
  28. ChiricoN. GramaticaP. Real external predictivity of QSAR models: How to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient.J. Chem. Inf. Model.20115192320233510.1021/ci200211n21800825
    [Google Scholar]
  29. ChiricoN. GramaticaP. Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection.J. Chem. Inf. Model.20125282044205810.1021/ci300084j22721530
    [Google Scholar]
  30. Crystal Structure of CDK4 in complex with a D-type cyclin.Available from: https://www.rcsb.org/structure/2W96 2022
  31. MueggeI. Selection criteria for drug-like compounds.Med. Res. Rev.200323330232110.1002/med.1004112647312
    [Google Scholar]
  32. Available from: https://metagenomics.iiserb.ac.in/ 2022
  33. FariaW.C.S. de OliveiraM.G. da ConceiçaoEC. Antioxidant efficacy and in silico toxicity prediction of free and spray-dried extracts of green Arabica and Robusta coffee fruits and their application in edible oil.Food Hydrocoll.2021108v14106004
    [Google Scholar]
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