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ISSN (Print): 2210-299X
ISSN (Online): 2210-3007

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Research Article

QSAR, Molecular Docking & ADMET Studies of Pyrrolo[2,3-d] Pyrimidine Derivatives as CDK4 Inhibitors for the Treatment of Cancer

Author(s): Shital M. Patil*, Varsha A. Patil, Kalyani Asgonkar, Vrushali Randive and Indrani Mahadik

Volume 1, 2023

Published on: 24 October, 2023

Article ID: e2210299X258569 Pages: 16

DOI: 10.2174/012210299X258569231006094309

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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-d] 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.

[1]
Vijayaraghavan, S.; Moulder, S.; Keyomarsi, K.; Layman, R.M. Inhibiting CDK in cancer therapy: Current evidence and future directions. Target. Oncol., 2018, 13(1), 21-38.
[http://dx.doi.org/10.1007/s11523-017-0541-2] [PMID: 29218622]
[2]
Ferlay, J.; Colombet, M.; Soerjomataram, I.; Parkin, D.M.; Piñeros, M.; Znaor, A.; Bray, F. Cancer statistics for the year 2020: An overview. Int. J. Cancer, 2021, 149(4), 778-789.
[http://dx.doi.org/10.1002/ijc.33588] [PMID: 33818764]
[3]
Malumbres, M. Protein family review- Cyclin-dependent kinases. Genome Biol., 2014, 15(122), 1-8.
[4]
Peyressatre, M.; Prével, C.; Pellerano, M.; Morris, M. Targeting cyclin-dependent kinases in human cancers: From small molecules to Peptide inhibitors. Cancers, 2015, 7(1), 179-237.
[http://dx.doi.org/10.3390/cancers7010179] [PMID: 25625291]
[5]
Whittaker, S.R.; Mallinger, A.; Workman, P.; Clarke, P.A. Inhibitors of cyclin-dependent kinases as cancer therapeutics. Pharmacol. Ther., 2017, 173, 83-105.
[http://dx.doi.org/10.1016/j.pharmthera.2017.02.008] [PMID: 28174091]
[6]
Heptinstall, A.B.; Adiyasa, I.W.S.; Cano, C.; Hardcastle, I.R. Recent advances in CDK inhibitors for cancer therapy. Future Med. Chem., 2018, 10(11), 1369-1388.
[http://dx.doi.org/10.4155/fmc-2017-0246] [PMID: 29846081]
[7]
Thangavel, C.; Boopathi, E.; Liu, Y.; McNair, C.; Haber, A.; Perepelyuk, M.; Bhardwaj, A.; Addya, S.; Ertel, A.; Shoyele, S.; Birbe, R.; Salvino, J.M.; Dicker, A.P.; Knudsen, K.E.; Den, R.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., 2018, 24(6), 1402-1414.
[http://dx.doi.org/10.1158/1078-0432.CCR-17-2074] [PMID: 29311118]
[8]
Spring, L.M.; Wander, S.A.; Zangardi, M.; Bardia, A. CDK 4/6 inhibitors in breast cancer: Current controversies and future directions. Curr. Oncol. Rep., 2019, 21(3), 25.
[http://dx.doi.org/10.1007/s11912-019-0769-3] [PMID: 30806829]
[9]
Mariaule, G.; Belmont, P. Cyclin-dependent kinase inhibitors as marketed anticancer drugs: Where are we now? A short survey. Molecules, 2014, 19(9), 14366-14382.
[http://dx.doi.org/10.3390/molecules190914366] [PMID: 25215591]
[10]
Shapiro, G.I. Cyclin-dependent kinase pathways as targets for cancer treatment. J. Clin. Oncol., 2006, 24(11), 1770-1783.
[http://dx.doi.org/10.1200/JCO.2005.03.7689] [PMID: 16603719]
[11]
Qin, A.; Reddy, H.G.; Weinberg, F.D.; Kalemkerian, G.P. Cyclin-dependent kinase inhibitors for the treatment of lung cancer. Expert Opin. Pharmacother., 2020, 21(8), 941-952.
[http://dx.doi.org/10.1080/14656566.2020.1738385] [PMID: 32164461]
[12]
Laderian, B.; Fojo, T. CDK4/6 Inhibition as a therapeutic strategy in breast cancer: Palbociclib, ribociclib, and abemaciclib. Semin. Oncol., 2017, 44(6), 395-403.
[http://dx.doi.org/10.1053/j.seminoncol.2018.03.006] [PMID: 29935901]
[13]
Li, Y.; Du, R.; Nie, Y.; Wang, T.; Ma, Y.; Fan, Y. Design, synthesis and biological assessment of novel CDK4 inhibitor with potent anticancer activity. Bioorg. Chem., 2021, 109, 104717.
[http://dx.doi.org/10.1016/j.bioorg.2021.104717] [PMID: 33647744]
[14]
Li, J.; Lei, B.; Liu, H.; Li, S.; Yao, X.; Liu, M.; Gramatica, P. QSAR study of malonyl-CoA decarboxylase inhibitors using GA-MLR and a new strategy of consensus modeling. J. Comput. Chem., 2008, 29(16), 2636-2647.
[http://dx.doi.org/10.1002/jcc.21002] [PMID: 18484640]
[15]
Gramatica, P.; Cassani, S.; Roy, P.P.; Kovarich, S.; Yap, C.W.; Papa, E. QSAR modeling is not “push a button and find a correlation”: A case study of toxicity of (Benzo‐)triazoles on algae. Mol. Inform., 2012, 31(11-12), 817-835.
[http://dx.doi.org/10.1002/minf.201200075] [PMID: 27476736]
[16]
Gramatica, P.; Chirico, N.; Papa, E.; Cassani, S.; Kovarich, S. QSARINS: A new software for the development, analysis, and validation of QSAR MLR models. J. Comput. Chem., 2013, 34(24), 2121-2132.
[http://dx.doi.org/10.1002/jcc.23361]
[17]
DRAGON for Windows (Software for molecular Descriptor Calculation), Talete srl. 2022. Available from: http://www.talete.mi
[18]
Anand Mariadoss, A.V.; Krishnan Dhanabalan, A.; Munusamy, H.; Gunasekaran, K.; David, E. In silico studies towards enhancing the anticancer activity of phytochemical phloretin against cancer drug targets. Curr. Drug Ther., 2018, 13(2), 174-188.
[http://dx.doi.org/10.2174/1574885513666180402134054]
[19]
Hassan, S.S.; Abbas, S.Q.; Ali, F.; Ishaq, M.; Bano, I.; Hassan, M.; Jin, H.Z.; Bungau, S.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. Molecules, 2022, 27(3), 917.
[http://dx.doi.org/10.3390/molecules27030917] [PMID: 35164181]
[20]
2022. Available from: https://portal.vlifesciences.com
[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. 2022. Available from: http://www.talete.mi
[22]
OECD Principles. 2022. Available from: http://www.oecd.org/dataoecd/33/37/37849783. pdf (Accessed on: 2022).
[23]
Aptula, A.O.; Jeliazkova, N.G.; Schultz, T.W.; Cronin, M.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., 2005, 24(3), 385-396.
[http://dx.doi.org/10.1002/qsar.200430909]
[24]
Shi, L.M.; Fang, H.; Tong, W.; Wu, J.; Perkins, R.; Blair, R.M.; Branham, W.S.; Dial, S.L.; Moland, C.L.; Sheehan, D.M. QSAR models using a large diverse set of estrogens. J. Chem. Inf. Comput. Sci., 2001, 41(1), 186-195.
[http://dx.doi.org/10.1021/ci000066d] [PMID: 11206373]
[25]
Schüürmann, G.; Ebert, R.U.; Chen, J.; Wang, B.; Kühne, R. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean. J. Chem. Inf. Model., 2008, 48(11), 2140-2145.
[http://dx.doi.org/10.1021/ci800253u] [PMID: 18954136]
[26]
Consonni, V.; Ballabio, D.; Todeschini, R. Evaluation of model predictive ability by external validation techniques. J. Chemometr., 2010, 24(3-4), 194-201.
[http://dx.doi.org/10.1002/cem.1290]
[27]
Lin, L.I.K. A concordance correlation coefficient to evaluate reproducibility. Biometrics, 1989, 45(1), 255-268.
[http://dx.doi.org/10.2307/2532051] [PMID: 2720055]
[28]
Chirico, N.; Gramatica, P. 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., 2011, 51(9), 2320-2335.
[http://dx.doi.org/10.1021/ci200211n] [PMID: 21800825]
[29]
Chirico, N.; Gramatica, P. 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., 2012, 52(8), 2044-2058.
[http://dx.doi.org/10.1021/ci300084j] [PMID: 22721530]
[30]
Crystal Structure of CDK4 in complex with a D-type cyclin. 2022. Available from: https://www.rcsb.org/structure/2W96
[31]
Muegge, I. Selection criteria for drug-like compounds. Med. Res. Rev., 2003, 23(3), 302-321.
[http://dx.doi.org/10.1002/med.10041] [PMID: 12647312]
[32]
2022. Available from: https://metagenomics.iiserb.ac.in/
[33]
Faria, W.C.S.; de Oliveira, M.G.; da Conceiçao, EC. 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., 2021, 108v(14), 106004.

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