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Current Enzyme Inhibition

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ISSN (Print): 1573-4080
ISSN (Online): 1875-6662

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Research Article

A QSAR and Pharmacophore Survey on Tyrosine Kinase Inhibitors with Effect on Epidermal Growth Factor Receptor

Author(s): Atefeh Hajiagha Bozorgi* and Fatemeh Samadi

Volume 20, Issue 1, 2024

Published on: 20 December, 2023

Page: [78 - 83] Pages: 6

DOI: 10.2174/0115734080272807231127050546

Price: $65

Abstract

Background: Tyrosine kinases are of great importance nowadays in cancer treatment. As designing new inhibitors with more potency is an optimal goal of pharmaceutical companies, using previous improvements in this area would be beneficial. One of the most popular and widely used methods is creating a QSAR model. Another useful way is to build a pharmacophoric map to address important features of inhibitors.

Methods: Upon this, a large dataset of molecules was applied to create a QSAR model for the prediction of the inhibitory activity of molecules against the epidermal growth factor receptor. Using MOE software, molecular descriptors were calculated in 3d, and a model was built.

Results: 9 descriptors were selected, which describe the energy, shape, and hydrophobicity of the molecules. A pharmacophoric map was also created, and 3 important features were selected: Hydrophobic areas, H-bond acceptor regions, and Aromatic moieties.

Conclusion: These findings proved the results obtained result from the QSAR model.

Keywords: QSAR, tyrosine kinase inhibitors, hydrophobicity, epidermal growth factor receptor, receptor tyrosine kinase, aromatic moieties.

« Previous
[1]
Xu MJ, Johnson DE, Grandis JR. EGFR-targeted therapies in the post-genomic era. Cancer Metastasis Rev 2017; 36(3): 463-73.
[http://dx.doi.org/10.1007/s10555-017-9687-8] [PMID: 28866730]
[2]
Grant SK. Therapeutic protein kinase inhibitors. Cell Mol Life Sci 2009; 66(7): 1163-77.
[http://dx.doi.org/10.1007/s00018-008-8539-7] [PMID: 19011754]
[3]
Noble MEM, Endicott JA, Johnson LN. Protein kinase inhibitors: Insights into drug design from structure. Science 2004; 303(5665): 1800-5.
[http://dx.doi.org/10.1126/science.1095920] [PMID: 15031492]
[4]
Arora A, Scholar EM. Role of tyrosine kinase inhibitors in cancer therapy. J Pharmacol Exp Ther 2005; 315(3): 971-9.
[http://dx.doi.org/10.1124/jpet.105.084145] [PMID: 16002463]
[5]
Madhusudan S, Ganesan TS. Tyrosine kinase inhibitors in cancer therapy. Clin Biochem 2004; 37(7): 618-35.
[http://dx.doi.org/10.1016/j.clinbiochem.2004.05.006] [PMID: 15234243]
[6]
Cohen MH, Williams GA, Sridhara R, Chen G, Pazdur R. FDA drug approval summary: Gefitinib (ZD1839) (Iressa) tablets. Oncologist 2003; 8(4): 303-6.
[http://dx.doi.org/10.1634/theoncologist.8-4-303] [PMID: 12897327]
[7]
Irmer D, Funk JO, Blaukat A. EGFR kinase domain mutations – functional impact and relevance for lung cancer therapy. Oncogene 2007; 26(39): 5693-701.
[http://dx.doi.org/10.1038/sj.onc.1210383] [PMID: 17353898]
[8]
Zhao Y, Bilal M, Raza A, et al. Tyrosine kinase inhibitors and their unique therapeutic potentialities to combat cancer. Int J Biol Macromol 2021; 168: 22-37.
[http://dx.doi.org/10.1016/j.ijbiomac.2020.12.009] [PMID: 33290765]
[9]
Roskoski R Jr. Properties of FDA-approved small molecule protein kinase inhibitors: A 2020 update. Pharmacol Res 2020; 152104609
[http://dx.doi.org/10.1016/j.phrs.2019.104609] [PMID: 31862477]
[10]
Sabe VT, Ntombela T, Jhamba LA, et al. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem 2021; 224113705
[http://dx.doi.org/10.1016/j.ejmech.2021.113705] [PMID: 34303871]
[11]
Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 2006; 20(10-11): 647-71.
[http://dx.doi.org/10.1007/s10822-006-9087-6] [PMID: 17124629]
[12]
Hajiagha Bozorgi A, Zarghi A. Search for the pharmacophore of histone deacetylase inhibitors using pharmacophore query and docking study. Iran J Pharm Res 2014; 13(4): 1165-72.
[PMID: 25587304]
[13]
Hajiagha Bozorgi A, Ghomi H. Tabatabaei, Jouyban A. QSAR and pharmacophore studies of telomerase inhibitors. Medicinal Chemistry Resreash 2011; 6: 853-66.
[14]
Roy K, Kar S. Das RN. Statistical methods in QSAR/QSPR. In: A primer on QSAR/QSPR modeling Springer. 2015; pp. 37-59.
[http://dx.doi.org/10.1007/978-3-319-17281-1_2]
[15]
González M, Terán C, Saíz-Urra L, Teijeira M. Variable selection methods in QSAR: An overview. Curr Top Med Chem 2008; 8(18): 1606-27.
[http://dx.doi.org/10.2174/156802608786786552] [PMID: 19075770]
[16]
Yoo C, Shahlaei M. The applications of PCA in QSAR studies: A case study on CCR5 antagonists. Chem Biol Drug Des 2018; 91(1): 137-52.
[http://dx.doi.org/10.1111/cbdd.13064] [PMID: 28656625]
[17]
Goodarzi M, Dejaegher B, Heyden YV. Feature selection methods in QSAR studies. J AOAC Int 2012; 95(3): 636-51.
[http://dx.doi.org/10.5740/jaoacint.SGE_Goodarzi] [PMID: 22816254]
[18]
Karegowda AG, Manjunath AS, Jayaram MA. Feature subset selection problem using wrapper approach in supervised learning. Int J Comput Appl 2010; 1(7): 13-7.
[http://dx.doi.org/10.5120/169-295]
[19]
https://www.chemcomp.com/ Available from: https://www.chemcomp.com/index.htm
[20]
Mosayebnia M, Hajiagha Bozorgi A, Rezaeianpour M, Kobarfard F. In silico prediction of SARS-CoV-2 main protease and polymerase inhibitors: 3D-Pharmacophore modelling. J Biomol Struct Dyn 2022; 40(14): 6569-86.
[http://dx.doi.org/10.1080/07391102.2021.1886991] [PMID: 33599180]
[21]
Liang J-w, et al. Discovery of ABCG2/VEGFR2 dual-target inhibitor with anti-drug resistance activity based on the ATP binding site. Research Square 2020.
[http://dx.doi.org/10.21203/rs.3.rs-127221/v1]
[22]
Prosser KE, Stokes RW, Cohen SM. Evaluation of 3-dimensionality in approved and experimental drug space. ACS Med Chem Lett 2020; 11(6): 1292-8.
[http://dx.doi.org/10.1021/acsmedchemlett.0c00121] [PMID: 32551014]
[23]
Available from:https://www.nlm.nih.gov/nichsr/stats_tutorial/section2/mod8_sd.html#:~:text=A%20standard%20deviation%20
[24]
Fatemeh Keramatnia, Ali Shayanfar, Hajiagha Bozorgi A, Marjan , Mottaghi &, Abolghasem Jouyban. Prediction of drug solubility data in polyethylene glycols + water mixtures at various temperatures. Lat Am J Pharm 2015; 34(8): 1614-21.
[25]
Jawarkar RD, Sharma P, Jain N, et al. QSAR, molecular docking, md simulation and mmgbsa calculations approaches to recognize concealed pharmacophoric features requisite for the optimization of alk tyrosine kinase inhibitors as anticancer leads. Molecules 2022; 27(15): 4951.
[http://dx.doi.org/10.3390/molecules27154951] [PMID: 35956900]
[26]
Sun X, Chen L, Li Y, et al. Structure-based ensemble-QSAR model: A novel approach to the study of the EGFR tyrosine kinase and its inhibitors. Acta Pharmacol Sin 2014; 35(2): 301-10.
[http://dx.doi.org/10.1038/aps.2013.148] [PMID: 24335842]
[27]
A QSAR study on a series of indolin-2-ones acting as non-receptor src tyrosine kinase inhibitors. Lett Drug Des Discov 2011; 8(10) [918 - 925].
[28]
Abdulilah ECE. E-pharmacophore mapping combined with virtual screening and molecular docking to identify potent and selective inhibitors of p90 ribosomal s6 kinase (rsk). Turk J Pharm Sci 2016; 13(2): 241-8.
[29]
Chang Y-S, Wang B-C, Yang L-L. Pharmacophore modeling of tyrosine kinase inhibitors: 4-anilinoquinazoline derivatives. J Chin Chem Soc 2010; 57(4B): 916-24.
[http://dx.doi.org/10.1002/jccs.201000127]

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