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In-Silico Identification of Anticancer Compounds; Ligand-Based Pharmacophore Approach against EGFR Involved in Breast Cancer

DOI: 10.4236/abcr.2021.103010, PP. 120-132

Keywords: EGFR, Breast Cancer, Lead Compound, Pharmacophore Modeling, HBA, HBD, Aromatic Ring, Pharmacophore Triangle, Molecular Docking

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Abstract:

Objective: Breast cancer is a public health challenge on a global scale that is caused by environmental or genetic factors. Breast cancer is affecting both males and females, but there is still a lack of effective drugs with improved potency and admissibility against breast cancer as many of the breast cancer drugs have severe side effects. Methods: The docking approach has been used to find a new compound for breast cancer with more efficacy and tolerance and with lesser side effects. A ligand-based pharmacophore approach has been generated for 39 anticancer compounds with significance for the development of new drugs. Result: Through docking, the approach found new lead compounds for breast cancer. The proposed pharmacophore model in this study contains two HBAs and one HYD, one hydrophobic domain and two Aromatic rings and the estimated distance range is minimum to maximum of derived pharmacophore features. Conclusion: Based on this research, it is proposed that these two lead compounds may be able to be used against EGFR in breast cancer. New compounds can be identified based on common features in the Pharmacophore model. 3D pharmacophore triangle could be used for further studies because this pharmacophore has better merging and in the future for more studies can suggest the same distance range of pharmacophore features as this pharmacophore.

References

[1]  Munir, A., et al. (2016) Structure-Based Pharmacophore Modeling, Virtual Screening and Molecular Docking for the Treatment of ESR1 Mutations in Breast Cancer. Drug Designing, 5, Article ID: 1000137.
https://doi.org/10.4172/2169-0138.1000137
[2]  Chanihoon, G.Q., et al. (2021) An AAS Dependent Method for Quantitative Essential Elements Analysis of Pakistani Female Breast Cancer Blood and Serum Samples. Advances in Breast Cancer Research, 10, 44-59.
https://doi.org/10.4236/abcr.2021.103004
[3]  Azim, H.A. and Ibrahim, A.S. (2014) Breast Cancer in Egypt, China and Chinese: Statistics and Beyond. Journal of Thoracic Disease, 6, 864.
[4]  Mustafa, M., et al. (2016) Breast Cancer: Detection Markers, Prognosis, and Prevention. IOSR Journal of Dental and Medical Sciences (IOSR-JDMS), 15, 73-80.
https://doi.org/10.9790/0853-1508117380
[5]  Aftab, A., et al. (2021) Computational Analysis of Cyclin D1 Gene SNPs and Association with Breast Cancer. Bioscience Reports, 41, BSR20202269.
https://doi.org/10.1042/BSR20202269
[6]  Mathers, C., Fat, D.M. and Boerma, J.T. (2008) The Global Burden of Disease: 2004 Update. World Health Organization, Genevan.
[7]  Asif, H.M., et al. (2014) Prevalence, Risk Factors and Disease Knowledge of Breast Cancer in Pakistan. Asian Pacific Journal of Cancer Prevention, 15, 4411-4416.
https://doi.org/10.7314/APJCP.2014.15.11.4411
[8]  Menhas, R. and Umer, S. (2015) Breast Cancer among Pakistani Women. Iranian Journal of Public Health, 44, 586-587.
[9]  Nicholson, R.I., Gee, J.M.W. and Harper, M.E. (2001) EGFR and Cancer Prognosis. European Journal of Cancer, 37, 9-15.
https://doi.org/10.1016/S0959-8049(01)00231-3
[10]  Yano, S., et al. (2002) Distribution and Function of EGFR in Human Tissue and the Effect of EGFR Tyrosine Kinase Inhibition. Anticancer Research, 23, 3639-3650.
[11]  Herbst, R.S. (2004) Review of Epidermal Growth Factor Receptor Biology. International Journal of Radiation Oncology Biology Physics, 59, S21-S26.
https://doi.org/10.1016/j.ijrobp.2003.11.041
[12]  Park, K., et al. (2007) EGFR Gene and Protein Expression in Breast Cancers. European Journal of Surgical Oncology (EJSO), 33, 956-960.
https://doi.org/10.1016/j.ejso.2007.01.033
[13]  Klijn, J., et al. (1992) The Clinical Significance of Epidermal Growth Factor Receptor (EGF-R) in Human Breast Cancer: A Review on 5232 Patients. Endocrine Reviews, 13, 3-17.
https://doi.org/10.1210/er.13.1.3
[14]  Berman, H.M. (2008) The Protein Data Bank: A Historical Perspective. Acta Crystallographica Section A: Foundations of Crystallography, 64, 88-95.
https://doi.org/10.1107/S0108767307035623
[15]  Gasteiger, E., et al. (2005) Protein Identification and Analysis Tools on the ExPASy Server. Springer, Berlin.
https://doi.org/10.1385/1-59259-890-0:571
[16]  Pettersen, E.F., et al. (2004) UCSF Chimera—A Visualization System for Exploratory Research and Analysis. Journal of Computational Chemistry, 25, 1605-1612.
https://doi.org/10.1002/jcc.20084
[17]  Huang, S.-Y. and Zou, X. (2010) Advances and Challenges in Protein-Ligand Docking. International Journal of Molecular Sciences, 11, 3016-3034.
https://doi.org/10.3390/ijms11083016
[18]  Wang, Y., et al. (2013) PubChem Bioassay: 2014 Update. Nucleic Acids Research, 42, D1075-D1082.
https://doi.org/10.1093/nar/gkt978
[19]  Dallakyan, S. and Olson, A.J. (2015) Small-Molecule Library Screening by Docking with PyRx. Chemical Biology: Methods and Protocols, 1263, 243-250.
https://doi.org/10.1007/978-1-4939-2269-7_19
[20]  Trott, O. and Olson, A.J. (2010) AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry, 31, 455-461.
https://doi.org/10.1002/jcc.21334
[21]  Salentin, S., et al. (2015) PLIP: Fully Automated Protein-Ligand Interaction Profiler. Nucleic Acids Research, 43, W443-W447.
https://doi.org/10.1093/nar/gkv315
[22]  Cheng, F., et al. (2012) admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties. Journal of Chemical Information and Modeling, 52, 3099-3105.
https://doi.org/10.1021/ci300367a
[23]  Dali, Y., Abbasi, S.M., Khan, S.A.F., Larra, S.A., Rasool, R., Ain, Q.T. and Jafar, T.H. (2019) Computational Drug Design and Exploration of Potent Phytochemicals against Cancer through in Silico Approaches. Biomedical Letters, 5, 21-26.
[24]  Malik, A., et al. (2018) In Silico and in Vivo Characterization of Cabralealactone, Solasodin and Salvadorin in a Rat Model: Potential Anti-Inflammatory Agents. Drug Design, Development and Therapy, 12, 1431-1443.
https://doi.org/10.2147/DDDT.S154169
[25]  Wolber, G. and Langer, T. (2005) Ligand Scout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters. Journal of Chemical Information and Modeling, 45, 160-169.
https://doi.org/10.1021/ci049885e
[26]  Haseeb, M., et al. (2014) Ligand Based Pharmacophore Development for Colorectal Cancer Drugs. Professional Medical Journal, 21, 856-863.

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