Abstract
Experimental evidence shows that most tumors express the histamine synthesizing enzyme, L-histidine decarboxylase [1]. These tumor cells trigger sporadic reactions when they come in contact with histamine cells. In order to suppress these reactions, antihistamine drugs are used which competitively block the receptor mediated response of a tumor cell. We describe the effect of antihistamine drugs on the tumor cells, confirming whether these antihistamine drugs tend to increase or rather decrease the development of a brain tumor.
Drug target sites will be analyzed for similar proteins surrounding the brain tumor .These target sites alignment with similar proteins will open up the conserved regions. Protein motifs in the conserved regions that are over expressed and involved in tumor growth will be identified. The proteins identified will be docked with antihistamine drugs. Results will be analyzed by the scoring function of the algorithms. Selection of drug molecules may help us to design an anti neo plastic agent that can be therapeutically targeted to treat brain tumor.
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References
Vanina, A.M.: Histamine and cancer pharmacology. Brit. J. Pharm. 161, 755–767 (2010)
Bland, K.I.: Surgical Oncology: Contemporary Principles and Practices, 4th edn. (2001)
Asher, A.: A Primer of Brain Tumors, 5th edn. (2002)
Bruno, B., Francesca: Histamine and Histamine Receptor Antagonists in Cancer Biology. Inflam. Allerg. Drug Targ. 9, 146–157 (2010)
Sander, K., et al.: Histamine H3 Receptor Antagonists Go to Clinics. Biological and Pharmaceutical Bulletin 31, 2163–2181 (2008)
Leurs, R., et al.: Molecular and Biochemical Pharmacology of the Histamine Receptor. Brit. J. Pharm. 157, 14–23 (2009)
Wishar, D.: A Comprehensive Resource for in Silico Drug Discovery and Exploration. Nucl. Acid Research 34 (2006)
Wheeler, D.: BLAST Quick Start. Comparative Genomics, 1st edn. (2007)
Sigrist, C.: PROSITE, a Protein Domain Database for Functional Characterization and Annotation. Nucl. Acids Research 38, 161–166 (2010)
Grinter, Z.: An Inverse Docking Approach for Identifying New Potential Anti-Cancer Targets. J. Molecular Graph. Model. 29, 795–799 (2011)
Chen, Y.Z.: Ligand-Protein Inverse Docking and Its Potential Use in Computer Search of Putative Protein Targets of a Small Molecule. Protein J. 43, 217–226 (2001)
Xiaofeng, L., et al.: Pharm Mapper Server: a Web Server for Potential Drug Target Identification via Pharmacophore Mapping Approach. Nucl. Acid Research 38, 609–614 (2010)
Morris, B.G.M., Olson, A.J.: J. Comput. Chem. 19, 1639–1662 (1998)
Henikoff, S.: Automated Assembly of Protein Blocks for Database Searching. Nucl. Acids Res. 19(23), 6565–6572, doi:10.1093/nar/19.23.6565. PMC 329220. PMID 1754394
Clustal, W.: FAQs, http://www.ebi.ac.uk/Tools/clustalw2/help.html#color
Concha, L.G.: New Pattern of EGFR Amplification in Glioblastoma and the Relationship of Gene Copy Number with Gene Expression Profile. Nature Modern Pathol. 23, 856–865 (2010)
Sreedhar, A.S.: Heat Shock Proteins in the Regulation of Apoptosis- New Strategies in Tumor Therapy. Elsevier Pharm. Therap. 101, 227–257 (2004)
Rempel, S.A.: Cathepsin B Expression and Localization in Glioma Progression and Invasion. Cancer Res. Dec. 54(23), 6027–6031 (1994)
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Feroz, S., Habib, A., Siddiqua, M., Saleem, S., Shar, N.A., Jafri, A.R. (2012). Association of Anti-Histamine Drugs with Brain Tumor. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_2
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DOI: https://doi.org/10.1007/978-3-642-34475-6_2
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