In silico method for identification of promising anticancer drug targets

• Published in: SAR and QSAR in environmental research Vol. 20; no. 7-8; pp. 755 - 766
• Main Authors: Koborova, O.N., Filimonov, D.A., Zakharov, A.V., Lagunin, A.A., Ivanov, S.M., Kel, A., Poroikov, V.V.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis Group 01-10-2009
• Subjects: Antineoplastic Agents - chemistry; Antineoplastic Agents - pharmacology; breast cancer; Breast Neoplasms - drug therapy; Computer Simulation; drug target identification; Female; Gene Expression Regulation - drug effects; Humans; Proteins - antagonists & inhibitors; Quantitative Structure-Activity Relationship; regulatory networks
• ISSN: 1062-936X; 1029-046X
• DOI: 10.1080/10629360903438628

In recent years, the accumulation of the genomics, proteomics, transcriptomics data for topological and functional organization of regulatory networks in a cell has provided the possibility of identifying the potential targets involved in pathological processes and of selecting the most promising targets for future drug development. We propose an approach for anticancer drug target identification, which, using microarray data, allows discrete modelling of regulatory network behaviour. The effect of drugs inhibiting a particular protein or a combination of proteins in a regulatory network is analysed by simulation of a blockade of single nodes or their combinations. The method was applied to the four groups of breast cancer, HER2/neu-positive breast carcinomas, ductal carcinoma, invasive ductal carcinoma and/or a nodal metastasis, and to generalized breast cancer. As a result, some promising specific molecular targets and their combinations were identified. Inhibitors of some identified targets are known as potential drugs for therapy of malignant diseases; for some other targets we identified hits in the commercially available sample databases.