In recent years, accumulation of “OMICs” data for topological and functional organization of regulatory networks in a cell provides possibility to identify the potential targets involved in pathological processes and to select the most promising targets for future drug development. We propose an approach for anticancer drug target identification, which using microarray data allows discrete modeling of a regulatory network behavior. The effect of drugs inhibiting particular protein or combination of proteins in regulatory network is analyzed by simulation of 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 the 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 samples databases.
SAR and QSAR in Environmental Research, 2009, 20 (7-8), 755-766.