In silico method for identification of promising anticancer drug targets.
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.
Koborova
O N
ON
Institute of Biomedical Chemistry of Russian Academy of Medical Sciences, Moscow, Russia. okoborova@gmail.com
Filimonov
D A
DA
Zakharov
A V
AV
Lagunin
A A
AA
Ivanov
S M
SM
Kel
A
A
Poroikov
V V
VV
D000970:Antineoplastic Agents / Q000737:chemistry* / Q000494:pharmacology*; D001943:Breast Neoplasms / Q000188:drug therapy*; D003198:Computer Simulation; D005260:Female; D005786:Gene Expression Regulation / Q000187:drug effects; D006801:Humans; D011506:Proteins / Q000037:antagonists & inhibitors; D021281:Quantitative Structure-Activity Relationship
D000970:Antineoplastic Agents; D011506:Proteins