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Advances in in vitro test systems about 30 years ago have shifted drug research from animal studies to target-oriented research. Together with genomic research, agents that specifically target a unique protein that is related to a certain disease have been found. However, drug resistance arises possibly owing to the diversity of mutations and other pathways. Furthermore, many others have been found to be inefficient or to cause severe side effects. So the limitations of the single protein targeted agent paradigm have come to surface.
Recent research has identified that a balanced multi-component therapies might be better than highly specific single component therapies in certain cases. Therefore, the interest in effective combinatorial drug discovery based on systems-oriented approaches has been increased steadily. For that reason, we developed CDA (Combinatorial Drug Assembler) for multi-signaling pathway targeting combinatorial drug discovery.
Process of CDA
CDA is a system for multi-signaling pathway targeting combinatorial drug discovery using gene expression profile. General expression pattern matching methods can be used to quantify the degree of functional similarity among genetic perturbation, disease, and drugs. CDA performs expression pattern matching within signaling pathway to measure functional similarity between input gene sets and 6,100 molecule-treated expression profiles using Kolmogorov-Smirnov statistics. Then it lists up best pattern matching single drugs/combinatorial drug pairs across the input gene set-related signaling pathways.
The result is presented on two different view; table view for scores and details and network view to visualize relationships between signaling pathway entities and known drugs, proteins and diseases. A network visualization tool phExplorer we developed provides integrated drug-protein-disease information. Using phExplorer, users can get clues how those chemicals could related to signaling pathways from known relationships.
Reference molecule-treated expression data was downloaded from Connectivity Map (build 02) (http://www.broadinstitute.org/cmap) It contains 6,100 expression profiles representing 1,309 molecules. Molecules were selectively applied to five different human cancer cell lines (MCF7 and ssMCF7: breast cancer, PC3: prostate cancer, HL60: leukemia, SKMEL: melanoma) for short duration.
Pathway gene set data was downloaded from Pathway Interaction Database (PID) on 09/03/2010 (http://pid.nci.nih.gov) Only the NCI-Nature Curated data was used.