CDA : combinatorial drug discovery using transcriptional response modules

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dc.contributor.authorJ H Lee-
dc.contributor.authorD G Kim-
dc.contributor.authorT J Bae-
dc.contributor.authorK Rho-
dc.contributor.authorJ T Kim-
dc.contributor.authorJ J Lee-
dc.contributor.authorYeongjun Jang-
dc.contributor.authorB C Kim-
dc.contributor.authorK M Park-
dc.contributor.authorS Kim-
dc.date.accessioned2017-04-19T09:33:14Z-
dc.date.available2017-04-19T09:33:14Z-
dc.date.issued2012-
dc.identifier.issn1932-6203-
dc.identifier.uri10.1371/journal.pone.0042573ko
dc.identifier.urihttps://oak.kribb.re.kr/handle/201005/10885-
dc.description.abstractBackground: Anticancer therapies that target single signal transduction pathways often fail to prevent proliferation of cancer cells because of overlapping functions and cross-talk between different signaling pathways. Recent research has identified that balanced multi-component therapies might be more efficacious than highly specific single component therapies in certain cases. Ideally, synergistic combinations can provide 1) increased efficacy of the therapeutic effect 2) reduced toxicity as a result of decreased dosage providing equivalent or increased efficacy 3) the avoidance or delayed onset of drug resistance. Therefore, the interest in combinatorial drug discovery based on systems-oriented approaches has been increasing steadily in recent years. Methodology: Here we describe the development of Combinatorial Drug Assembler (CDA), a genomics and bioinformatics system, whereby using gene expression profiling, multiple signaling pathways are targeted for combinatorial drug discovery. CDA performs expression pattern matching of signaling pathway components to compare genes expressed in an input cell line (or patient sample data), with expression patterns in cell lines treated with different small molecules. Then it detects best pattern matching combinatorial drug pairs across the input gene set-related signaling pathways to detect where gene expression patterns overlap and those predicted drug pairs could likely be applied as combination therapy. We carried out in vitro validations on non-small cell lung cancer cells and triple-negative breast cancer (TNBC) cells. We found two combinatorial drug pairs that showed synergistic effect on lung cancer cells. Furthermore, we also observed that halofantrine and vinblastine were synergistic on TNBC cells. Conclusions: CDA provides a new way for rational drug combination. Together with phExplorer, CDA also provides functional insights into combinatorial drugs. CDA is freely available at http://cda.i-pharm.org.-
dc.publisherPublic Library of Science-
dc.titleCDA : combinatorial drug discovery using transcriptional response modules-
dc.title.alternativeCDA : combinatorial drug discovery using transcriptional response modules-
dc.typeArticle-
dc.citation.titlePLoS One-
dc.citation.number8-
dc.citation.endPagee42573-
dc.citation.startPagee42573-
dc.citation.volume7-
dc.contributor.affiliatedAuthorYeongjun Jang-
dc.contributor.alternativeName이지현-
dc.contributor.alternativeName김대규-
dc.contributor.alternativeName배태정-
dc.contributor.alternativeName노규형-
dc.contributor.alternativeName김지태-
dc.contributor.alternativeName이종준-
dc.contributor.alternativeName장영준-
dc.contributor.alternativeName김병철-
dc.contributor.alternativeName박경미-
dc.contributor.alternativeName김성훈-
dc.identifier.bibliographicCitationPLoS One, vol. 7, no. 8, pp. e42573-e42573-
dc.identifier.doi10.1371/journal.pone.0042573-
dc.description.journalClassY-
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