系統性治療大腸直腸癌症之候選藥物預測、篩選與驗證的整合性策略;An Integrated Approach for the Prediction, Screening and Validation of Systems Therapeutic Candidates for Colorectal Cancer

NCUIR > college of Health Sciences and Technology > Institute of Systems Biology and Bioinformatics > Research Project > Item 987654321/62678

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/62678

Title:	系統性治療大腸直腸癌症之候選藥物預測、篩選與驗證的整合性策略;An Integrated Approach for the Prediction, Screening and Validation of Systems Therapeutic Candidates for Colorectal Cancer
Authors:	蘇立仁;徐士蘭
Contributors:	國立中央大學系統生物與生物資訊研究所
Keywords:	藥學
Date:	2012-12-01
Issue Date:	2014-03-17 11:58:10 (UTC+8)
Publisher:	行政院國家科學委員會
Abstract:	研究期間：10108~10207;Colorectal cancer is now recognized as is a disease caused by a break down of a large part, including the epigenome, of the biological system in the tumor, not just of the failure of one or two of its biological functions. Therefore, one cannot expect cancer to be effectively treated by one or a few single-target drugs. Rather, a systems approach to cancer treatment is required. In what has been termed a crisis in new drug discovery, total annual R&D expenditure by pharmaceutical companies has grown approximately linearly with time, while the number of approved new drugs has decreased. At the same time, many previously thought successful drugs have been withdrawn, mostly due to side-effect issues. This points to a weakness in the prevalent single-target based approach to drug design. Here, we propose an interdisciplinary project that employs novel application of network theory, strict principles statistics, and general methods of systems biology to the design and development of side-effect free repurposed drug compounds for a systems treatment of cancer. We also want to find out candidate drugs for cancer therapy and reducing chemotherapy resistant effects from traditional Chinese medicine and old drugs. This project includes a prospective pathology-based colorectal tissue bank core establishment, in silico drug prediction, and in vitro experimental cell and mouse model validation, and clinical sample validation, and data management. The drug prediction integrates multi-cohort microarray gene expression data, protein-protein interaction data, and gene ontology, and employs network theory to extract differential (biological) functional gene sets, which will then be applied to the Harvard-MIT (drug) “connectivity map” database and use the "gene set enrichment analysis" (GSEA) to select repurposed drug compounds for cancer treatment. Novelties in the validation portion will be the high-throughput cell-model experiments and the mouse xenograft and carcinoma-in-situ model experiments. We expect this project will establish a new interdisciplinary paradigm for finding systems treatment drugs for cancer, and will find drug compounds that are promising for a systems treatment of colorectal cancer.
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[Institute of Systems Biology and Bioinformatics] Research Project

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