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药物重定位——网络药理学的重要应用领域
引用本文:张永祥,程肖蕊,周文霞.药物重定位——网络药理学的重要应用领域[J].中国药理学与毒理学杂志,2012,26(6):779-786.
作者姓名:张永祥  程肖蕊  周文霞
作者单位:军事医学科学院毒物药物研究所,北京,100850
基金项目:国家科技支撑计划课题,国家"重大新药创制"科技重大专项
摘    要:药物重定位是指发现已上市药物的新适应症,是网络药理学的重要应用领域。药物重定位策略是目前已知的药物研发策略中风险与效益比最好的策略之一,也是一种解决新药开发高投入低成功率困境的有效方法之一。目前已成功进行重定位的药物已超过百余种(国内有老药新用专著收载123种),药物重定位研究已超越了随机发现药物新适应症的阶段,进入了基于计算机技术的崭新研究阶段。现有研究方法主要有基于小分子(或配体)特征的方法、基于蛋白靶点(或受体)特征的方法、基于表型(或网络)特征的方法。随着对防治重大疾病有效药物需求的不断增加,以及系统生物学、计算生物学、网络药理学等相关学科的快速发展,面对新药研发难度越来越大的严峻形势,药物重定位已成为世界范围内关注的热点,在药物研发领域占据重要地位。

关 键 词:药物重定位  网络药理学  相似性扰动  相似性搜索
收稿时间:2012-12-11

Drug repositioning: an important application of network pharmacology
ZHANG Yong-xiang , CHENG Xiao-rui , ZHOU Wen-xia.Drug repositioning: an important application of network pharmacology[J].Chinese Journal of Pharmacology and Toxicology,2012,26(6):779-786.
Authors:ZHANG Yong-xiang  CHENG Xiao-rui  ZHOU Wen-xia
Institution:(Institute of Pharmacology and Toxicology,Academy of Military Medical Sciences,Beijing 100850,China)
Abstract:Drug repositioning, as an important application of network pharmacology, is concerned with identification of new indications for existing drugs. Drug repositioning can offer a better risk vs reward trade-off than other drug development strategies. There are more than 100 successfully repositioned drugs. Beyond serendipitous observations, there are several in silico methods that have been established to address the issues of drug-target interaction prediction and drug repositioning. These methods base on the feature of ligand (including quantitative structure-activity relationships and similarity search) or receptor (including reverse docking) or phenotype-based (including drug-based similarity inference, target-based similarity inference and network-based inference). Drug repositioning is playing a vital role in drug research and development with the increasing demand on market and rapid advanvement in systems biology, computational biology and network pharmacology.
Keywords:drug repositioning  network pharmacology  similarity inference  similarity search
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