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利用系统生物学分析药物发现中的微阵列数据。

Analysing microarray data in drug discovery using systems biology.

机构信息

National Tsing Hua University, Laboratory of Control and Systems Biology, 101, Sec 2, Kuang Fu Road, Hsinchu, 300, Taiwan.

出版信息

Expert Opin Drug Discov. 2007 May;2(5):755-68. doi: 10.1517/17460441.2.5.755.

Abstract

The innovation of present drug design focuses on new targets. However, compound efficacy and safety in human metabolism, including toxicity and pharmacokinetic profiles, but not target selection, are the criteria that determine which drug candidates enter the clinic. Systems biology approaches to disease are developed from the idea that disease-perturbed regulatory networks differ from their normal counterparts. Microarray data analyses reveal global changes in gene or protein expression in response to genetic and environmental changes and, accordingly, are well suited to construct the normal, disease-perturbed and drug-affected networks, which are useful for drug discovery in the pharmaceutical industry. The integration of modelling, microarray data and systems biology approaches will allow for a true breakthrough in in silico absorption, distribution, metabolism, excretion and toxicity assessment in drug design. Therefore, drug discovery through systems biology by means of microarray analyses could significantly reduce the time and cost of new drug development.

摘要

目前药物设计的创新点主要集中在新靶点上。然而,化合物在人体代谢中的疗效和安全性,包括毒性和药代动力学特征,而不是靶点选择,是决定候选药物进入临床的标准。疾病的系统生物学方法是基于这样一种观点发展起来的,即疾病相关的调控网络与正常网络不同。微阵列数据分析揭示了基因或蛋白质表达的全局变化,以响应遗传和环境变化,因此非常适合构建正常、疾病干扰和药物影响的网络,这对于制药行业的药物发现很有用。通过建模、微阵列数据分析和系统生物学方法的整合,将实现在药物设计中的计算机吸收、分布、代谢、排泄和毒性评估方面的真正突破。因此,通过微阵列分析进行系统生物学的药物发现可以显著减少新药开发的时间和成本。

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