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美国食品药品监督管理局(FDA)批准药物及其靶点的网络分析

Network analysis of FDA approved drugs and their targets.

作者信息

Ma'ayan Avi, Jenkins Sherry L, Goldfarb Joseph, Iyengar Ravi

机构信息

Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, New York 10029, USA.

出版信息

Mt Sinai J Med. 2007 Apr;74(1):27-32. doi: 10.1002/msj.20002.

Abstract

The global relationship between drugs that are approved for therapeutic use and the human genome is not known. We employed graph-theory methods to analyze the Federal Food and Drug Administration (FDA) approved drugs and their known molecular targets. We used the FDA Approved Drug Products with Therapeutic Equivalence Evaluations 26(th) Edition Electronic Orange Book (EOB) to identify all FDA approved drugs and their active ingredients. We then connected the list of active ingredients extracted from the EOB to those known human protein targets included in the DrugBank database and constructed a bipartite network. We computed network statistics and conducted Gene Ontology analysis on the drug targets and drug categories. We find that drug to drug-target relationship in the bipartite network is scale-free. Several classes of proteins in the human genome appear to be better targets for drugs since they appear to be selectively enriched as drug targets for the currently FDA approved drugs. These initial observations allow for development of an integrated research methodology to identify general principles of the drug discovery process.

摘要

已获批用于治疗的药物与人类基因组之间的全球关系尚不清楚。我们采用图论方法来分析美国食品药品监督管理局(FDA)批准的药物及其已知的分子靶点。我们使用《联邦食品药品监督管理局批准的具有治疗等效性评估的药品第26版电子橙皮书》(EOB)来识别所有FDA批准的药物及其活性成分。然后,我们将从EOB中提取的活性成分列表与DrugBank数据库中包含的已知人类蛋白质靶点进行关联,并构建了一个二分网络。我们计算了网络统计数据,并对药物靶点和药物类别进行了基因本体分析。我们发现二分网络中药物与药物靶点的关系是无标度的。人类基因组中的几类蛋白质似乎是更好的药物靶点,因为它们作为当前FDA批准药物的靶点似乎有选择性地富集。这些初步观察结果有助于开发一种综合研究方法,以确定药物发现过程的一般原则。

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