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Characterizing and optimizing human anticancer drug targets based on topological properties in the context of biological pathways

机译:在生物学途径的背景下基于拓扑特性表征和优化人类抗癌药物靶标

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摘要

One of the challenging problems in drug discovery is to identify the novel targets for drugs. Most of the traditional methods for drug targets optimization focused on identifying the particular families of "druggable targets", but ignored their topological properties based on the biological pathways. In this study, we characterized the topological properties of human anticancer drug targets (ADTs) in the context of biological pathways. We found that the ADTs tended to present the following seven topological properties: influence the number of the pathways related to cancer, be localized at the start or end of the pathways, interact with cancer related genes, exhibit higher connectivity, vulnerability, betweenness, and closeness than other genes. We first ranked ADTs based on their topological property values respectively, then fused them into one global-rank using the joint cumulative distribution of an N-dimensional order statistic to optimize human ADTs. We applied the optimization method to 13 anticancer drugs, respectively. Results demonstrated that over 70% of known ADTs were ranked in the top 20%. Furthermore, the performance for mercaptopurine was significant: 6 known targets (ADSL, GMPR2, GMPR, HPRT1, AMPD3, AMPD2) were ranked in the top 15 and other four out of the top 15 (MAT2A, CDKN1A, AREG, JUN) have the potentialities to become new targets for cancer therapy. (C) 2015 Elsevier Inc. All rights reserved.
机译:药物发现中具有挑战性的问题之一是确定药物的新靶标。用于药物靶标优化的大多数传统方法都着眼于识别“可药物性靶标”的特定家族,但忽略了基于生物学途径的拓扑特性。在这项研究中,我们在生物途径的背景下表征了人类抗癌药物靶标(ADT)的拓扑特性。我们发现,ADT倾向于呈现以下七个拓扑特性:影响与癌症相关的途径的数量,位于途径的开始或末端,与癌症相关的基因相互作用,表现出更高的连通性,脆弱性,中间性和亲密性高于其他基因。我们首先分别基于ADT的拓扑属性值对ADT进行排名,然后使用N维顺序统计量的联合累积分布将它们融合到一个全局排名中,以优化人类ADT。我们将优化方法分别应用于13种抗癌药物。结果表明,超过70%的已知ADT排名前20%。此外,巯基嘌呤的性能非常重要:6个已知目标(ADSL,GMPR2,GMPR,HPRT1,AMPD3,AMPD2)排在前15位,而其他15个前四个目标(MAT2A,CDKN1A,AREG,JUN)具有成为癌症治疗新目标的潜力。 (C)2015 Elsevier Inc.保留所有权利。

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