Quantitative and systems pharmacology 4. Network-based analysis of drug pleiotropy on coronary artery disease.
Quantitative and systems pharmacology 4. Network-based analysis of drug pleiotropy on coronary artery disease.
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DOI：
10.1016/j.ejmech.2018.10.020
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发表时间：
2019-01-01
影响因子：
6.7
通讯作者：
Cheng F
中科院分区：
文献类型：
--
作者：
Fang J;Cai C;Chai Y;Zhou J;Huang Y;Gao L;Wang Q;Cheng F
冠状动脉疾病（coronary artery disease，CAD）是目前世界上最严重的疾病之一。基因组学和系统生物学方法的使用为药物发现和开发提供了新靶点识别和计算机药物再利用的可能性。在这项研究中，我们提出了一个基于网络的系统药理学框架，用于CAD的药物治疗和化学预防中的靶点识别和药物再利用。具体来说，我们通过整合已知的药物-靶点相互作用、来自遗传和基因组研究的CAD基因以及人类蛋白质-蛋白质相互作用组来构建计算机模型。我们证明，提出的计算机模型可以成功地发现批准的药物和新的天然产物，在潜在的治疗和预防CAD。在案例研究中，我们强调了几种已批准的药物（例如，法舒地尔、帕瑞考昔和地塞米松）或天然产物（例如，白藜芦醇、木犀草素、大豆苷元和咖啡酸）与CAD化学干预的新作用机制。总之，本研究为CAD的靶点识别和计算机模拟药物再利用提供了一种强大的系统药理学方法。
Despite recent advance of therapeutic development, coronary artery disease (CAD) remains one of the major issues to public health. The use of genomics and systems biology approaches to inform drug discovery and development have offered the possibilities for new target identification and in silico drug repurposing. In this study, we propose a network-based, systems pharmacology framework for target identification and drug repurposing in pharmacologic treatment and chemoprevention of CAD. Specifically, we build in silico models by integrating known drug-target interactions, CAD genes derived from the genetic and genomic studies, and the human protein-protein interactome. We demonstrate that the proposed in silico models can successfully uncover approved drugs and novel natural products in potentially treating and preventing CAD. In case studies, we highlight several approved drugs (e.g., fasudil, parecoxib, and dexamethasone) or natural products (e.g., resveratrol, luteolin, daidzein and caffeic acid) with new mechanism-of-action in chemical intervention of CAD by network analysis. In summary, this study offers a powerful systems pharmacology approach for target identification and in silico drug repurposing on CAD.
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影响因子：
16.6
作者：
Cheng F;Desai RJ;Handy DE;Wang R;Schneeweiss S;Barabási AL;Loscalzo J
通讯作者：
Loscalzo J
影响因子：
37.8
作者：
Baggish AL;Weiner RB;Kanayama G;Hudson JI;Lu MT;Hoffmann U;Pope HG Jr
通讯作者：
Pope HG Jr
影响因子：
3.6
作者：
Egert, Sarah;Bosy-Westphal, Anja;Mueller, Manfred J.
通讯作者：
Mueller, Manfred J.
影响因子：
3.1
作者：
Fang, Fang;Li, Dongye;Sun, Hong
通讯作者：
Sun, Hong
影响因子：
4.2
作者：
Gaziano, Thomas A.;Bitton, Asaf;Anand, Shuchi;Abrahams-Gessel, Shafika;Murphy, Adrianna
通讯作者：
Murphy, Adrianna