Network-based approach to prediction and population-based validation of in silico drug repurposing.
Network-based approach to prediction and population-based validation of in silico drug repurposing.
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DOI：
10.1038/s41467-018-05116-5
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发表时间：
2018-07-12
影响因子：
16.6
通讯作者：
Loscalzo J
中科院分区：
文献类型：
--
作者：
Cheng F;Desai RJ;Handy DE;Wang R;Schneeweiss S;Barabási AL;Loscalzo J
在这里，我们通过量化人类（蛋白质-蛋白质）相互作用组中疾病基因和药物靶点的网络接近度，为900多种FDA批准的药物确定了数百种新的药物-疾病关联。我们选择了四个网络预测的关联，使用拥有超过2.2亿患者的大型医疗数据库和最先进的药物流行病学分析来测试它们的因果关系。使用倾向评分匹配，在患者水平数据中验证了四种基于网络的预测中的两种：卡马西平与冠状动脉疾病（CAD）风险增加相关[风险比（HR）1.56，95%置信区间（CI）1.12-2.18]，羟氯喹与CAD风险降低相关（HR 0.76，95% CI 0.59-0.97）。体外实验表明，羟氯喹减弱人主动脉内皮细胞中促炎性奎宁介导的活化，在机制上支持其在CAD中的潜在有益作用。总之，我们证明了蛋白质-蛋白质相互作用网络邻近性和大规模患者水平纵向数据的独特整合，以及体外机制研究的补充，可以促进药物再利用。重新使用已批准的药物可以加快各种疾病的治疗选择。在这里，作者使用人类蛋白质相互作用组中疾病基因产物和药物靶点的网络接近性来识别心血管疾病的药物-疾病关联，并使用纵向医疗数据验证这些关联。
Here we identify hundreds of new drug-disease associations for over 900 FDA-approved drugs by quantifying the network proximity of disease genes and drug targets in the human (protein–protein) interactome. We select four network-predicted associations to test their causal relationship using large healthcare databases with over 220 million patients and state-of-the-art pharmacoepidemiologic analyses. Using propensity score matching, two of four network-based predictions are validated in patient-level data: carbamazepine is associated with an increased risk of coronary artery disease (CAD) [hazard ratio (HR) 1.56, 95% confidence interval (CI) 1.12–2.18], and hydroxychloroquine is associated with a decreased risk of CAD (HR 0.76, 95% CI 0.59–0.97). In vitro experiments show that hydroxychloroquine attenuates pro-inflammatory cytokine-mediated activation in human aortic endothelial cells, supporting mechanistically its potential beneficial effect in CAD. In summary, we demonstrate that a unique integration of protein-protein interaction network proximity and large-scale patient-level longitudinal data complemented by mechanistic in vitro studies can facilitate drug repurposing. Repurposing approved drugs could accelerate treatment options for various diseases. Here, the authors use network proximity of disease gene products and drug targets in the human protein interactome to identify drug-disease associations for cardiovascular disease, and validate these using longitudinal healthcare data.
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影响因子：
--
作者：
Fazekas D;Koltai M;Türei D;Módos D;Pálfy M;Dúl Z;Zsákai L;Szalay-Bekő M;Lenti K;Farkas IJ;Vellai T;Csermely P;Korcsmáros T
通讯作者：
Korcsmáros T
影响因子：
46.9
作者：
通讯作者：
--
影响因子：
14.9
作者：
Dinkel H;Chica C;Via A;Gould CM;Jensen LJ;Gibson TJ;Diella F
通讯作者：
Diella F
DOI：
10.1016/0197-2456(86)90046-2
发表时间：
1986-09-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
--
作者：
DERSIMONIAN, R;LAIRD, N
通讯作者：
LAIRD, N
影响因子：
4.3
作者：
Ghiassian SD;Menche J;Barabási AL
通讯作者：
Barabási AL