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DOI | 10.1038/s41467-018-05592-9 |
Network-based approach to prediction and population-based validation of in silico drug repurposing | |
Cheng, Feixiong1,2,3,4; Desai, Rishi J.5; Handy, Diane E.6; Wang, Ruisheng6; Schneeweiss, Sebastian5; Barabasi, Albert-Laszlo1,3,4,7,8; Loscalzo, Joseph6 | |
2018-07-12 | |
发表期刊 | NATURE COMMUNICATIONS
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ISSN | 2041-1723 |
出版年 | 2018 |
卷号 | 9 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Hungary |
英文摘要 | 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. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000438347000006 |
WOS关键词 | CORONARY-HEART-DISEASE ; GENOME-WIDE ASSOCIATION ; PROTEOME-SCALE MAP ; MYOCARDIAL-INFARCTION ; PROPENSITY-SCORE ; INFLAMMATORY CYTOKINES ; RHEUMATOID-ARTHRITIS ; BRUGADA-SYNDROME ; CLINICAL-TRIALS ; RISK-FACTORS |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/203937 |
专题 | 资源环境科学 |
作者单位 | 1.Northeastern Univ, Ctr Complex Networks Res, Boston, MA 02115 USA; 2.Northeastern Univ, Dept Phys, Boston, MA 02115 USA; 3.Dana Farber Canc Inst, Ctr Canc Syst Biol, Boston, MA 02215 USA; 4.Dana Farber Canc Inst, Dept Canc Biol, Boston, MA 02215 USA; 5.Harvard Med Sch, Brigham & Womens Hosp, Div Pharmacoepidemiol & Pharmacoecon, Dept Med, Boston, MA 02115 USA; 6.Harvard Med Sch, Brigham & Womens Hosp, Dept Med, Boston, MA 02115 USA; 7.Harvard Med Sch, Brigham & Womens Hosp, Channing Div Network Med, Dept Med, Boston, MA 02115 USA; 8.Cent European Univ, Ctr Network Sci, H-1051 Budapest, Hungary |
推荐引用方式 GB/T 7714 | Cheng, Feixiong,Desai, Rishi J.,Handy, Diane E.,et al. Network-based approach to prediction and population-based validation of in silico drug repurposing[J]. NATURE COMMUNICATIONS,2018,9. |
APA | Cheng, Feixiong.,Desai, Rishi J..,Handy, Diane E..,Wang, Ruisheng.,Schneeweiss, Sebastian.,...&Loscalzo, Joseph.(2018).Network-based approach to prediction and population-based validation of in silico drug repurposing.NATURE COMMUNICATIONS,9. |
MLA | Cheng, Feixiong,et al."Network-based approach to prediction and population-based validation of in silico drug repurposing".NATURE COMMUNICATIONS 9(2018). |
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