Insights from systems pharmacology into cardiovascular drug discovery and therapy

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• 作者：Peng Li ; Yingxue Fu ; Jinlong Ru ; Chao Huang ; Jiangfeng Du…
• 关键词：Cardiovascular disease ; Network pharmacology ; Network analysis ; Drug discovery ; Drug ; target network ; Gene ; disease network
• 刊名：BMC Systems Biology
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：8
• 期：1
• 全文大小：3,417 KB
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• 刊物主题：Bioinformatics; Systems Biology; Simulation and Modeling; Computational Biology/Bioinformatics; Physiological, Cellular and Medical Topics; Algorithms;
• 出版者：BioMed Central
• ISSN：1752-0509

文摘

Background Given the complex nature of cardiovascular disease (CVD), information derived from a systems-level will allow us to fully interrogate features of CVD to better understand disease pathogenesis and to identify new drug targets. Results Here, we describe a systematic assessment of the multi-layer interactions underlying cardiovascular drugs, targets, genes and disorders to reveal comprehensive insights into cardiovascular systems biology and pharmacology. We have identified 206 effect-mediating drug targets, which are modulated by 254 unique drugs, of which, 43% display activities across different protein families (sequence similarity--0%), highlighting the fact that multitarget therapy is suitable for CVD. Although there is little overlap between cardiovascular protein targets and disease genes, the two groups have similar pleiotropy and intimate relationships in the human disease gene-gene and cellular networks, supporting their similar characteristics in disease development and response to therapy. We also characterize the relationships between different cardiovascular disorders, which reveal that they share more etiological commonalities with each other rooted in the global disease-disease networks. Furthermore, the disease modular analysis demonstrates apparent molecular connection between 227 cardiovascular disease pairs. Conclusions All these provide important consensus as to the cause, prevention, and treatment of various CVD disorders from systems-level perspective.