Literature DB >> 27334200

Drug Repositioning Through Network Pharmacology.

Hao Ye, Jia Wei, Kailin Tang, Ritchie Feuers, Huixiao Hong1.   

Abstract

Low drug productivity has been a significant problem of the pharmaceutical industry for several decades even though numerous novel technologies were introduced during this period. Currently pharmacologic dogma, "single drug, single target, single disease", is at the root of the lack of drug productivity. From a systems biology viewpoint, network pharmacology has been proposed to complement the established guiding pharmacologic approaches. The rationale for network pharmacology as a major component of drug discovery and development is that a disease can be caused by perturbation of the disease-causing network and a drug may be designed to interact with multiple targets for modulation of such a network from the disease status toward normal status. Therefore, network pharmacology has been applied to guide and assist in drug repositioning. Drugs exerting their therapeutic effects may directly target disease-associated proteins, but they may also modulate the pathways involved in the pathological process. In this review, we discuss the progresses and prospects in network pharmacology, focusing on drug off-targets discovery, disease-associated protein identification, and pathway analysis for elucidating relationships between drug targets and disease-associated proteins.

Mesh:

Year:  2016        PMID: 27334200     DOI: 10.2174/1568026616666160530181328

Source DB:  PubMed          Journal:  Curr Top Med Chem        ISSN: 1568-0266            Impact factor:   3.295


  20 in total

1.  Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

Authors:  Jiansong Fang; Zengrui Wu; Chuipu Cai; Qi Wang; Yun Tang; Feixiong Cheng
Journal:  J Chem Inf Model       Date:  2017-10-13       Impact factor: 4.956

2.  Prediction of Targets of Curculigoside A in Osteoporosis and Rheumatoid Arthritis Using Network Pharmacology and Experimental Verification.

Authors:  Jiawen Han; Minjie Wan; Zhanchuan Ma; Cong Hu; Huanfa Yi
Journal:  Drug Des Devel Ther       Date:  2020-11-26       Impact factor: 4.162

3.  A novel artificial intelligence protocol to investigate potential leads for Parkinson's disease.

Authors:  Zhi-Dong Chen; Lu Zhao; Hsin-Yi Chen; Jia-Ning Gong; Xu Chen; Calvin Yu-Chian Chen
Journal:  RSC Adv       Date:  2020-06-16       Impact factor: 4.036

Review 4.  Towards reproducible computational drug discovery.

Authors:  Nalini Schaduangrat; Samuel Lampa; Saw Simeon; Matthew Paul Gleeson; Ola Spjuth; Chanin Nantasenamat
Journal:  J Cheminform       Date:  2020-01-28       Impact factor: 5.514

5.  Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Huashi Baidu formula in the treatment of COVID-19.

Authors:  Quyuan Tao; Jiaxin Du; Xiantao Li; Jingyan Zeng; Bo Tan; Jianhua Xu; Wenjia Lin; Xin-Lin Chen
Journal:  Drug Dev Ind Pharm       Date:  2020-07-08       Impact factor: 3.225

Review 6.  The greater inflammatory pathway-high clinical potential by innovative predictive, preventive, and personalized medical approach.

Authors:  Greg Gibson; Luigi Manni; Christine Nardini; Maria Giovanna Maturo; Marzia Soligo
Journal:  EPMA J       Date:  2019-12-10       Impact factor: 6.543

7.  Mechanism research of Salvia miltiorrhiza on treating myocardial ischemia reperfusion injury according to network pharmacology combined with molecular docking technique.

Authors:  Zhiyan Jiang
Journal:  Medicine (Baltimore)       Date:  2021-12-03       Impact factor: 1.817

8.  Repositioning of Omarigliptin as a once-weekly intranasal Anti-parkinsonian Agent.

Authors:  Bassam M Ayoub; Shereen Mowaka; Marwa M Safar; Nermeen Ashoush; Mona G Arafa; Haidy E Michel; Mariam M Tadros; Mohamed M Elmazar; Shaker A Mousa
Journal:  Sci Rep       Date:  2018-06-12       Impact factor: 4.379

9.  Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data.

Authors:  Yanjun Xu; Qun Dong; Feng Li; Yingqi Xu; Congxue Hu; Jingwen Wang; Desi Shang; Xuan Zheng; Haixiu Yang; Chunlong Zhang; Mengting Shao; Mohan Meng; Zhiying Xiong; Xia Li; Yunpeng Zhang
Journal:  J Transl Med       Date:  2019-08-06       Impact factor: 5.531

10.  Network Pharmacology-Based Approach to Investigate the Molecular Targets of Rhubarb for Treating Cancer.

Authors:  Lan Jiang; Zhongquan Shi; Yi Yang
Journal:  Evid Based Complement Alternat Med       Date:  2021-06-08       Impact factor: 2.629

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