Network pharmacology-based analysis of Chinese herbal Naodesheng formula for application to Alzheimer＇s disease  

作　　者：PANG Xiao-Cong KANG De FANG Jian-Song ZHAO Ying XU Lv-Jie LIAN Wen-Wen LIU Ai-Lin DU Guan-Hua 

机构地区：[1]Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China [2]Beijing Key Laboratory of Drug Target and Screening Research, Beijing 100050, China [3]State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Beijing 100050, China [4]Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510006, China

出　　处：《Chinese Journal of Natural Medicines》2018年第1期53-62,共10页中国天然药物（英文版）

基　　金：supported by the Research Special Fund for the National Great Science and Technology Projects(No.2012ZX09301002-001-001);the International Collaboration Project(No.2011DFR31240);Peking Union Medical College graduate student innovation fund(No.2013-1007-18)

摘　　要：Naodesheng(NDS) formula, which consists of Rhizoma Chuanxiong, Lobed Kudzuvine, Carthamus tinctorius, Radix Notoginseng, and Crataegus pinnatifida, is widely applied for the treatment of cardio/cerebrovascular ischemic diseases, ischemic stroke, and sequelae of cerebral hemorrhage, etc. At present, the studies on NDS formula for Alzheimer's disease(AD) only focus on single component of this prescription, and there is no report about the synergistic mechanism of the constituents in NDS formula for the potential treatment of dementia. Therefore, the present study aimed to predict the potential targets and uncover the mechanisms of NDS formula for the treatment of AD. Firstly, we collected the constituents in NDS formula and key targets toward AD. Then, drug-likeness, oral bioavailability, and blood-brain barrier permeability were evaluated to find drug-like and lead-like constituents for treatment of central nervous system diseases. By combining the advantages of machine learning, molecular docking, and pharmacophore mapping, we attempted to predict the targets of constituents and find potential multi-target compounds from NDS formula. Finally, we built constituent-target network, constituent-target-target network and target-biological pathway network to study the network pharmacology of the constituents in NDS formula. To the best of our knowledge, this represented the first to study the mechanism of NDS formula for potential efficacy for AD treatment by means of the virtual screening and network pharmacology methods.Naodesheng （NDS） formula, which consists of Rhizoma Chuanxiong, Lobed Kudzuvine, Carthamus tinctorius, Radix Notoginseng, and Crataegus pinnatifida, is widely applied for the treatment of cardio/cerebrovascular ischemic diseases, ischemic stroke, and sequelae of cerebral hemorrhage, etc. At present, the studies on NDS formula for Alzheimer＇s disease （AD） only focus on single component of this prescription, and there is no report about the synergistic mechanism of the constituents in NDS formula for the potential treatment of dementia. Therefore, the present study aimed to predict the potential targets and uncover the mechanisms of NDS formula for the treatment of AD. Firstly, we collected the constituents in NDS formula and key targets toward AD. Then, drug-likeness, oral bioavailability, and blood-brain barrier permeability were evaluated to find drug-like and lead-like constituents for treatment of central nervous system diseases. By combining the advantages of machine learning, molecular docking, and pharma- cophore mapping, we attempted to predict the targets of constituents and find potential multi-target compounds from NDS formula. Finally, we built constituent-target network, constituent-target-target network and target-biological pathway network to study the network pharmacology of the constituents in NDS formula. To the best of our knowledge, this represented the first to study the mechanism of NDS formula for potential efficacy for AD treatment by means of the virtual screening and network pharmacology methods.

关 键 词：Alzheimer’s disease Network pharmacology DOCKING PHARMACOPHORE Machine learning 

分 类 号：R965[医药卫生—药理学]