背景:由于高成本,阿尔茨海默病(AD)的全球社会费用已增加到1万亿美元,副作用,和目前的AD疗法的低效率。另一个原因是缺乏预防药物和亚洲和非洲国家的低收入状况。因此,患者更喜欢传统的草药。网络药理学已成为可视化和构建疾病靶蛋白-药物框架的完善方法。这可以帮助鉴定药物的分子机制。
目的:本研究的目的是研究北非植物中可能针对阿尔茨海默病的植物化学成分。这可以通过基于分子网络药理学的方法探索其可能的作用机制来完成。
方法:北非植物的植物化学化合物(NAP)已从开放数据库中获取。已使用Qikprop软件进行了ADME筛选,以过滤NAP植物化学成分。开放的STITCH数据库已用于预测植物化学成分靶蛋白;UniProt和TDD-DB数据库已用于区分AD相关蛋白。植物化学成分-靶蛋白(C-T)和植物-植物化学成分-靶蛋白(P-C-T)框架已利用Cytoscape组装,以解释靶向植物化学成分的抗阿尔茨海默病作用机制。
结果:NAP6842植物化学成分(来自1000多种植物)已暴露于ADME和CNS调节过滤,产生94种植物化学成分,这些成分已经过目标预测研究。94个植物化学成分和4个AD鉴定的靶标已通过155个边缘相关联,这些边缘形成了与AD相关的主要途径。Cuparene,α-硒烯,β-倍半色兰,calamenene,2-4-二甲基庚烷,十一烷,正十四烷,十六烷,十九烷,正二十烷,和heneicosane有C-T网络最高的综合得分,而蛋白质MAO-B,HMG-CoA,BACE1和GCR通过包含C-T的最高组合分数而成为最丰富的。贯叶连翘,PiperNigrum,Juniperuscommunis,灯盏细辛,牛至获得了最多数量的P-C-靶标相互作用。
结论:利用基于分子网络药理学的研究预测NAP的植物化学靶标为多靶标联网铺平了道路,多成分,和多途径机制。这可能为阿尔茨海默病的调节和管理带来潜在的未来目标。
■阿尔茨海默病,网络药理学,基于计算机的方法。
BACKGROUND: The global social expenses of Alzheimer\'s disease (AD) have been increased to US$1 trillion due to high cost, side-effects, and low efficiency of the current AD-therapies. Another reason is the lack of preventive drugs and the low-income situation of Asian and African countries. Accordingly, patients rather prefer traditional herbal remedies. Network-pharmacology has been a well-established method for the visualization and the construction of disorder target protein-drug framework. This could aid in the identification of drugs molecular-mechanisms.
OBJECTIVE: The aim of this study is to investigate the phytochemical constituents that could target Alzheimer\'s disease from the North African plants. This could be done by exploring their possible mechanisms of action through molecular network pharmacology-based approach.
METHODS: The Phytochemical-compounds of North-African plants (NAP) have been accessed from open-databank. ADME-screening has been conducted for filtering of the NAP phytochemical-constituents utilizing Qikprop-software. The open STITCH databank has been utilized for the prediction of the phytochemical-constituents target-proteins; UniProt and TDD-DB databanks have been utilized for distinguishing AD-related proteins. Phytochemical constituent-target protein (C-T) and plant-phytochemical constituent-target protein (P-C-T) frameworks have been assembled utilizing Cytoscape to interpret the anti-Alzheimer\'s disease mechanism of action of the targeted phytochemical constituents.
RESULTS: The NAP 6842 phytochemical-constituents (from more than 1000 plants) have been exposed to ADME and CNS modulating filtration, generating 94 phytochemical-constituents which have been subjected to target-prediction investigation. The 94 phytochemical-constituents and the 4 AD-identified targets have been associated through 155 edges which formed the main pathways related to AD. Cuparene, alpha-selinene, beta-sesquiphellandrene, calamenene, 2-4-dimethylheptane, undecane, n-tetradecane, hexadecane, nonadecane, n-eicosane, and heneicosane have had C-T network highest combined-score, whilst the proteins MAO-B, HMG-CoA, BACE1, and GCR have been the most enriched ones by comprising the uppermost combined-scores of C-T. Hypericum perforatum, Piper nigrum, Juniperus communis, Levisticum officinale, Origanum vulgare acquired the uppermost number of P-C-Target interactions.
CONCLUSIONS: The phytochemical-targets prediction of NAP utilizing molecular-network pharmacology-based investigation has paved the way for networking multi-target, multi-constituent, and multi-pathway mechanisms. This may introduce potential future targets for the regulation and the management of Alzheimer\'s disease.
UNASSIGNED: Alzheimer\'s disease, Network pharmacology, In-silico computer based approach.