药食两用高良姜调控疾病的网络药理学研究

Network Pharmacology Study on the Mechanism of Food and Drug Dual Purpose of Alpinia Offi cinarum Hance

目的:探索药食两用高良姜多成分、多靶点调控疾病的网络机制,预测高良姜的药效物质及作用机制,为高良姜的临床应用和大健康产品开发提供科学依据。方法:运用网络药理学方法,通过数据库检索和文献挖掘构建高良姜全成分化学库;利用口服生物利用度(OB)筛选候选活性分子;对候选活性成分进行靶标及疾病预测;采用Cytoscape构建成分–靶点(C–T)、靶点–靶点(PPI)、靶点–疾病(T–D)网络,预测高良姜调控疾病网络的药效物质和作用机制。结果:构建了高良姜全成分化学库(共138个),其中74个候选活性成分OB≥30%;预测了153个相关靶点,与25类疾病防治相关。C–T网络分析发现高良姜的关键靶点涉及丝氨酸蛋白酶1(PRSS1)、二肽基肽酶4(DPP4)、前列腺素内过氧化物合酶2(PTGS2)等;PPI网络分析发现50个蛋白,包括关键蛋白TP53、HSP90AB1存在相互作用;T–D网络分析发现高良姜对肿瘤、中枢神经系统、心血管系统疾病具有潜在的作用。结论:采用网络药理学方法从分子层面揭示高良姜多成分、多靶点作用的物质基础和作用机制,有助于挖掘高良姜潜在药理效应,为高良姜在肿瘤、老年痴呆和心血管疾病防治方面的深入研究和临床应用提供了参考。

Objective Explore the ＂multi-component, multi-target＂ pharmacological mechanism and predict the pharmacological effect and pharmacodynamic materials of Alpinia offi cinarum Hance. Methods Build the whole components bank of Galangal by database and literature searching. Screening of candidate active components by choosing oral bioavailability value（OB） more than 30%. Predict the target and disease of the candidate active components. Construct component-target（C-T）, target-target（PPI）, target-disease（T-D） network. Predict the pharmacological effects and mechanism of Galangal. Results The whole component bank of Galangal consists of 138 chemical compositions. OB vales of 74 candidate active ingredients were not less than 30%. Predict 153 targets and 25 types of disease for the 74 candidate active components. C-T network analysis found that the key targets of Galangal including of PRSS1, DPP4, PTGS2, etal. PPI network analysis found that 50 proteins interact with each other, including the key protein TP53, HSP90 AB1. T-D network analysis found that Galangal had potential roles on cancer, central nervous system disease and cardiovascular disease. Conclusions Network pharmacologyis beneficial to reveal the pharmacodynamic material basis and potential pharmacological mechanismof galangal in the aspects of tumor, senile dementia and cardiovascular disease prevention and control.