目的 通過網(wǎng)絡(luò)藥理學(xué)和分子對接的方法,探究糖脈康顆粒治療糖尿病周圍神經(jīng)病變(DPN)的機(jī)制。方法 利用檢索中藥系統(tǒng)藥理學(xué)數(shù)據(jù)庫及分析平臺(TCMSP)對篩選糖脈康顆粒的組方藥物的化學(xué)活性物質(zhì),藥物靶標(biāo)采用Swiss Target Prediction平臺進(jìn)行預(yù)測,并搜索Genecards數(shù)據(jù)庫、OMIM數(shù)據(jù)庫、TTD數(shù)據(jù)庫和DisGeNET數(shù)據(jù)庫預(yù)測DPN相關(guān)靶點(diǎn)。對藥物與疾病的靶標(biāo)基因取交集后采用Cytoscape軟件構(gòu)建活性成分-靶點(diǎn)網(wǎng)絡(luò)圖,并導(dǎo)入String數(shù)據(jù)庫構(gòu)建PPI網(wǎng)絡(luò),進(jìn)行拓?fù)浞治鰧ふ液诵幕?。采用R軟件進(jìn)行基因本體(GO)注釋和京都基因與基因組百科全書(KEGG)信號通路富集分析。結(jié)果 糖脈康顆粒治療糖尿病周圍神經(jīng)病變的關(guān)鍵化學(xué)成分為巴馬汀、廣玉蘭內(nèi)酯和11,14,17-三烯酸甲酯等;藥物-疾病的共同靶點(diǎn)共331個(gè),關(guān)鍵蛋白包括細(xì)胞腫瘤抗原p53、連環(huán)蛋白β-1(CTNNB1)、熱休克蛋白90-α(HSP90AA1)等。GO富集分析顯示,生物過程主要包括細(xì)胞對化學(xué)應(yīng)激的反應(yīng)、對營養(yǎng)素水平的反應(yīng)和肽基酪氨酸磷酸化等,細(xì)胞組成主要包括膜筏膜微域和膜區(qū)等,分子功能主要包括蛋白酪氨酸激酶活性、細(xì)胞表面受體蛋白酪氨酸激酶活性和細(xì)胞表面受體蛋白激酶活性等。KEGG富集分析所涉及的通路有糖尿病并發(fā)癥中的高糖基化終產(chǎn)物-高糖基化終產(chǎn)物受體信號通路、脂質(zhì)和動脈粥樣硬化、前列腺癌、表皮生長因子受體酪氨酸激酶抑制劑耐藥性和癌癥的中樞碳代謝等。分子對接結(jié)果顯示,糖脈康顆粒中的7個(gè)關(guān)鍵活性成分與5個(gè)核心靶點(diǎn)蛋白的結(jié)合能均 ≤ −5.0 kcal/mol,說明成分和靶點(diǎn)蛋白結(jié)合性可能較好。結(jié)論 利用網(wǎng)絡(luò)藥理學(xué)和分子對接揭示了糖脈康顆粒通過多種途徑,多種信號通路作用于多種靶點(diǎn)發(fā)揮藥效,為進(jìn)一步闡釋糖脈康顆粒治療糖尿病周圍神經(jīng)病變的作用機(jī)制提供了依據(jù)。

Objective To explore the mechanism of Tangmaikang Granules in treatment of diabetes peripheral neuropathy based on network pharmacology and molecular docking. Methods The Traditional Chinese Medicine SystemsPharmacology Database and Analysis Platform (TCMSP) was searched to screen the chemical active ingredients in the Tangmaikang Granules. The drug targets were predicted by the SwissTargetPrediction Platform. Genecards Database, OMIM database, TTD database, and DisGeNET database were searched to predict DPN-related targets. After crossing the target genes of drugs and diseases, Cytoscape software was used to construct the drug-ingredient-target-disease network, and the PPI network plot was constructed by importing the String database. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment were conducted by R software. Results The key chemical ingredients in Tangmaikang Granules for DPN are palmatine, magnograndiolide, and icosa-11,14,17-trienoic acid methyl ester, etc., there are 331 common targets for drug-disease, the key proteins include cellular tumor antigen p53 (TP53), catenin beta-1 (CTNNB1), heat shock protein HSP 90-alpha (HSP90AA1). GO enrichment analysis showed that biological process included cellular response to chemical stress, response to nutrient levels, and peptidyl-tyrosine phosphorylation. Celelular component included membrane raft, membrane microdomain, and membrane region. Molecular function included protein tyrosine kinase activity, transmembrane receptor protein tyrosine kinase activity, and transmembrane receptor protein kinase activity. The pathways involved in KEGG enrichment analysis are the AGE-RAGE signaling pathway in diabetic complications, lipid and atherosclerosis, prostate cancer, EGFR tyrosine kinase inhibitor resistance, and central carbon metabolism in cancer, etc. The results of molecular docking showed that the binding energies of the seven key active components with the five core target proteins in Tangmaikang Granules were mostly ≤ −5.0 kcal/mol, indicating that the components may possessed good binding ability with the target proteins. Conclusion Network pharmacology and molecular docking have revealed that Tangmaikang Granules exerts its pharmacodynamic effect through multiple pathways and multiple signal pathways acting on multiple targets, it provides a basis for further explaining the mechanism of Tangmaikang Granule in the treatment of DPN.

R914

中醫(yī)藥防治糖尿病國際聯(lián)合研究中心基金項(xiàng)目（2015B01022）；兵團(tuán)財(cái)政科技計(jì)劃項(xiàng)目（2020AA005）