目的 通過網(wǎng)絡藥理學、分子對接技術(shù)和斑馬魚實驗探究仙鶴草治療糖皮質(zhì)激素性骨質(zhì)疏松癥可能的作用機制。方法 通過中藥系統(tǒng)藥理學數(shù)據(jù)庫與分析平臺（TCMSP）和本草組鑒（HERB）數(shù)據(jù)庫初步篩查出仙鶴草相關的活性成分；利用SEA和SwissTargetPrediction平臺預測活性成分的相關靶點；利用OMIM、GeneCards數(shù)據(jù)庫檢索糖皮質(zhì)激素性骨質(zhì)疏松癥相關的疾病靶點；利用韋恩圖繪制平臺獲取共有的關鍵作用靶點，并將信息導入Cytoscope 3.7.2軟件和STRING在線分析平臺，進行可視化分析；使用DAVID數(shù)據(jù)庫進行基因本體（GO）和京都基因與基因組百科全書（KEGG）富集分析；然后通過Autodock Tools 1.5.6軟件對有效成分-核心作用靶點進行分子對接驗證。通過斑馬魚幼魚骨形成抑制模型探討仙鶴草水提液對糖皮質(zhì)激素所致的斑馬魚幼魚骨形成抑制的影響及機制。結(jié)果 通過TCMSP和HERB數(shù)據(jù)庫篩選出仙鶴草的32種潛在活性成分，通過SEA和SwissTargetPrediction平臺預測并篩選后得277個藥物靶點；通過OMIM和GeneCards數(shù)據(jù)庫檢索糖皮質(zhì)激素性骨質(zhì)疏松癥相關的潛在疾病靶點共654個。將藥物-疾病的36個交集靶點進行富集分析，GO功能富集主要包括通過RNA聚合酶Ⅱ的轉(zhuǎn)錄正調(diào)控、信號轉(zhuǎn)導、DNA模板的正向調(diào)控等；KEGG富集通路主要富集在磷脂酰肌醇3-激酶（PI3K）/蛋白激酶B（Akt）信號通路等信號通路。將degree值前5個的有效成分（槲皮素、木犀草素、鞣花酸、芹菜素和山柰酚）與核心靶點[絲氨酸/蘇氨酸蛋白激酶1（Akt1）、非受體酪氨酸激酶（SRC）、雌激素受體1（ESR1）、基質(zhì)金屬蛋白酶-9（MMP9）、核因子κB亞基1（NFKB1）、糖原合成酶激酶-3β（GSK-3β）、前列腺素內(nèi)過氧化物合酶2（PTGS2）和Jun原癌基因]進行分子對接，結(jié)果顯示仙鶴草的5種活性成分與糖皮質(zhì)激素性骨質(zhì)疏松癥核心靶點的結(jié)合活性均較好。通過斑馬魚幼魚骨形成抑制模型驗證仙鶴草水提液可部分通過PI3K/Akt信號通路緩解糖皮質(zhì)激素所致的斑馬魚幼魚骨形成抑制的現(xiàn)象。結(jié)論 揭示了仙鶴草治療糖皮質(zhì)激素性骨質(zhì)疏松癥的有效成分和作用機制，為仙鶴草治療糖皮質(zhì)激素性骨質(zhì)疏松癥的臨床實踐及后續(xù)研究提供參考。

Objective To explore the mechanism of action of Agrimoniae Herba in treatment of glucocorticoid-induced osteoporosis based on network pharmacology, molecular docking technology, and zebrafish experiments. Methods Preliminary screening of active ingredients related to Agrimoniae Herba was conducted through the TCMSP and the HERB database; Using SEA and SwissTarget Prediction platforms to predict the relevant targets of active ingredients. Retrieve disease targets related to glucocorticoid induced osteoporosis using OMIM and GeneCards databases. Using the Venn diagram drawing platform to obtain common key targets, and importing the information into Cytoscope 3.7.2 software and STRING online analysis platform for visualization analysis. Perform GO and KEGG enrichment analysis using the DAVID database. Molecular docking validation was performed on the active ingredient core target using Autodock Tools 1.5.6 software. Through a zebrafish larval bone formation inhibition model, the effects and mechanisms of the water extract of Agrimoniae Herba on glucocorticoid-induced inhibition of bone formation in zebrafish larvae were explored. Results 32 Active ingredients of Agrimoniae Herba were screened through TCMSP and HERB databases, with the top 5 core active ingredients (quercetin, luteolin, ellagic acid, apigenin, and kaempferol). After prediction and screening using SEA and SwissTarget Prediction platforms, 277 drug targets were identified. A total of 654 potential disease targets related to glucocorticoid induced osteoporosis were retrieved through OMIM and GeneCards databases. Enrichment analysis of the 36 intersecting targets of drugs and diseases showed that GO functional enrichment mainly included positive regulation of transcription by RNA polymerase II, signal transduction, and positive regulation of DNA-templated processes. KEGG enrichment pathways were mainly focused on the PI3K/Akt signaling pathway. Molecular docking of the top 5 effective components (quercetin, luteolin, ellagic acid, apigenin, and kaempferol) with the core targets (Akt1, SRC, ESR1, MMP9, NFKB1, GSK-3β, PTGS2, and JUN) showed had good binding activity. The zebrafish larval bone formation inhibition model verified that the water extract of Agrimoniae Herba could partially alleviate the glucocorticoid-induced inhibition of bone formation in zebrafish larvae through the PI3K/Akt signaling pathway. Conclusion This study preliminarily revealed the effective ingredients and mechanism of action of Agrimoniae Herba in treatment of glucocorticoid-induced osteoporosis, providing reference for the clinical practice and subsequent research of Agrimoniae Herba in treatment of glucocorticoid-induced osteoporosis.

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廣東省醫(yī)學科學技術(shù)研究基金資助項目（A2024138）；廣東省藥學會科學研究基金資助項目（2023QNTJ39）