目的 采用數(shù)據(jù)挖掘、網(wǎng)絡(luò)藥理學(xué)和分子對接技術(shù)，探討中藥抗動(dòng)脈粥樣硬化（AS）的用藥規(guī)律、核心靶點(diǎn)及潛在作用機(jī)制。方法 以中國學(xué)術(shù)期刊全文數(shù)據(jù)庫（CNKI）、萬方數(shù)據(jù)庫（Wanfang Data）、維普生物醫(yī)學(xué)數(shù)據(jù)庫（VIP） 3大數(shù)據(jù)庫近10年治療AS的文獻(xiàn)為數(shù)據(jù)來源，借助古今醫(yī)案云平臺(tái)及Apriori關(guān)聯(lián)規(guī)則函數(shù)等進(jìn)行數(shù)據(jù)挖掘，以確定治療AS的核心藥物；通過TCMSP、Swiss Target Prediction數(shù)據(jù)庫得到核心中藥活性成分及潛在靶點(diǎn)，與Genecards數(shù)據(jù)庫得到的疾病靶點(diǎn)取交集，利用Cytoscape3.10.0構(gòu)建核心中藥-活性成分-交集靶點(diǎn)網(wǎng)絡(luò)以及蛋白質(zhì)-蛋白質(zhì)相互作用（PPI）網(wǎng)絡(luò)，通過DAVID數(shù)據(jù)庫進(jìn)行基因本體（GO）功能和京都基因與基因組百科全書（KEGG）通路富集分析；對中藥的核心活性成分及AS的核心靶點(diǎn)進(jìn)行分子對接以驗(yàn)證其有效性。結(jié)果 從數(shù)據(jù)庫中共篩選出143首方劑，涉及201味中藥，藥物的性味以甘、苦、辛為主，歸肝、脾、心經(jīng)居多。Apriori算法結(jié)果表明，“紅花-桃仁-赤芍”為支持度和置信度較高的組合；通過篩選獲得該組合靶點(diǎn)有332個(gè)，AS靶點(diǎn)1 663個(gè)，交集靶點(diǎn)228個(gè)；核心成分為黃芩素、β-谷甾醇、沒食子酸120（GA120）、豆甾醇；核心靶點(diǎn)為腫瘤蛋白p53（TP53）、原癌基因酪氨酸蛋白激酶Src（SRC）、AKT絲氨酸/蘇氨酸激酶（AKT1）、信號轉(zhuǎn)導(dǎo)及轉(zhuǎn)錄活化因子3（STAT3）； GO分析條目共1 275個(gè)，KEGG通路富集分析共162條通路，根據(jù)KEGG分析，預(yù)測主要通過癌癥通路、AGE-RAGE信號通路、脂質(zhì)與動(dòng)脈粥樣硬化等信號通路發(fā)揮治療作用；分子對接結(jié)果顯示，藥物核心成分與核心靶點(diǎn)都有一定的結(jié)合親和力，其中SRC、AKT1靶點(diǎn)與分子結(jié)合較好。結(jié)論 “紅花-桃仁-赤芍”通過多成分、多靶點(diǎn)、多通路發(fā)揮抗AS的作用，其靶點(diǎn)可能與TP53、SRC、AKT1、STAT3有關(guān)。

Objective To explore the dosing pattern, core targets, and potential mechanism of action of traditional Chinese medicine (TCM) against atherosclerosis (AS) using data mining, network pharmacology, and molecular docking techniques. Methods Literature from the past 10 years on AS treatment was retrieved from three major databases: China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP Chinese Biomedical Database. The Ancient and Modern Medical Case Cloud Platform and Apriori association rule algorithm were used to identify core herbs for AS treatment. Active components and potential targets of core herbs were obtained from the TCMSP and Swiss Target Prediction databases. Intersection targets between herb-related targets and AS-related targets (from the Genecards database) were identified. The core herb-active component-intersection target network and protein-protein interaction (PPI) network were constructed using Cytoscape 3.10.0. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database. Molecular docking was performed to validate the binding affinity between core active components of TCM and core targets of AS. Results A total of 143 prescriptions involving 201 herbs were screened. The herbs were primarily characterized by sweet, bitter, and pungent flavors and mainly acted on the liver, spleen, and heart meridians. The Apriori algorithm indicated that the combination of Carthami Flos-Persicae Semen-Paeoniae Radix Rubra had high support and confidence levels. A total of 332 herb-related targets and 1 663 ASrelated targets were identified, with 228 intersection targets. Core active components included baicalein, β-sitosterol, gallic acid (GA120), and stigmasterol. Core targets included tumor protein p53 (TP53), proto-oncogene tyrosine-protein kinase Src (SRC), AKT serine/threonine kinase 1 (AKT1), and signal transducer and activator of transcription 3 (STAT3). GO analysis yielded 1 275 functional terms, and KEGG pathway enrichment analysis identified 162 pathways, including cancer pathways, AGE-RAGE signaling pathway, and lipid and atherosclerosis pathways. The core active components exhibited binding affinity with the core targets, with SRC and AKT1 showing particularly strong interactions. Conclusion The combination of Carthami Flos-Persicae Semen-Paeoniae Radix Rubra exerts anti-AS effects through multiple components, targets, and pathways, with potential involvement of TP53, SRC, AKT1, and STAT3. This study provides a scientific basis for the clinical application of TCM in AS treatment.

R285.5

國家自然科學(xué)基金面上項(xiàng)目（82274488）;2023科技創(chuàng)新專項(xiàng)（ DZMKJCX-2023-025）