基于网络药理学及分子对接探讨桂枝去芍药加麻辛附子汤治疗脓毒症心功能障碍的作用机制
Investigating the Mechanism of Cinnamon Twig Decoction without Peony Combined with Ephedra and Aconite and Asarum Decoction in Treating SIMD Based on Network Pharmacology and Molecular Docking

目的：本研究通过网络药理学及分子对接对桂枝去芍药加麻辛附子汤的有效成分及其治疗脓毒症心功能障碍的作用机制进行研究。方法：首先利用TCMSP平台检索桂枝去芍药加麻辛附子汤中7味中药的活性成分，在PubChem、Swiss Target Prediction平台搜索得到药物作用靶点，并通过GeneCards等数据库筛选出治疗SIMD的疾病作用靶点，取药物–疾病交集靶点通过Cytoscape 3.10.2构建药物–成分–靶点–疾病网络，从中筛选得到关键活性成分，并将交集靶点导入STRING数据库中处理后将结果导入Cytoscape软件，进一步构建PPI网络图，筛选到核心靶点，再将交集靶点通过DAVID数据库处理后进行GO功能富集分析，最后将从药物–成分–靶点–疾病网络中进行拓扑分析得到的关键活性成分与核心靶点进行分子对接。结果：分析结果显示根据条件筛选后得到甘草查尔酮A、6-甲基姜二醇双乙酸2、2,7-二去乙酰基-2,7-二苯酰–云南紫杉宁等142个中药活性成分和1172个药物成分作用靶点和2379个疾病作用靶点，取交集得到丝氨酸/苏氨酸激酶1、肿瘤坏死因子、白介素-6、甘油醛-3-磷酸脱氢酶等418个交集靶点，基因本体(GO)功能富集分析得到1746个GO条目(P < 0.01)，京都基因与基因组百科全书(KEGG)通路富集分析富集得到197条信号通路(P < 0.05)，主要涉及癌症中的通路、脂质与动脉粥样硬化、AGE-RAGE信号通路在糖尿病并发症中的作用等信号通路。分子对接结果显示关键活性成分与核心靶点均能稳定结合，结合能均<？5.0 kcal/mol。结论：研究初步揭示了桂枝去芍药加麻辛附子汤通过多成分–多靶点–多通路治疗SIMD的作用机制，为桂枝去芍药加麻辛附子汤治疗SIMD提供了理论依据。
Objective: This study investigated the effective components and action mechanisms of cinnamon twig decoction without peony combined with ephedra and aconite and asarum decoction in treating sepsis-induced myocardial dysfunction (SIMD) through network pharmacology and molecular docking. Methods: First, the active components of the seven herbs in cinnamon twig decoction without peony combined with ephedra and aconite and asarum decoction were retrieved using the TCMSP platform. The drug targets were identified via PubChem and Swiss Target Prediction platforms, while disease targets for SIMD were selected using GeneCards and other databases. The intersection of drug and disease targets was used to construct a drug-component-target-disease network using Cytoscape 3.10.2. Key active components were then selected from this network. The intersection targets were further processed in the STRING database and used to construct a PPI network in Cytoscape. Core targets were identified, and GO function enrichment analysis was performed using the DAVID database. Finally, molecular docking was conducted between the key active components and core targets identified through topological analysis of the drug-component-target-disease network. Results: The analysis revealed 142 active compounds, including licochalcone A, 6-methylgingediacetate2, and 2,7-Dideacetyl-2,7-dibenzoyl-taxayunnanine F, as well as 1172 drug targets and 2379 disease targets. The intersection of these targets included 418 common targets, such as serine/threonine kinase 1, tumor necrosis factor (TNF), interleukin-6 (IL-6), and