目的 基于辣木Moringa oleifera叶的特征图谱并结合网络药理学，筛选辣木叶的质量标志物（quality markers，Q-Marker），建立其定量分析方法，为辣木叶的质量评价提供科学依据。方法 采用HPLC法建立辣木叶的特征图谱，条件如下：Phenomenex Luna C₁₈(2) 100A色谱柱（250 mm×4.6 mm，5 µm），流动相乙腈（A）-0.5%甲酸水溶液（B）梯度洗脱，检测波长254 nm，体积流量0.8 mL/min，柱温35 ℃，进样量5 µL。应用化学计量学方法筛选不同产地的辣木叶差异标志物，基于网络药理学获取辣木叶特征成分的关键靶点及关键通路，绘制出“特征成分-关键靶点-关键通路”网络，以预测辣木叶的调血脂Q-Marker。再以15批辣木叶药材为研究对象，对质量标志物进行含量测定。以五原则为核心确定出最终的Q-Marker。结果 建立了15批辣木叶药材的特征图谱，结合高效液相色谱-四级杆飞行时间串联质谱法（high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry，HPLC-Q-TOF-MS/MS）确认了8个共有峰，通过对照品指认出其中6个色谱峰，分别为新绿原酸、L-色氨酸、隐绿原酸、维采宁-2、异槲皮素、紫云英苷；经化学计量学分析，初步确认新绿原酸、L-色氨酸、隐绿原酸、异槲皮素、维采宁-2为不同产地的差异性标志物。经网络药理学确认新绿原酸、L-色氨酸、隐绿原酸、异槲皮素、维采宁-2为活性成分，可作用于白细胞介素6（interleukin-6，IL-6）、白细胞介素-2（interleukin-2，IL-2）、基质金属蛋白酶9（matrix metalloprotein 9，MMP9）等5个核心靶点，影响3条关键通路[细胞因子-细胞因子受体相互作用、产生免疫球蛋白A（IgA）的肠道免疫网络、磷脂酰肌醇3-激酶/蛋白激酶B（phosphoinositide 3 kinase-protein kinase B，PI3K-Akt）信号通路]发挥调血脂作用。结合“五原则”初步预测新绿原酸、L-色氨酸、隐绿原酸、异槲皮素、维采宁-2为辣木叶调血脂的潜在的Q-Marker，质量分数分别为5.81、1.91、2.23、6.59、0.94 mg/g。结论 建立的质量评价方法准确可靠，在此基础上结合网络药理学所筛选出的质量标志物可为辣木叶的质量控制提供参考，为阐明其药效物质基础的作用机制奠定基础。

Objective Based on the specific chromatogram and network pharmacology, the quality markers (Q-Markers) of Moringa oleifera leaves (MOLs) were screened and its quantitative analysis methods were established to provide scientific basis for the quality evaluation of MOLs. Methods The specific chromatogram was established by high-performance liquid chromatography (HPLC) on Phenomenex Luna C₁₈(2)100A column (250 mm × 4.6 mm, 5 μm), mobile phase acetonitrile (A) -0.5% formic acid aqueous solution (B) for gradient elution, detection wavelength was 254 nm, the flow rate was 0.8 mL/min, the column temperature was 35 ℃, and the injection volume was 5 μL. Then, the Q-Markers of MOLs from different habitats were screened based on chemometrics. In order to predict the Q-Markers for blood lipid-lowering effect of MOLs, the network of “characteristic component-key target-key pathway” was drawn based on key targets and pathways obtained from network pharmacology. Moreover, 15 batches of MOLs were taken as the research object, and the content of Q-Marker was determined. The final Q-Markers were determined taking “five principles” as the core. Results The specific chromatogram of 15 batches were established, and eight common peaks were identified by high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (HPLC-Q-TOF-MS/MS), and six chromatographic peaks (neochlorogenic acid, L-tryptophan, cryptochlorogenic acid, vicenin-2, isoquercetin and astragalin) were identified by reference substance. By chemometrics analysis, it was confirmed that neochlorogenic acid, L-tryptophan, cryptochlorogenic acid, isoquercetin and vicenin-2 were the Q-Markers in different habitats. Network pharmacology confirmed that neochlorogenic acid, L-tryptophan, cryptochlorogenic acid, isoquercetin and vicenin-2 were the active components. They can act on five core targets including interleukin-6 (IL6), interleukin-2 (IL2) and matrix metalloproteinase (MMP9), and affect three core pathways [cytokine-cytokine receptor interaction, intestinal immune network that produces immunoglobulin A (IgA), and phosphatidylinositol 3-kinase-protein kinase/protein kinase B (PI3K-Akt) signaling pathway]. It was preliminarily predicted that neochlorogenic acid, L-tryptophan, cryptochlorogenic acid, isoquercetin and vicenin-2 were potential Q-Markers for blood lipid-lowering effect of MOLs, the content was 5.81, 1.91, 2.23, 6.59, 0.94 mg/g, respectively. Conclusion The quality evaluation method established in this paper is accurate and reliable, and the Q-Markers screened by network pharmacology can provide reference for the quality control of MOLs, which lays a foundation for elucidating the mechanism of action of its pharmacodynamic material basis.

云南省重大科技专项计划项目（2017ZF004）