Xue Xiaoxia, Jiao Qishu, Jin Runa, Wang Xueyuan, Li Pengyue, Shi Shougang, Huang Zhengjun, Dai Yuntao, Chen Shilin
Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
Shanxi University of Chinese Medicine, Jinzhong, 030619, Shanxi, China.
Chin Med. 2021 Jul 2;16(1):50. doi: 10.1186/s13020-021-00459-6.
It is essential to identify the chemical components for the quality control methods establishment of Chinese Classical Formula (CCF). However, CCF are complex mixture of several herbal medicines with huge number of different compounds and they are not equal to the combination of chemical components from each herb due to particular formula ratio and preparation techniques. Therefore, it is time-consuming to identify compounds in a CCF by analyzing the LC-MS/MS data one by one, especially for unknown components.
An ultra-high pressure liquid chromatography-linear ion trap-orbitrap high resolution mass spectrometry (UHPLC-LTQ-Orbitrap-MS/MS) approach was developed to comprehensively profile and characterize multi-components in CCF with Erdong decoction composed of eight herbal medicines as an example. Then the MS data of Erdong decoction was analyzed by MS/MS-based molecular networking and these compounds with similar structures were connected to each other into a cluster in the network map. Then the unknown compounds connected to known compounds in a cluster of the network map were identified due to their similar structures.
Based on the clusters of the molecular networking, 113 compounds were rapidly tentative identification from Erdong decoction for the first time in the negative mode, which including steroidal saponins, triterpenoid saponins, flavonoid O-glycosides and flavonoid C-glycosides. In addition, 10 alkaloids were tentatively identified in the positive mode from Nelumbinis folium by comparison with literatures.
MS/MS-based molecular networking technique is very useful for the rapid identification of components in CCF. In Erdong decoction, this method was very suitable for the identification of major steroidal saponins, triterpenoid saponins, and flavonoid C-glycosides.
确定中药经典方剂（CCF）质量控制方法的化学成分至关重要。然而，CCF是几种草药的复杂混合物，含有大量不同的化合物，由于特定的配方比例和制备技术，它们并不等同于每种草药化学成分的组合。因此，通过逐一分析LC-MS/MS数据来鉴定CCF中的化合物非常耗时，尤其是对于未知成分。
开发了一种超高压液相色谱-线性离子阱-轨道阱高分辨率质谱（UHPLC-LTQ-Orbitrap-MS/MS）方法，以由八种草药组成的二冬汤为例，全面分析和表征CCF中的多成分。然后通过基于MS/MS的分子网络分析二冬汤的质谱数据，将这些结构相似的化合物在网络图中相互连接成一个簇。然后，由于网络图中一个簇内与已知化合物相连的未知化合物结构相似，从而对其进行鉴定。
基于分子网络的簇，首次在负离子模式下从二冬汤中快速初步鉴定出113种化合物，包括甾体皂苷、三萜皂苷、黄酮O-糖苷和黄酮C-糖苷。此外，通过与文献比较，在正离子模式下从荷叶中初步鉴定出10种生物碱。
基于MS/MS的分子网络技术对快速鉴定CCF中的成分非常有用。在二冬汤中，该方法非常适合鉴定主要的甾体皂苷、三萜皂苷和黄酮C-糖苷。
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