Comprehensive metabolic profiling of modified gegen qinlian decoction by ultra-high-performance liquid chromatography-diode array detection-Q-exactive-orbitrap-electrospray ionization-mass spectrometry/mass spectrometry and application of high-performance thin-layer chromatography for its fingerprint analysis

Abstract

Objective: Gegen Qinlian decoction (GQD) is a classical traditional Chinese medicine formulation which has been used for almost 2000 years. At Guang'anmen Hospital, Beijing, a modified GQD version (mGQD) with seven instead of four herbal ingredients has been applied to treat Type 2 diabetes. Quality control is a crucial prerequisite for the therapeutic application of herbal medicines. For the identification of products derived from classical GQD, the Chinese Pharmacopeia requires the analysis of only three marker compounds. Because mGQD is a more complex mixture containing seven herbs and hundreds of constituents, the pharmacopoeia method for GQD is inadequate. Materials and Methods: A more comprehensive characterization of the formula's constituents has been developed using ultra-high-performance liquid chromatography-diode array detection (UHPLC-DAD)-Q-Exactive-mass spectrometry (MS) in electrospray ionization positive and negative mode. Moreover, a new method for the fingerprint analysis of mGQD via high-performance thin-layer chromatography (HPTLC) has been established. Results: Altogether, 91 compounds have been assigned to their originating plants and 84 substances were identified either by comparison with authentic references or with data from the literature. The HPTLC method is based on the application of two different mobile phases and is able to detect both lipophilic and hydrophilic constituents of mGQD. Conclusions: The modified GQD was extensively characterized by UHPLC combined with DAD and Q-Exactive Orbitrap high-resolution MS detection, leading to the assignment and identification of compounds present in the decoction. In addition, a new method for the fingerprint analysis of the mGQD using HPTLC was established, which allows fast and simple identification of the herbal ingredients in the mixture.