Codonopsis radix was commonly used as food materials or herbal medicines in many countries. However, the comprehensive analysis of chemical constituents, and in vivo xenobiotics of Codonopsis radix remain unclear. In the present study, an integrated strategy with feature-based molecular networking using ultrahigh-performance liquid chromatography coupled with mass spectrometry was established to systematically screen the chemical constituents and the in vivo xenobiotics of Codonopsis radix. A step-by-step manner based on a composition database, visual structure classification, discriminant ions, and metabolite software prediction was proposed to overcome the complexities due to the similar structure of chemical constituents and metabolites of Codonopsis radix. As a result, 103 compounds were tentatively characterized, 20 of which were identified by reference standards. Besides, a total of 50 xenobiotics were detected in vivo, including 26 prototypes and 24 metabolites, while the metabolic features of the pyrrolidine alkaloids were elucidated for the first time. The metabolism reactions of pyrrolidine alkaloids and sesquiterpene lactones included oxidation, methylation, hydration, hydrogenation, demethylation, glucuronidation, and sulfation. This study provided a generally applicable approach to the comprehensive investigation of the chemical and metabolic profile of traditional Chinese medicine and offered reasonable guidelines for further screening of quality control indicators and pharmacodynamics mechanism of Codonopsis radix.

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Introduction: Alismatis rhizoma (AR), a distinguished diuretic traditional Chinese herbal medicine, is widely used for the treatment of diarrhea, edema, nephropathy, hyperlipidemia, and tumors in clinical settings. Most beneficial effects of AR are attributed to the major triterpenoids, whose contents are relatively high in AR. To date, only 25 triterpenoids in AR have been characterized by LC-MS because the low-mass diagnostic ions are hardly triggered in MS, impeding structural identification. Herein, we developed an advanced data post-processing method with abundant characteristic fragments (CFs) and neutral losses (NLs) for rapid identification and classification of the major triterpenoids in AR by UPLC-Q-TOF-MS E .Objective: We aimed to establish a systematic method for rapid identification and classification of the major triterpenoids of AR.Methods: UPLC-Q-TOF-MS E coupled with an advanced data post-processing method was established to characterize the major triterpenoids of AR. The abundant CFs and NLs of different types of triterpenoids were discovered and systematically summarized. The rapid identification and classification of the major triterpenoids of AR were realized by processing the data and comparing with information described in the literature.Results: In this study, a total of 44 triterpenoids were identified from AR, including three potentially new compounds and 41 known ones, which were classified into six types. Conclusion:The newly established approach is suitable for the chemical profiling of the major triterpenoids in AR, which could provide useful information about chemical constituents and a basis for further exploration of its active ingredients in vivo.