Search

Abstract Background Deciphering the in vivo processes of traditional Chinese medicine (TCM) is crucial for identifying new pharmacodynamic substances and new drugs. Due to the complexity and diversity of components, investigating the exposure, metabolism, and disposition remains a major challenge in TCM research. In recent years, a number of non-targeted smart mass-spectrometry (MS) techniques, such as precise-and-thorough background-subtraction (PATBS) and metabolomics, have realized the intelligent identification of in vivo components of TCM. However, the metabolites characterization still largely relies on manual identification in combination with online databases. Results We developed a scoring approach based on the structural similarity and minimal mass defect variations between metabolites and prototypes. The current method integrates three dimensions of mass spectral data including m/z, mass defect of MS1 and MS2, and the similarity of MS2 fragments, which was sequentially analyzed by a R-based mass dataset relevance bridging (MDRB) data post-processing technique. The MDRB technology constructed a component relationship network for TCM, significantly improving metabolite identification efficiency and facilitating the mapping of translational metabolic pathways. By combining MDRB with PATBS through this non-targeted identification technology, we developed a comprehensive strategy for identification, characterization and bridging analysis of TCM metabolite in vivo. As a proof of concept, we adopted the proposed strategy to investigate the process of exposure, metabolism, and disposition of Semen Armeniacae Amarum (CKXR) in mice. Significance The currently proposed analytical approach is universally applicable and demonstrates its effectiveness in analyzing complex components of TCMs in vitro and in vivo. Furthermore, it enables the correlation of in vitro and in vivo data, providing insights into the metabolic transformations among components sharing the same parent nucleus structure. Finally, the developed MDRB platform is publicly available for ( https://github.com/933ZhangDD/MDRB ) for accelerating TCM research for the scientific community. Graphical Abstract

Lianhuaqingwen (LHQW) capsules, a Chinese patent medicine composed of 13 herbal ingredients, are widely used for respiratory diseases. However, the complex composition of LHQW poses challenges in assessing its quality and consistency. In this study, a comprehensive network of LHQW was constructed by integrating Digital RNA with pertUrbation of Genes (DRUG)-seq, RNA sequencing, and pharmacodynamic data. This approach enables rapid and systematic screening of compounds in LHQW that exhibit high-exposure in vivo and significant activity potential, serving as potential quality control markers. Specifically, DRUG-seq was employed to evaluate gene expression alterations in peripheral blood mononuclear cells derived from healthy volunteers. Ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS) identified 505 compounds in LHQW-treated rats. Additionally, absorption, distribution, metabolism, and excretion (ADME) profiles were plotted for 27 primary components of LHQW. Furthermore, an HPLC-MS/MS method quantified 46 compounds from LHQW, with 15 of them identified as potential quality markers with high exposure levels. These markers exhibited significant inhibitory effects on lipopolysaccharide (LPS)-induced pneumonia in mice, with mechanisms predicted by RNA-seq and verified by RT-qPCR. In summary, this study successfully constructed an ''Herbs- in vivo Compounds-targets-pathways'' network, offering novel insights into the mechanisms of LHQW and establishing a foundation for enhancing quality control measures.