A strategy for intelligent chemical profiling-guided precise quantitation of multi-components in traditional Chinese medicine formulae-QiangHuoShengShi decoction

Intelligent characterization multi-components in Yangxinshi tablet by online comprehensive two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry combined with deep learning-assisted mass defect filtering classification and multidimensional data annotation strategy

A comprehensive characterization strategy for the intelligent analysis of multiple chemical components in Yangxinshi Tablet (YXST) was established. The strategy developed the deep learning-assisted mass defect filtering intelligent classification, preferred ions capture list and active exclusion (DLA-MDF-PIL-AE) data acquisition mode by online comprehensive two-dimensional liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (2DLC-Q-TOF-MS/MS). Firstly, the online 2DLC-Q-TOF-MS/MS system was constructed and the orthogonality was evaluated. Secondly, the user interface for deep learning-assisted MDF intelligent classification technology was developed and applied to compounds classification to generate preferred ion capture lists of various types compounds. Finally, molecular networking (MN), associated neutral loss (NL) fragments, and characteristic diagnosis ion (CDI) were utilized for the automatic and manual annotation of compounds, respectively. As a result, a total of 228 compounds including 80 flavanoids, 52 alkaloids, 36 phenolic acids, 15 terpenoids, 17 saponins and 28 others were preliminary identified from YXST and source attribution was assigned to them. Furthermore, 39 compounds were simultaneously quantified by ultra-high performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-MS/MS) method. Conclusively, the proposed integrated strategy proved to be a powerful method for characterizing multiple components in complex natural products.

Digital intelligence technology: new quality productivity for precision traditional Chinese medicine

Traditional Chinese medicine is a complex medical system characterized by multiple metabolites, targets, and pathways, known for its low drug resistance and significant efficacy. However, challenges persist within Traditional Chinese medicine, including difficulties in assessing the quality of Botanical drugs, reliance on experiential knowledge for disease diagnosis and treatment, and a lack of clarity regarding the pharmacological mechanisms of Traditional Chinese medicine. The advancement of digital intelligence technology is driving a shift towards precision medicine within the Traditional Chinese medicine model. This transition propels Traditional Chinese medicine into an era of precision, intelligence, and digitalization. This paper introduces standard digital intelligence technologies and explores the application of digital intelligence technologies in quality control and evaluation of Traditional Chinese medicine, studies the research status of digital intelligence technologies in assisting diagnosis, treatment and prevention of diseases, and further promotes the application and development of digital intelligence technologies in the field of Traditional Chinese medicine.

Enhancing coverage of annotated compounds in traditional Chinese medicine formulas: Integrating MSE and Fast-DDA molecular network with AntDAS-Case study of Xiao Jian Zhong Tang

The chemical characterisation of traditional Chinese medicine formulas (TCMFs) using mass spectrometry poses notable challenges owing to their complex and diverse chemical compositions. While acquisition modes such as data-dependent acquisition (DDA) and data-independent acquisition (DIA) offer new insights, DDA's tendency to overlook low-abundance ions and DIA's complicated data processing, particularly in matching MS1 and MS2 information, limit the effective annotation of valuable compounds in TCMFs. Herein, we present a new integrated strategy to enhance the coverage of annotated compounds in TCMFs, using Xiao Jian Zhong Tang (XJZ) as a case study. First, we characterised the components of XJZ through UNIFI software in Fast-DDA and DIA modes. We then summarised the diagnostic ions and substituent information of the identified compounds based on the Fast-DDA data, integrating molecular networks and AntDAS to predict unknown components and uncover potential components. Ultimately, we characterised a total of 785 components in XJZ, including 43 that were unique to XJZ when compared to the individual herbs involved. The presence of these new components may result from the recombination of substituents during compatibility. In conclusion, this new integrated strategy facilitates more in-depth characterisation of components in TCMFs, providing a new direction for exploring the compatibility principles among TCMFs.

AI empowering traditional Chinese medicine?

For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.

Chemical profiling and quality evaluation of Liuweizhiji Gegen-Sangshen oral liquid by UPLC-Q-TOF-MS and HPLC-diode array detector fingerprinting

INTRODUCTION

Liuweizhiji Gegen-Sangshen (LGS) oral liquid is a Chinese patent medicine that is widely used for the prevention and treatment of alcoholic liver disease in clinical practice. However, the chemical complexity of LGS has not yet been investigated.

OBJECTIVE

The aim of this study was to rapidly identify chemical constituents of LGS and establish a quality control method based on fingerprint and quantitative analysis.

METHODOLOGY

A comprehensive strategy was used by combining qualitative analysis by ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and fingerprint analysis by high-performance liquid chromatography with diode array detection (HPLC-DAD).

RESULTS

A total of 162 chemical components in LGS, including 91 flavonoids, 31 organic acids, and 20 phenolic compounds, were identified or preliminarily characterized in both positive and negative ion modes based on the UPLC-Q-TOF-MS results. Of these, 37 were confirmed with the reference standards. In fingerprint analysis, 23 peaks were chosen as common peaks and used to evaluate the similarity of different batches of LGS. Subsequently, a rapid quantification method was optimized and validated for the simultaneous determination of multiple chemical markers in LGS. The validated quantitative method was successfully used to analyze different batches of LGS samples.

CONCLUSION

The proposed comprehensive strategy combining HPLC-DAD fingerprinting and multi-component quantification demonstrated satisfactory results with high efficiency, accuracy, and reliability. This can be used as a reference for the overall quality consistency evaluation of Chinese patent medicines.

Quality Consistency Evaluation of Traditional Chinese Medicines: Current Status and Future Perspectives

Quality consistency evaluation of traditional Chinese medicines (TCMs) is a crucial factor that determines the safe and effective application in clinical settings. However, TCMs exhibit diverse, heterogeneous, complex, and flexible chemical compositions, as well as variability in preparation processes. These characteristics pose greater challenges in researching the consistency of TCMs compared to chemically synthesized and biological drugs. Therefore, it is paramount to develop effective strategies for evaluating the quality consistency of TCMs. From the starting point of quality properties, this review explores the strategy used to evaluate quality consistency in terms of chemistry-based strategy (chemical consistency) and the biology-based strategy (bioequivalence). Among them, the chemistry-based strategy is the mainstream, and biology-based strategy complements the chemistry-based strategy each other. Furthermore, the emerging chemistry-biology strategies (overall evaluation) is discussed, including individually combining strategy and integration strategy. Finally, this review provides insights into the challenges and future perspectives in this field. By highlighting current status and trends in TCMs quality consistency, this review aims to contribute to establishment of generally applicable chemistry-biology integrated evaluation strategy for TCMs. This will facilitate the advancement toward a higher stage of overall quality evaluation.

A molecular networking-assisted automatic database screening strategy for comprehensive annotation of small molecules in complex matrices

Liquid chromatography-tandem with high-resolution mass spectrometry (LCHRMS) has proven challenging for annotating multiple small molecules within complex matrices due to the complexities of chemical structure and raw LCHRMS data, as well as limitations in previous literatures and reference spectra related to those molecules. In this study, we developed a molecular networking assisted automatic database screening (MN/auto-DBS) strategy to examine the combined effect of MS1 exact mass screening and MS2 similarity analysis. We compiled all previously reported compounds from the relevant literatures. With the development of a Python software, the in-house database (DB) was created by automatically calculating the m/z and data from experimental MS1 hits were rapid screened with DB. We then performed a feature-based molecular network analysis on the auto-MS2 data for supplementary identification of unreported compounds, including clustered FBMN and annotated GNPS compounds. Finally, the results from both strategies were merged and manually curated for correct structural assignment. To demonstrate the applicability of MN/auto-DBS, we selected the Huangqi-Danshen herb pair (HD), commonly used in prescriptions or patent medicines to treat diabetic nephropathy and cerebrovascular disease. A total of 223 compounds were annotated, including 65 molecules not previously reported in HD, such as aromatic polyketides, coumarins, and diarylheptanoids. Using MN/auto-DBS, we can profile and mine a wide range of complex matrices for potentially new compounds.

A global profiling strategy for identification of the total constituents in Chinese herbal medicine based on online comprehensive two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry combined with intelligentized chemical classification guidance

A comprehensive strategy for effective identification of total constituents in Chinese patent medicine has been advanced applying full scan-preferred parent ions capture-static and active exclusion (FS-PIC-SAE) acquisition coupled with intelligent deep-learning supported mass defect filter (MDF) process, with Naoxintong capsule (NXT) as a case. Online comprehensive two-dimensional liquid chromatography (2DLC) coupled with Q-TOF-MS/MS system was established for obtaining the excellent separation and detection performance of total components, which could exhibit excellent peak capacity with 1052 and orthogonality with 0.69. In addition, a total of 901 unknown compounds could be classified into nine chemical classes rapidly and effectively, based on the intelligent deep-learning algorithm supported MDF model with 96.4% accuracy. Consequently, 276 compounds were successfully identified from NXT, especially including 44 flavonoids, 27 phenolic acids, 25 fatty acids, 17 saponins, 21 phthalocyanines, 20 triterpenes, 10 monoterpenes, 13 diterpenoid ketones, 14 amino acids, and others. It is concluded that the proposed program is an effective and practical strategy enabling the in-depth chemical profiling of complex herbal and biological samples.

Identification and quantitative analysis of the chemical constituents of Gandouling tablets using ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry

Gandouling tablets are used in a clinical agent for the treatment of hepatocellular degeneration; however, their chemical constituents have not been elucidated. Here, we screened and identified the chemical constituents of Gandouling tablets using ultra-high-performance liquid chromatography (UHPLC)-quadrupole time of flight/mass spectrometry. A method for the quality evaluation of Gandouling tablets was developed by combining the UHPLC fingerprints and the simultaneous quantitative analysis of multiple active ingredients. For fingerprint analysis, 20 shared peaks were identified to assess the similarities among the 10 batches of Gandouling tablets and the similarity was >0.9. The levels of nine representative active ingredients were simultaneously determined to ensure consistency in quality. A total of 99 chemical components were identified, including 18 alkaloids, 20 anthraquinones, 13 flavonoids, 11 phenolic acids, 9 polyphenols, 7 phenanthrenes, 5 sesquiterpenes, 3 curcuminoids, 2 lignans, 2 isoflavones, 2 dianthranones, and 7 other components. The retention times, molecular formulae, and secondary fragmentation information of these compounds were analyzed, and the cleavage pathways and characteristic fragments of some of the representative compounds were elucidated. This systematic analysis used to identify the chemical components of Gandouling tablets lays the foundation for its further quality control and research on their pharmacodynamic substances.

Integrated gut microbiota and metabolome analysis reveals the mechanism of Xiaoai Jiedu recipe in ameliorating colorectal cancer

Introduction

Xiaoai Jiedu recipe (XJR), a classical prescription of traditional Chinese medicine (TCM), has been clinically proven to be effective in ameliorating colorectal cancer (CRC). However, its exact mechanism of action is still elusive, limiting its clinical application and promotion to a certain extent. This study aims to evaluate the effect of XJR on CRC and further illustrate mechanism underlying its action.

Methods

We investigated the anti-tumor efficacy of XJR in vitro and vivo experiments. An integrated 16S rRNA gene sequencing and UPLC-MS based metabolomics approach were performed to explore possible mechanism of XJR anti-CRC on the gut microbiota and serum metabolic profiles. The correlation between altered gut microbiota and disturbed serum metabolites was investigated using Pearson's correlation analysis.

Results

XJR effectively displayed anti-CRC effect both in vitro and in vivo. The abundance of aggressive bacteria such as Bacteroidetes, Bacteroides, and Prevotellaceae decreased, while the levels of beneficial bacteria increased (Firmicutes, Roseburia, and Actinobacteria). Metabolomics analysis identified 12 potential metabolic pathways and 50 serum metabolites with different abundances possibly affected by XJR. Correlation analysis showed that the relative abundance of aggressive bacteria was positively correlated with the levels of Arachidonic acid, Adrenic acid, 15(S)-HpETE, DL-Arginine, and Lysopc 18:2, which was different from the beneficial bacteria.

Discussion

The regulation of gut microbiota and related metabolites may be potential breakthrough point to elucidate the mechanism of XJR in the treatment of the CRC. The strategy employed would provide theoretical basis for clinical application of TCM.

An efficient method for qualitation and quantitation of multi-components of the herbal medicine Qingjin Yiqi Granules

Qingjin Yiqi Granules (QJYQ) is a Traditional Chinese Medicines (TCMs) prescription for the patients with post-COVID-19 condition. It is essential to carry out the quality evaluation of QJYQ. A comprehensive investigation was conducted by establishing deep-learning assisted mass defect filter (deep-learning MDF) mode for qualitative analysis, ultra-high performance liquid chromatography and scheduled multiple reaction monitoring method (UHPLC-sMRM) for precise quantitation to evaluate the quality of QJYQ. Firstly, a deep-learning MDF was used to classify and characterize the whole phytochemical components of QJYQ based on the mass spectrum (MS) data of ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS). Secondly, the highly sensitive UHPLC-sMRM data-acquisition method was established to quantify the multi-ingredients of QJYQ. Totally, nine major types of phytochemical compounds in QJYQ were intelligently classified and 163 phytochemicals were initially identified. Furthermore, fifty components were rapidly quantified. The comprehensive evaluation strategy established in this study would provide an effective tool for accurately evaluating the quality of QJYQ as a whole.

Multi-component immune knockout: A strategy for studying the effective components of traditional Chinese medicine

Periploca forrestii Schltr., a traditional Chinese medicine (TCM), is commonly used to treat autoimmune diseases such as rheumatoid arthritis (RA). However, its mechanism, involving a variety of cardiac glycosides, remains largely unknown. The immune knockout strategy can highly selectively deplete target components by immunoaffinity chromatography (IAC). We aimed to identify the common structural features of cardiac glycosides in P. forrestii and design IAC to specifically recognize these features to achieve the multi-component knockout of potential active substances from the extracts of P. forrestii. A content detection experiment confirmed that the content of a compound with periplogenin structure (CPS) in the extract of P. forrestii was reduced by 45% by IAC of periplogenin. The immunosuppressive ability of the extract on H9 human T lymphocytic cells was weakened after CPS knockout from P. forrestii extract. Molecular biology experiments showed that mRNA expression of interferon-γ (IFN-γ), interleukin-2 (IL-2), and interleukin-6 (IL-6) in H9 cells was up-regulated after CPS knockout, while no significant changes in the expression of interleukin-4 (IL-4) were found. CPS knockout from P. forrestii extract did not cause significant changes in the proliferation of lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells incubated with this extract. These results indicate that CPS exhibited immunosuppressive effects via inhibiting the T helper 1 (Th1) cell immune response and not via the anti-inflammatory components in P. forrestii. This is the first use of IAC to achieve multi-component knockout in TCM extracts for identifying effective compounds. This method is effective and reliable and warrants further exploration.

Unraveling the mystery of efficacy in Chinese medicine formula: New approaches and technologies for research on pharmacodynamic substances

Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble "mystery boxes". Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.

Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine

It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine.

Lonicerae Japonicae Flos Attenuates Neutrophilic Inflammation by Inhibiting Oxidative Stress

Lonicerae japonicae flos (LJ) is an Asian traditional herb that is used as a dietary supplement, tea, and beverage to clear heat and quench thirst. However, no studies investigated its effect on activated human neutrophils, which played a crucial role in the bad prognosis of coronavirus disease of 2019 (COVID-19) patients by aggravating lung inflammation and respiratory failure. Herein, we evaluated the anti-inflammatory effect of LJ ethanol extract (LJEE) on human neutrophils activated by N-formyl-methionyl-leucyl-phenylalanine (fMLF). Our experimental results indicated that LJEE suppressed fMLF-activated superoxide anion (O₂^•−) generation, the expression of CD11b, and cell adhesion and migration, as well as the formation of neutrophil extracellular traps in human neutrophils. Further in-depth mechanical investigation revealed that pretreatment with LJEE accelerated the Ca²⁺ clearance, but did not affect the phosphorylation of mitogen-activated protein kinases (MAPKs) and protein kinase B (Akt) in activated human neutrophils. In addition, LJEE displayed a dose-dependent reactive oxygen species (ROS) scavenger activity, which assisted its anti-inflammatory activity. From the bioassay-coupled chromatographic profile, chlorogenic acids were found to dominate the anti-inflammatory effects of LJEE. Moreover, LJ water extract (LJWE) demonstrated an interrupting effect on the severe acute respiratory syndrome coronavirus-2 spike protein (SARS-CoV-2-Spike)/angiotensin-converting enzyme 2 (ACE2) binding. In conclusion, the obtained results not only supported the traditional use of LJ for heat-clearance, but also suggested its potential application in daily health care during the COVID-19 pandemic.

Prinsepiae Nux Extract Activates NRF2 Activity and Protects UVB-Induced Damage in Keratinocyte

Ultraviolet B (UVB) is one of the most important environmental factors that cause extrinsic aging through increasing intracellular reactive oxygen species (ROS) production in the skin. Due to its protective roles against oxidative stress, nuclear factor erythroid-2-related factor (NRF2) has been traditionally considered as a target for skin aging prevention. Here, we identified the extract of Prinsepiae Nux, a top-grade drug listed in Shen Nong Ben Cao Jing, as a potent NRF2 activator by high-throughput screening. A bioassay-guided fractionation experiment revealed that NRF2-activating components were concentrated in the 90% methanol (MP) fraction. MP fraction significantly increased the expression of NRF2 and HO-1 protein and upregulated HO-1 and NQO1 mRNA expression in HaCaT cells. Moreover, MP fraction pre-treatment dramatically reversed UVB-induced depletion of NRF2 and HO-1, accumulation of intracellular ROS, NF-κB activation, and the upregulation of pro-inflammatory genes. Finally, the qualitative analysis using UPLC-tandem mass spectroscopy revealed the most abundant ion peak in MP fraction was identified as α-linolenic acid, which was further proved to activate NRF2 signaling. Altogether, the molecular evidence suggested that MP fraction has the potential to be an excellent source for the discovery of natural medicine to treat/prevent UVB-induced skin damage.

Quality Evaluation of Traditional Chinese Medicine Prescription in Naolingsu Capsule Based on Combinative Method of Fingerprint, Quantitative Determination, and Chemometrics

BACKGROUND

Naolingsu capsule (NLSC) is a well-known traditional Chinese medicine (TCM) prescription in China. It is widely used to treat neurasthenia, insomnia, cardiovascular and cerebrovascular disease, and other diseases. However, its inalienable chemical groups have not been carried out.

METHODS

We first established the nontargeted investigation based on fingerprinting coupled with UHPLC-Q/TOF-MS/MS. Second, the quantitative methods based on HPLC-DAD and LC-MS/MS were connected to the synchronous quantitative assurance of eleven and fourteen marker compounds. Finally, the quantitative information was processed with SIMCA-P for differentiating the distinctive bunches of samples to screen the foremost appropriate chemical markers.

RESULTS

The similarity of HPLC fingerprints of 24 batches of NLSC samples was 0.645-0.992. In total, 37 flavonoids, 21 organic acids, 22 lignans, 13 saponins, and 20 other compounds were recognized in NLSC by the UHPLC-Q/TOF-MS/MS method. The quantitative determination was approved for linearity, discovery limits, accuracy, repeatability, soundness, and precision. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models accomplished the great classification of the samples from the five enterprises, respectively. Rehmannioside D (RD), methylophiopogonanone A (MPA), 3,6'-disinapoyl sucrose (DS), schisandrin B (SSB), epimedin C (EC), icariin (ICA), and jujuboside B (JB) were considered as the potential chemical markers for NLSC quality control.

CONCLUSION

The experimental results illustrated that the combinative strategy was valuable for quick pharmaceutical quality assessment, which can potentially differentiate the origin, decide the realness, and assess the overall quality of the formulation.

Characterization of effective phytochemicals in traditional Chinese medicine by mass spectrometry

Traditional Chinese medicines (TCMs) have been widely used in clinical and healthcare applications around the world. The characterization of the phytochemical components in TCMs is very important for studying the therapeutic mechanism of TCMs. In the analysis process, sample preparation and instrument analysis are key steps to improve analysis performance and accuracy. In recent years, chromatography combined with mass spectrometry (MS) has been widely used for the separation and detection of trace components in complex TCM samples. This article reviews various sample preparation techniques and chromatography-MS techniques, including the application of gas chromatography-MS and liquid chromatography-MS and other MS techniques in the characterization of phytochemicals in TCM materials and Chinese medicine products. This article also describes a new ambient ionization MS method for rapid and high-throughput analysis of TCM components.

Delineating the dynamic metabolic profile of Qi-Yu-San-Long decoction in rat urine using UPLC-QTOF-MSE coupled with a post-targeted screening strategy

Qi-Yu-San-Long decoction (QYSLD) is a traditional Chinese medicine that has been clinically used in the treatment of non-small-cell lung cancer (NSCLC) for more than 20 years. However, to date, metabolic-related studies on QYSLD have not been performed. In this study, a post-targeted screening strategy based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight full information tandem mass spectrometry (UPLC-QTOF-MS^E) was developed to identify QYSLD-related xenobiotics in rat urine. The chemical compound database of QYSLD constituents was established from previous research, and metabolites related to these compounds were predicted in combination with their possible metabolic pathways. The metabolites were identified by extracted ion chromatograms using predicted m/z values as well as retention time, excimer ions, and fragmentation behavior. Overall, 85 QYSLD-related xenobiotics (20 prototype compounds and 65 metabolites) were characterized from rat urine. The main metabolic reactions and elimination features of QYSLD included oxidation, reduction, decarboxylation, hydrolysis, demethylation, glucuronidation, sulfation, methylation, deglycosylation, acetylation, and associated combination reactions. Of the identified molecules, 14 prototype compounds and 58 metabolites were slowly eliminated, thus accumulating in vivo over an extended period, while five prototypes and two metabolites were present in vivo for a short duration. Furthermore, one prototype and five metabolites underwent the process of “appearing-disappearing-reappearing” in vivo. Overall, the metabolic profile and characteristics of QYSLD in rat urine were determined, which is useful in elucidating the active components of the decoction in vivo, thus providing the basis for studying its mechanism of action.

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A post-targeted screening strategy based on UPLC-QTOF-MS^E was developed.

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Twenty prototype compounds and 65 metabolites of QYSLD were identified in rat urine.

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The main metabolic reactions and elimination features of QYSLD were determined in vivo.

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Dynamic metabolic profiles of QYSLD-related xenobiotics at multiple time intervals were delineated.

Chemicalome and metabolome profiling of Chai-Gui Decoction using an integrated strategy based on UHPLC-Q-TOF-MS/MS analysis

Traditional Chinese medicine prescriptions are widely believed to exert therapeutic benefits via a multiple-component and multiple-target mode. The systemic profiling of their in vitro chemicalome and in vivo metabolome is of great importance for further understanding their clinical value. Herein, an integrated strategy using ultra-performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry was proposed to profile the chemicalome and metabolome of Chai-Gui Decoction. Particularly, an approach combined mass defect filter, characteristic product ion filter, and neutral loss filter was adopted to identify metabolites in plasma, urine, bile, and feces by MetabolitePilot. Consequently, a total of 174 constituents were identified or tentatively characterized and 70 metabolites that related to 21 representative structural components were matched in rat biofluids. Among them, 19 prototypes and 7 metabolites that contributed to flavonoids, monoterpenes, and phenylpropanoids were detected distribution in brain, heart, kidney, liver, lung or spleen. This study provided a generally applicable approach to comprehensive investigation on chemicalome and metabolome of traditional Chinese medicine prescriptions, and offered reasonable guidelines for further screening of quality control indicators of Chai-Gui Decoction.

Integrated analysis of the chemical-material basis and molecular mechanisms for the classic herbal formula of Lily Bulb and Rehmannia Decoction in alleviating depression

BACKGROUND

Lily Bulb and Rehmannia Decoction (LBRD), is a traditional Chinese formula that has been shown to be safe and effective against depression; however, its material basis and pharmacological mechanisms remain unknown.

METHODS

Here, ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) and high-performance liquid chromatography (HPLC) were used to identify the chemical spectrum and qualitatively identify the major active ingredients in the LBRD standard decoction, respectively. Subsequently, we assessed the behavior, neuronal function and morphology, neurotransmitter levels, hypothalamic-pituitary-adrenal (HPA)-axis associated hormones, inflammatory cytokine levels, and miRNA/mRNA expression alterations in an in vitro/vivo depression model treated by the LBRD standard decoction. Finally, miRNA/mRNA regulatory networks were created through bioinformatics analysis, followed by functional experiments to verify its role in LBRD standard decoction treatment.

RESULTS

A total of 32 prototype compounds were identified in the LBRD standard decoction, and the average quality of verbascoside in the fresh lily bulb decoction, fresh raw Rehmannia juice, and the LBRD standard decoction were 0.001264%, 0.002767%, and 0.009046% (w/w), respectively. Administration of the LBRD standard decoction ameliorated chronic unpredictable mild stress (CUMS)-induced depression-like phenotypes and protected PC12 cells against chronic corticosterone (CORT)-induced injury. The levels of neurotransmitter, cytokine, stress hormones and neuronal morphology were disrupted in the depression model, while LBRD standard decoction could work on these alterations. After LBRD standard decoction administration, four differentially expressed miRNAs, rno-miR-144-3p, rno-miR-495, rno-miR-34c-5p, and rno-miR-24-3p, and six differentially expressed mRNAs, Calml4, Ntrk2, VGAT, Gad1, Nr1d1, and Bdnf overlapped in the in vivo/vitro depression model. Among them, miR-144-3p directly mediated GABA synthesis and release by targeting Gad1 and VGAT, and miR-495 negatively regulated BDNF expression. The LBRD standard decoction can reverse the above miRNA/mRNA network-mediated GABA and BDNF expression in the in vivo/vitro depression model.

CONCLUSION

Collectively, the multi-components of the LBRD standard decoction altered a series of miRNAs in depression through mediating GABAergic synapse, circadian rhythm, and neurotrophic signaling pathway etc., thereby abolishing inhibitory/excitatory neurotransmitter deficits, recovering the pro-/anti-inflammatory cytokine levels and regulating the HPA-axis hormone secretion to achieve balance of the physiological function of the whole body.