ABSTRACT Aim of the Study Integrating metabolomics, network pharmacology, and zebrafish depression models, this study explored the pharmacodynamic material basis and potential quality markers (Q‐markers) of Yueju Wan (YJW) against depression. Methods Chronic unpredictable mild stress (CUMS) was adopted to establish a rat depression model for evaluating the antidepressant efficacy of YJW. UPLC‐QTOF‐MS/MS was utilized to characterize the chemical constituents of YJW, and relevant databases were employed to predict its potential therapeutic targets. Metabolomics analysis was performed to screen differential metabolites in serum and urine, followed by pathway enrichment analysis. A compound–target–metabolite–pathway interaction network was constructed to screen core therapeutic targets and candidate pharmacodynamic substances, and RT‐qPCR, Western blot, and zebrafish depression models were applied to validate the identified targets and pharmacological effects. Finally, a quantitative detection method for the candidate quality markers was established. Results One hundred eight chemical components and 105 core targets were characterized and predicted from YJW, respectively. YJW reversed 87 depression‐associated differential metabolites. Further integrated analysis together with RT‐qPCR and Western blot validation suggested that Alox15 and Cyp19a1 may be involved in the antidepressant‐like effects of YJW, possibly through modulation of arachidonic acid metabolism, microglial polarization, and neuroinflammatory responses. Seventeen compounds including α‐cyperone, geniposide, ferulic acid, and atractylodin were verified to exert antidepressant activities via molecular docking and zebrafish models. Ferulic acid, α‐cyperone, geniposide, and atractylodin show satisfactory detectability and are proposed as potential Q‐markers. Conclusions This study provides new evidence for the candidate antidepressant pharmacodynamic substances and potential quality markers of YJW and provides a scientific basis for the new drug development and quality control of YJW.
ABSTRACT Introduction As one of the most common chronic liver diseases worldwide, metabolic dysfunction‐associated steatotic liver disease (MASLD) imposes an escalating burden on global health systems. However, the efficacy of Guizhi Fuling decoction (GZFLD) against MASLD and its potential pharmacodynamic material basis remain elusive. Objectives This work aims to explore the potential bioactive constituents of GZFLD against MASLD by integrating a multitarget affinity ultrafiltration strategy with UPLC‐Q‐TOF‐MS/MS. Methods The efficacy of GZFLD against MASLD was initially evaluated using an in vitro HepG2 model of MASLD. Subsequently, potential active ingredients of GZFLD against MASLD were screened out via affinity ultrafiltration coupled with liquid chromatography–tandem mass spectrometry (AUF‐LC‐MS/MS) targeting multiple enzymes, including pancreatic lipase (PL), cyclooxygenase‐2 (COX‐2), proprotein convertase subtilisin/kexin type 9 (PCSK9), and interleukin‐6 (IL‐6). Finally, molecular docking simulations, enzyme inhibition assays, and cell experiments were performed to validate the activities of the candidate components. Results The results demonstrated that GZFLD significantly alleviated lipid accumulation and oxidative stress in the HepG2 model of MASLD, and a total of 18 potential bioactive ingredients were identified in GZFLD. Among them, benzoylpaeoniflorin, galloylpaeoniflorin, and ellagic acid are the main active compounds of GZFLD against MASLD. Furthermore, the results of molecular docking revealed that these components could strongly bind their corresponding target by various forces. The result of COX‐2 inhibition assay indicated that ellagic acid exhibited a strong COX‐2 inhibitory activity. Moreover, the results of cell verifications implied that ellagic acid and benzoylpaeoniflorin exhibited significant therapeutic potential on MASLD by modulating lipid metabolism and inflammatory responses. Conclusion GZFLD demonstrated noteworthy effects against MASLD by alleviating lipid accumulation and oxidative stress, and the potential bioactive components of GZFLD and their interaction network against MASLD were first explored and constructed by combining AUF‐LC‐MS/MS with multiple targets, which showcased a powerful new integrating strategy for the simultaneous screening and identification of bioactive compounds in complex Chinese medicines.
ABSTRACT Introduction Colorectal cancer (CRC) remains one of the leading causes of cancer‐related mortality worldwide. Combination therapies, which integrate traditional Chinese medicine (TCM) with conventional chemotherapeutic agents, offer a promising new strategy for the management of CRC. Objective This study aimed to investigate the potential of Aloe vera (L.) Burm.f. ( A. vera ) as an adjuvant to hydroxycamptothecin (HCPT) for CRC treatment and to explore the underlying mechanisms, specifically focusing on nucleotide metabolism, as perturbations in intracellular nucleotide pools are closely linked to various human pathologies. Methods The aqueous extract of A. vera adjuvantly ameliorated the progression of colon carcinogenesis was investigated in Apc Min/+ mouse model. For the underlying mechanism studies, cytotoxicity assessment, DNA damage, cell cycle, RNA and DNA synthesis analysis, endogenous nucleotides determination, and Western blot were performed in HCT 116 cells. Results Major constituents of A. vera , including aloin A, aloin B, aloesin, aloenin, and aloe‐emodin, were quantified and found to meet Chinese Pharmacopoeia standards. A. vera synergistically enhanced the anticancer activity of HCPT. This effect was mediated by exacerbated DNA damage, imbalanced purine/pyrimidine metabolism, and inhibition of the Wnt/β‐catenin signaling pathway. Conclusion This study provided preliminary evidence that A. vera could be developed as an adjuvant therapy for CRC, contributing to its further clinical application in this field.
ABSTRACT Introduction In‐depth profiling of plant‐derived metabolites is essential for unraveling chemically diverse group of bioactive compounds that play essential roles in plant defense, adaptation, ecological interactions, and human health. Comprehensive profiling of these metabolites is important for understanding their biochemical diversity and exploring their pharmaceutical, agricultural, and industrial potentials. Metabolomics is an emerging platform for qualitative and quantitative assessment of metabolites, employing GC–MS, LC–MS, and NMR. Objective To compare and critically assess the extraction, data acquisition, and data‐processing protocols including chemometric analysis and applications of metabolomics. Methodology Metabolomics studies largely depend on optimized sample collection, extraction procedures, analytical platforms, and data‐processing workflows for the chemical profiling of plant‐derived metabolites. Following metabolite extraction from different plant parts, analytical data generated by GC–MS, LC–MS, and NMR are further analyzed, employing chemometric statistical approaches for identification of metabolic signatures, classification of plant samples, and to reveal biochemical variations associated with genetic and environmental factors. Results This review focuses on several applications of metabolomics in plant sciences, including elucidating plant responses to abiotic and biotic stresses, evaluating the effects of nutrient availability on metabolite biosynthesis, investigating genetic and phenotypic diversity among wild‐type, cultivated, and transgenic plants, supporting crop improvement and metabolic engineering, authenticating medicinal plants, ensuring quality control, and accelerating the discovery of bioactive natural products. Conclusion This review provides comprehensive comparison of various extraction and characterization techniques of secondary metabolites, which will provide valuable insights as well as further directions to researchers working in allied field.
INTRODUCTION:Anoectochilus roxburghii (AR) is a prized medicinal herb valued for its hepatoprotective effects. Its quality varies depending on geographical origin. The primary bioactive constituents include rutin, quercetin-7-O-glucoside, kaempferol-3-O-rutinoside, narcissin, quercetin, and kinsenoside. A method that enables simultaneous determination of both the content and bioactivity of the herb is therefore essential for effective quality control. OBJECTIVE:To develop a rapid, nondestructive approach for simultaneously predicting the contents of primary bioactive constituents and hepatoprotective effects of AR using hyperspectral camera imaging (HCI) combined with deep learning models. METHOD:Hyperspectral images of 100 AR batches were acquired using a portable Vis-NIR HCI system (389.81-1048.18 nm). The contents of six active compounds were quantified via HPLC-UV and HPLC-ELSD, while hepatoprotective activity was evaluated using an APAP-induced L02 cell injury model. Quantitative calibration models were constructed using partial least squares regression (PLSR) and the following three deep learning architectures: liquid neural network (LNN), Mamba state space model, and graph convolutional network (GCN). Their predictive performances were systematically compared. Shewhart control charts were employed to visualize batch-to-batch quality variation. RESULT:The Mamba model demonstrated superior predictive performance across all seven quality attributes, achieving the highest coefficients of determination (Rp 2 up to 0.9972) and the lowest prediction errors. It significantly outperformed PLSR, LNN, and GCN models. Furthermore, the integration of Shewhart charts enabled effective visualization of quality consistency across batches. CONCLUSION:This study presents a novel HCI-Mamba framework designed for the rapid, nondestructive, and multicomponent quality assessment of AR. The proposed strategy offers a high-throughput solution for AR quality control while providing a methodological framework applicable to other complex herbal medicines.
