School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan, 430074, China.
College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
Comput Biol Med. 2024 Sep;180:108985. doi: 10.1016/j.compbiomed.2024.108985. Epub 2024 Aug 13.
Chrysanthemi Flos as a medicine food homology species is widely used in the prevention and treatment of diseases, whereas comprehensive research of its active compounds related to multi-pharmacological effects remains limited. This study aimed to systematically explore the active compounds through artificial intelligence-based target prediction and activity evaluation.
The information on compounds in Chrysanthemi Flos was obtained from six cultivars containing Gongju, Chuju, Huaiju, Boju, Hangbaiju, and Fubaiju, using UPLC-Q-TOF/MS. The main differential metabolites in six cultivars were also screened through the PLS-DA model. Then the potential targets of differential compounds were predicted via the DrugBAN model. Enrichment and topological analysis of compound-target networks were performed to identify key pharmaceutical compounds. Subsequently, the pharmacological effects of predictively active compounds were confirmed in vitro. Based on the active compounds, the pharmacological activities of Chrysanthemi Flos from the six origins were also investigated and compared for the further evaluation of medicinal quality.
A total of 155 secondary metabolites were obtained from Chrysanthemi Flos. Among them, 26 differential components were screened, and 9 key pharmacological compounds with 1141 targets were identified. Enrichment analysis indicated the main pharmacological effects of Chrysanthemi Flos related to inflammation, oxidative stress, and lipid metabolism. In addition, 9 key pharmaceutical compounds were evaluated in vitro experiments, indicating the significant therapeutic effect in regulating inflammation, oxidative stress, and lipid metabolism.
This study successfully identified 9 key pharmaceutical compounds in Chrysanthemi Flos and predicted the pharmacodynamic advantages of six origins. The findings would provide improved guidance for the discovery of active constituents and the assessment of pharmacodynamic advantages of different geographical origins.
作为一种药食同源的品种，菊花被广泛应用于疾病的预防和治疗，然而，其与多药效相关的活性化合物的综合研究仍然有限。本研究旨在通过基于人工智能的目标预测和活性评估系统地探索其活性化合物。
采用 UPLC-Q-TOF/MS 从贡菊、滁菊、怀菊、亳菊、杭白菊和福白菊 6 个品种中获取菊花中的化合物信息，通过 PLS-DA 模型筛选 6 个品种间的主要差异代谢物，然后利用 DrugBAN 模型预测差异化合物的潜在靶点。通过化合物-靶点网络的富集和拓扑分析，确定关键的药物化合物。随后，体外验证预测的活性化合物的药理作用。基于活性化合物，进一步评价菊花的药用质量，对来自 6 个产地的菊花进行药理活性研究和比较。
从菊花中获得了 155 种次生代谢产物。其中筛选出 26 种差异成分，鉴定出 9 种关键的具有 1141 个靶点的药理活性化合物。富集分析表明，菊花的主要药理作用与炎症、氧化应激和脂质代谢有关。此外，9 种关键药物化合物在体外实验中进行了评价，表明它们在调节炎症、氧化应激和脂质代谢方面具有显著的治疗作用。
本研究成功鉴定了菊花中的 9 种关键药物化合物，并预测了 6 个产地的药效优势。研究结果为发现活性成分和评估不同产地药效优势提供了改进的指导。
