Screening diagnostic biomarkers can be challenging due to the complexity of traditional Chinese medicine (TCM) and ambiguous pharmacological mechanisms. In this study, we reported an integrated strategy for accurately screening diagnostic biomarkers based on metabolomics coupled with network pharmacology. First, a feasible pharmacological model was established through systems pharmacology and based on metabolomics-based techniques to explore diagnostic biomarkers. While the components satisfying the q-value < 0.05, fold change (FC) ≥ 1.2 or FC ≤ 0.8, coefficient of variance (CV) ≤ 30%(QC) and the variable importance in the project (VIP) value > 1 are considered to be diagnostic biomarkers. Second, the ingredients were retained only when oral bioavailability (OB), Caco-2 permeability, drug half-life, TPSA and drug likeness (DL) satisfied the criteria (OB ≥ 40%; Caco-2 ≥ -0.4; HL ≥ 4 h; TPSA˂140; DL ≥ 0.18) suggested by the TCMSP database. Moreover, ingredients that exhibit extensive biological activity in TCM are also retained. Third, the effect targets of TCM were screened using the TCMSP database, Swiss Target Prediction and STICH online software. Disease targets were gathered from the therapeutic target database (TTD), PharmGkb and TCMSP database. Hub genes were screened by potential protein-protein interaction (PPI) network pharmacology analysis. Finally, a metabolic network pathway is established between the diagnostic biomarker and the hub gene. In the network analysis of metabolic pathways, most of the genes involved in this pathway are the second-step-obtained hub genes, which can explain the accuracy of the identified biomarkers. The proposed integrated strategy was successfully applied to explore the mechanism of action of Pulsatilla decoction (PD) in the treatment of acute ulcerative colitis (UC). Based on this integrated strategy, 23 potential biomarkers of acute UC treated with PD were identified. In conclusion, the integrated strategy provides novel insights into network pharmacology and metabolomics as effective tools to illuminate the mechanism of action of TCM.

中文翻译：

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基于代谢组学和网络药理学准确筛选生物标志物的综合策略。

由于中药（TCM）的复杂性和药理机制不明确，因此筛选诊断性生物标志物可能具有挑战性。在这项研究中，我们报告了一种基于代谢组学和网络药理学准确筛选诊断性生物标志物的综合策略。首先，通过系统药理学并基于基于代谢组学的技术建立了可行的药理模型，以探索诊断性生物标志物。当满足q值<0.05的组件，倍数变化（FC）≥1.2或FC≤0.8，方差系数（CV）≤30％（QC）和项目中变量重要性（VIP）值> 1时被考虑成为诊断性生物标志物。其次，仅当口服生物利用度（OB），Caco-2渗透性，药物半衰期，TPSA和药物相似性（DL）满足TCMSP数据库提出的标准（OB≥40％; Caco-2≥-0.4; HL≥4 h;TPSA˂140; DL≥0.18）。此外，还保留了在TCM中具有广泛生物活性的成分。第三，使用TCMSP数据库，Swiss Target Prediction和STICH在线软件筛选中药的疗效指标。从治疗目标数据库（TTD），PharmGkb和TCMSP数据库收集疾病目标。通过潜在的蛋白质-蛋白质相互作用（PPI）网络药理学分析筛选了Hub基因。最后，在诊断生物标志物和集线器基因之间建立了代谢网络途径。在代谢途径的网络分析中，参与该途径的大多数基因是第二步获得的中枢基因，可以解释所鉴定生物标志物的准确性。所提出的综合策略已成功地用于探讨白头翁汤（PD）在治疗急性溃疡性结肠炎（UC）中的作用机制。基于这种综合策略，确定了PD治疗的23种急性UC潜在生物标志物。总之，综合策略为网络药理学和代谢组学提供了新颖的见解，是阐明中药作用机制的有效工具。