Characterization and Anti-Allergic Mechanisms of Bioactive Compounds in a Traditional Chinese Medicine Prescription Using UHPLC-Q-TOF-MS/MS, Network Pharmacology and Computational Simulations

Background/Objectives: Allergic diseases (e.g., asthma, chronic urticaria) are increasing globally, but current anti-allergic drugs exhibit limitations in efficacy and safety. Traditional Chinese Medicine (TCM) emphasizes constitutional regulation for allergic diseases management. The allergic constitution prescription (ACP), a TCM formulation, lacks clear mechanistic insights. Methods: This study employs a novel network pharmacology approach integrating ultra-high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS) to identify ACP’s chemical components and compare its mechanisms with anti-allergic drugs. Chemical components of ACP were analyzed via UHPLC-Q-TOF-MS/MS, and allergic disease-related targets were collected from public databases. Anti-allergic drug targets were intersected with ACP-disease targets to identify unique and common pathways. Molecular docking and dynamics simulations assessed binding affinity between key compounds and core targets. Results: We identified 126 compounds in ACP. Compared to anti-allergic drugs, ACP targeted 10 unique and five common key pathways (e.g., MAPK signaling), 10 unique and nine common core targets (e.g., Tumor Necrosis Factor (TNF), IL-6), and 14 unique and 15 common key compounds. Simulations confirmed high binding affinity of ACP compounds to core targets. Conclusions: These findings highlight ACP’s potential multi-target mechanisms for allergic diseases treatment, identifying unique and shared pathways, targets, and compounds compared to anti-allergic drugs, offering new insights for further mechanistic studies. However, it is crucial to note that these mechanistic predictions and compound-target interactions are primarily derived from computational analyses, and experimental validation (e.g., in vitro or in vivo assays) is essential to confirm these computational findings.