Respiratory viral infections are highly prevalent infectious diseases worldwide, characterized by complex pathogens and rapid mutations, posing limitations to existing prevention and treatment strategies. Traditional Chinese medicine (TCM) demonstrates unique advantages in holistic regulation and multi-target intervention for these infections. However, the complex pharmacodynamic basis and systemic mechanisms of TCM have presented challenges in achieving comprehensive evaluation and systematic mechanism elucidation. Inspired by systems philosophy and systems biology, this paper proposes an integrative research pathway centered on metabolomics as a phenotypic hub. At the observational level, it incorporates conventional metabolomics, stable isotope tracing, spatial metabolomics, and single-cell/subcellular metabolic analyses. At the association level, it combines multi-omics data such as transcriptomics, proteomics, and microbiome data. At the computational level, artificial intelligence is utilized for feature extraction and network modeling. This pathway aims to advance systematic research in biomarker discovery, syndrome substance basis elucidation, and the synergistic mechanisms of complex formulas in the prevention and treatment of respiratory viral infections with TCM. By promoting a new paradigm of systems biology research driven by metabolomics, it is expected to enhance the interpretation of TCM-based preventive and therapeutic mechanisms, thereby significantly promoting the modernization and internationalization of TCM.

respiratory viral infection  /  traditional Chinese medicine  /  metabolomics  /  systems biology  /  multi-omics integration  /  artificial intelligence analysis