Future perspectives

Despite these limitations, network pharmacology offers a promising platform for advancing the scientific understanding and clinical application of herbal medicines. A key direction is the development of more comprehensive network models that integrate intracellular signalling with extracellular crosstalk and multisystem physiology across organs. Such models would better capture the systemic nature of diseases such as asthma and reflect how herbal interventions modulate interactions among immune, respiratory and other regulatory systems.

Another important frontier is mechanistic and translational research. Well-designed preclinical and clinical studies are needed to elucidate the precise modes of action of botanical and hybrid preparations, thereby strengthening the scientific credibility of traditional herbal interventions. Emerging analytical technologies, including advanced mass spectrometry and twodimensional liquid chromatography, will play a crucial role in characterizing complex herbal mixtures and tracking their pharmacologically active components, enabling more accurate mapping of compound-target relationships.

The field is also poised to benefit from the holistic integration of multi-omics data and artificial intelligence. By combining genomics, transcriptomics, proteomics and metabolomics with machinelearning approaches, network pharmacology can more rationally decode polypharmacology, predict adverse effects and refine precisionmedicine strategies in phytomedicine. Closely linked to this is the need for rigorous experimental validation of networkbased predictions through systematic analyses of gene and protein expression in cellular and animal models, as well as in clinical settings, to bridge the gap between computational inference and biological reality [57].

In parallel with these analytical advances, the quality and safety standards for herbal and botanical medicines must be strengthened. Adoption of internationally recognized guidelines for standardization, pharmacological characterization and safety assessment will improve the reproducibility and clinical reliability of herbal interventions [58]. At the same time, existing databases should be expanded and enriched to include information on component content, dosage and the effects of different preparation methods, as well as links between compounds, targets, diseases and traditional classification systems (e.g. TCM syndromes). Incorporating dose-effect relationships into network models by explicitly linking herbal medicine dosages, tissue concentrations, target engagement and therapeutic outcomes would further enhance the predictive power of the approach.

Finally, the integration of molecular docking and other experimental techniques within the network--pharmacology pipeline will strengthen confidence in predicted compound-target interactions and provide deeper mechanistic insights. By combining large-scale network analysis with fine-grained structural and functional validation, future research can transform network pharmacology from a descriptive tool into a robust, hypothesis-driven framework for the rational development of herbal-based therapies for asthma and other complex diseases [58].