Principles of network pharmacology

Network pharmacology is a systemoriented approach that integrates polypharmacology and network biology to study drug-disease interactions at the level of biological networks rather than individual molecular targets. It recognizes that complex diseases such as asthma arise from dysregulation of interconnected signalling pathways and regulatory networks, rather than from isolated gene or protein defects. By mapping relationships among bioactive compounds, their molecular targets and downstream biological pathways, this approach provides a framework for understanding how multicomponent agents, such as herbal extracts, exert synergistic effects across multiple nodes within the disease network. In contrast to the classical “onedrug-onetarget” paradigm, network pharmacology embraces multitarget regulation, in which a single compound or a mixture (e.g. herbal formulation) may simultaneously modulate several proteins, thereby shifting the overall state of the pathological network toward a healthier configuration. This is particularly suitable for herbal medicines, which contain numerous phytochemicals that collectively influence complex disease phenotypes. By combining systems medicine with information science, network pharmacology enables systematic identification of key pharmacological targets, enrichment of relevant biological processes and signalling pathways, and prioritization of candidate compounds for further experimental validation [18,19].

Operationally, network pharmacology follows a structured workflow in which bioactive compounds are first identified and pharmacokinetically screened (for example, using oral bioavailability and druglikeness criteria) to retain only those with a higher likelihood of systemic exposure [7,20]. Targets of the screened compounds are predicted using in silico tools and literature evidence and then intersected with disease--related genes. Compound-target and protein-protein interaction (PPI) networks are constructed and analysed using networktopology metrics such as degree, betweenness centrality and closeness centrality to identify hub genes and critical regulatory nodes [21]. Functional enrichment analysis, such as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) [22,23] pathway mapping, translates these network features into biological mechanisms, while molecular docking and, where feasible, experimental validation confirm the plausibility of predicted compound-target interactions at the structural and functional levels [7,21].

In the context of asthma, where multiple inflammatory, remodelling and immuneregulatory pathways interact dynamically, network pharmacology offers a rational strategy for deconvoluting the complex pharmacology of herbal extracts, identifying key modulated targets and pathways and guiding the design of more targeted and effective therapeutic interventions [20,24]. By integrating computational predictions with pharmacokinetic and pharmacodynamic considerations, this approach supports the discovery of novel antiasthmatic mechanisms and helps prioritize highvalue compounds for focused preclinical and Drug Metabolism and Pharmacokinetics (DMPK) studies [19].