ADMET & DMPK · 2026
Leveraging network pharmacology in the treatment of asthma.
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Ahuja Sarthi, Kashindye Richard C, Yadav Divya, Chaudhary Priyanka, Yadav Rakesh
Аннотация
BACKGROUND AND PURPOSE: Asthma, a chronic airway inflammatory disorder driven by multifaceted genetic, cellular and molecular interactions, remains inadequately managed by single-target therapies due to incomplete disease control and adverse effects; this review aimed to explore network pharmacology's role in elucidating multi-target mechanisms of phytotherapeutic agents for asthma, with a focus on integrating ADMET/DMPK profiling to predict clinical translatability and safety.
EXPERIMENTAL APPROACH: We employed network pharmacology methodologies including target prediction (e.g. via PharmMapper, PubChem), protein-protein interaction network construction (STRING, Cytoscape), pathway enrichment analysis (KEGG, Reactome), molecular docking (AutoDock) and ADMET/DMPK modelling (SwissADME, pkCSM) to dissect multi-component herbal formulations, complemented by literature-mined experimental validations.
KEY RESULTS: Analyses identified key asthma-related targets (e.g. IL-17, TNF) and pathways (JAK-STAT, PI3K-AKT), revealing quercetin and kaempferol's multi-target efficacy in reducing airway inflammation and immune dysregulation; favourable ADMET profiles (high oral bioavailability, low toxicity) and DMPK parameters (metabolic stability via CYP inhibition) supported their therapeutic potential in herbal combinations.
CONCLUSION: Network pharmacology, enhanced by ADMET/DMPK integration, advances a holistic understanding of herbal asthma treatments, promoting safe, multi-target drug development. Limitations include data gaps in multi-omics validation and herbal standardization, with future directions leveraging AI-driven predictions for personalized pharmacotherapy.
EXPERIMENTAL APPROACH: We employed network pharmacology methodologies including target prediction (e.g. via PharmMapper, PubChem), protein-protein interaction network construction (STRING, Cytoscape), pathway enrichment analysis (KEGG, Reactome), molecular docking (AutoDock) and ADMET/DMPK modelling (SwissADME, pkCSM) to dissect multi-component herbal formulations, complemented by literature-mined experimental validations.
KEY RESULTS: Analyses identified key asthma-related targets (e.g. IL-17, TNF) and pathways (JAK-STAT, PI3K-AKT), revealing quercetin and kaempferol's multi-target efficacy in reducing airway inflammation and immune dysregulation; favourable ADMET profiles (high oral bioavailability, low toxicity) and DMPK parameters (metabolic stability via CYP inhibition) supported their therapeutic potential in herbal combinations.
CONCLUSION: Network pharmacology, enhanced by ADMET/DMPK integration, advances a holistic understanding of herbal asthma treatments, promoting safe, multi-target drug development. Limitations include data gaps in multi-omics validation and herbal standardization, with future directions leveraging AI-driven predictions for personalized pharmacotherapy.