CBD identified as a top drug candidate through advanced network analysis

The systematic integration of heterogeneous host-pathogen interaction data with disease modules and pharmacological knowledge remains a major challenge in translational biomedical informatics. Network medicine offers a promising strategy for identifying conserved regulatory vulnerabilities and therapeutic repositioning opportunities across distinct mucosal ecosystems. We developed a scalable multilayer network integration framework that unifies pathogen-host protein interactions, disease-risk gene modules, and drug-target associations into a consolidated human interactome. The integrated network comprised 7,262 human proteins, 17,016 high-confidence protein-protein interactions, nine bacterial pathogens, four respiratory viruses, and 514 FDA-approved drugs. Network topology was quantitatively characterized using complementary centrality metrics (degree, betweenness, closeness, clustering coefficient, and topological coefficient) to identify high-influence host regulators. Drug prioritization employed a multi-criteria ranking pipeline integrating functional network scoring (CoDReS), structural similarity clustering (Tanimoto-based hierarchical modeling), and pharmacokinetic constraint filtering (ADMET profiling). Pathway enrichment analysis was performed to identify convergent biological mechanisms. The integrative framework identified conserved cross-ecosystem regulatory hubs, including PPARG, CDC42, JUN, RHOA, and CAV1, which link microbial perturbations to cardiometabolic and inflammatory disease pathways. Centrality-weighted drug prioritization consistently ranked indomethacin, ibuprofen, dexibuprofen, mesalazine, and cannabidiol as high-confidence repositioning candidates for densely connected host networks. Enrichment analyses demonstrated convergence on immune signaling pathways, cytoskeletal remodeling, PPAR signaling, and focal adhesion networks. This study presents a reproducible and generalizable network medicine workflow that formalizes interactome construction, multi-metric centrality assessment, and composite drug ranking in a unified analytical framework. The proposed strategy enables the systematic identification of conserved host regulatory vulnerabilities and repositionable therapeutics across infectious and chronic inflammatory diseases, thereby advancing host-directed therapeutic discovery in translational biomedical informatics.