Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.08147 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912820034011136 |
|---|---|
| author | Fujisaki, Koyo Horikoshi, Osei Nagahara, Yukitoshi Morohashi, Kengo |
| author_facet | Fujisaki, Koyo Horikoshi, Osei Nagahara, Yukitoshi Morohashi, Kengo |
| contents | Dietary flavonoids associate with disease prevention in epidemiological studies, yet their polypharmacological mechanisms remain unclear. We establish network pharmacology as a systematic framework to characterize flavonoid therapeutic properties through integrated computational, experimental, and epidemiological validation. We constructed a master network of 17,869 human proteins, 14 dietary flavonoids, and 1,496 FDA-approved drugs with 278,768 interactions. Flavonoids averaged 45.3 target proteins per compound compared to 16.8 for FDA-approved drugs (2.7-fold higher; p=7.5x10^-4), reflecting multi-target architecture. Statistical analysis revealed that 71.4% of flavonoids targeted proteins associated with cardiovascular drugs and 78.6% aligned with antineoplastic drug targets. MTT-based Jurkat cell assays confirmed network predictions: high-association flavonoids (luteolin LC50=31.4 microM, myricetin=29.5 microM) produced strong cytotoxicity, while low-association flavonoids showed minimal activity (LC50>200 microM). Network-predicted association strengths correlated with experimental bioactivity (Pearson r=0.918; R^2=0.843). We translated network associations into food-level predictions across 506 foods, identifying 685 food-drug therapeutic combinations. Systematic literature searches confirmed 96 associations supported by 132 unique references. Cardiovascular domains achieved 47.1% validation. Top-validated foods included tea (31 evidence items), blueberries (18 items), tomato (13 items), grape juice (10 items), and plum (9 items). Network pharmacology characterizes dietary polypharmacological properties and generates evidence-based food-therapeutic predictions, bridging nutritional science and systems pharmacology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08147 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Network Pharmacology Framework Characterizes Polypharmacological Properties of Dietary Flavonoids: Integration of Computational, Experimental, and Epidemiological Evidence Fujisaki, Koyo Horikoshi, Osei Nagahara, Yukitoshi Morohashi, Kengo Quantitative Methods Biomolecules Dietary flavonoids associate with disease prevention in epidemiological studies, yet their polypharmacological mechanisms remain unclear. We establish network pharmacology as a systematic framework to characterize flavonoid therapeutic properties through integrated computational, experimental, and epidemiological validation. We constructed a master network of 17,869 human proteins, 14 dietary flavonoids, and 1,496 FDA-approved drugs with 278,768 interactions. Flavonoids averaged 45.3 target proteins per compound compared to 16.8 for FDA-approved drugs (2.7-fold higher; p=7.5x10^-4), reflecting multi-target architecture. Statistical analysis revealed that 71.4% of flavonoids targeted proteins associated with cardiovascular drugs and 78.6% aligned with antineoplastic drug targets. MTT-based Jurkat cell assays confirmed network predictions: high-association flavonoids (luteolin LC50=31.4 microM, myricetin=29.5 microM) produced strong cytotoxicity, while low-association flavonoids showed minimal activity (LC50>200 microM). Network-predicted association strengths correlated with experimental bioactivity (Pearson r=0.918; R^2=0.843). We translated network associations into food-level predictions across 506 foods, identifying 685 food-drug therapeutic combinations. Systematic literature searches confirmed 96 associations supported by 132 unique references. Cardiovascular domains achieved 47.1% validation. Top-validated foods included tea (31 evidence items), blueberries (18 items), tomato (13 items), grape juice (10 items), and plum (9 items). Network pharmacology characterizes dietary polypharmacological properties and generates evidence-based food-therapeutic predictions, bridging nutritional science and systems pharmacology. |
| title | Network Pharmacology Framework Characterizes Polypharmacological Properties of Dietary Flavonoids: Integration of Computational, Experimental, and Epidemiological Evidence |
| topic | Quantitative Methods Biomolecules |
| url | https://arxiv.org/abs/2601.08147 |