{
  "annotation_id": "a55db088-a916-4ad7-b879-9b7d69b4446c",
  "date_created": "2026-02-17T05:53:15.100000Z",
  "date_modified": "2026-02-17T05:53:15.100000Z",
  "file_hash": "fb958e27a3eac1c08c4ce3e2bb5e86943d6aacac684e6099f74a13f13a3abd87",
  "private": false,
  "record": {
    "abstract": "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\u003e200 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.",
    "arxiv_id": "2601.08147",
    "authors": [
      "Koyo Fujisaki",
      "Osei Horikoshi",
      "Yukitoshi Nagahara",
      "Kengo Morohashi"
    ],
    "categories": [
      "q-bio.QM",
      "q-bio.BM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Network Pharmacology Framework Characterizes Polypharmacological Properties of Dietary Flavonoids: Integration of Computational, Experimental, and Epidemiological Evidence",
    "url": "https://arxiv.org/abs/2601.08147",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "aed7c6a2-aaf1-491a-afd4-ddc8231f4319",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}