Network Pharmacology Framework Characterizes Polypharmacological Properties of Dietary Flavonoids: Integration of Computational, Experimental, and Epidemiological Evidence

TL;DR

This study develops a network pharmacology framework integrating computational, experimental, and epidemiological data to understand how dietary flavonoids interact with multiple proteins, revealing their potential therapeutic effects and guiding food-based interventions.

Contribution

It introduces a systematic network pharmacology approach to characterize flavonoid polypharmacology, combining large-scale data analysis with experimental validation and food-level predictions.

Findings

Flavonoids target more proteins than FDA-approved drugs, indicating multi-target properties.

Network predictions of flavonoid-protein interactions correlate strongly with experimental bioactivity.

Identified foods like tea and blueberries have high potential for therapeutic benefits based on network analysis.

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>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.