Molecular Modeling and Systems Pharmacology Approaches to Polyphenols in Urolithiasis Therapy

Urolithiasis, a condition characterized by the formation of stones in the urinary tract, remains prevalent and frequently recurring. Its development is driven by multiple factors, including oxidative stress, inflammation, crystal aggregation, and injury to renal epithelial cells. Current treatment options often provide only short-term relief and fail to effectively prevent recurrence or target the complex biological pathways involved. In this context, natural polyphenols-bioactive compounds found abundantly in plant-based foods and traditional medicinal herbs-have gained attention for their potential therapeutic effects, including antioxidant, anti-inflammatory, and crystal nucleation inhibition, as well as kidney-protective actions. However, their precise molecular mechanisms and biological targets are still not fully understood, largely due to their interactions with diverse cellular pathways. Systems pharmacology and molecular modeling offer promising tools for exploring the antiurolithiatic potential of polyphenolic compounds. Systems pharmacology allows researchers to map polyphenol interactions across various biological networks, linking them to key mechanisms implicated in stone formation, such as oxidative balance via Nrf2/HO-1, inflammation through NF-κB and TNF-α signaling, and regulation of crystallization by proteins like osteopontin and uromodulin. Complementary to this, molecular modeling approaches such as molecular docking, dynamics simulations, and structure-activity relationship (SAR) studies enable visualization and analysis of the binding affinities between polyphenols and urolithiasis-related targets at the atomic level. Together, these computational approaches provide a powerful platform for the rational design and optimization of polyphenol-based therapeutics. This integrative strategy not only deepens our understanding of how these compounds work but also supports the development of multitarget plant-derived drugs for the prevention and management of kidney stones. Moving forward, combining these techniques with experimental validation and incorporating artificial intelligence could greatly enhance the efficiency and accuracy of natural product drug discovery.