A review of advances in research on mechanisms inhibiting kidney stone formation from a network pharmacology perspective.

Nephrolithiasis is a prevalent, highly recurrent urological disorder affecting 1%-15% of the global population, with 5-year recurrence rates reaching up to 50%. While surgical interventions achieve acute stone clearance, they do not resolve underlying metabolic derangements, crystal-induced tubular injury, or chronic inflammatory cascades. Natural products from global ethnomedicinal systems (including Traditional Chinese Medicine, Indian Ayurveda, Persian Unani medicine, European phytotherapy, and African ethnopharmacology) demonstrate potent multi-target anti-urolithic properties. However, decoding their intricate polypharmacology remains a major challenge. Network pharmacology, combining multi-omics profiling, computational target prediction, and network topology, has emerged as a promising paradigm to decipher these multi-component mechanisms. Unlike previous descriptive reviews that largely focused on narrow botanical catalogs or local traditions, this review establishes a critical, globally inclusive, and methodologically rigorous synthesis of network pharmacology-driven nephrolithiasis research from 2022 to 2026. We detail the fundamental pathophysiology across major stone types (calcium oxalate, calcium phosphate, uric acid, struvite, cystine) and the comprehensive cellular cascade encompassing crystal nucleation, Randall's plaque/plug formation, CD44-mediated adhesion, mitochondrial reactive oxygen species burst, endoplasmic reticulum stress, NLRP3 inflammasome activation, macrophage M1/M2 polarization, immunometabolism, extracellular vesicle communication, and programmed cell death (apoptosis, necroptosis, pyroptosis, ferroptosis, and PANoptosis). We critically synthesize evidence across diverse chemical classes (flavonoids, polyphenols, pentacyclic triterpenes, alkaloids, anthraquinones, tannins, polysaccharides, and marine bioactive metabolites), bridging computational predictions with established mechanistic milestones. Furthermore, we provide a deep critical appraisal of core methodological bottlenecks-target prediction bias, publication bias of over-studied hub genes, arbitrary pharmacokinetic cutoffs, circular virtual docking, and lack of renal bioavailability validation. Finally, we formulate a forward-looking translational roadmap integrating single-cell transcriptomics, humanized 3D organoid co-cultures, spatial multi-omics, reverse validation in longitudinal clinical registries, and machine learning feature selection, providing rigorous guidance for precision anti-urolithic drug discovery.