Integrating network pharmacology and experimental validation to advance psoriasis treatment: Multi-target mechanistic elucidation of medicinal herbs and natural compounds.

BACKGROUND

Psoriasis, a chronic immune-mediated inflammatory disease (IMID), presents significant therapeutic challenges, necessitating exploration of alternative treatments like medicinal herbs (MH) and natural compounds (NC). Network pharmacology offers predictive insights, yet a systematic evaluation connecting these predictions with experimental validation outcomes specifically for MH/NC in psoriasis is lacking. This review specifically fills this gap by comprehensively integrating and analyzing studies that combine network pharmacology predictions with subsequent experimental validation.

METHODS

A systematic literature search identified 44 studies employing both network pharmacology and in vitro or in vivo experimental methods for MH/NC targeting psoriasis. This review provides a systematic analysis of the specific network pharmacology platforms, predicted targets/pathways, in vivo and in vitro experimental validation models, and key biomarker changes reported across these integrated studies. Methodological approaches and the consistency between predictions and empirical findings were critically evaluated.

RESULTS

This first comprehensive analysis reveals that network pharmacology predictions regarding MH/NC mechanisms in psoriasis are frequently corroborated by experimental data. Key signaling pathways, including the IL-17/IL-23 axis, MAPK, and NF-κB, emerge as consistently predicted and experimentally validated targets across diverse natural products. The review maps the specific network pharmacology tools and experimental designs utilized, establishing a methodological benchmark for the field and highlighting the successful synergy between computational prediction and empirical verification.

CONCLUSION

By systematically integrating and critically assessing the linkage between network pharmacology predictions and experimental validation for MH/NC in psoriasis, this review offers a unique clarification of the current, validated state-of-the-art, differentiating it from previous literature. It confirms network pharmacology's predictive power for natural products, identifies robustly validated therapeutic pathways, and provides a crucial benchmark, offering data-driven insights for future research into artificial intelligence-enhanced natural product-based therapies for psoriasis and other IMIDs.