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.