Multi-Target Mechanism of Compound Qingdai Capsule for Treatment of Psoriasis: Multi-Omics Analysis and Experimental Verification.

Background: Psoriasis is a chronic skin disease affected by genetic and autoimmunity. The traditional Chinese medicine, Compound Qingdai Capsule (CQC), has shown potential benefits in treating psoriasis in clinical settings. Despite its efficacy, the molecular mechanisms underpinning its therapeutic action remain unclear. Purpose: This study aimed to unravel the molecular mechanism of Compound Qingdai Capsule for psoriasis based on the psoriasis pathogenic pathway network, integrating multi-omics analysis, systems pharmacology, machine learning modeling, and animal experimentation. Methods: Psoriasis pathogenic pathway network was constructed through employing bioinformatics analysis and psoriasis-related multi-omics data mining. The ingredients of CQC were detected by UPLC-MS/MS, and target prediction was performed by systems pharmacology. Machine learning, including Lasso regression, Random Forest, and Support Vector Machine (SVM), were utilized to screen core targets of psoriasis. Molecular docking was employed to evaluate the binding affinity between ingredients and core targets. The expression levels of core targets were determined using qRT-PCR and ELISA. Results: Psoriasis-related datasets GSE201827 and GSE174763 were comprehensively analyzed to obtain 635 psoriasis-related genes. These genes were further enriched to elucidate signaling pathways involved, leading to the construction of psoriasis pathogenic pathway network. Utilizing UPLC-MS/MS, 29 main ingredients of CQC were characterized. CQC ingredients-targets network was constructed using these ingredients and their targets. Screening of CQC anti-psoriasis core targets using machine learning algorithm. Molecular docking confirmed good binding affinity between these targets and ingredients. Imiquimod (IMQ) induced psoriasis-like rat validated the anti-psoriasis effect of CQC by alleviating symptoms, reducing spleen and thymus index, and modulating the expressions of core targets at mRNA and protein levels. Conclusion: CQC effectively modulates the expression levels of AURKB, CCNB1, CCNB2, CCNE1, CDK1, and JAK3 through various ingredients, such as astilbin, salvianolic acid A, and engeletin, via multiple pathways, thereby alleviating psoriasis-like symptoms.