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Network pharmacology-guided optimization of Semen Sojae Praeparatum fermentation for enhanced anti-influenza efficacy
Wenping Liu1, Wenlong Zhao2, Qian Zhang1
1College of Traditional Chinese Medicine, Bozhou University, No. 2266, Tangwang Avenue, High-tech Zone, Bozhou, 236800, Anhui Province, China.
Abstract:
Semen Sojae Praeparatum (SSP), a traditional Chinese medicine derived from fermented black soybeans, has been historically used to treat febrile epidemics ("Wen Bing") with symptoms akin to influenza. This study integrated network pharmacology, molecular docking, in vitro pharmacodynamics and HPLC-based fingerprint and quality evaluation to elucidate SSP's anti-influenza mechanisms and optimize its fermentation process. Network pharmacology identified genistein and daidzein as key bioactive constituents targeting hub proteins TNF, AKT1, EGFR, SRC, and MMP9, which mediate pathological process in influenza. Molecular docking confirmed their strong binding affinities, particularly to lung-expressed AKT1 and SRC both of which are upstream activating kinases of the NF-κB p65 signaling pathway. Guided by these computational predictions, genistein and daidzein were evaluated in A549 cells using LPS and H2O2-induced influenza lung injury models. They significantly inhibited NF-κB p65 nuclear translocation, suppressed transcription and secretion of pro-inflammatory cytokines (TNF-α, IL-1β, IL-8, PTGS2), upregulated antioxidant defense genes (NQO1, GCLM, SOD2), and reduced oxidative stress markers (ROS, MDA), thereby highlighting SSP's potential protective effects during influenza-induced pulmonary infection. Concurrently, SSP fermentation parameters were optimized (water-to-material ratio of 20:1 (v/w), 30 ± 2 °C, open conditions), yielding nine SSP batches with high chromatographic similarity (> 0.989) and genistein/daidzein total contents exceeding Chinese Pharmacopoeia standards. Compared to commercial products, optimized SSP showed significantly higher indicator constituents' levels and superior quality consistency. This study establishes a robust, scalable framework for standardized SSP production, enhancing its clinical reliability and industrial applicability in influenza management.
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