s Predictive Analysis of Yi-Gai-San's Multifaceted Mechanisms for Tremor-dominant Parkinson's Disease via Network Pharmacology and Molecular Docking Validation

Introduction: Based on comprehensive network-pharmacology and molecular docking analysis, this study was intended to unveil the multiple mechanisms of Yi- Gai-San (YGS) in treating the tremor-dominant subtype of Parkinson's disease (PD-DT). The compounds of YGS were meticulously analyzed, selected, and standardized with references to their pharmacological attributes. Its components included Gouteng (Uncaria rhynchophylla), Chaihu (Radix Bupleuri), Chuanxiong (Chuanxiong Rhizoma), Danggui (Angelicae sinensis radix), Fuling (Wolfiporia extensa), Baizhu (Atractylodis macrocephalae rhizoma), and Gancao (Licorice, Glycyrrhizae radix). Methods: We identified 75 active compounds within YGS. From these, we predicted 110 gene targets, which exhibited a direct association with PD-DT. PPI network results highlighted core target proteins, including TP53, SLC6A3, GAPDH, MAOB, AKT, BAX, IL6, BCL2, PKA, and CASP3. These proteins potentially alleviate PD-DT by targeting inflammation, modulating neuronal cell apoptosis, and regulating the dopamine system. Furthermore, GO and KEGG enrichment analyses emphasized that YGS might influence various mechanisms, such as the apoptotic process, mitochondrial autophagy, Age-Rage signaling, and dopaminergic and serotonergic synapses. The core proteins from the PPI analysis were selected for the docking experiment. Results: The docking results demonstrated that the most stable ligand-receptor conformations were kaempferol with CASP3 (-9.5 kcal/mol), stigmasterol with SLC6A3 (-10.5 kcal/mol), shinpterocarpin with BCL2L1 (-9.6 kcal/mol), hirsutine with MAOB (-9.7 kcal/mol), hederagenin with PRKACA (-9.8 kcal/mol), and yatein with GAPDH (-9.8 kcal/mol). These results provide us with research objectives for future endeavors in extracting single compounds for drug manufacturing or in-depth studies on drug mechanisms. Conclusion: From these computational findings, we suggested that YGS might mitigate PD-DT via “multi-compounds, multi-targets, and multi-pathways.”