目的 运用网络药理学和分子对接探讨补肾益精方异病同治阿尔茨海默病（AD）和帕金森病（PD）的作用 机制。方法 使用 TCMSP、HERB 和 Swiss ADME 数据库筛选补肾益精方的活性成分，以 PubChem 和 Swiss TargetPrediction 数据库获取对应靶点，并利用 UniProt 数据库校正靶点名称。由 GeneCard、OMIM 和 TTD 数据 库获取阿尔茨海默病和帕金森病的疾病靶点，使用 Venny 在线工具获得药物与疾病的交集靶点，将交集靶点 导入 STRING 数据库，在 Cytoscape3.10.3 构建疾病-药物-成分-交集靶点网络和 PPI 网络并筛选关键靶点，采 用 Metascape 数据库对疾病共有交集靶点进行 GO 分析和 KEGG 通路分析。使用 AutoDockTools 进行分子对接 预测。结果 筛选后得到补肾益精方活性成分 76 个，靶点 884 个，与两者疾病交集靶点 179 个，依据药物-活 性成分-疾病靶点网络得到脱氧三尖形酯碱、（2R，3R，3aS）-3a-烯丙基-2-（1，3-苯并二氧杂环戊烯-5-基） -5-甲氧基-3-甲基-2，3-二氢苯并呋喃-6-酮、戈米辛 B、海风藤酮、（6aR，11aR）-9，10-二甲氧基-6a， 11a-二氢-6H-苯并呋喃并[3，2-c]色烯-3-醇等 10 个药物活性成分。PPI 网络中核心靶点为 AKT1、TNF、 TP53 和 EGFR。GO 分析包含生物学过程（BP）2 344 条、细胞组成（CC）141 条、分子功能（MF）239 条。KEGG 分析得到 207 条通路，主要涉及癌症相关通路、阿尔茨海默病通路、神经退行性病变通路、PI3K-Akt 信号通 路、脂质与动脉粥样硬化通路、癌症中的蛋白聚糖通路、MAPK 信号传导通路等。分子对接结果显示上述 5 个 核心成分与 4 个核心靶点均有较好的结合能力。结论 补肾益精方可通过脱氧三尖杉酯碱、戈米辛 B 等核心 成分，作用于 AKT1、TNF、TP53 等关键靶点，调节 MAPK 与 PI3K-Akt 信号通路，从而抑制神经元凋亡、减 轻神经炎症与氧化应激，这可能是其实现阿尔茨海默病与帕金森病“异病同治”的共同作用机制。

Objective To explore the mechanism of Bushen Yijing Formula （BSYJF） in treating both Alzheimer’s disease （AD） and Parkinson’s disease （PD） with the same therapeutic principle using network pharmacology and molecular docking. Methods Active components of BSYJF were screened using the TCMSP， HERB， and Swiss ADME databases. Corresponding targets were obtained from PubChem and Swiss TargetPrediction，and target names were standardized using the UniProt database. Disease targets for AD and PD were retrieved from the GeneCard，OMIM， and TTD databases. Intersection targets between the drug and the diseases were identified using the Venny online tool. These intersection targets were imported into the STRING database to construct both a disease-drug-component-intersection target network and a protein-protein interaction （PPI） network using Cytoscape 3.10.3， followed by screening for key targets. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analyses were performed on the shared intersection targets using the Metascape database. Molecular docking predictions were conducted using AutoDockTools. Results After screening，76 active components and 884 targets of BSYJF were identified，with 179 intersection targets shared with both diseases. Based on the drug-active component-disease target network diagram，10 key active components were identified，including Deoxyharringtonine，（2R，3R，3aS）-3aAllyl-2-（1，3-benzodioxol-5-yl）-5-methoxy-3-methyl-2，3-dihydrobenzofuran-6-one，Gomisin B，Kadstrenone， and （6aR，11aR）-9，10-Dimethoxy-6a，11a-dihydro-6H-benzofuro[3，2-c]chromen-3-ol. Core targets from the PPI network were AKT1， TNF， TP53， and EGFR. GO analysis yielded 2 344 biological process （BP） terms， 141 cellular component （CC） terms，and 239 molecular function （MF） terms. KEGG analysis identified 207 pathways， primarily involving pathways in cancer，Alzheimer’s disease，neurodegenerative diseases，the PI3K-Akt signaling pathway， lipid and atherosclerosis， proteoglycans in cancer， and the MAPK signaling pathway. Molecular docking results indicated that the aforementioned five core components all exhibited good binding affinity with the four core targets. Conclusion BSYJF may exert its effects through core components such as Deoxyharringtonine and Gomisin B， acting on key targets like AKT1，TNF，and TP53，and by modulating the MAPK and PI3K-Akt signaling pathways. This likely underlies its common mechanism in “treating different diseases with the same method” for both AD and PD， potentially by inhibiting neuronal apoptosis and alleviating neuroinflammation and oxidative stress.

R285.5

国家重点研发计划“中医药现代化”重点专项（2022YFC3501400）；江苏省院士工作站项目（BM2024101）。