BACKGROUND: This study aimed to identify the pharmacological targets and mechanisms of action of the traditional Chinese medicine, formononetin, in the treatment of Alzheimer’s disease (AD) using network pharmacological analysis.

MATERIAL AND METHODS: Targets of AD were obtained by using DisGeNET gene discovery platform, the herbal ingredients target (HIT) database, the SuperPred, and the SwissTargetPrediction compound target prediction platforms. Pathogenic and therapeutic targets were imported to the STRING biological database, and Cytoscape network integration software was used to construct component-target and disease-target interaction networks. Core targets were identified by topological analysis and were further tested to identify the biological processes and signaling pathways.

RESULTS: Seven key target genes for formononetin in the treatment of patients with AD were identified, including estrogen receptor alpha (ESR1), peroxisome proliferator-activated receptor gamma (PPARG), tumor protein p53 (TP53), sirtuin 1 (SIRT1), tumor necrosis factor (TNF), cytochrome P450 19A1 (CYP19A1), and nuclear factor (erythroid-derived 2)-like 2 (NFE2L2). The biological processes included hormone metabolism, regulation of nucleoside, nucleotide and nucleic acid metabolism, apoptosis, energy pathways, metabolism, cell communication, and signal transduction. The signaling pathways included histone acetylation and deacetylation (HDAC) class I, regulation of p38-alpha/beta, p38 mitogen-activated protein kinase (MAPK) signaling pathway, bone morphogenetic protein (BMP) receptor signaling, interleukin-1 (IL1) mediated signaling events, the tumor necrosis factor (TNF) receptor signaling pathway, and cytoplasmic and nuclear Smad2/3 signaling.

CONCLUSIONS: Pharmacological network analysis was used to identify the gene targets and mechanisms of formononetin treatment in patients with AD.

Keywords: Alzheimer Disease, Behavior and Behavior Mechanisms, Neuropharmacology, Computational Biology, Drugs, Chinese Herbal, Gene Expression Regulation, Gene Regulatory Networks