RESUMEN
BACKGROUND: Osteoporosis is a chronic bone disease characterized by bone loss and decreased bone strength. However, current anti-resorptive drugs carry a risk of various complications. The deep learning-based efficacy prediction system (DLEPS) is a forecasting tool that can effectively compete in drug screening and prediction based on gene expression changes. This study aimed to explore the protective effect and potential mechanisms of cinobufotalin (CB), a traditional Chinese medicine (TCM), on bone loss. METHODS: DLEPS was employed for screening anti-osteoporotic agents according to gene profile changes in primary osteoporosis. Micro-CT, histological and morphological analysis were applied for the bone protective detection of CB, and the osteogenic differentiation/function in human bone marrow mesenchymal stem cells (hBMMSCs) were also investigated. The underlying mechanism was verified using qRT-PCR, Western blot (WB), immunofluorescence (IF), etc. RESULTS: A safe concentration (0.25 mg/kg in vivo, 0.05 µM in vitro) of CB could effectively preserve bone mass in estrogen deficiency-induced bone loss and promote osteogenic differentiation/function of hBMMSCs. Both BMPs/SMAD and Wnt/ß-catenin signaling pathways participated in CB-induced osteogenic differentiation, further regulating the expression of osteogenesis-associated factors, and ultimately promoting osteogenesis. CONCLUSION: Our study demonstrated that CB could significantly reverse estrogen deficiency-induced bone loss, further promoting osteogenic differentiation/function of hBMMSCs, with BMPs/SMAD and Wnt/ß-catenin signaling pathways involved.