Computational Drug Discovery in Ankylosing Spondylitis-Induced Osteoporosis Based on Data Mining and Bioinformatics Analysis.

Background: Ankylosing spondylitis (AS) and osteoporosis (OP) are both prevalent illnesses in spine surgery, with OP being a possible consequence of AS. However, the mechanism of AS-induced OP (AS-OP) remains unknown, limiting etiologic research and therapy of the illness. To mine targetable medicine for the prevention and treatment of AS-OP, this study analyzes public data sets using bioinformatics to identify genes and biological pathways relevant to AS-OP.

Methods: First, text mining was used to identify common genes associated with AS and OP, after which functional analysis was carried out. The STRING database and Cytoscape software were used to create protein-protein interaction networks. Hub genes and potential drugs were discovered using drug-gene interaction analysis and transcription factors-gene interaction analysis.

Results: The results of text mining showed 241 genes common to AS and OP, from which 115 key symbols were sorted out by functional analysis. As options for treating AS-OP, protein-protein interaction analysis yielded 20 genes, which may be targeted by 13 medications.

Conclusions: Carlumab, bermekimab, rilonacept, rilotumumab, and ficlatuzumab were first identified as the potential drugs for the treatment of AS-OP, proving the value of text mining and pathway analysis in drug discovery.