Machine Learning-Driven Identification of Therapeutic Checkpoints in Rheumatoid Arthritis: A Network-Based Approach to Discover Resolution- Promoting Agents

Background: Current therapeutic strategies for Rheumatoid Arthritis (RA) primarily focus on anti-inflammatory approaches, which often fail to induce sustained remission. Shifting the paradigm toward "Resolution Pharmacology" requires identifying therapeutic checkpoints within the synovial microenvironment capable of activating resolution-promoting pathways. Methods: We employed a systems biology and machine learning framework analyzing synovial transcriptomic data. Following differential gene expression (DEG) analysis, Reactome pathway enrichment was utilized to isolate resolution-specific networks. Machine learning algorithms, particularly Random Forest , were trained and externally validated to classify RA phenotypes. Furthermore, reverse signature analysis was applied to computationally repurpose drugs capable of reverting the pathological transcriptomic profile. To facilitate clinical translation, an interactive web-based dashboard was developed for real-time data exploration. Results: Analysis identified 2,135 DEGs (1,168 upregulated, 967 downregulated) significantly discriminating RA patients from healthy controls. The machine learning model achieved a high classification accuracy of 86.96% on the validation dataset ( GSE77298 ). Protein-protein interaction network analysis pinpointed critical therapeutic checkpoints, prominently including MYC, PTPRC, and JUN . Computational drug repurposing revealed Manumycin A and Salermide as the top candidate resolution-promoting agents capable of reversing the RA transcriptomic signature. Conclusion: This study provides a validated machine learning pipeline identifying novel therapeutic checkpoints and repurposable drugs to promote inflammation resolution in RA. The integration of these findings into an open-source interactive dashboard offers a scalable tool to accelerate drug discovery and precision medicine in autoimmune diseases.