Computational Approaches in Drug Repurposing for Rheumatoid Arthritis: From Network Pharmacology to Molecular Docking

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease characterized by persistent synovial inflammation, progressive joint destruction, and systemic complications that significantly affect patient quality of life. Although several therapeutic options, including conventional synthetic disease-modifying antirheumatic drugs (csDMARDs), biologics, and targeted synthetic DMARDs, have improved disease management, limitations such as adverse effects, treatment resistance, and inability to regenerate damaged tissue remain major challenges. Drug repurposing has emerged as a promising strategy to accelerate the identification of novel therapeutic agents by utilizing approved drugs with established safety profiles. Computational approaches have revolutionized this process through the integration of network pharmacology, molecular docking, and molecular dynamics simulations. Network pharmacology enables the identification of complex interactions among genes, proteins, and signaling pathways involved in RA pathogenesis, facilitating multi-target therapeutic discovery. Molecular docking and molecular dynamics simulations further provide insights into ligand-target interactions, binding affinity, and structural stability at the atomic level. Recent advances involving artificial intelligence (AI), machine learning (ML), protein–protein interaction networks, and multi-omics integration have significantly enhanced predictive capabilities and drug candidate prioritization. Computational studies have identified promising repurposed compounds such as rifampicin and guggulsterones as potential therapeutic candidates for RA. Future research should focus on integrating deep learning and graph-based approaches with experimental validation through in vitro, ex vivo, and clinical investigations to bridge translational gaps. Overall, computational drug repurposing presents a cost-effective, time-efficient, and precision-driven strategy for discovering novel therapies for RA and other complex autoimmune diseases.