Precision Therapeutics in Rheumatoid Arthritis: Emerging Strategies in Active, Stimuli-responsive, and AI-Enabled Drug Delivery

Background: Rheumatoid Arthritis (RA) is a chronic auto-immune condition marked by swelling of synovial membrane, leading to joint discomfort, swelling, and the erosion of bone and cartilage. While the exact origin of rheumatoid arthritis remains uncertain, various medications such as nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, disease-modifying antirheumatic drugs (DMARDs), and biologic drugs have been utilized for its treatment. However, upon administration, these drugs distribute throughout the body, resulting in both wastage and potential side effects. Objective: Targeted drug delivery has been proposed to overcome the limitations of conventional therapy, while precision medicine is widely acknowledged to tailor treatments according to individual patient needs. This narrative review focuses on various advanced drug delivery strategies for management of RA, with a particular emphasis on active targeting approaches, stimuli responsive drug delivery and progressive integration of artificial intelligence in enabling precision diagnosis, treatment, and optimized therapeutic outcomes. Methods: An exhaustive literature review was conducted using PubMed, Scopus and Web of sciences databases on various targeted drug delivery strategies for management of RA. Results: Mechanistically, folate, CD44, scavenger receptor or CD163 receptor targeting coupled with stimuli-responsive approach ensures targeted delivery of therapeutic agents, leading to reduction of TNF and IL-6 biomarkers. AI models, including deep learning, supervised and unsupervised machine learning models based on datasets, help in early detection of joint erosion, bone damage, and synovial inflammation more accurately than traditional methods. It helps to identify individuals at high risk of developing RA by analyzing multiple data sources including electronic health records (EHR) data, lab biomarkers, medication history, and clinical documentation. Supervised machine learning helps to predict disease severity and treatment responses. This allows for individualized dose adjustments and precision therapeutics. Further, AI enables treatment optimization based on each patient’s clinical profile. Conclusion: This narrative review concluded that various targeting strategies enhance the efficacy of therapeutic agents while the integration of AI in RA helps in early diagnosis, individualized treatment, and substantial precision in therapy. Furthermore, it emphasizes the need for future research to concentrate on creating versatile nanocarrier systems that can target multiple pathways related to RA simultaneously.