Machine learning-assisted rheumatoid arthritis formulations: A review on smart pharmaceutical design

Rheumatoid arthritis (RA) is a long-lasting autoimmune condition that causes significant suffering among those affected. The medications used to treat this disease, including NSAIDs (nonsteroidal anti-inflammatory drugs), glucocorticoids, DMARDs (disease-modifying antirheumatic drugs), and biologic agents, come with various drawbacks due to their inherent physicochemical properties and potential side effects. Utilizing pharmaceutical processes, formulating, and employing nanoparticle-based drug delivery approaches could potentially maximize the benefits of these drugs. However, developing suitable formulations and optimized drug delivery systems can be challenging in the laboratory, as incorrect formulas might lead to insufficient bioavailability and effectiveness. Different artificial intelligence techniques, particularly machine learning, have been applied in various aspects of RA research. These include utilizing AI to develop, optimize, and enhance drug delivery systems and predicting and enhancing the diagnosis and treatment methods employed for this disease. This review article explored the use of machine learning in manufacturing diverse pharmaceutical formulations and improving the diagnosis and treatment of RA disease.