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Machine Learning-Assisted Rheumatoid Arthritis Formulations: A Review on Smart Pharmaceutical Design Publisher



Pouyanfar N1 ; Anvari Z1 ; Davarikia K2 ; Aftabi P2 ; Tajik N2 ; Shoara Y2 ; Ahmadi M3, 4 ; Ayyoubzadeh SM5, 6 ; Shahbazi MA7 ; Ghorbanibidkorpeh F1
Authors
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Authors Affiliations
  1. 1. Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  2. 2. Student research committee, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  3. 3. Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  4. 4. Medical Nanotechnology and Tissue Engineering Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  5. 5. Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
  6. 6. Health Information Management Research Center, Tehran University of Medical Sciences, Tehran, Iran
  7. 7. Department of Biomedical Engineering, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, Groningen, 9713 AV, Netherlands

Source: Materials Today Communications Published:2024


Abstract

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. © 2024 Elsevier Ltd
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