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

Aug 3, 2026

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Rheumatoid arthritis (RA) is a chronic autoimmune disorder causing substantial patient burden. Conventional therapies such as NSAIDs, glucocorticoids, DMARDs, and biologics face limitations stemming from unfavorable physicochemical characteristics and possible adverse effects. Nanoparticle-based drug delivery and tailored pharmaceutical formulations may improve therapeutic value, but laboratory optimization is difficult because formulation errors can reduce bioavailability and efficacy. Machine learning-driven approaches are reviewed for enhancing drug delivery design, supporting diagnosis and treatment prediction, and improving manufacturing of RA-related pharmaceutical formulations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Introduction

Background of rheumatoid arthritis and treatment challenges

Abstract

Summary of AI and machine learning applications in RA

Keywords

Artificial intelligence, machine learning, and drug delivery systems

University of Groningen Machine learning-assisted rheumatoid arthritis formulations Pouyanfar, Niki; Anvari, Zahra; Davarikia, Kamyar; Aftabi, Parnia; Tajik, Negin; Shoara, Yasaman; Ahmadi, Mahnaz; Ayyoubzadeh, Seyed Mohammad; Shahbazi, Mohammad Ali; Ghorbani-Bidkorpeh, Fatemeh Published in: Materials today communications DOI: 10.1016/j.mtcomm.2024.110208 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2024 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Pouyanfar, N. , Anvari, Z. , Davarikia, K. , Aftabi, P. , Tajik, N. , Shoara, Y. , Ahmadi, M. , Ayyoubzadeh, S. M. , Shahbazi, M. A. , & Ghorbani-Bidkorpeh, F. (2024) . Machine learning-assisted rheumatoid arthritis formulations: A review on smart pharmaceutical design. Materials today communications , 41, Article 110208. [https://doi.org/10.1016/j.mtcomm.2024.110208](https://doi.org/10.1016/j.mtcomm.2024.110208) Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) . The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.) Download date: 29-12-2025 Materials Today Communications 41 (2024) 110208 Contents lists available at ScienceDirect Materials Today Communications journal [homepage: www.elsevier.com/locate/mtcomm](homepage: www.elsevier.com/locate/mtcomm) | Machine learning-assisted rheumatoid arthritis formulations: A review on smart pharmaceutical design
Niki Pouyanfar a,1, Zahra Anvari a,1, Kamyar Davarikiab, Parnia Aftabib, Negin Tajik b, Yasaman Shoarab, Mahnaz Ahmadic,d,*, Seyed Mohammad Ayyoubzadehe,f, Mohammad-Ali Shahbazig, Fatemeh Ghorbani-Bidkorpeha,**
a Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran b Student research committee, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
c Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
d Medical Nanotechnology and Tissue Engineering Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran e Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran f Health Information Management Research Center, Tehran University of Medical Sciences, Tehran, Iran
g Department of Biomedical Engineering, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, Groningen 9713 AV, the Netherlands | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords

FAQs

What is rheumatoid arthritis and why are current treatments challenging?

Rheumatoid arthritis is a chronic autoimmune inflammatory disease affecting small joints. Treatments such as NSAIDs, glucocorticoids, DMARDs, and biologics can be limited by physicochemical drawbacks and potential side effects.

How can nanoparticle-based drug delivery approaches help in RA?

Nanoparticle-based delivery and optimized pharmaceutical formulations may maximize therapeutic benefits of RA medications. However, achieving suitable formulations and delivery systems is difficult in laboratory settings.

Which machine learning tasks are discussed for RA research and smart design?

The review highlights AI and machine learning for developing, optimizing, and improving drug delivery systems, as well as predicting and enhancing diagnosis and treatment strategies for RA.

Aug 3, 2026

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