REVIEW article
Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1654117
This article is part of the Research TopicAdvancing Stem Cell and Extracellular Vesicle Therapies for Autoimmune and Cancer TreatmentView all articles
How Artificial Intelligence can enable Personalized Mesenchymal Stem Cell–Based Therapeutic Strategies in Systemic Lupus Erythematosus
Provisionally accepted- 1National Institutes of Biotechnology Malaysia, Serdang, Malaysia
- 2University of Malaya, Kuala Lumpur, Malaysia
- 3Universiti Kebangsaan Malaysia Fakulti Perubatan, Cheras, Malaysia
- 4UPES, Dehradun, India
- 5Algarve Biomedical Center Research Institute, Faro, Portugal
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Mesenchymal Stromal Cells (MSCs) are increasingly recognized as promising candidates for treating Systemic Lupus Erythematosus (SLE) due to their immunomodulatory and regenerative properties. However, their therapeutic efficacy remains inconsistent, largely due to the heterogeneity of MSC origins, culture conditions, cell quality, host immune interactions, and the influence of immunosuppressive treatments. Artificial Intelligence (AI) offers powerful tools to address these challenges by optimising MSC modification and application. This review explores how AI can identify optimal genetic and epigenetic targets, predict MSC behaviour under different environmental and priming conditions, and design personalise therapies tailored to individual patients. Moreover, AI enables the analysis of extensive datasets to refine dosing strategies and improve the integration of MSC therapy with immunosuppressants. By enhancing the precision, consistency, and personalisation of MSC-based interventions, AI has the potential to significantly improve therapeutic outcomes in SLE, advancing the field toward more effective and patient-centred autoimmune disease management.
Keywords: machine learning, Stem Cells, lupus, Genetic modifications, Personalised medicine
Received: 25 Jun 2025; Accepted: 17 Oct 2025.
Copyright: © 2025 Rajeev Kumar, Kai He, Lokanathan, Gaurav, Yusoff, Macedo and Bhassu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Fatima Macedo, mfmacedo@ualg.pt
Subha Bhassu, subhabhassu@um.edu.my
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