Machine Learning Models to Predict Methotrexate Treatment Response in Rheumatoid Arthritis: A Scoping Review
- 1Faculty of Medicine, University College London, London WC1E 6BT, UK
- 2Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
- 3Department of Tissue Engineering and Regenerative Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
Abstract
Methotrexate (MTX) remains the anchor drug in rheumatoid arthritis (RA) despite the advent of newer disease-modifying anti-rheumatic drugs (DMARDs) that have transformed the treatment landscape of RA. A significant proportion of patients fail to respond to MTX monotherapy. Machine learning (ML) methods have identified predictors of the MTX response to refine RA treatment strategies. Objective: This scoping review (ScR) aimed to provide a comprehensive overview of the current landscape of ML applications for predicting the MTX response in RA. Methods: A thorough online search in PubMed and Web of Science databases identified studies using ML to predict MTX treatment responses in RA. We extracted and synthesised data on study design, ML methodologies, data sources, and outcomes. Findings were summarised descriptively. Of the 266 articles identified, 14 studies met the inclusion criteria. Data sources included electronic medical records, clinical and genetic biomarkers. Results: ML models primarily employed supervised techniques (e.g., random forests, logistic regression). Most clinical studies consistently identified baseline DAS28 as the strongest predictor of response to MTX. Other clinical predictors included Health Assessment Questionnaire (HAQ) scores, erythrocyte folate, lymphocyte count, platelet count, anti-citrullinated protein antibodies (ACPA), and rheumatoid factor (RF). Several genotypes (e.g., ABCB1 rs1045642 and ABCC3 rs4793665) and single nucleotide polymorphisms (SNPs), i.e., (pfcHap), were reported as predictors. Conclusions: Higher baseline disease activity based on DAS28 was the most common predictor of poor response across the clinical studies.
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BibTeX
@article{rajalingham2026machine,
title = {{Machine Learning Models to Predict Methotrexate Treatment Response in Rheumatoid Arthritis: A Scoping Review}},
author = {Ishana Darshini Rajalingham and Rajalingham Sakthiswary and Syahrul Sazliyana Shaharir and Yogeswaran Lokanathan},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15218463},
url = {https://www.mdpi.com/2077-0383/15/21/8463},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Machine Learning Models to Predict Methotrexate Treatment Response in Rheumatoid Arthritis: A Scoping Review
AU - Ishana Darshini Rajalingham
AU - Rajalingham Sakthiswary
AU - Syahrul Sazliyana Shaharir
AU - Yogeswaran Lokanathan
PY - 2026
DA - 2026/10/30
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15218463
UR - https://www.mdpi.com/2077-0383/15/21/8463
AB - Methotrexate (MTX) remains the anchor drug in rheumatoid arthritis (RA) despite the advent of newer disease-modifying anti-rheumatic drugs (DMARDs) that have transformed the treatment landscape of RA. A significant proportion of patients fail to respond to MTX monotherapy. Machine learning (ML) methods have identified predictors of the MTX response to refine RA treatment strategies. Objective: This scoping review (ScR) aimed to provide a comprehensive overview of the current landscape of ML applications for predicting the MTX response in RA. Methods: A thorough online search in PubMed and Web of Science databases identified studies using ML to predict MTX treatment responses in RA. We extracted and synthesised data on study design, ML methodologies, data sources, and outcomes. Findings were summarised descriptively. Of the 266 articles identified, 14 studies met the inclusion criteria. Data sources included electronic medical records, clinical and genetic biomarkers. Results: ML models primarily employed supervised techniques (e.g., random forests, logistic regression). Most clinical studies consistently identified baseline DAS28 as the strongest predictor of response to MTX. Other clinical predictors included Health Assessment Questionnaire (HAQ) scores, erythrocyte folate, lymphocyte count, platelet count, anti-citrullinated protein antibodies (ACPA), and rheumatoid factor (RF). Several genotypes (e.g., ABCB1 rs1045642 and ABCC3 rs4793665) and single nucleotide polymorphisms (SNPs), i.e., (pfcHap), were reported as predictors. Conclusions: Higher baseline disease activity based on DAS28 was the most common predictor of poor response across the clinical studies.
SN - 2077-0383
ER -