โก Quick Summary
This scoping review highlights the urgent need for biomarkers to predict drug treatment responses in rheumatoid arthritis (RA), a condition where only a minority of patients achieve sustained remission. The study emphasizes the potential of artificial intelligence in developing predictive models that integrate clinical and molecular data.
๐ Key Details
- ๐ Review Date: Articles published until July 25, 2025
- ๐ฌ Focus Areas: Blood immunophenotyping, circulating proteins, proteomics, transcriptomics, metabolomics, lipidomics, cortisol production, and histopathology
- ๐ค Technology: Artificial intelligence-based approaches for response prediction
- โ ๏ธ Current Limitations: Lack of established biomarkers for predicting responses to methotrexate and biologic agents
๐ Key Takeaways
- ๐งฌ Biomarkers are essential for predicting treatment responses in RA.
- ๐ Only a minority of RA patients achieve long-term remission with current therapies.
- ๐ก AI technologies show promise in developing predictive models.
- ๐ Current knowledge does not allow for effective differentiation between responders and non-responders.
- ๐ ๏ธ Standardization in research approaches is lacking, hindering biomarker discovery.
- ๐ Advanced analytical techniques are proposed for future research.
- ๐ฑ Cellular therapies may benefit early-stage RA patients who are refractory to approved drugs.
๐ Background
Rheumatoid arthritis is the most prevalent systemic rheumatic disease, characterized by chronic inflammation and joint damage. The initiation of effective drug treatment is crucial for controlling inflammation and preventing disease progression. However, the variability in patient responses to therapies necessitates the identification of reliable biomarkers that can predict treatment outcomes.
๐๏ธ Study
This review critically examines the literature on various biomarkers, including blood immunophenotyping, circulating proteins, and advanced omics technologies. The authors also explore the role of artificial intelligence in creating models that integrate clinical features with molecular profiling to enhance prediction accuracy for drug responses in RA.
๐ Results
The findings indicate that current methodologies do not provide sufficient insight to distinguish future responders from non-responders to treatments like methotrexate and biologic agents. The authors highlight the absence of standardized research approaches, which has been a significant barrier to discovering effective biomarkers.
๐ Impact and Implications
The implications of this study are profound, as identifying reliable biomarkers could transform the management of rheumatoid arthritis. By leveraging advanced analytical techniques and machine learning, researchers can pave the way for personalized treatment strategies, ultimately improving patient outcomes and quality of life.
๐ฎ Conclusion
This review underscores the critical need for ongoing research into biomarkers for predicting drug treatment responses in rheumatoid arthritis. The integration of artificial intelligence and advanced analytical methods holds promise for future breakthroughs in this field. Continued efforts are essential to overcome current limitations and enhance treatment efficacy for RA patients.
๐ฌ Your comments
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In search of biomarkers for prediction of drug treatment responses in rheumatoid arthritis: Lessons learned and future perspectives.
Abstract
Prompt initiation of effective drug treatment is crucial for controlling inflammation and preventing disease progression in rheumatoid arthritis, the most prevalent systemic rheumatic disease. The growing range of drug therapies over the past three decades and the fact that only a minority of patients achieve sustained long-term remission with any given therapy, make imperative the need for biomarkers predicting responses to specific drugs. Moreover, promising therapeutic approaches under development, namely cellular therapies, could be promptly applicable at earlier disease stages in about 10-15โฏ% of RA patients who will be refractory to all approved drugs. In this scoping review of original articles published until 25th of July 2025, we present a critical overview of the literature pertaining to the prognostic value of blood immunophenotyping, circulating proteins and blood proteomics, transcriptomics, metabolomics and lipidomics, as well as of endogenous cortisol production and synovial histopathology. We also discuss the emerging use of artificial intelligence-based approaches for developing response prediction models that integrate clinical features with molecular profiling. We conclude that current knowledge does not allow to discern future responders to methotrexate and/or to different biologic agents from non-responders because established biomarkers to identify those patients who will benefit the most from each therapeutic option are lacking. We also emphasize the lack of standardized research approaches to discover biomarkers predicting drug treatment responses and try to identify the relevant pitfalls and describe the lessons learned over the years. Finally, we propose a roadmap and the application of advanced analytical and machine learning techniques for future research in this area.
Author: [‘Dara A’, ‘Vlachogiannis NI’, ‘Fragoulis GE’, ‘Tektonidou MG’, ‘Sfikakis PP’]
Journal: Autoimmun Rev
Citation: Dara A, et al. In search of biomarkers for prediction of drug treatment responses in rheumatoid arthritis: Lessons learned and future perspectives. In search of biomarkers for prediction of drug treatment responses in rheumatoid arthritis: Lessons learned and future perspectives. 2025; (unknown volume):103914. doi: 10.1016/j.autrev.2025.103914