Predictors of treatment response in rheumatoid arthritis
Abstract
The expanding array of drugs available for treating rheumatoid arthritis is creating challenges in drug selection for the individual patient. The identification of biomarkers that predict the treatment response prior to drug exposure is therefore a current priority. This new approach, known as theranostics, is a component of personalized medicine, which involves selecting the management strategies that are most effective for a given patient at a given point in time. Antibodies to citrullinated peptides, rheumatoid factor, and the interferon signature are the most robust and best validated biomarkers identified to date. Matrices containing clinical or laboratory parameters of diagnostic or prognostic relevance may help to select the best treatment for the individual patient. Furthermore, the development of large-scale approaches requiring no a priori knowledge, such as functional genomics and metabolomics, hold considerable promise, despite persistent difficulties in replicating findings. The complexity of the treatment response in a given patient and substantial variability across patients suggest that biomarkers may be more helpful in combination than singly. The objectives of this review article are to discuss the approaches used to identify theranostic biomarkers and to present an overview of currently available biomarkers and of their performance in everyday clinical practice. However, the range of biomarkers suitable for use in daily practice remains extremely narrow.
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