Predicting the success of therapies for the treatment of rheumatoid arthritis
Disease - Rheumatoid arthritis
Lead applicant - Professor Costantino Pitzalis
Organisation - Queen Mary University of London
Type of grant - Special Strategic Award
Status of grant - Active
Amount of the original award - £1,000,000
Start date - 3 March 2014
Reference - 20670
What are the aims of this research?
The aim of this study is to identify biological molecules, for example those found in the blood or joint tissue, which can be used to predict whether a patient will respond to drugs such as methotrexate, anti-TNF, rituximab and tocilizumab. Identifying this kind of biological marker will help doctors to give patients with rheumatoid arthritis the drug that they are most likely to respond to, as soon as they are diagnosed. This research study also aims to address whether combinations of markers predict response better than studying one marker at a time, and identify genetic changes that are thought to be associated with treatment success.
Why is this research important?
Rheumatoid arthritis is an autoimmune disease which affects an estimated 400,000 people in the UK. There are many drugs available for the treatment of rheumatoid arthritis, yet not all patients respond to every treatment. At the moment, there is no way of predicting which patients will respond best to which drug and so the drugs are prescribed on a trial-and-error basis. The longer it takes to find an effective therapy, the more joint damage accumulates and the worse the long-term outlook is for patients.
How will the findings benefit patients?
The outcome of this study will be the identification of predictive markers, either biological molecules or genetic changes, which will help doctors to target the right treatments to the right patients with rheumatoid arthritis. This is particularly important as the sooner the condition is effectively controlled the better the long term health of the patient. This would also lead to a reduction in the prescription of drugs that do not work; lowering exposure to the potential side effects of these drugs and increasing cost-effectiveness.