Personalised Medicine Strategy to Predict Drug Choice, Improve Efficacy and Outcome

Inflammatory Arthritis is a common autoimmune disease which causes joint tissue destruction, work instability due to disability and ultimately premature mortality. In addition, IA reduces mobility, increases social isolation and is significantly associated with obesity and diabetes. Targeted biotherapeutics have a major effect on the outcome of IA, however responses may be sub-optimal or associated with adverse events. Firstly, the disease modifying anti-rheumatic drug (DMARD) methotrexate (MTX) is the drug of first choice; patients who fail MTX are eligible for anti-TNF, those who fail to respond to anti-TNF can then switch to B-cell depleting therapy, rituximab (RTX), Abatacept, anti-IL6 or IL-17 blocking therapies. Secondly, a significant non–response rate at 2 years is well established for these agents, MTX – 45%, anti-TNF – 30% and RTX – 40%. Thirdly, these biologic agents when introduced early more consistently produce improvement in long-term outcomes including joint damage, disability and employment. They are all expensive and potentially associated with serious adverse events; so, identifying those patients most likely to respond and not develop toxicity would improve the cost-benefit. Therefore, the aim of this study is to identify predictors of treatment response to allow stratification of patients to receive the most appropriate therapy early in the disease process. This programme is a coordinated approach between Molecular Rheumatology – led by the Fearon Group, The Centre for Arthritis and Rheumatic Diseases (SVUH, UCD) led by Prof Doug Veale and Tallaght Rheumatology Research, led by Dr Ronan Mullan.  Biologic samples (blood, synovial fluid, synovial tissue and specific synovial tissue cell types) and full clinical assessment at baseline/follow-up are being collected from well-defined clinical cohorts, specifically RA (ACPA+ vs ACPA-), PsA and OA.  Furthermore, ACPA+ pre-RA patients are also being recruited. Molecular, phenotypic and functional genomics will be performed on specific biologic samples and an in-depth systems biology computational analysis performed to identify key differential inflammatory pathways between specific patient cohorts and in Pre- RA. Furthermore, this approach will allow us to identify key synovial inflammatory pathways that are common in systemic correlates for new targets or biomarkers for RA.