Predictive Analytics for Theranosis in RA

RA is the most common inflammatory, persistent and progressive disease of the joints with serious co-morbidities and huge health and socio-economic impact worldwide.

The current standard therapeutic strategy for RA patients is to initiate a DMARDs therapy, (e.g. MTX) with serious side effects. This also applies to some PSO or SNSA patients. MTX is however inefficient in about 40% of the cases. Other treatments (biological DMARDs) must thus be initiated, which have an overall similar effectiveness and side effects and an higher cost (around 13kEUR/year/patient). Therapeutic choices are based on symptoms and blood tests including markers of inflammation which are inadequate to predict disease evolution and response to treatments. Improving RA management requires to improve the adequacy of the therapeutic strategies. The earlier the disease is correctly addressed, the more likely its progression and irreversible damages to the joints will be limited. DNAlytics recently developed RheumaKit a differential diagnostic solution for UA patients. UA is a condition in which joint inflammation is present, but a precise diagnosis cannot be made, due to the lack of sensitivity of presently available diagnostic techniques. RheumaKit is a multi-gene expression solution that discriminates RA from other joint conditions. A diagnostic model train to identify patients suffering from RA, SNSA or OA. RheumaKit diagnostic accuracy is higher than 90%, a performance that is better than any other diagnostic solution designed until now, including the ACR/EULAR 2010 criteria for the diagnosis of RA. See working principle below. Beyond diagnosis, DNAlytics wants to make RheumaKit evolve towards treatment recommendation applications (theranostic applications) for patients eligible for biological DMARDs. On one hand, the diseases of these patients have been more and more described in terms of the activity of several metabolic pathways (T \& B cells activation, Extra cellular matrix, Inteferon, TNF). On the other hand, the existing treatments also have been more and more described in terms of the pathways they target. The RheumaKit signature contains many markers that are representative of these pathways of interest. RheumaKit thus now provides a snapshot of the activity of seven metabolic pathways known from literature to be related to diseases mechanisms, or to be target of existing treatments. In this study, DNAlytics wants to show that based on a score defined on the RheumaKit platform, the response or non- response to anti-TNFs, representing the largest category of biological DMARDs, can be predicted before treatment initiation.

Eligibility Criteria:
Inclusion Criteria:

Patients must satisfy all of the following criteria:

* Signed the ICF and covered by health insurance.
* At least 18 years old.
* For women, use of a reliable method of birth control or remain abstinent during the study, or have had surgical sterilization or women above 60 years of age.
* Having undergone at least 3 months of synthetic DMARD treatment while being strictly eligible for it (diagnosed with RA) and being at a stable dose at least for the last month, and showing no satisfactory response to this therapy.
* Be eligible for biological DMARD treatment according to local regulation and practice.
* Willing and able to comply with scheduled visits, treatment plan, tests and other protocol procedures.

Exclusion Criteria:

Patients must satisfy none of the following criteria:

* Arthritis history longer than 5 years.
* Biological DMARD therapy already initiated.
* Be diagnosed with septic arthritis.
* Be pregnant or breastfeeding/lactating women.
* Diagnosed with HIV, hepatitis B, hepatitis C, Crohn's disease, fibromyalgia.
* Diagnosed with other inflammatory arthritic syndrome than RA.
* have a chronic pain condition that would confound evaluation of the patient.
* Be identified as at too high risk for biopsy or for biologic therapy.
* Be identified as having psychological, familial, social or geographical conditions which could potentially hamper compliance with the study protocol and follow-up schedule.