Projet: Neural activity of splenic nerves as predictive biomarker of inflammatory arthritis relapses

Présentation

Rheumatoid arthritis (RA) is a chronic autoimmune disease affecting approximately 1% of the population, and is characterized by joint inflammation leading to structural damage and disability. RA often exhibits variable disease activity over time with exacerbations (relapses) and periods of low disease activity. RA patients are currently heavily treated by administration of pharmacological drugs or biologics. In addition to compliance and side-effect issues of these drugs, relapses occur in one third of cases. While rheumatologists assess clinical symptoms of RA and try to adjust treatment in case of relapse or adverse side effects, they only intervene when symptoms worsen due to the lack of reliable point-of-care biomarkers for RA relapses. This likely results in sub-optimal therapeutic drug levels. A relapse-responsive drug delivery system is highly desirable because it would titrate drug release to match the disease activity that would notably allow personalized medicine to be developed, tailoring therapy to the individual, shortening time from onset to effective treatment, improving cost and risk-benefit ratios of drugs, and ultimately achieving high response rate with minimal toxicity and/or desensitisation.

In parallel of the pharmacological approach, Bioelectronic Medicine is using electronic devices to modulate the activity of the human autonomic nervous system. This new approach has been tested with some sucess in PR patients. However, not only less than one third of patients achieved real remission following electrostimulation but, some patients also exhibited adverse effects.

Both pharmacological and bioelectronic approaches would benefit from personalized-treatment through the anticipation of relapse phases for early treatment. One approach to improve efficacy and reduce adverse effects could be to deliver on-demand quality-controlled electrostimulation based on information extracted from neural signals using advanced signal processing, control and machine learning approaches.

The aim of this project is to provide the proof-of-concept in mice that sympathetic neural activity can be used as a new class of biomarker for a personalized automated real-time monitoring to prevent relapse in arthritis.

Pertaining to this proof-of-concept, our specific objectives are:

•       Objective 1: To identify the signals that generate neural activity changes in splenic nerve following inflammation;

•       Objective 2: Building on this neural signature, to identify an electrical fingerprint in the splenic nerve that predicts relapse in a mouse model of inflammatory arthritis;

•       Objective 3: To investigate whether therapeutic electrostimulation can prevent relapse when delivered automatically following detection of electrical fingerprints identified in the previous objective (closed-loop system).

The main innovation of this project relies on the identification of real-time and reliable biomarkers based on machine learning methods that could discriminate neurograms associated with the relapse periods in RA patients to anticipate therapeutic intervention.

To achieve our goal, we have assembled a multidisciplinary consortium of two academics partners, which are both internationally recognized leaders in immunology and biomedical signal processing fields. We also have obtained preliminary results suggesting that spontaneous splenic nerve activity could be used as a biomarker for inflammation. Combining disease biology with advanced analysis on neural decoding will be the strength of the consortium.

This approach will allow us to establish the scientific foundation of a new field of research at the interface between neurology and immunology that may be beneficial for patients suffering not only from RA but also with other Immune-mediated inflammatory diseases including Crohn’s diseases or Multiple Sclerosis.