We believe that the present project is clinically relevant and could have a positive impact in MS patients management through the identification of clinical, molecular and environmental parameters associated with disease activity. This information will be combined in order to build a predictive model of disease activity that could be easily transferred into the clinical practice, given that no major invasive procedures are required to obtain the biological samples that are necessary to perform the multi-omics profiling coupled with clinical, paraclinical and environmental information.
FindingMS will deliver a predictive algorithm for disease activity that will be implemented into a prototypical tool to be used in an experimental setting. The results of the present project will have to be further confirmed in the context of a prospective clinical trial; however we expect that, when successful, our findings could contribute to guide in the future a more tailored use of currently available DMDs. Specifically, more aggressive treatments could be proposed earlier to subjects with higher predicted risk of developing breakthrough disease activity, avoiding potentially dangerous empirical treatment trials. On the other hand, less powerful but safer drugs would be preferred for patients with lower predicted inflammatory activity, thus reducing the risk of severe adverse events. This approach should enhance treatment efficacy, reducing uncertainty of patients and physicians and promoting efficiency of MS care.
Moreover, the prediction of disease activity early in disease course should at the end contribute to slow disease progression and accumulation of disability that derives from recurrent inflammatory events, in turn reducing health costs. MS patients could obtain a significant benefit from such a personalized management, with an improvement of their quality of life and potential slowing of disability progression. Such an effort could also be translated into a reduction of health care and social costs related to patients’ hospitalization for treatment of acute relapses, loss of productivity secondary to disability accumulation and chronic symptoms’ management.
Finally, the identification of clinical, molecular and environmental factors associated with disease activity, together with the biological pathways and networks they are involved in, could provide a better understanding of the key mechanisms contributing to disease pathogenesis, unraveling the biological and clinical heterogeneity of MS. This should contribute to identify subsets of patients with distinct pathogenic mechanisms and the obtained information could be used in the future to develop new treatments, targeting the specific biological processes that appear to be altered in the different disease states.