Patients with a chronic inflammatory disease still need better treatment, since approximately 90% of the available drugs fail in over 60% of the patients. Despite extensive research over the past years, the underlying molecular pathways that drive chronic inflammation are still unclear. The complex nature of the chronic inflammatory disease and the lack of comprehensive understanding of the underlying molecular mechanisms is hindering the development of effective and personalized therapies.
In our group, we use a Systems Medicine approach to identify key molecular mechanisms and pathways that lead to chronic inflammation. Using the Systems Medicine approach, we aim to achieve disease interception, prediction of mode of action, prognosis and therapy response. Since different patients can exhibit differences in phenotypes and/or molecular footprints, we ultimately aim to predict and develop therapies for personalized medicine (i.e. who to treat with what and for how long).
Within the Systems Medicine approach, we isolate immune cell subsets and study multiple OMICS layers on each cell subset. We use different computational approaches to analyze each OMICS layer and finally integrate these extensive data sets. Using computational analysis and statistical modeling, we predict molecular candidates which further stems Proof-of-Concept and Proof-of-Mechanism studies using exeperimental and disease in dish models of (chronic) inflammation.