Identification of long-term therapeutic strategy in IgE-mediated allergy by leveraging humanized models and patient samples

IgE is a key driver of allergic diseases, which affect approximately one-third of the worlds population.

Monoclonal antibodies (mAbs) targeting IgE are approved for the treatment of allergic asthma, and show clinical benefit in a number of other allergic diseases. Yet, a sizable portion of patients do not respond to the drug despite high levels of IgE.

There is a clear need to better define which allergy features depend (or not) on IgE, and to find predictive biomarkers in order to identify patients who will benefit from anti-IgE therapy.

In addition, use of anti-IgE mAbs is limited by very high cost and the need to perform frequent reinfusion to maintain clinical efficiency.

Combining unique mouse models humanized for IgE and its two receptors FceRI and CD23, clinical samples from patients undergoing FDA-approved anti-IgE therapy, and a novel high-throughput IgE repertoire analysis method, the project addresses three key questions: (1) Which IgE features distinguish responders vs. non-responders to anti-IgE therapy, and can it be used as predictive biomarker? (2) Which key allergy features depend on IgE, and through which mechanisms? (3) Can we induce long-term protection against IgE-mediated allergies with a vaccine approach?

This translational project will increase our understanding of the basic mechanisms underlying allergic diseases, and has the potential to identify important new therapeutic strategies.