Back

Ovalbumin-loaded mesoporous silica nanoparticles for allergen specific immunotherapy

Perez-Moreno, A. M.; Torres, P.; Martin-Astorga, M. d. C.; Cuevas-Delgado, P.; Garcia-Esteban, I.; Cespedes, J. A.; Montanez, M. I.; Torres, M. J.; Aranda, C. J.; Mayorga, C.; Paris, J. L.

2024-12-26 immunology
10.1101/2024.12.26.630362 bioRxiv
Show abstract

Allergic diseases are caused by an unnecessary immune response against harmless external substances (allergens), and they pose an important economic burden for healthcare systems with a large impact on the quality of life of patients. Allergen-specific immunotherapy (AIT) is the only treatment option capable of modifying the natural history of the disease, but current AIT schemes present safety and efficacy limitations. One possible strategy to address these limitations is to encapsulate the allergen in nanoparticle carriers that can deliver it to antigen presenting cells while hiding it from effector cells responsible for the allergic reaction. In this work, we evaluate the use of allergen-loaded mesoporous silica nanoparticles (MSNs) as AIT agents. MSNs of different pore sizes were prepared and characterized, evaluating their capacity to load and release ovalbumin (OVA) as a model allergen. Extra-large pore MSNs (XL-MSNs) showed the optimal loading and release behavior, presenting also enhanced activation of the dendritic cell line DC2.4 and reduced allergenic capacity in pre-sensitized RBL-2H3 cells, both compared to free OVA. After evaluating their biodistribution following subcutaneous, sublingual or intravenous administration, their therapeutic potential in AIT was further assessed in an in vivo murine model of OVA systemic anaphylaxis. The results showed that intravenous administration of OVA-loaded XL-MSNs significantly protected the mice from anaphylaxis and induced a Th1/Treg-immune profile, while administration through other routes failed to prevent the development of an anaphylactic reaction upon provocation with OVA. These findings establish MSNs, particularly via intravenous administration, as a promising platform to develop safer and more effective AIT.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.