Delivery vehicles are necessary for many therapeutics to overcome the various challenges in their path. It is clear, however, that the relationship between delivery vehicles and the immune system is a complex one. One such delivery vehicle is the lipidoid nanoparticle, which has been shown to be potent in several cell types. This thesis details the first time lipidoids have been used for wound delivery, and demonstrates the successful silencing of an inflammatory protein, TNFα, in the context of diabetic ulcers. Knockdown is seen in an in vitro macrophage-fibroblast coculture model, as well as in nondiabetic and diabetic mice wound models. Lipidoids silence roughly half of the TNFα gene expression in the diabetic wound and have been shown to help the wound close faster than untreated controls. Of course, immune activation can decrease therapeutic efficacy or trigger dangerous reactions in the patient. Learning more about what chemical moieties cause an immune response would allow for the design of a particle that could better resist immune clearance and avoid the creation of a secondary response. This thesis investigated the effect of a lipidoid library on the immune system using a two pronged approach. The lipidoids were first tested against human peripheral blood mononuclear cells and then were injected into mice to probe the in situ immune response. Several types of B cells were examined in this latter case, namely germinal center B cells, plasma cells, and memory B cells. A T cell dependent response occurred, favoring memory B cells for most of the lipidoids tested. There was an increase in free antibody in the blood that reflected this increase in antibody producing cells. Nitrogen rings and carbon tail lengths of eleven and twelve carbons were particularly reactive, though it appears that the amine head group determines immune response more than the tail. Further work will analyze whether these increases in immune cells reflect a loss of therapeutic efficacy, as current ramifications are unclear. An in-depth T cell subset analysis with flow cytometry would also help complete the picture.