Development and in vitro Evaluation of Antigen-Loaded Poly(amidoamine) Nanoparticles for Respiratory Epithelium Applications

A poly(amidoamine) with disulfide linkages in the main chain and 4‐hydroxybutyl and ω‐carboxy‐PEG groups (9:1 ratio) as side chains was prepared by Michael addition polymerization of cystamine bisacrylamide with 4‐hydroxybutylamine and ω‐carboxy‐PEG‐amine. To develop therapeutic protein formulations for improved delivery of antigen via the intranasal route, nanoparticles were prepared from this polymer by self‐assembly with p24 or ovalbumin as the model proteins and CpG as the adjuvant. The nanoparticles incorporated the antigens and adjuvant from the feed solution with high efficiency (∼90 %) and have sizes of 112 and 169 nm, respectively, with low positive surface charge (∼+2 mV). Formulations of the nanoparticles were shown to be nontoxic and stable for at least 10 days at room temperature. Their capacity to pass through epithelial and endothelial cell layers was evaluated in vitro by using a respiratory mucosa‐like barrier model in which monolayers of NCI H441 respiratory epithelial cells and ISO‐HAS‐1 endothelial cells were co‐cultured on both sides of a transwell filter membrane. It was shown that p24 incorporated in the nanoparticles was transported with >140 % greater efficiency through the two contact‐inhibited layers than p24 in its free form, whereas incorporation of ovalbumin in the nanoparticles leads to a 40 % decrease in transport efficiency relative to the free antigen. A nose for good transport: Nanoparticles were prepared by self‐assembly of a biodegradable poly(amidoamine), using p24 or ovalbumin as model antigens and CpG as the adjuvant, with the goal of developing therapeutic protein formulations for improved delivery of antigen via the intranasal route. The nanoparticles are nontoxic and stable for at least 10 days at room temperature. Their capacity to pass through epithelial and endothelial cell layers was evaluated in vitro by using a respiratory mucosa‐like barrier model.