Unravelling the interactions of biodegradable dendritic nucleic acid carriers and neural cells.

Nanomedicines based on nanoparticles as carriers of therapeutics are expected to drastically influence the future of healthcare. However, clinical translation of these technologies can be very challenging. The development process of nanoparticles for biological applications encompasses the analysis and understanding of several steps , before , and subsequent clinical applications, namely, the in-depth study of biosafety, cellular interaction, and intracellular trafficking. Recently, we proposed a new family of fully biodegradable PEG-GATGE (Poly(Ethylene Glycol)-Gallic Acid-Triethylene Glycol Ester) dendritic block copolymers to act as versatile delivery vectors in nanomedicine. These nanosystems showed great promise in complexing, protecting, and delivering nucleic acids to cells, forming nanoscaled complexes called dendriplexes. Due to these favourable features, in the present study, the dendriplexes' characterization was expanded and, in addition, their biocompatibility, cellular uptake, and cellular path in neuronal cells from the peripheral and central nervous systems were assessed. Our fully biodegradable dendritic nanosystem was found to be biocompatible in all the studied neuronal cells and mediates fast cellular interaction and endocytosis in both cell line tested and primary mouse cortical neurons. Nevertheless, the mechanism of dendriplex cell entry and intracellular fate was found to be different in cell lines and primary cultures. Dendriplexes' internalization was observed to be mediated by clathrin in ND7/23 and HT22 cells, while caveolin-mediated endocytosis occurred in primary mouse cortical neurons, in which, after internalization, dendriplexes were not colocalized with lysosomes or autophagosomes. Taken together, these results further point to PEG-GATGE dendrimers as biosafe delivery vectors of nucleic acids to neuronal cells , suggesting their feasibility as carriers in the context of nervous system applications. Furthermore, our data reinforce the importance of testing the performance of new vectors in different models to verify their potential applicability and/or .