Dynamic bonds and polyion complex (PIC) nanoparticles for targeted intracellular peptide delivery

ended

The therapeutic action of many peptides is limited by their poor stability in biological media and lack of cell specificity. In particular, cationic peptide amphiphiles are promising antimicrobial tools, but their targeted delivery inside infected host cells to kill intracellular pathogens remains challenging. Our goal is to develop a new supramolecular strategy to protect these peptides and provide them with cell specificity towards their safe and efficient intracellular delivery. In this project, we will combine dynamic covalent bonds, supramolecular, peptide and polymer chemistry, together with chemical biology, to develop a conceptually new targeted drug delivery platform: Hybrid polymer/peptide self-assembled nanoparticles combining stimuli-responsive, cell-penetrating and cell-targeting properties will be applied to the intracellular delivery of cationic peptide amphiphiles inside infected cells. We will prepare combinatorial libraries of cationic peptide amphiphiles that will be screened for membrane permeation and antimicrobial activity to find suitable dual-action peptides for intracellular application. These amphiphilic peptides will be self-assembled with anionic stimuli-responsive polymers into organic nanoparticles designed to disassemble and release their cargo upon cellular uptake, in response to pH reduction in the endosome. Under these conditions, the dynamic bonds present in the peptide amphiphiles can be broken to release conjugated molecules of interest (e.g. drugs, fluorescent probes, etc.). Cell targeting will be achieved by controlling nanoparticle size and by decorating the polymers with ligands for specific recognition by cellular receptors. Dual-action peptides will diffuse across cellular compartments to attack pathogens in the cytosol. The biological activity of peptides and nanoparticles will be assessed with advanced chemical biology and imaging techniques in suitable biological models.