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Lipid nanoparticles (LNPs) for RNA delivery have exploded onto the biomedical research scene with the success of mRNA vaccines for COVID-19. In addition to their promise as mRNA vaccines for infectious disease and cancer, LNPs have the potential to treat or cure patients with deadly lung diseases such as cystic fibrosis. However, gene therapy in the lung is a notoriously difficult challenge that has frustrated decades of researchers. Here, I take two approaches to identifying LNPs capable of addressing this challenge. First, I developed an in vitro primary cell platform to screen LNPs for lung mRNA delivery, and used this platform to identify two state-of-the-art LNPs for respiratory tract delivery to mice. Second, I developed Lipid Optimization using Neural networks (LiON), a deep learning strategy for LNP design. Using LiON I evaluated 1.6 million possible LNPs and identified two, FO-32 and FO-35, with even better mRNA delivery to the mouse lung than my previous work. Furthermore, both LNPs efficiently delivered mRNA to ferret lungs, a key preclinical lung model. This is the first academic report of broadly distributed nonviral ferret lung mRNA delivery, and suggests great translational promise for FO-32 and FO-35. Overall, this work shows the potential of next-generation LNPs to bring gene therapy to patients suffering from lung disease.