Mae M. Lewis

Biological systems use post-translational modifications (PTMs) to control the structure, location, and function of proteins after expression. Despite the ubiquity of PTMs in biology, their use to create genetically encoded recombinant biomaterials is limited. We have utilized a natural lipidation PTM (hedgehog-mediated cholesterol modification of proteins) to create a class of hybrid biomaterials called cholesterol-modified polypeptides (CHaMPs) that exhibit programmable self-assembly at the nanoscale. To demonstrate the biomedical utility of CHaMPs, we used this approach to append cholesterol to biologically active peptide exendin-4 that is an approved drug for the treatment of type II diabetes. The exendin-cholesterol conjugate self-assembled into micelles, and these micelles activate the glucagon-like peptide-1 receptor with a potency comparable to that of current gold standard treatments.

Although mRNA lipid nanoparticles (LNPs) are highly effective as vaccines, their efficacy for pulmonary delivery has not yet fully been established. A major barrier to this therapeutic goal is their instability during aerosolization for local delivery. This imparts a shear force that degrades the mRNA cargo and therefore reduces cell transfection. In addition to remaining stable upon aerosolization, mRNA LNPs must also possess the aerodynamic properties to achieve deposition in clinically relevant areas of the lungs. We addressed these challenges by formulating mRNA LNPs with SM-102, the clinically approved ionizable lipid in the Spikevax COVID-19 vaccine. Our lead candidate, B-1, had the highest mRNA expression in both a physiologically relevant air-liquid interface (ALI) human lung cell model and in healthy mice lungs upon aerosolization. Further, B-1 showed selective transfection in vivo of lung epithelial cells compared to immune cells and endothelial cells. These results show that the formulation can target therapeutically relevant cells in pulmonary diseases such as cystic fibrosis. Morphological studies of B-1 revealed differences in the surface structure compared to LNPs with lower transfection efficiency. Importantly, the formulation maintained critical aerodynamic properties in simulated human airways upon next generation impaction. Finally, structure-function analysis of SM-102 revealed that small changes in the number of carbons can improve upon mRNA delivery in ALI human lung cells. Overall, our study expands the application of SM-102 and its analogs to aerosolized pulmonary delivery and identifies a potent lead candidate for future therapeutically active mRNA therapies.

For cystic fibrosis patients, a lung-targeted gene therapy would significantly alleviate pulmonary complications associated with morbidity and mortality. However, mucus in the airways and cell entry pose huge delivery barriers for local gene therapy. Here, we used phage display technology to select for and identify mucus- and cell-penetrating peptides against primary human bronchial epithelial cells from cystic fibrosis patients cultured at the air-liquid interface. At the air-liquid interface, primary human bronchial epithelial cells produce mucus and reflect cystic fibrosis disease pathology, making it a clinically relevant model. Using this model, we discovered a lead candidate peptide and incorporated it into lipid nanoparticles to deliver mRNA to primary human bronchial epithelia <i>in vitro</i> and mouse lungs <i>in vivo</i>. Compared to lipid nanoparticles without our peptide, peptide-lipid nanoparticles demonstrated up to 7.8-fold and 3.4-fold higher reporter luciferase bioactivity <i>in vitro</i> and <i>in vivo</i>, respectively. Importantly, these peptides facilitated higher specific uptake of nanoparticles into lung epithelia relative to other cell types. Since gene delivery to primary human bronchial epithelia is a significant challenge, we are encouraged by these results and anticipate that our peptide could be used to successfully deliver cystic fibrosis gene therapies in future work.

Abstract mRNA lipid nanoparticles (LNPs) have a tremendous potential to treat, cure, or prevent many diseases. To identify promising candidates for each application, most studies screen dozens to hundreds of formulations with ionizable lipids synthesized using a single type of chemistry. However, this technique leaves the ionizable lipids synthesized through multi-step chemistries underexplored. This gap in the repertoire of structures is of particular significance because it affects the screening of analogs that are structurally similar to SM-102 and ALC-0315, the ionizable lipids used to formulate the clinically approved mRNA LNP COVID-19 vaccines. Herein, we address this by employing LightGBM, a machine learning algorithm, to reduce the burden of screening these types of ionizable lipids by learning from the breadth and diversity of lipids that have already been tested. We first evaluate the ability of LightGBM to predict LNP potency across heterogeneous chemistries from different studies to achieve an R 2 of 0.94. After establishing the predictive capacity of the model, we then identify the number of outside carbons in the ionizable lipid as the most important factor contributing to transfection efficiency. From this finding, we subsequently apply the algorithm to predict the effect of formulating nanoluciferase mRNA LNPs using analogs of SM-102 and ALC-0315 with small changes in the number of outside carbons on luciferase activity in HEK293T cells and achieve an R 2 of 0.83. Importantly, this correlation encompasses novel lipids not included within the database used to train the algorithm. Overall, this study demonstrates the potential of machine learning to accelerate the development of new ionizable lipids by simplifying the screening process.