Optimization of lipid nanoparticles for gene editing of the liver via intraduodenal delivery.

Lipid nanoparticles (LNPs) have recently emerged as successful gene delivery platforms for a diverse array of disease treatments. Efforts to optimize their design for common administration methods such as intravenous injection, intramuscular injection, or inhalation, revolve primarily around the addition of targeting ligands or the choice of ionizable lipid. Here, we employed a multi-step screening method to optimize the type of helper lipid and component ratios in a plasmid DNA (pDNA) LNP library to efficiently deliver pDNA through intraduodenal delivery as an indicative route for oral administration. By addressing different physiological barriers in a stepwise manner, we down-selected effective LNP candidates from a library of over 1000 formulations. Beyond reporter protein expression, we assessed the efficiency in non-viral gene editing in mouse liver mediated by LNPs to knockdown PCSK9 and ANGPTL3 expression, thereby lowering low-density lipoprotein (LDL) cholesterol levels. Utilizing an all-in-one pDNA construct with Strep. pyogenes Cas9 and gRNAs, our results showcased that intraduodenal administration of selected LNPs facilitated targeted gene knockdown in the liver, resulting in a 27% reduction in the serum LDL cholesterol level. This LNP-based all-in-one pDNA-mediated gene editing strategy highlights its potential as an oral therapeutic approach for hypercholesterolemia, opening up new possibilities for DNA-based gene medicine applications.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Hai-Quan Mao reports financial support was provided by National Institute of Allergy and Infectious Diseases. Kam W. Leong reports financial support was provided by National Institutes of Health. Shuting Sarah Cai reports financial support was provided by National Science Foundation. Hai-Quan Mao, Yining Zhu, Jingyao Ma, Ruochen Shen, and Leonardo Cheng has patent pending to Johns Hopkins Technology Ventures. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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