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Correlating the Structure and Gene Silencing Activity of Oligonucleotide-Loaded Lipid Nanoparticles Using Small-Angle X-ray Scattering.

Michal HammelYuchen FanApoorva SarodeAmy E ByrnesNanzhi ZangPonien KouKarthik NagapudiDennis LeungCasper C HoogenraadTao ChenChun-Wan YenGreg L Hura
Published in: ACS nano (2023)
With three FDA-approved products, lipid nanoparticles (LNPs) are under intensive development for delivering wide-ranging nucleic acid therapeutics. A significant challenge for LNP development is insufficient understanding of structure-activity relationship (SAR). Small changes in chemical composition and process parameters can affect LNP structure, significantly impacting performance in vitro and in vivo . The choice of polyethylene glycol lipid (PEG-lipid), one of the essential lipids for LNP, has been proven to govern particle size. Here we find that PEG-lipids can further modify the core organization of antisense oligonucleotide (ASO)-loaded LNPs to govern its gene silencing activity. Furthermore, we also have found that the extent of compartmentalization, measured by the ratio of disordered vs ordered inverted hexagonal phases within an ASO-lipid core, is predictive of in vitro gene silencing. In this work, we propose that a lower ratio of disordered/ordered core phases correlates with stronger gene knockdown efficacy. To establish these findings, we developed a seamless high-throughput screening approach that integrated an automated LNP formulation system with structural analysis by small-angle X-ray scattering (SAXS) and in vitro TMEM106b mRNA knockdown assessment. We applied this approach to screen 54 ASO-LNP formulations while varying the type and concentration of PEG-lipids. Representative formulations with diverse SAXS profiles were further visualized using cryogenic electron microscopy (cryo-EM) to help structural elucidation. The proposed SAR was built by combining this structural analysis with in vitro data. Our integrated methods, analysis, and resulting findings on PEG-lipid can be applied to rapidly optimize other LNP formulations in a complex design space.
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