Lipid nanoparticles (LNPs) are gaining traction in the field of nucleic acid delivery following the success of two mRNA vaccines against COVID-19. As one of the constituent lipids on LNP surfaces, PEGylated lipids (PEG-lipids) play an important role in defining LNP physicochemical properties and biological interactions.
Previous studies indicate that LNP performance is modulated by tuning PEG-lipid parameters, including PEG size and architecture, carbon tail type and length, and PEG-lipid concentration. Owing to these numerous degrees of freedom, a high-throughput approach is necessary to fully understand LNP behavioral trends over a broad range of PEG-lipid variables.
We report a low-volume, high-throughput screening (HTS) workflow for the preparation, characterization, and in vitro assessment of LNPs loaded with a therapeutic antisense oligonucleotide (ASO). A library of 54 ASO-LNP formulations with distinct PEG-lipid compositions was prepared using a liquid handling robot and assessed for their gene silencing efficacy in murine neurons.
Our results show anionic PEG-lipid concentration regulates LNP particle size. In contrast, PEG-lipid carbon tail length controls ASO-LNP gene silencing activity, with up to 5-fold lower mRNA expression achieved in neurons treated with ASO-LNPs as compared to naked ASO. We then scaled up the HTS hits using a well-established microfluidic formulation technique, demonstrating a smooth translation of ASO-LNP properties and in vitro efficacy across different formulation scales.
The HTS workflow can be used to screen additional LNP multivariate parameters with significant time and material savings, guiding the selection and scale-up of optimal formulations for nucleic acid delivery to various cellular targets.