Senior Scientist Genentech, Inc, California, United States
Lipid nanoparticles (LNPs) are increasingly employed to improve delivery efficiency and therapeutic efficacy of new pharmaceutical modalities, especially nucleic acids. LNP is one of the most effective delivery platforms for mRNA antigens and has led to the success of mRNA-based COVID vaccines. LNPs are also complex formulations composed of helper lipids, PEGylated lipids, as well as charged lipid species for encapsulating nucleic acids, resulting in their unique nanoscale structures. Various formulation parameters can affect the quality attributes of LNP formulations, but currently there is a lack of systemic screening approaches to address this challenge. In this work, we developed an automated high-throughput screening (HTS) workflow for streamline preparation and analytical characterization of LNPs loaded with antisense oligonucleotides (ASOs) in a full 96-well plate within a timeframe of ~3 hrs. ASO-loaded LNPs were formulated by an automated solvent-injection method using a robotic liquid handler, and assessed for particle size distribution, nanoparticle structure, encapsulation efficiency, and stability with different formulation compositions and ASO loadings. Results indicate that the PEGylated lipid content significantly affected the particle size distribution; whereas the ionizable lipid / ASO charge ratio determined the encapsulation efficiency of ASOs. The HTS approach has also been successfully applied to formulation screenings of various LNPs including liposomes loaded with small molecules and LNPs loaded with mRNA. Furthermore, results from our HTS approach correlated with those from the state-of-the-art, scale-up method using a microfluidic formulator, therefore opening up a new avenue for robust formulation development and design of experiment methods, while reducing material usage by 10 folds and improving analytical outputs and accumulation of information by 100 folds.