Comparison and validation of a high-throughput lipid nanoparticle production and characterization workflow

Lipid nanoparticle-borne, RNA-based therapeutics have emerged as transformative tools in nanomedicine. However, to optimize lipid nanoparticle (LNP) formulations against different pathologies, it will be necessary to change LNP payload, lipid composition, and production parameters. High-throughput formulation screening provides a way to rapidly develop and assess new LNP formulations, however this requires the capacity for high-throughput LNP physico-chemical characterization methods. When considering a shift towards automated/semi-automated high-throughput methods, it is pertinent to evaluate whether such characterization is comparable to so-called “tried and true” methods, especially in the context of scaling hit formulations from the screening phase to production. Here, we show that combining a semi-automated microfluidic system with a high-throughput characterization instrument enables the rapid production and characterization of LNP. Compared to conventional methods, the high-throughput plate reader DLS provided comparable hydrodynamic diameter data and faster analysis, albeit with lower sensitivity for RNA quantification. Additionally, we conducted an independent analysis of raw autocorrelation function data from dynamic light scattering measurements to mitigate functional differences between the high-throughput and single sample instruments. Fluorescence-based assays, also capable for high-throughput workflows, were demonstrated to be more sensitive for RNA quantification. These results illustrate that high-throughput systems can streamline LNP development, and be integrated into a translational workflow, i.e. screening to identify hit formulations, transition of hit formulations to scalable production methods, and validation of screening characterization results. This integrated workflow represents an important step for RNA therapeutic development pipelines, where increasing characterization capacity can accelerate nanomedicine development.