Solution biophysics identifies lipid nanoparticle non-sphericity, polydispersity, and dependence on internal ordering for efficacious mRNA delivery.

Lipid nanoparticles (LNPs) are the most advanced delivery system currently available for RNA therapeutics. Their development has accelerated since the success of Patisiran, the first siRNA-LNP therapeutic, and the mRNA vaccines that emerged during the COVID-19 pandemic. Designing LNPs with specific targeting, high potency, and minimal side effects is crucial for their successful clinical use. However, our understanding of how the composition and mixing method influences the structural, biophysical, and biological properties of the resulting LNPs remains limited, hindering the development of LNPs. Our lack of structural understanding extends from the physical and compositional polydispersity of LNPs, which render traditional characterization methods, such as dynamic light scattering (DLS), unable to accurately quantitate the physicochemical characteristics of LNPs. In this study, we address the challenge of structurally characterizing polydisperse LNP formulations using emerging solution-based biophysical methods that have higher resolution and provide biophysical data beyond size and polydispersity. These techniques include sedimentation velocity analytical ultracentrifugation (SV-AUC), field-flow fractionation followed by multi-angle light scattering (FFF-MALS), and size-exclusion chromatography in-line with synchrotron small-angle X-ray scattering (SEC-SAXS). Here, we show that the LNPs have intrinsic polydispersity in size, RNA loading, and shape, and that these parameters are dependent on both the formulation technique and lipid composition. Lastly, we demonstrate that these biophysical methods can be employed to predict transfection in human primary T cells, intravenous administration, and intramuscular administration by examining the relationship between mRNA translation and physicochemical characteristics. We envision that employing solution-based biophysical methods will be essential for determining LNP structure-function relationships, facilitating the creation of new design rules for LNPs.