High-Resolution Characterization of Protein-Conjugated, mRNA-Loaded Lipid Nanoparticles by Analytical...

The study describes a novel use for the Custom Grid (CG) algorithm in UltraScan targeting lipid nanoparticles (LNPs) with cargos ranging from empty LNPs, LNPs loaded with messenger RNA (mRNA), and LNPs conjugated with proteins, or both. The CG method is used to fit sedimentation velocity analytical ultracentrifugation experiments performed in density matching mode to derive partial specific volume, molar mass, and hydrodynamic radius distributions for LNPs. Because LNP cargos often differ in density from the encapsulating lipids, density (or partial specific volume) is a critical quality attribute to quantify LNP composition and cargo loading. It is shown that the CG approach, in combination with D₂O density matching, faithfully fits even complex cases that exhibit both sedimenting and floating analytes in the same sample without sacrificing generality, and derives density distributions confirming successful cargo loading. In addition, the method provides distributions for hydrodynamic radii, molar mass, and sedimentation coefficients. Analysis of the same samples with the parametrically constrained spectrum analysis provides orthogonal validation in good agreement with the CG analysis. The results show that polydispersity assessment and other metrics alone are unreliable in determining the fraction of empty LNPs present in a formulation, but density profiles obtained here clearly distinguish mRNA-loaded from empty LNPs.