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

Area of Science:

• Biophysical Chemistry
• Nanotechnology
• Pharmaceutical Sciences

Background:

• Lipid nanoparticles (LNPs) are key delivery vehicles for therapeutics like mRNA.
• Accurate characterization of LNP composition, especially cargo loading, is critical for efficacy and safety.
• Existing methods struggle to reliably quantify empty versus loaded LNPs.

Purpose of the Study:

• To introduce and validate a novel application of the Custom Grid (CG) algorithm for analyzing LNPs.
• To demonstrate the CG algorithm's capability in determining LNP properties like density, molar mass, and size distributions.
• To establish a reliable method for distinguishing empty LNPs from those loaded with cargo, such as mRNA or proteins.

Main Methods:

• Utilized sedimentation velocity analytical ultracentrifugation (SV-AUC) experiments.
• Employed density matching with D2O and the Custom Grid (CG) algorithm for data analysis.
• Applied parametrically constrained spectrum analysis for orthogonal validation.

Main Results:

• The CG algorithm successfully derived partial specific volume, molar mass, and hydrodynamic radius distributions for various LNPs.
• Density profiles generated by the CG method confirmed successful cargo loading and distinguished between empty and mRNA-loaded LNPs.
• Orthogonal validation using parametrically constrained spectrum analysis showed good agreement with CG results.

Conclusions:

• The CG algorithm, combined with density matching, provides a robust method for characterizing LNP composition and cargo loading.
• This approach overcomes limitations of polydispersity assessment for determining empty LNP fractions.
• The method offers a reliable tool for quality control in LNP formulation development.