Controlling Payload Heterogeneity in Lipid Nanoparticles for RNA-Based Therapeutics

Abstract Lipid nanoparticles (LNPs) are a leading platform for nucleic acid delivery, yet conventional assembly by mixing lipids with RNA yields particles with heterogeneous, bimodal payload distribution. Our transfection experiments demonstrate that heterogeneous siRNA distribution within LNPs significantly reduces gene knockdown efficiency. To systematically investigate the origin and extent of payload heterogeneity, we integrate coarse-grained molecular dynamics and kinetic Monte Carlo simulations with single-particle characterization via cylindrical illumination confocal spectroscopy and machine learning analysis. We find that the balance between RNA diffusion kinetics and lipid self-assembly dynamics is the dominant driver of payload heterogeneity. Leveraging this mechanistic insight, we show that (i) finely controlled turbulent mixing minimizes payload variance and increases the uniformity of RNA distribution without altering LNP size, and (ii) systematic adjustment of salt concentration and PEG-lipid content tunes RNA loading in a volume-dependent manner. Together, these results elucidate the self-assembly landscape of RNA-LNPs and provide actionable design principles for crafting more uniform, potent, and safer LNP-based nucleic acid therapies.