Electrostatic adsorption of polyanions onto lipid nanoparticles controls uptake, trafficking, and transfection of RNA and DNA therapies.

Rapid advances in nucleic acid therapies highlight the immense therapeutic potential of genetic therapeutics. Lipid nanoparticles (LNPs) are highly potent nonviral transfection agents that can encapsulate and deliver various nucleic acid therapeutics, including but not limited to messenger RNA (mRNA), silencing RNA (siRNA), and plasmid DNA (pDNA). However, a major challenge of targeted LNP-mediated systemic delivery is the nanoparticles' nonspecific uptake by the liver and the mononuclear phagocytic system, due partly to the adsorption of endogenous serum proteins onto LNP surfaces. Tunable LNP surface chemistries may enable efficacious delivery across a range of organs and cell types. Here, we describe a method to electrostatically adsorb bioactive polyelectrolytes onto LNPs to create layered LNPs (LLNPs). LNP cores varying in nucleic acid cargo and component lipids were stably layered with four biologically relevant polyanions: hyaluronate (HA), poly-L-aspartate (PLD), poly-L-glutamate (PLE), and polyacrylate (PAA). We further investigated the impact of the four surface polyanions on the transfection and uptake of mRNA- and pDNA-loaded LNPs in cell cultures. PLD- and PLE-LLNPs increased mRNA transfection twofold over unlayered LNPs in immune cells. HA-LLNPs increased pDNA transfection rates by more than twofold in epithelial and immune cells. In a healthy C57BL/6 murine model, PLE- and HA-LLNPs increased transfection by 1.8-fold to 2.5-fold over unlayered LNPs in the liver and spleen. These results suggest that LbL assembly is a generalizable, highly tunable platform to modify the targeting specificity, stability, and transfection efficacy of LNPs, as well as incorporate other charged targeting and therapeutic molecules into these systems.

Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery.

To accelerate the translation of cancer nanomedicine, we used an integrated genomic approach to improve our understanding of the cellular processes that govern nanoparticle trafficking. We developed a massively parallel screen that leverages barcoded, pooled cancer cell lines annotated with multiomic data to investigate cell association patterns across a nanoparticle library spanning a range of formulations with clinical potential. We identified both materials properties and cell-intrinsic features that mediate nanoparticle-cell association. Using machine learning algorithms, we constructed genomic nanoparticle trafficking networks and identified nanoparticle-specific biomarkers. We validated one such biomarker: gene expression of SLC46A3, which inversely predicts lipid-based nanoparticle uptake in vitro and in vivo. Our work establishes the power of integrated screens for nanoparticle delivery and enables the identification and utilization of biomarkers to rationally design nanoformulations.