Coarse-Grained Modeling of Nucleic Acid-Loaded Lipid Nanoparticle Formulations

We will start off with a problem statement describing how the complicated nature of lipid nanoparticle formulations makes efficient formulation optimization a challenge. Additionally, the effectiveness of LNP formulations is believed to be strongly correlated to the LNP morphology, but this is difficult to characterize, making predictive, in silico measurements extremely valuable.

Then we will provide a brief description of molecular modeling, emphasizing that for LNP self-assembly length-scales (~100 nm) coarse-grained modeling is required. In addition, high-throughput screening studies require that the building of CG models be automated as much as possible. We briefly review techniques for the automation of this process.

We demonstrate the application of coarse-grained modeling to RNA-encapsulating LNPs, with a case study focusing on the Pfizer-BioNTech COVID-19 vaccine formulation. In this case study, we examine various aspects:
- Self-assembly of an mRNA-containing LNP at low pH
- Evolution of LNP morphology, and mRNA encapsulation stability, under pH changes
- Formation of mRNA-encapsulating blebs at physiological pH
Additionally, we examine how modifying the LNP formulation, by changing lipid composition and chemistry, can affect the morphology of the resulting LNPs, according to the predictions of our coarse-grained approach.

We will then describe future case studies in which we will use CG modeling to examine:
- Role of ethanol in LNP self-assembly
- Interaction between LNPs and model endosomal membranes, to investigate the mechanism of RNA release
- Role of PEGylated lipid in regulating the LNP size and stability

Finally, we will summarize and provide some additional context for our CG approach and results. We will:
- Compare and contrast our approach to other CG models of RNA-loaded LNPs
- Discuss accuracy and reliability
- Address limitations of our approach