Atomistic Insights into Organization of RNA-Loaded Lipid Nanoparticles

Abstract

RNA-based therapies have shown promise in a wide range of applications, from cancer therapy, treatment of inherited diseases to vaccination. Encapsulation of RNA into ionizable lipid (IL) containing lipid nanoparticles (LNPs) has enabled its safe and targeted delivery. We present here the simulations of the self-assembly process of pH-sensitive RNA-carrying LNPs and their internal morphology. At low pH, the simulations confirm a lipid core encapsulating RNA in the hexagonal phase. Our all-atom and coarse-grained simulations show that an RNA molecule inside an LNP is protected from interactions with ions by being enveloped in the charged ILs. At neutral pH, representing the environment after LNP administration into human tissues, LNPs expelled most of the encapsulated RNA and water and formed separate bulk IL-rich and ordered the helper-lipid-rich phase. Helper lipids arranged themselves to be in contact with RNA or water. The presented models provide atomistic understanding of the LNP structure and open a way to investigate them in silico, varying the LNP composition or interacting with other biostructures aiming at increasing the efficiency of RNA-based medicine.

Calculating Apparent pKa Values of Ionizable Lipids in Lipid Nanoparticle

“…At smaller distances the free energy rises approaching or exceeding that for bulk water (0 kcal/mol) in the center of the bilayer, reflecting the free energy cost for burying the charged headgroup inside a low-dielectric region. The indicate that the neutral head groups exist and perhaps favor burial inside membranes while the positive head groups remain on the surface of lipid structures and retain their exposure to water [22][23][24] . The contrast between the positioning of the neutral and charged ILs seems most extreme for MC3 in our studies.…”

Molecular Dynamics Simulation Studies of Bile, Bile Salts, Lipid-Based Drug Formulations, and mRNA–Lipid Nanoparticles: A Review

“…The authors of this work propose that at low pH, when the amino lipid is protonated, this LNP material makes stable bilayers and forms vesicles (Figure 11a,d). At physiological pH, the aminolipid exists in the deprotonated state and the LNPs form an oil-like core composed of the aminolipid ALC-0315 110 Simulations were run under low pH and neutral conditions using CG MARTINI and all-atom models. The nanoparticles were composed of ALC-0315, ALC-0159, DSPC, and cholesterol.…”

“…28 Although, compared to atomistic models, CG models lose some level of detail, they enable much larger model systems, allowing us to study physical phenomena that occur on a larger scale. CG models are being successfully used to model the large lipid particles present in mRNA formulations, 110 and more remarkably, CG models have been used in the simulation of an entire cell. 138 The combination of CG models with atomistic models to create multiscale models can potentially enable the creation of large models that retain much of the accuracy and detail of atomistic models.…”

“…When RNA is incorporated into the particle at physiological pH (Figure c,f), it resides within the core of the LNP and the anionic RNA phosphate backbone is surrounded by molecules of protonated aminolipid. Similarly, a recent study by Paloncýová et al investigated RNA-containing LNPs similar to the Pfizer and BioNTech COVID-19 vaccines . Simulations were run under low pH and neutral conditions using CG MARTINI and all-atom models.…”

Single-Particle Spectroscopic Chromatography Reveals Heterogeneous RNA Loading and Size Correlations in Lipid Nanoparticles

“…Moreover, there is a lack of convincing assessment of how the data obtained can quantitatively translate to structural information and size-loading correlations. Theoretical modeling and molecular dynamics simulation also could only provide limited information on structural changes of individual nanoparticles during complexation and purification due to computational complexity and the large time scale of these processes. − Here, we combine the strength of single-fluorophore sensitivity and absolute quantification by confocal fluorescence spectroscopy with unbiased chromatographic nanoparticle sizing with high resolution and high throughput, and carry out a thorough examination of single-LNP loading-size correlations at a high level of capability and accuracy.…”