Speaker
Description
Lipid nanoparticles (LNPs) have emerged as key vehicles for nucleic acid (NA) delivery, promising novel tool for vaccination, cancer and rare diseases therapy. LNPs are complex structures, composed of ionizable lipids (ILs), helper lipids, PEGylated lipids and cargos. ILs are responsible for efficient encapsulation of the NA cargo and for NA release during endosome maturation. The dynamic nature of LNP pH dependent behavior inside a target cell is a major challenge in experimental research of LNP behavior, therefore a full understanding of LNP structure and processes related to the cargo release are still missing.
Molecular dynamics simulations with multiscale resolution offer a powerful approach to explore LNP organization and function in various environments. All-atom simulations can describe the interactions of individual lipid functional groups with NAs and their mutual effect on their structure and stability, but are limited to either small models or a short simulation of a prebuilt LNP. On the other hand, coarse-grained (CG) simulations can be used to simulate the formation of a whole LNP in tens of nanometers and microseconds scale, predicting the internal LNP organization, distinguishing between lipid inverse hexagonal and lamellar phase. The lower computational costs allow CG simulations to study LNP in a systematic way, manipulating the composition and ratio of lipid species or in a desired bioenvironment in its path through the body, getting us closer to the description of the mechanism of the endosomal escape process.
The potentials and limitations of both the resolutions can be efficiently combined to a valuable workflow, advancing our understanding of LNP structure, stability and behavior. The provided insight can lead to a targeted in-silico design of next-generation delivery platforms, increasing the cargo delivery efficiency and decreasing the costs. Their integration into formulation workflows represents a promising direction for predictive, mechanism-informed design of therapeutic systems.
