Back

Development and optimisation of cationic lipid nanoparticles for mRNA delivery

Yan, D.; Lu, H.; Bhamra, A. K.; Fu, R.; Wang, N.; Teh, J. H.; Lou, H.; Chen, R.; Aboagye, E. O.

2023-02-07 bioengineering
10.1101/2023.02.07.524134 bioRxiv
Show abstract

Messenger RNA (mRNA) has been proposed as a therapeutic agent for various diseases, including cancer. To ensure effective transfection of cancer cells, mRNA needs to be transported with a delivery system that protects its integrity and functionality. In this regard, cationic lipid nanoparticles composed of dioleoylphosphatidylethanolamine (DOPE) and 3{beta}-[N-(N,N-dimethylaminoethane)-carbamoyl] cholesterol (DC-Chol) have emerged as common vectors to deliver mRNA. In this project, we aim to use luciferase mRNA as a reporter to synthesise mRNA-loaded cationic lipid nanoparticles, and optimise their mRNA encapsulation and transfection efficiency in ovarian cancer cells. The optimisation process included: 1) adjusting the lipid formulation; 2) adjusting the input mRNA concentration before lipid nanoparticle extrusion; and 3) adjusting the extrusion methods. After optimisation, the encapsulation efficiency was optimised to 62%, thus achieving a relatively high transfection luminescence signal (9.4 times compared to baseline). The lipid nanoparticles also demonstrated stable physical characteristics and high biocompatibility (above 75% cell viability after treatment) within 24 hours. Overall, this project evaluated the synthesis of DOPE/DC-Chol cationic lipid nanoparticles, and optimised their mRNA encapsulation and transfection efficiency in ovarian cancer cell lines. The optimised lipid nanoparticles can be utilised as an ideal system for mRNA delivery, which could be further developed as a potential platform for the immunotherapy in ovarian cancer.

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.