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Optimization of Lipid Nanoparticles for saRNA Expression and Cellular Activation Using a Design-of-Experiment
Han Han Ly1, Simon Daniel2, Shekinah K V Soriano1
1Michael Smith Laboratories, School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada.
Lipid nanoparticles (LNPs) optimization for larger RNA payloads like self-amplifying RNA (saRNA) was achieved using design of experiments. This study enhances RNA delivery technology for vaccines and protein replacement therapies.
Area of Science:
- Biotechnology
- Nanomedicine
- Molecular Biology
Background:
- Lipid nanoparticles (LNPs) are crucial for RNA delivery, demonstrated by mRNA vaccines and siRNA therapies.
- Optimization of LNPs for larger, complex RNA payloads like self-amplifying RNA (saRNA) remains underexplored.
- Understanding LNP process parameters, critical quality attributes (CQAs), and functional outcomes (protein expression, cellular activation) is limited.
Purpose of the Study:
- To optimize lipid nanoparticle (LNP) formulations for self-amplifying RNA (saRNA) delivery.
- To investigate the impact of process parameters and ionizable lipids on saRNA encapsulation and function.
- To establish relationships between LNP composition, CQAs, and functional readouts for saRNA.
Main Methods:
- Employed two iterations of design of experiments (DoE): definitive screening design and Box-Behnken design.
- Utilized FDA-approved ionizable lipids (MC3, ALC-0315, SM-102) for saRNA formulation optimization.
- Evaluated LNP formulations based on critical quality attributes (CQAs) and functional assays, including protein expression and cellular activation.
Main Results:
- Polyethylene glycol (PEG) is essential for maintaining saRNA LNP critical quality attributes (CQAs).
- Self-amplifying RNA (saRNA) presents greater challenges in encapsulation and preservation compared to messenger RNA (mRNA).
- Identified three distinct LNP formulations optimized for specific outcomes: minimized cellular activation, maximized cellular activation, or balanced CQAs with protein expression.
Conclusions:
- Design of experiments (DoE) and response surface modeling effectively optimize saRNA LNP formulations.
- The findings provide a framework for designing LNP formulations for diverse RNA cargoes and therapeutic applications.
- This research advances the development of LNP-based vaccines and protein replacement therapies utilizing larger RNA molecules.
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