Impact of Aqueous Buffer Composition on RNA-LNP Physicochemical Properties
Bibliographic record
Abstract
Background RNA-encapsulating lipid nanoparticles (RNA-LNPs) therapeutics offer a rational avenue for enhanced delivery to extra-hepatic tissues, particularly for treating solid tumors. In this study, the impact of different formulation variables, as the aqueous buffer species, defined by their kosmotropic effects and molarity (1), on the structural integrity and stability of RNA-LNPs was studied. Methods RNA-LNPs were initially produced by pipette mixing to screen the effects of various aqueous buffer species and molarities on particles’ physical properties. Subsequently, RNA-LNPs prepared with varied lipid compositions were formulated in the presence of different aqueous buffers, using the Nanoassemblr™ microfluidics platform (Precision NanoSystems, Vancouver, Canada). Particle size and polydispersity index, zeta potential, and encapsulation efficiency were characterized, using dynamic light scattering (DLS), electrophoretic light scattering (ELS) and the RiboGreen™ fluorescence assay, respectively, to evaluate particle stability and integrity during manufacturing. Results Our findings highlight the potential impact of aqueous buffer composition on RNA-LNP physicochemical properties. Pipette-mixed RNA-LNPs prepared in citrate buffer, with varying molarities, or acetate buffer both at pH 4.0, exhibited substantial variation in particle size, underscoring the role of aqueous buffer type and ionic strength in lipid packing dynamics. For the formulations produced via microfluidics, citrate buffer appeared to provide superior stability, with minimal size increase and reduced aggregation during manufacturing, compared to the other conditions/buffers tested. Conclusions These results emphasize the critical role of aqueous buffer selection in modulating lipid molecular arrangement during the self-assembly process of RNA-LNPs, that ultimately impact their physicochemical integrity during manufacturing. Thus, our study provides valuable insights into the biophysical interplay between lipid components and aqueous environments, offering practical guidance for optimizing RNA-LNP formulations for targeted therapeutic applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".