Investigation on a Freeze-Drying Process for Long-Term Stability of mRNA-LNPs
Bibliographic record
Abstract
Background: Thermostability remains a key bottleneck for equitable access to mRNA–LNP vaccines, largely due to cold-chain requirements. Objectives and methods: Here, we optimized freeze-drying formulations by screening excipients (sugars, sugar-alcohols, and proteins) and buffers to preserve mRNA–LNP physicochemical (size, polydispersity index -PDI and encapsulation efficiency -EE) and fluorescence intensity-based, functional integrity assay (in vitro transfection) during long-term storage of up to 6 months. Results: In a preliminary screening study, different sugars (sucrose, trehalose), sugar alcohol (mannitol), protein (gelatin) and different buffers (Tris, PBS, histidine) were evaluated. The preliminary result showed that sucrose and trehalose, along with Tris and histidine buffers, had a positive effect on maintaining the physicochemical properties during freeze-drying, while mannitol, gelatin and PBS buffer had a negative effect. Based on these findings, the optimized formulations containing sucrose/Tris, sucrose/histidine, trehalose/Tris and trehalose/histidine were chosen, and a stability study was performed at −80, −20, 4, and 20 °C for six months. Conclusions: Overall, except for the samples maintained at 20 °C, no significant changes in the physicochemical quality of the freeze-dried mRNA-LNPs were observed over six months at −80, −20, and 4 °C. The in vitro stability study demonstrated stability at 4 °C for four months across all formulations, while a formulation with sucrose/Tris maintained satisfactory stability even at 20 °C for the same duration. The main results of this study demonstrate the feasibility of storing mRNA drug products as solid formulations at non-freezing temperatures (≤ 4 °C).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".