Preliminary Evaluation of Formulations for Stability of mRNA-LNPs Through Freeze-Thaw Stresses and Long-Term Storage
Bibliographic record
Abstract
Ionizable lipid nanoparticles were a crucial contribution to the effective packaging and delivery of mRNA informational drugs for vaccines and therapeutic applications. However, thermal instability and the need for ultracold storage present significant challenges during distribution and administration, even during non-pandemic periods like epidemics. Ongoing efforts include engineering novel lipids or optimizing mRNA-LNP formulations with different buffers, excipients, and surfactants to enable extended storage at room temperature (RT) or refrigerated conditions (4 ˚C). Methods: In this study, six sugar-surfactant excipient combinations in tris buffer were evaluated for extending the stability of mRNA-LNPs. The experimentation included two phases of screening: first, the evaluation of six formulations under repeated freeze-thaw (−20 °C) cycles, and second, a long-term storage evaluation of the best formulations at RT, 4 °C, − 20 °C and −80 °C. LNPs were formulated with the ionizable lipids ALC-0315 or SM-102. Results: Sucrose-P188 and mannitol-F127 combinations effectively preserved physicochemical properties like encapsulation efficiency (EE), polydispersity index (PDI), and z-average (size) of mRNA-LNPs. Surfactants in the screened formulations reduced the aggregation of LNPs. Storage at −20 °C with sucrose-P188 extended LNP stability beyond ultra-low temperature requirements, showing potential for removing logistical hurdles to improve global delivery by eliminating the need for ultracold storage. Excipients in non-frozen conditions preserved the LNP quality attributes, but the RNA integrity was affected. On the other hand, mRNA degradation was minimized when frozen, but LNP quality was affected, underscoring the challenging trade-off during storage condition optimization. Conclusions: This work highlights the potential of improved formulations to preserve mRNA-LNP functionality and emphasizes the need for further systematic studies on a library of excipient and surfactant combinations, paving the way for stable storage under less stringent conditions.
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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) | 0.000 | 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".