Lipid Nanoparticle and Liposome Reference Materials: Assessment of Size Homogeneity and Long-Term −70 °C and 4 °C Storage Stability
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide With recent advances and anticipated proliferation of lipid nanoparticle (LNP)-delivered vaccines and therapeutics, there is a need for the availability of internationally recognized reference materials of LNP systems. Accordingly, we developed six LNP and liposome (anionic, neutral, and cationic each) candidate reference material formulations and thoroughly characterized by dynamic light scattering their particle hydrodynamic size (Z-avr) and polydispersity. We also evaluated the particle size homogeneity and long-term −70 °C and 4 °C storage stability using multiple large sets of randomly selected vials for each formulation. The formulations stored at −70 °C remained stable and homogeneous for a minimum of 9 months. The Z-avr relative combined uncertainty and the long-term variability were both <1.3% for liposome formulations and anionic LNPs, (3.9% and 1.7%) for neutral LNPs, and (6.7% and 4.4%) for cationic LNPs. An inadvertent few-hour-long storage temperature increase to −35 °C due to a freezer malfunction resulted in a small change of the size and size distribution of anionic liposomes and LNPs but, unexpectedly, a larger size increase of the neutral and cationic liposomes (≤5%) and LNPs (≤25%). The mean Z-avr values of the LNPs stored at 4 °C appeared to slowly increase with t 1/3, where t is the storage time, and the Z-avr between-vial heterogeneity and mean polydispersity index values appeared to decrease; no change was observed for liposomes. The size and size distribution evolution of LNPs stored at 4 °C was attributed to an incomplete equilibration of the formulations following the addition of sucrose prior to the initial freezing. Such a process of size increase and size distribution narrowing has not been previously discussed nor observed in the context of LNPs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".