RNA lipid nanoparticles stabilized during nebulization through excipient selection
Bibliographic record
Abstract
therapeutic RNA delivery. However, during the nebulization process, LNPs are subjected to high shear forces that result in particle destabilization and consequent loss of cargo. Here, we provide a generalizable approach to stabilize LNPs by adjusting the nebulization buffer composition. We investigated the effect of buffer composition on nanoparticle size, RNA encapsulation efficiency, and LNP material recovery. We found that pH 5.0 citrate buffer reduces the loss of encapsulated RNA, poloxamer 188 maintains nanoparticle size and improves recovery, and glucose is important for an iso-osmotic solution. RNA encapsulated in nebulized LNPs maintained bioactivity as demonstrated with cellular uptake and functional siRNA delivery to Vero cells expressing nano luciferase. Together, this work shows a versatile strategy for the delivery of inhalable LNP-based RNA therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".