Targeted nasal delivery of LNP-mRNAs aerosolised by Rayleigh breakup technology
Bibliographic record
Abstract
• Rayleigh breakup technology for aerosolisation was explored. • Rayleigh breakup droplets had the physical size of ~25µm and > 70% with aerodynamic size > 10 µm. • Physiochemical properties of lipid nanoparticles-mRNAs were preserved by Rayleigh breakup technology for aerosolisation. • Targeted nasal delivery of mRNA at maximum can be achieved at optimized parameters. • Fast inhalation flowrate may cause undesired delivery of aerosols to the lungs. The nasal delivery of mRNA vaccines attracts great interests in both academia and industry. While the lipid nanoparticle (LNP)-mRNA complexes are vulnerable and need a subtle process for aerosolisation. In this study, a new nasal atomizer, based on the working rationale of Rayleigh breakup, was employed to aerosolise LNP-mRNAs. The data revealed no statistical differences in physiochemical properties before and after aerosolisation, strongly suggesting LNP-mRNAs be well preserved upon aerosolisation by Rayleigh breakup technology. Additionally, these Rayleigh breakup droplets showed a physical size of ∼25 µm in mean with a narrow size distribution (Span: 1.24) and demonstrated a large portion (70–80 % w/w) greater than 10 µm in aerodynamic diameter, strongly suggesting a predominate deposition in the upper airway and designating a great appropriateness for nasal drug delivery. Furthermore, the recently developed Alberta Idealized Nasal Inlet (AINI) was utilized to evaluate the regional nasal deposition of LNP-mRNA aerosols. It was demonstrated that, at the administration angle of 45°, the major deposition of mRNAs (>50 % w/w) was in the target region of turbinates. The inhalation airflow at 7.5 L/min can maximize the targeted delivery of mRNA (∼64 % w/w) and minimize the undesirable depositions in other segments. This study provides a new approach to aerosolise LNP-mRNAs with undisturbed stabilities for targeted nasal vaccine delivery.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".