Evaluation of rayleigh jet atomizer for intranasal delivery of lipid nanoparticle-siRNA formulations: stability, deposition, and device performance
Bibliographic record
Abstract
The COVID-19 pandemic has emphasised the need for innovative and efficient drug delivery systems, particularly for nucleic acid-based therapeutics. Lipid nanoparticle (LNP)-based small interfering RNA (siRNA) technology provides a promising strategy for gene therapy, immune modulation, and targeted molecular medicine. Intranasal delivery of LNP-siRNA formulations offers advantages such as efficient gene silencing and non-invasive administration. However, the nasal spray device plays a crucial role in determining the deposition patterns within the nasal cavity and can impact the physicochemical stability of LNP formulations during aerosolisation. In this study, the Rayleigh Jet Nasal Atomizer was evaluated for its performance in delivering three LNP-siRNA formulations designed based on the LNP structures of Moderna, Pfizer, and Alnylam (Onpattro) marketed formulations, respectively. Key nanoparticle characteristics, including particle size distribution, polydispersity index (PDI), zeta potential, and encapsulation efficiency, as well as aerosol properties such as droplet size, were analyzed before and after aerosolisation. Deposition patterns were assessed using the Alberta Idealized Nasal Inlet (AINI) model to determine the distribution of aerosolized LNPs. The results demonstrate that the Rayleigh Jet Nasal Atomizer efficiently delivers all the three formulations to the nasal cavity, primarily targeting the nasopharynx, while minimizing deposition in the lower respiratory tract. Additionally, the device maintained LNPs structural integrity, although a reduction in encapsulated siRNA concentration suggests partial LNP disruption during aerosolisation. These findings indicate that the Rayleigh Jet Nasal Atomizer is a suitable device for intranasal delivery of LNP-based siRNA therapeutics, offering a promising approach for nasal administration of RNA-based drug 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 | 0.003 | 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) | 0.000 | 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".