CFTR mRNA delivery with a novel non-LNP nanoemulsion formulation to cystic fibrosis differentiated human airway epithelium and airway organoid
Bibliographic record
Abstract
Background: Cystic fibrosis (CF) is caused by dysfunctional mutations in the CF transmembrane regulator (CFTR) chloride channel. Currently approved CFTR modulators are not effective in all genotypes leaving some patients without a therapeutic option. A single universally applicable therapy that produces functional CFTR protein expression by CFTR gene delivery would benefit for all patients. Aim: To assess wild-type CFTR mRNA delivery with a novel non-LNP formulation [RIGImmune Inc., Nano-Emulsion Enhanced Delivery (NEEDTM) platform] to human airway epithelium and organoid from CF. Methods: Optimised wild-type CFTR mRNA (Northern RNA, Canada) formulated with NEEDTM (RIG-301) was apically applied to differentiated human alveolar or bronchial epithelium (hAE or hBE, Epithelix Sarl) from healthy or CF (ΔF508) subjects. Cells were collected at different timepoints up to 72hrs post-delivery, and CFTR protein was detected by Western blotting. A human induced pluripotent stem cell derived CF ΔF508- airway apical-in organoids were treated with RIG-301, and effects of forskolin-induced swelling were evaluated (HiLung Inc., Japan). Results: RIG-301 induced a significant CFTR protein expression with longer duration than unformulated CFTR mRNA delivery in hAE and hBE. RIG-301 also showed an increased forskolin-induced swelling of CF-airway organoid, which effects were comparable to those of a triple combinations of CFTR modulators (VX-661/VX-445/VX-770). Conclusion: Our data demonstrate the capability of the NEEDTM platform to deliver optimised CFTR mRNA in airway cells. These preclinical data warrant further investigations of CFTR delivery in vivo.
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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 | 0.000 | 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) | 0.001 | 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".