Enhancing peptide and PMO delivery to mouse airway epithelia by chemical conjugation with the amphiphilic peptide S10
Bibliographic record
Abstract
Delivery of antisense oligonucleotides (ASOs) to airway epithelial cells is arduous due to the physiological barriers that protect the lungs and the endosomal entrapment phenomenon, which prevents ASOs from reaching their intracellular targets. Various delivery strategies involving peptide-, lipid-, and polymer-based carriers are being investigated, yet the challenge remains. S10 is a peptide-based delivery agent that enables the intracellular delivery of biomolecules such as GFP, CRISPR-associated nuclease ribonucleoprotein (RNP), base editor RNP, and a fluorescent peptide into lung cells after intranasal or intratracheal administrations to mice, ferrets, and rhesus monkeys. Herein, we demonstrate that covalently attaching S10 to a fluorescently labeled peptide or a functional splice-switching phosphorodiamidate morpholino oligomer improves their intracellular delivery to airway epithelia in mice after a single intranasal instillation. Data reveal a homogeneous delivery from the trachea to the distal region of the lungs, specifically into the cells lining the airway. Quantitative measurements further highlight that conjugation via a disulfide bond through a pegylated (PEG) linker was the most beneficial strategy compared with direct conjugation (without the PEG linker) or conjugation via a permanent thiol-maleimide bond. We believe that S10-based conjugation provides a great strategy to achieve intracellular delivery of peptides and ASOs with therapeutic properties in lungs.
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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) | 0.001 | 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 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".