Systemic delivery of proteins using novel peptides via the sublingual route
Bibliographic record
Abstract
Therapeutic proteins often require needle-based injections, which compromise medication adherence especially for those with chronic diseases. Sublingual administration provides a simple and non-invasive alternative. Herein, two novel peptides (lipid-conjugated protamine and a protamine dimer) were synthesized to enable sublingual delivery of proteins through simple physical mixing with the payloads. It was found that the novel peptides promoted intracellular delivery of proteins via increased pore formation on the cell surface. Results from in vitro models of cell spheroids and human sublingual tissue substitute indicated that the novel peptides enhanced protein penetration through multiple cell layers compared to protamine. The novel peptides were mixed with insulin or semaglutide and sublingually delivered to mice for blood glucose (BG) control. The effects of these sublingual formulations were comparable to the subcutaneous preparations and superior to protamine. In addition to peptide drugs, the novel peptides were shown to enable sublingual absorption of larger proteins with molecular weights from 22 to 150 kDa in mice, including human recombinant growth hormone (rhGH), bovine serum albumin (BSA) and Immunoglobulin G (IgG). The novel peptides given sublingually did not induce any measurable toxicities in mice.
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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 | 0.001 |
| 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".