Enzymatic absorption promoters for non-invasive peptide delivery
Bibliographic record
Abstract
Peptide drugs offer considerable potential for treating a diverse range of diseases. Yet, their clinical application is generally restricted to injectable therapies. The main challenge hindering their broader use through globally accessible, patient-friendly, and non-invasive delivery routes such as oral or buccal, lies in their poor ability to cross biological barriers effectively. Here, we demonstrate that enzymes can be harnessed to transiently reduce these barriers and improve absorption. As a proof of concept, we employ a mucin-specific protease (mucinase) and a phospholipase to increase mucus diffusivity and epithelial cell membrane permeability, respectively. In a canine model, we show that enteric capsules containing both enzymes, and the peptide drug desmopressin achieved a relative bioavailability of 155 % compared to the drug alone. Additionally, a buccal patch loaded with phospholipase and semaglutide displayed a 5-fold higher bioavailability and lower variability (71.5 % reduction in the coefficient of variation) compared to the commercially available oral tablet. These results suggest that enzymatic modulation of biological barriers holds promise as a strategy to improve non-invasive delivery of peptides and potentially other macromolecular drugs.
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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".