Ionic liquids for oral insulin delivery
Bibliographic record
Abstract
With the rise in diabetes mellitus cases worldwide and lack of patient adherence to glycemia management using injectable insulin, there is an urgent need for the development of efficient oral insulin formulations. However, the gastrointestinal tract presents a formidable barrier to oral delivery of biologics. Here we report the development of a highly effective oral insulin formulation using choline and geranate (CAGE) ionic liquid. CAGE significantly enhanced paracellular transport of insulin, while protecting it from enzymatic degradation and by interacting with the mucus layer resulting in its thinning. In vivo, insulin-CAGE demonstrated exceptional pharmacokinetic and pharmacodynamic outcome after jejunal administration in rats. Low insulin doses (3-10 U/kg) brought about a significant decrease in blood glucose levels, which were sustained for longer periods (up to 12 hours), unlike s.c. injected insulin. When 10 U/kg insulin-CAGE was orally delivered in enterically coated capsules using an oral gavage, a sustained decrease in blood glucose of up to 45% was observed. The formulation exhibited high biocompatibility and was stable for 2 months at room temperature and for at least 4 months under refrigeration. Taken together, the results indicate that CAGE is a promising oral delivery vehicle and should be further explored for oral delivery of insulin and other biologics that are currently marketed as injectables.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".