Enhanced oral absorption of insulin: hydrophobic ion pairing and a self-microemulsifying drug delivery system using a D-optimal mixture design
Bibliographic record
Abstract
The lipophilicity of a peptide drug can be considerably increased by hydrophobic ion pairing with amphiphilic counterions for successful incorporation into lipid-based formulations. Herein, to enhance the oral absorption of insulin (INS), a self-microemulsifying drug delivery system (SMEDDS) formulation was developed. Prior to optimization, INS was complexed with sodium n-octadecyl sulfate (SOS) to increase the loading into the SMEDDS. The INS–SOS complex was characterized via scanning electron microscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and its dissociation behavior. The SMEDDS was optimized using a D-optimal mixture design with three independent variables including Capmul MCM (X1, 9.31%), Labrasol (X2, 49.77%), and Tetraglycol (X3, 40.92%) and three response variables including droplet size (Y1, 115.2 nm), INS stability (Y2, 46.75%), and INS leakage (Y3, 17.67%). The desirability function was 0.766, indicating excellent agreement between the predicted and experimental values. The stability of INS-SOS against gastrointestinal enzymes was noticeably improved in the SMEDDS, and the majority of INS remained in oil droplets during release. Following oral administration in diabetic rats, the optimized SMEDDS resulted in pharmacological availabilities of 3.23% (50 IU/kg) and 2.13% (100 IU/kg). Thus, the optimized SMEDDS is a good candidate for the practical development of oral delivery of peptide drugs such as INS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".