Novel artificial cell microencapsulation of a complex gliclazide-deoxycholic bile acid formulation: a characterization study
Bibliographic record
Abstract
Gliclazide (G) is an antidiabetic drug commonly used in type 2 diabetes. It has extrapancreatic hypoglycemic effects, which makes it a good candidate in type 1 diabetes (T1D). In previous studies, we have shown that a gliclazide-bile acid mixture exerted a hypoglycemic effect in a rat model of T1D. We have also shown that a gliclazide-deoxycholic acid (G-DCA) mixture resulted in better G permeation in vivo, but did not produce a hypoglycemic effect. In this study, we aimed to develop a novel microencapsulated formulation of G-DCA with uniform structure, which has the potential to enhance G pharmacokinetic and pharmacodynamic effects in our rat model of T1D. We also aimed to examine the effect that DCA will have when formulated with our new G microcapsules, in terms of morphology, structure, and excipients' compatibility. Microencapsulation was carried out using the Büchi-based microencapsulating system developed in our laboratory. Using sodium alginate (SA) polymer, both formulations were prepared: G-SA (control) at a ratio of 1:30, and G-DCA-SA (test) at a ratio of 1:3:30. Complete characterization of microcapsules was carried out. The new G-DCA-SA formulation was further optimized by the addition of DCA, exhibiting pseudoplastic-thixotropic rheological characteristics. The size of microcapsules remained similar after DCA addition, and these microcapsules showed no chemical interactions between the excipients. This was supported further by the spectral and microscopy studies, suggesting microcapsule stability. The new microencapsulated formulation has good structural properties and may be useful for the oral delivery of G in T1D.
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) | 0.000 | 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".