Substituted amylose matrices for oral drug delivery
Bibliographic record
Abstract
High amylose corn starch was used to obtain substituted amylose (SA) polymers by chemically modifying hydroxyl groups by an etherification process using 1,2-epoxypropanol. Tablets for drug-controlled release were prepared by direct compression and their release properties assessed by an in vitro dissolution test (USP XXIII no 2). The polymer swelling was characterized by measuring gravimetrically the water uptake ability of polymer tablets. SA hydrophilic matrix tablets present sequentially a burst effect, typical of hydrophilic matrices, and a near constant release, typical of reservoir systems. After the burst effect, surface pores disappear progressively by molecular association of amylose chains; this allows the creation of a polymer layer acting as a diffusion barrier and explains the peculiar behaviour of SA polymers. Several formulation parameters such as compression force, drug loading, tablet weight and insoluble diluent concentration were investigated. On the other hand, tablet thickness, scanning electron microscope analysis and mercury intrusion porosimetry showed that the high crushing strength values observed for SA tablets were due to an unusual melting process occurring during tabletting although the tablet external layer went only through densification, deformation and partial melting. In contrast, HPMC tablets did not show any traces of a melting process.
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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) | 0.002 | 0.001 |
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".