Characterization of Binary Mixtures Consisting of Cross‐Linked High Amylose Starch and Hydroxypropylmethylcellulose Used in the Preparation of Controlled Release Tablets
Bibliographic record
Abstract
Cross-linked amylose starch (CLA), hydroxypropylmethylcellulose (HPMC), and HPMC/CLA matrices were prepared by direct compression. HPMC was used to slow down the enzymatic degradation of CLA matrices. CLA was either granulated alone and mixed with HPMC or cogranulated with the latter. Compaction characteristics of the powder, hydration and mechanical properties of the resulting matrices, as well as the release profiles of three model drugs were investigated. The results showed that wet granulation of CLA in the presence of 10% HPMC improved significantly the flow properties of the powder without compromising its compactibility. Both CLA and HPMC deformed mainly by plastic flow (yield pressures are 75 and 124 MPa, respectively), but CLA exhibited a stronger elastic component (elastic recoveries are 18.4 and 11.5%, respectively). The values of yield pressure increased linearly with the concentration of HPMC. The addition of HPMC to CLA slightly decreased the resistance to consolidation but the crushing force of the final compacts was found to be proportional to the HPMC concentration. Mechanical studies on swollen matrices revealed that CLA formed a stronger gel than HPMC or CLA/HPMC mixture, and swelling and erosion of the tablets increased with HPMC content and incubation time. The in vitro release kinetics of three model drugs (pseudeoephedrine sulfate, sodium diclofenac, and prednisone) showed a clear effect of drug solubility and presence of alpha-amylase in the dissolution medium on the release rate. The addition of HPMC to CLA protected the tablets against alpha-amylase hydrolysis and reduced the release rate of prednisone and sodium diclofenac. The release of pseudoephedrine sulfate was fast and independent of HPMC and occurred mainly by diffusion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".