Effect of sodium tripolyphosphate concentration and simulated gastrointestinal fluids on release profile of paracetamol from chitosan microsphere
Bibliographic record
Abstract
The problem to overcome in oral drug administration is the significant pH changes present in the human digestive system. In this study, ionotropic gelation method employing 2-8% (w/v) tripolyphosphate solutions were used to crosslink chitosan microspheres for a controlled release of paracetamol as a model drug. The release profiles of paracetamol from chitosan microspheres were determined using simulated gastrointestinal fluids having pH values of 1.2, 6.8, and 7.4. The results showed that the paracetamol loading and the encapsulation efficiency values increased with increasing concentration of tripolyphosphate solutions used in the preparation step. Paracetamol released at pH 1.2 and 6.8 buffer solutions was significantly higher than that at pH 7.4; also, more paracetamol was released in the presence of α-amylase and β-glucosidase enzymes. The release profiles showed zero-order release behaviour up to 8 hours where the highest drug release was 39% of the paracetamol loaded in the chitosan microspheres, indicating a strong crosslinking between chitosan and TPP anions. The relatively low accumulated drug release could be compensated by employing suitable enzymes, lower TPP solution concentration, and addition of other biodegradable polymer to reduce the TPP crosslink.
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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 | 0.000 | 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".