Thermal Treating of Acrylic Matrices as a Tool for Controlling Drug Release
Bibliographic record
Abstract
The purpose of the present study was to investigate the effect of thermal-treating on the release of ibuprofen from the granules prepared using aqueous dispersions of Eudragit. To accomplish this goal, different formulations were prepared using wet granulation method containing two different types of Eudragit aqueous dispersions, RS30D, RL30D and Avicel as filler. Tablets were prepared using direct compression method. The prepared tablets were thermally treated at 50 and 70 degrees C for 24 h. The drug release from tablets was assessed before and after thermal-treating. The results of release study showed that, thermally-treating the tablets at the temperatures higher than glass transition temperature (Tg) of the polymer can decrease the drug release from matrices. For mechanistic evaluation of the effect of thermal-treating, powder X-ray diffraction (XPD), scanning electron microscopy (SEM), differential scanning calorimeter (DSC), Fourier transform infrared (FT-IR) and helium pycnometer have been employed. The SEM graphs showed that the tablets have smoother surface with less porosity after thermal-treating. FT-IR spectra showed no change in the spectrum of thermally-treated tablet compared to control. In DSC graphs, no crystalline change was seen in the heat-treated samples of ibuprofen tablets, but decreased and widened peak size were related to the probable formation of solid solution of ibuprofen in Eudragit matrix. The results of helium pycnometer showed a significant decrease in the total porosity of some heat-treated samples. This study revealed the importance of thermal treating on the drug release from sustained release tablets containing Eudragit polymer.
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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 | 0.000 |
| 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".