Physicochemical Characterization of Solid Dispersions of Indomethacin with PEG 6000, Myrj 52, Lactose, Sorbitol, Dextrin, and Eudragit® E100
Bibliographic record
Abstract
The purpose of this study was to prepare and characterize solid dispersions of indomethacin with polyethylene glycol (PEG) 6000, Myrj 52, Eudragit E100, and different carbohydrates such as lactose, mannitol, sorbitol, and dextrin. Indomethacin is a class II substance according to the Biopharmaceutics Classification System. It is a poorly water soluble antirheumatic agent. The goal was to investigate whether the solid dispersion can improve the dissolution properties of indomethacin. The solid dispersions were prepared by three different methods depending on the type of carrier. The evaluation of the properties of the dispersions was performed using solubility measurements, dissolution studies, Fourier-transform infrared spectroscopy, and x-ray powder diffractometery. The results indicate that lactose, mannitol, sorbitol, and especially Myrj 52 are suitable carriers to enhance the in vitro dissolution rate of indomethacin at pH 7.2. Eudragit E100, Myrj 52, and mannitol increase the dissolution properties at pH 1.2. The data from the x-ray diffraction showed that the drug was still detectable in its solid state in all solid dispersions except solid dispersions with dextrin and high amounts of mannitol. However, the results from infrared spectroscopy together with those from x-ray diffraction showed well-defined drug-carrier interactions for dextrin coevaporates.
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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) | 0.000 | 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".