Application of a hot-melt granulation process to enhance fenofibrate solid dose manufacturing
Bibliographic record
Abstract
Evaluation of hot-melt granulation of fenofibrate and croscarmellose sodium and its cooling time for the molten mass in a ratio of 55:45 was conducted to assess the manufacturing process capability to produce an acceptable granulation which flows well on Korsch PH300 tablet compression machine. The formation of the drug-polymer eutectic mixture was investigated by differential scanning calorimetry, scanning electron microscopy and X-ray powder diffraction. The physical properties of the hot-melt was determined by examining the milled blocks after solidification and milling after cooling periods of 10, 20 and 30 d. The milled material was assessed for the effect of hold time of the blend on the solid dose compression characteristics. The impact of cooling on the processing of the blocks was assessed after 10, 20 and 30 d of cooling. The study suggests that after the hot-melt formed the fenofibrate crystallized independently and a solid solution with croscarmellose sodium was not formed. The age of the blocks determined the hardness of the crystals, changing the processing nature of the granules with respect to compression and powder flow characteristics. The blocks processed after 20 d and beyond produced granules with a characteristic suitable for holding the blend for 14 d in the bin with no impact on flow properties and compressibility of the blend. There was no chipping, capping, sticking or picking observed and a higher compression speed was achieved.
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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 | 0.001 |
| 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".