Roller Compaction and Tabletting of St. John's Wort Plant Dry Extract Using a Gap Width and Force Controlled Roller Compactor. I. Granulation and Tabletting of Eight Different Extract Batches
Bibliographic record
Abstract
The purpose of this study was to investigate the influence of roller compaction parameters on granule and tablet quality of a dry herbal extract from St. John's wort (Hypericum perforatum L.), which is widely used in the treatment of mild to moderate depressive disorders. Eight different extract batches were blended with 0.5, 2, and 5% of magnesium stearate and were compacted at different compaction forces using a gap width and force controlled roller compactor. The ribbon formed was milled into granules having mean particle sizes up to 700 microns. The roller compaction of the extracts decreased significantly the angle of repose from about 45 to 32 degrees and the Hausner ratio from about 1.2 to 1.1. Tabletting of granulated extract instead of extract powder effectively reduced not only dust and feeding problems during the tabletting process but also prevented capping. The incorporation of 2 and 5% of magnesium stearate into the roller compacted extract reduced significantly the sticking of the dry herbal extracts to the punch faces without affecting the crushing strength of the tablets. Tablets containing granulated extracts exhibited a 3-fold lower disintegration time of about 12 min compared to tablets containing extract powder. Dissolution studies revealed that hyperforin, hypericin, and rutin were more rapidly released from tablets containing granulated extract. Therefore, roller compaction leveled out the differences in technological properties between the eight dry herbal extracts and compression of granulated extract significantly improved tablet quality.
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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".