Effect of Kollidon VA<sup>®</sup>64 particle size and morphology as directly compressible excipient on tablet compression properties
Bibliographic record
Abstract
The study evaluates use of Kollidon VA®64 and a combination of Kollidon VA®64 with Kollidon VA®64 Fine as excipient in direct compression process of tablets. The combination of the two grades of material is evaluated for capping, lamination and excessive friability. Inter particulate void space is higher for such excipient due to the hollow structure of the Kollidon VA®64 particles. During tablet compression air remains trapped in the blend exhibiting poor compression with compromised physical properties of the tablets. Composition of Kollidon VA®64 and Kollidon VA®64 Fine is evaluated by design of experiment (DoE). A scanning electron microscopy (SEM) of two grades of Kollidon VA®64 exhibits morphological differences between coarse and fine grade. The tablet compression process is evaluated with a mix consisting of entirely Kollidon VA®64 and two mixes containing Kollidon VA®64 and Kollidon VA®64 Fine in ratio of 77:23 and 65:35. A statistical modeling on the results from the DoE trials resulted in the optimum composition for direct tablet compression as combination of Kollidon VA®64 and Kollidon VA®64 Fine in ratio of 77:23. This combination compressed with the predicted parameters based on the statistical modeling and applying main compression force between 5 and 15 kN, pre-compression force between 2 and 3 kN, feeder speed fixed at 25 rpm and compression range of 45–49 rpm produced tablets with hardness ranging between 19 and 21 kp, with no friability, capping, or lamination issue.
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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".