Effects of Co-Processed and conventional excipients on content uniformity
Bibliographic record
Abstract
Silicification of microcrystalline cellulose (MCC) has previously been shown to have positive effects on the powder's flowability and tabletability compared to plain MCC or physical blends of colloidal silicon dioxide (CSD) and MCC [1]. A further characteristic of silicified MCC (SMCC) is its specific surface area, which is approximately five times larger than that of plain MCC [1]. It was hypothesized, therefore, that SMCC might have beneficial effects in terms of blend and content uniformity via effects of interactive blending. This study was designed to compare the blending efficacy of silicified microcrystalline cellulose and a co-processed SMCC-based multifunctional excipient to that of physical blends comprising the same nominal components. Near infrared spectroscopy (NIRS) was used to probe blend uniformity during the blending process of the excipients and a model active pharmaceutical ingredient (API), caffeine, presenting morphological and electrostatic challenges with regard to content uniformity. In addition to NIRS, both particle size analysis and scanning electron microscopy (SEM) were used to investigate the resulting blends. Content uniformity on the tableted blends, obtained by caffeine dissolution, was used to investigate the effects of differing blend uniformity on a final oral solid dosage form. For this non-optimized formulation with a challenging API, use of SMCC and a co-processed SMCC-based multifunctional excipient yielded formulations with significant benefits over using standard MCC, or MCC blended with colloidal silicon dioxide (CSD). These benefits included a faster blend uniformity, prevention of particle attrition, and a reduced impact of blender type and materials. Additionally, use of silicified microcrystalline cellulose yielded formulations with increased tablet hardness and reduced ejection forces.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".