Optimizing paracetamol-ascorbic acid effervescent tablet characteristics: a quality by design approach
Bibliographic record
Abstract
OBJECTIVE: This study aims to optimize paracetamol-ascorbic acid (PCM-AA) effervescent tablet characteristics through a Quality-by-Design (QbD) approach, investigating the effects of binder concentration, granulation time, and effervescent agents' ratio on hardness, disintegration, and dissolution of the tablets. METHODS: The QbD approach was implemented by identifying the quality target product profile, critical quality attributes (CQAs), critical material attributes (CMAs), and critical process parameters for formulating PCM-AA effervescent tablets. An Ishikawa diagram identified risk factors for CQAs. A risk estimation matrix evaluated the levels of associated risks. A central composite design-based response surface methodology with 20 experimental runs, including six center points, identified key factors (binder concentration, granulation time, and effervescent agents' ratio) influencing tablet characteristics (hardness, disintegration, dissolution). The optimum formulation, determined by numerical analysis, was characterized for weight uniformity, tablet thickness and diameter, friability, and PCM and AA assay. RESULTS: > 0.05), indicating consistent results. CONCLUSION: The study successfully optimized the hardness, disintegration, and dissolution rate of PCM-AA effervescent tablets via the QbD approach. Granulation time affects hardness and PCM dissolution, binder concentration influences disintegration time, and the effervescent agents' ratio impacts both disintegration time and AA dissolution. This research enhances the understanding of pharmaceutical formulation processes, risk management, and optimization in effervescent tablet development.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".