FACTORIAL DESIGN USED IN OPTIMIZATION IMMEDIATE RELEASE SOLID DOSAGE RANITIDINE HYDROCHLORIC
Bibliographic record
Abstract
The aims of this study were to develop a predictive immediate release tablet formulation system for soluble drugs. Ranitidine hydrochloride, silicifiedmicrocrystallinecellulose (SMCC), polyplasdone XL and hydroxyprophylmethylcellulose (HPMC) E6 were evaluated for powder properties. The effects of binder (HPMC E6) and disintegrant (Polyplasdone XL) were investigated. A 32 factorial design was applied to optimize the drug release profile. The amount of binder and disintegrant were selected as independent variables. The times required for 50% (t50) and 80% (t80) drug dissolution and similarity factor (f2) were chosen as dependent variables. The results of factorial design indicated that a high amount of binder and low amount of disintegrate favored the preparation of drug release. The difference (f1) and similarity (f2) factors were used to measure the relative error and the closeness (similarity) between the factorial design batches and brand name drugs. No significant difference was observed between the brand drug and ranitidine batches F1, F2, F5, F6 and F9. Ranitidine batch F2 yielded the highest value of f2(71%)and the lowest of f1(10%). This research indicates that the proper amount of binder and disintegrant can produce drug dissolution profiles comparable to their brands.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".