Dosage unit uniformity and dissolution testing of extended-release pharmaceutical products marketed in the U.S.
Bibliographic record
Abstract
An international sampling study yielded 69 samples of extended-release prescription pharmaceuticals for legal sale in the U.S. Samples included 29 lots of innovator and 40 lots of generic solid oral extended-release drugs manufactured at 16 different facilities and containing 6 different active ingredients. Dosage unit uniformity and dissolution were tested for each lot. All samples met the relevant testing criteria for dosage unit uniformity and dissolution. There were no indications that manufacturer or region impacted a product's acceptability for use by patients. The variability of attributes was used to calculate a process performance index (Ppk) for each facility. Higher Ppk values suggest less variability relative to specification limits. Only two manufacturers fell below a 4-sigma manufacturing benchmark Ppk of 1.33 for dosage unit uniformity: a European manufacturer of a brand drug and an Asian manufacturer of a generic drug. Conversely, all but four manufacturers fell below a 4-sigma benchmark for the minimum Ppk across their product's dissolution timepoints: generic drug manufacturers in India (two), the U.S., and Canada. Compared to the immediate-release products of a previous study, Ppks were generally lower for extended-release products. A retrospective analysis found that manufacturers performing below median Ppks submitted more Field Alert Reports after the end of the sampling period.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".