Quality Testing of Difficult-to-Make Prescription Pharmaceutical Products Marketed in the US
Bibliographic record
Abstract
Importance: Health care practitioners and patients must have information to support their confidence in the quality of prescription pharmaceuticals. Objective: To determine whether there were clear and substantive differences in major quality attributes between difficult-to-make solid oral dosage form pharmaceutical products marketed in the US. Design, Setting, and Participants: This quality improvement study analyzed US Food and Drug Administration-collected samples of 252 drug products marketed in the US and manufactured in the US, Canada, Europe, India, and the rest of Asia. These drug products were immediate-release solid oral dosage forms considered difficult to make on the basis of product quality history. This sampling included 35 innovator and 217 generic drug samples manufactured by 46 different firms containing 17 different active ingredients. Statistical analysis was performed from February to November 2019. Main Outcomes and Measures: All products were tested within their shelf life on the basis of the legally recognized tests of the US Pharmacopeia for the major quality attributes of dosage unit uniformity and dissolution. These tests measure dosage consistency and drug release, respectively. The consistency of either attribute was used to calculate a process performance index to describe the variability in manufacturing. Results: All 252 drug product samples met the US market standards for dosage unit uniformity and dissolution, although the process performance index (Ppk) for dissolution fell below the level of 4-sigma capability (ie, <1 error per 1600) for 11 different manufacturers and for generics in 4 of 5 regions, including the US. As part of a retrospective analysis, manufacturers performing above the median Ppk for either dissolution or dosage unit uniformity submitted fewer product quality defect reports (mean field alert reports of 0.22 and 0.63, respectively) than those falling at or below the median Ppk for these attributes (mean field alert reports of 2.1 and 1.7, respectively). Conclusions and Relevance: All samples met the US market standards for dosage unit uniformity and dissolution, indicating acceptability for use by patients regardless of manufacturer or region. To our knowledge, this is the largest sampling study of pharmaceutical manufacturers for the US market and these data provide objective insight into the quality of prescription drugs with high manufacturing risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".