Review of Quality Deficiencies Found in Active Pharmaceutical Ingredient Master Files Submitted to the WHO Prequalification of Medicines Programme
Bibliographic record
Abstract
PURPOSE: The aim of this work was to determine the number and type of active pharmaceutical ingredient (API) quality deficiencies in API Master Files (APIMFs) as submitted to the World Health Organization (WHO) Prequalification of Medicines Programme (PQP). METHODS: We conducted a retrospective review of API quality deficiencies identified following the assessment of new APIMFs for non-sterile APIs during a 6-year period from 1 January 2007 to 31 December 2012. All deficiencies were collected, classified and quantified according to the Common Technical Document (CTD) sections and subsections and as groups of commonly raised questions. RESULTS: There were 5446 deficiencies collected from 159 APIMF deficiency letters by CTD section, by selected CTD subsections and by selected CTD subsections and year. More than 50% of the total number of deficiencies related to the manufacturing sections of the CTD, followed by deficiencies concerning the impurities, the API specification and the stability sections of the CTD. A pattern of API deficiencies across the different CTD subsections and over time was identified. CONCLUSIONS: The most frequent critical deficiencies were related to how the specific manufacturing process and the key materials used, in particular the API starting material, impact the API impurities content. The number and pattern of APIMF deficiencies did not change over time. The results are compared to the findings in similar studies as reported by the United States Food and Drug Administration (USFDA), the European Directorate for the Quality of Medicines (EDQM) and the European Medicines Agency (EMA) and similarities and differences are discussed. Our findings highlight the need for greater guidance and technical assistance for API manufacturers submitting APIMFs to the PQP.
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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 | 0.019 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".