Similarities and Differences of International Practices and Procedures for the Regulation for Active Substance Master Files/Drug Master Files of Human Use: Moving Toward Regulatory Convergence
Bibliographic record
Abstract
PURPOSE: A gap analysis survey of international practices for Active Substance Master Files (ASMFs)/Drug Master Files (DMFs) of human use was conducted as a project of the ASMF/DMF working group of the International Generic Drug Regulators Pilot (IGDRP) to identify similarities and differences among ASMF/DMF procedures of 10 IGDRP members and 2 observers. METHODS: We conducted a questionnaire survey and compared the following aspects: overall ASMF/DMF procedures, submission requirements for ASMFs/DMFs, assessment processes for ASMFs/DMFs, the technical requirements for active pharmaceutical ingredients (APIs), generation of assessment reports for ASMFs/DMFs, procedures for changing ASMF/DMF details, and Good Manufacturing Practice (GMP) inspection/certification of API manufacturers. Twelve organizations participated in this project: the Brazilian Health Surveillance Agency (Anvisa), the European Union (EU), Health Canada (HC), the Singapore Health Sciences Authority (HSA), the South African Medicines Control Council (MCC), the South Korean Ministry of Food and Drug Safety (MFDS), the Japanese Pharmaceuticals and Medical Devices Agency (PMDA), the Swiss Agency for Therapeutic Products (Swissmedic), the Taiwan Food and Drug Administration (TFDA), the Australian Therapeutic Goods Administration (TGA), the European Directorate for the Quality of Medicines & HealthCare (EDQM) (Observer) and the Prequalification Team (PQT) of the World Health Organization (WHO), which includes the PQT-Medicines (Observer). RESULTS: Although there were many similarities among the participating agencies surveyed, there were also differences that should be discussed such as assessment processes of ASMFs/DMFs and Technical requirements for APIs. CONCLUSIONS: These differences revealed by this survey will be key considerations in order to facilitate the filing of ASMFs/DMFs globally and to establish a framework for sharing and utilizing information related to ASMFs/DMFs among IGDRP members in the future. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".