The Saudi Arabia Food and Drug Authority: An Evaluation of the Registration Process and Good Review Practices in Saudi Arabia in Comparison with Australia, Canada and Singapore
Bibliographic record
Abstract
OBJECTIVE: This study compares the current regulatory review process and good review practices at the Saudi Food and Drug Authority (SFDA) with those of regulatory agencies in Australia, Canada, and Singapore and identifies opportunities for developing the SFDA as a Regional Centre of Excellence. METHODS: A questionnaire completed by the SFDA included data regarding the organisation, key milestones, review timelines, and good review practices of the agency. Similar information was obtained within the same timeframe (2014/2015) through the same standard questionnaire regarding the processes and practices for Health Canada, Singapore's Health Sciences Authority, and Australia's Therapeutic Goods Administration. RESULTS: All four regulatory agencies have established target times for scientific assessment and regulatory review, examine dossier sections in parallel, and separate company response time from overall timing. Additionally, all four agencies have instituted good review practices including standard operating procedures, templates, dossier monitoring, and continuous improvement processes, and assign a high priority to transparency in their relationships with the public, healthcare professionals and industry. Of the four agencies, however, only the SFDA requires a Certificate of Pharmaceutical Product (CPP) at the time of the submission and pricing negotiations before final product approval. CONCLUSIONS: To assist the SFDA in its efforts to become a Regional Centre of Excellence, it is suggested that the agency explore a risk stratification approach to select dossiers for verification, abridged, or full reviews; use forms of certification other than the CPP; make pricing negotiations independent to the review process; and introduce a feedback process for the quality of the dossier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".