The Jordan Food and Drug Administration: Comparison of its Registration Process with Australia, Canada, Saudi Arabia and Singapore
Bibliographic record
Abstract
OBJECTIVE: This study outlines the current regulatory review process and good review practices (GRevPs) at the Jordan Food and Drug Administration (JFDA) and compares them with those of regulatory agencies in Australia, Canada, Saudi Arabia and Singapore to gauge how well the JFDA is performing. We identify opportunities for further development of the JFDA as a key global reference agency. METHODS: Personnel within the JFDA completed a questionnaire comprising four sections: organisation, key milestones, review timelines, and GRevPs. The same questionnaire was used concurrently to gather information from Australia's Therapeutic Goods Administration (TGA), Health Canada, the Saudi Food and Drug Authority (SFDA) and Singapore's Health Sciences Authority (HSA). RESULTS: The JFDA conducts an abridged review for new active substances and requires a certificate of pharmaceutical product (CPP) at the time of submission and 6 months of pharmacovigilance data at the time of the final review as well as full pharmaceutical, chemistry, manufacturing and controls (CMC) and clinical data at the time of submission. A written summary and tabulated data are required for non-clinical data. The four comparator agencies conduct full assessments; the SFDA also requires a CPP, and the JFDA and SFDA both require pricing information at submission. All agencies have established target timelines, and the JFDA, SFDA, TGA and HSA currently exceed those targets. All agencies have also developed GRevPs as well as training and continuous-improvement processes. CONCLUSIONS: The JFDA has achieved significant success in its role as a regulatory agency by setting and implementing clear regulations in line with international guidance. It is recognised as a training centre in the region, with considerable achievements in the development of its activities by simplifying and improving requirements, procedures and actions. It also publishes information regarding guidance, procedures, drug application submissions and registration dates for all new chemical entities on its website. The relationship between the JFDA and the pharmaceutical sector in Jordan has resulted in balanced, practical, internationally compatible regulations and demonstrates a viable model of collaboration. To assist the JFDA in its efforts to become a key global reference agency, it is suggested that the agency explore a risk-stratification approach to the regulatory review; accept CPPs after dossier submission or use alternatives to the CPP; conduct pricing evaluations in parallel with scientific assessments; establish defined target times for review milestones and improve internal tracking systems to monitor these milestones; and make certain information transparent to all stakeholders by publishing a summary basis of approval.
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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 | 0.027 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".