The South African Medicines Control Council: Comparison of Its Registration Process With Australia, Canada, Singapore, and Switzerland
Bibliographic record
Abstract
Introduction: Comparisons between regulatory authorities of similar size and regulatory characteristics facilitate value-added benchmarking and provide insight into regulatory performance. Such comparisons highlight areas for improvement as authorities move toward achieving their regulatory goals and stakeholders’ demands. The aims of this study were to compare the registration process and the regulatory review model of the South African Medicines Control Council (MCC) to that of four other similar-sized regulatory authorities and to identify areas for improvement that may inform recommendations to the South African Health Products Regulatory Authority (SAHPRA) as it looks to re-engineer and enhance the registration process in South Africa. Methods: A questionnaire was completed by the MCC to describe its organisational structure, the registration process, good review and decision-making practices. Similar questionnaires were also completed and validated by Australia’s Therapeutic Goods Administration (TGA), Canada’s Health Canada, Singapore’s Health Science Authority (HSA) and Switzerland’s Swissmedic. Results: A comparison of the MCC regulatory process with the four comparative agencies indicated that they all have similar requirements and employ a full-review model although the timelines for the MCC were considerably longer. However, similar quality measures were implemented by all authorities as part of their good review practices (GRevP) including prioritising transparency, communication, continuous improvement initiatives and training. Conclusion: Comparisons made through this study provided insight into the areas of the MCC registration process that may be improved and have informed recommendations to SAHPRA including the implementation of facilitated regulatory pathways, definition of targets for key milestones in regulatory review and formal implementation and monitoring of GRevP. In order to build quality into the review process the application of a standardised template for the clinical assessment of medicines such as the Universal Methodology for Benefit-Risk Assessment (UMBRA) could be considered as well as enhancing transparency and communication through the application of an electronic management system and the development of publicly available summaries for the 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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".