Worldwide Regulatory Reliance: Launching a Pilot on a Chemistry, Manufacturing, and Control Post Approval Change for a Vaccine
Bibliographic record
Abstract
When an initial marketing authorization of a pharmaceutical product is granted, a substantial number of chemistry, manufacturing, and control (CMC) post approval changes (PACs) have to be managed by the manufacturers. Despite efforts undertaken over the years by multiple regulatory jurisdictions, there is still heterogeneity in terms of regulatory requirements and timelines across national regulatory authorities (NRAs). This creates complexity in managing global CMC PACs, putting the supply of medical products at risk. Regulators have developed regulatory mechanisms that aim at accelerating the reviews and approvals of PACs by NRAs. The World Health Organization (WHO) is supporting the concept of "reliance" among NRAs, which are encouraged to rely on the assessment completed by a "highly performing authority". The objective is to accelerate the overall process for PACs, ultimately fostering more equitable and timely access to medical products for populations who need them. With the support of Health Canada, WHO, Pan American Health Organization, and the Paul-Ehrlich-Institut, Sanofi has launched a pilot using the principles of reliance for a CMC PAC for a vaccine, with 21 NRAs who accepted to participate in the pilot. The objective of this pilot was to apply these principles to reduce the approval timeline to a maximum of 6 months in all countries after an initial approval is granted by a reference authority. We discuss the opportunities and challenges of implementing reliance principles for CMC PACs. We also describe the pilot experience by sharing initial lessons learned from the Step 1 of this pilot, which consisted of engaging the reference authority and the NRAs.
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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 | 0.054 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".