Worldwide Regulatory Reliance: Results of an Executed Chemistry, Manufacturing and Control Post Approval Change Pilot
Bibliographic record
Abstract
Post-approval changes (PACs) are integral to pharmaceutical product life cycle management, ensuring that the product remains safe, effective, and compliant with evolving standards. However, managing these changes across multiple regulatory jurisdictions remains a challenging endeavor due to diverse regulatory requirements and timelines across national regulatory authorities (NRAs). This results in delays in obtaining approval from NRAs, impacting global supply chains and ultimately jeopardizing timely access to essential medical products by patients. In 2021, the World Health Organization issued the Good Reliance Practices (GReIP) guidance to encourage streamlined PAC review and approval process while maintaining access to quality-assured, safe, and effective medicinal products. NRAs are encouraged to rely on the assessment completed by a reference authority that agrees to provide the outcomes of its regulatory expertise. The ultimate objective of this guidance is to accelerate the overall process for PACs, ultimately fostering more equitable and timely access to medical products by the populations who need them. This approach was tested in a chemistry, manufacturing, and control PAC pilot to determine the feasibility of using the principles of regulatory reliance based on the recommendations outlined in the GReIP with the goal of establishing a predictable, 6-month approval timeframe across multiple NRAs. The design and management of this pilot is described in Gastineau et al. This paper describes the outcomes of the pilot, which demonstrated that regulatory reliance is feasible. Of the 21 NRAs that agreed to participate, 55% were able to complete the review within 6 months; within 10 months, 95% of approvals were received and, after 16 months, all participating countries had approved the PAC. The use of a Q&A SharePoint Tool allowed for visibility of the questions raised and the company responses among the NRAs. Feedback on this reliance pilot was solicited from the participating NRAs and provides further support for future CMC PAC reliance cases.
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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 | 0.034 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".