Regulatory reliance to approve new medicinal products in Latin American and Caribbean countries
Bibliographic record
Abstract
Objective.To describe the current status of regulatory reliance in Latin America and the Caribbean (LAC) by assessing the countries' regulatory frameworks to approve new medicines, and to ascertain, for each country, which foreign regulators are considered as trusted regulatory authorities to rely on. Methods.Websites from LAC regulators were searched to identify the official regulations to approve new drugs.Data collection was carried out in December 2019 and completed in June 2020 for the Caribbean countries.Two independent teams collected information regarding direct recognition or abbreviated processes to approve new drugs and the reference (trusted) regulators defined as such by the corresponding national legislation.Results.Regulatory documents regarding marketing authorization were found in 20 LAC regulators' websites, covering 34 countries.Seven countries do not accept reliance on foreign regulators.Thirteen regulatory authorities (Argentina, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Mexico, Panama, Paraguay, Peru, Uruguay, and the unique Caribbean Regulatory System for 15 Caribbean States) explicitly accept relying on marketing authorizations issued by the European Medicines Agency, United States Food and Drug Administration, and Health Canada.Ten countries rely also on marketing authorizations from Australia, Japan, and Switzerland.Argentina, Brazil, Chile, and Mexico are reference authorities for eight LAC regulators.Conclusions.Regulatory reliance has become a common practice in the LAC region.Thirteen out of 20 regulators directly recognize or abbreviate the marketing authorization process in case of earlier approval by a regulator from another jurisdiction.The regulators most relied upon are the European Medicines Agency, United States Food and Drug Administration, and Health Canada.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".