Regulating Medicines in a Globalized World With Increased Recognition and Reliance Among Regulators: A National Academies Report
Bibliographic record
Abstract
Research and development of pharmaceuticals are now complex global endeavors, with drug companies operating worldwide using global supply chains. Pharmaceutical companies source their products from many countries, conduct trials in multiple sites, and market essential drugs and vaccines globally. Yet oversight of drug safety and effectiveness is primarily the responsibility of national regulators of variable capacities. National agencies often undertake product reviews without recognizing that similar reviews are occurring elsewhere, sometimes simultaneously. The result is duplication and redundancy, which benefits neither national nor global public health. Supported by the US Food and Drug Administration (FDA), the National Academies of Sciences, Engineering, and Medicine convened an expert committee to explore the benefits of mutual recognition and other reliance activities among regulators. Even well-resourced regulators (for example, the FDA, the European Medicines Agency, the Pharmaceutical and Medical Devices Agency Japan, and Health Canada) find it difficult to ensure the safety, efficacy, and quality of medicines in a globalized world. Regulatory failures cause harm to the population and undermine public trust in government. In 2008, following discovery of contaminated heparin originating from China, the Bush administration authorized the FDA to coordinate certain product manufacturing inspections with Australian and European regulators in China and India — setting the stage for “third country” inspections (ie, inspections conducted outside the jurisdiction of either regulator). Yet concerns about the quality of active pharmaceutical ingredients and finished pharmaceutical products persist. For example, in 2018, the FDA recalled generic medications used to treat hypertension and cardiovascular disease because of contamination.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".