Regulatory Review of Novel Therapeutics — Comparison of Three Regulatory Agencies
Bibliographic record
Abstract
BACKGROUND: The upcoming reauthorization of the Prescription Drug User Fee Act focuses on improving the review process for new drug applications at the Food and Drug Administration (FDA). METHODS: Using publicly available information from the FDA, the European Medicines Agency (EMA), and Health Canada, we compared the time for completion of the first review and the total review time for all applications involving novel therapeutic agents approved by the three regulatory agencies from 2001 through 2010 and determined the geographic area in which each novel therapeutic agent was first approved for use. RESULTS: There were 510 applications for novel therapeutic agents approved from 2001 through 2010--225 by the FDA, 186 by the EMA, and 99 by Health Canada; among the applications, there were 289 unique agents. The median length of time for completion of the first review was 303 days (interquartile range, 185 to 372) for applications approved by the FDA, 366 days (interquartile range, 310 to 445) for those approved by the EMA, and 352 days (interquartile range, 255 to 420) for those approved by Health Canada (P<0.001 for the comparison across the three agencies). The median total review time was also shorter at the FDA than at the EMA or Health Canada (P=0.002). Among the 289 unique novel therapeutic agents, 190 were approved in both the United States and Europe (either by the EMA or through the mutual recognition process), of which 121 (63.7%) were first approved in the United States; similarly, 154 were approved in both the United States and Canada, of which 132 (85.7%) were first approved in the United States. CONCLUSIONS: For novel therapeutic agents approved between 2001 and 2010, the FDA reviewed applications involving novel therapeutics more quickly, on average, than did the EMA or Health Canada, and the vast majority of these new therapeutic agents were first approved for use in the United States. (Funded by the Pew Charitable Trusts.).
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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 | 0.055 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".