Canadian and US Drug Approval Times and Safety Considerations
Bibliographic record
Abstract
BACKGROUND: Approval times of new drugs are frequently longer in Canada than in the US, but it has been argued that reducing approval times might lead to unsafe drugs receiving marketing approval. OBJECTIVE: To compare new drug approval times in Canada and the US over a 10-year period and to relate them to safety discontinuations. METHODS: Application and approval dates of all new drugs except diagnostic products, new salts, esters, isomers, and dosage forms of already-marketed drugs, as well as combinations containing previously approved substances approved in the US and Canada between January 1992 and December 2001 were obtained from the respective drug regulatory agencies and other sources. Information about drugs discontinued for safety reasons was obtained from the agencies' publications and Web sites and from journal articles. RESULTS: New drug approval times were significantly longer in Canada than in the US. The difference occurs in all drug categories and by review type (priority/standard). However, the proportion of new drugs approved and later discontinued for safety reasons from the Canadian market (2.0%) was just over half that in the US (3.6%). CONCLUSIONS: When serious drug safety problems were identified in a timely manner after US approval, the products were not subsequently approved in Canada. Canada avoided potential dangers because its longer approval times provided an opportunity to observe actual market experience in other countries. However, the trade-off is that new drugs, including those for conditions for which current therapy has limited efficacy, take significantly longer to be approved in Canada and, hence, to be available to Canadians.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".