Comparison of drug approval between health Canada (HC) and the U.S. Food and Drug Administration (FDA).
Bibliographic record
Abstract
6082 Background: Differences in drug approval processes between countries can impact patient access to new therapies. In Canada, patients can freely access a new treatment after regulatory approval by Health Canada (HC) followed by funding approval from the provincial government. The aims of this study were to delineate the Canadian drug approval timeline and to compare the time to drug approval between HC and the US FDA. Methods: Cancer drugs approved by the FDA from 1989 to 2011 were reviewed. For each drug, the following endpoints were determined: publication date of phase I and pivotal phase III trial, date of FDA and HC approval, HC submission date, and funding approval in Alberta (AB). Time intervals between the aforementioned endpoints were calculated. Results: Of 55 FDA-approved drugs, 51 drugs are approved by HC with 40 of these drugs funded in AB. HC approval occurs an average of 14.4 months post FDA approval (95% CI -36.9 to 66.1, sign rank test p<0.0001). However, there was no significant difference between the mean time from Phase I to FDA approval (48.5 months; 95% CI 21.2 to 75.8) and Phase I to HC approval (61.5 months; 95% CI 32.4 to 90.5). Most drugs (74%) were approved by the FDA prior to publication of the phase III trial. There was a trend towards faster drug approval from Phase III to FDA approval compared to HC (-14.97 versus 0.1 months, p = 0.05). HC submission occurs before FDA drug approval 77% of the time (mean 3.0 months prior; 95%CI: -59.1 to 43.4, p = 0.0206). HC approval occurs on average 17 months post HC submission. AB funding approval occurs on average 22 months after HC approval. The time interval from Phase I to AB funding approval was significantly shorter for targeted compared to cytotoxic agents (mean time 58 vs. 120 months; p = 0.039). Conclusions: HC drug approval lags behind FDA approval by about 14 months. Time from Phase III to drug approval tends to be shorter for the FDA compared to HC. This is the first documentation, to our knowledge, of the time required to bring a drug from phase I trial to provincial funding approval.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".