Development Time and Patent Extension for Prescription Drugs in Canada: A Cohort Study
Bibliographic record
Abstract
The Comprehensive Economic and Trade Agreement between Canada and the European Union provides for an extension of Canadian patents for prescription drugs by up to 2 years. One of the arguments advanced for longer patent time is to compensate companies for the length of the overall drug development time (the time between patent application and market approval). This study investigates overall development time in Canada for different groups of drugs approved between January 1, 2009 and December 31, 2018 and how many of these drugs are eligible for up to 2 years of patent term extension. Based on a list of patents and dates of market approval, the change in overall development time for all drugs was calculated along with whether there were differences in development time between different groups of drugs. Using Canadian patent filing dates, overall development time for all drugs went from a mean of 2240 days (95% CI: 1832, 2648) in 2009 to 4197 days (95% CI: 3728, 4665) in 2018 (analysis of variance [ANOVA], P<.0001). Using first global patent filing dates, overall development time went from a mean of 4481 days (95% CI: 3053, 5908) in 2009 to 6298 days (95% CI: 4839, 7756) in 2018 (ANOVA, P=.0118). There was a statistically significant difference in the overall development mean time between small molecule drugs (3553, 95% CI: 3361, 3746) and biologics (3903, 95% CI: 3595, 4212), (t test, P=.0487) when using Canadian patent filing dates but not when first global patent filing dates were used. There was no statistically significant change in overall development time among drugs that were substantial, moderate or little to none additional therapeutic value compared to existing drugs. Out of 238 drugs, 218 (91.6%) would have been eligible for patent term extension with 195 (80.7%) eligible for the full 2 years. Patent term extension does not appear to be justified based on changes in overall development time, except possibly in the case of biologics. There are also trade offs in terms of increased expenditures that need to be considered if patent terms are lengthened.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".