A new Canadian intellectual property right : the protection of data submitted for marketing approval of pharmaceutical drugs
Bibliographic record
Abstract
In order to market and sell a new pharmaceutical drug in Canada, the Minister of Health requires the initial applicant to submit clinical test results demonstrating that the drug is safe and effective for human use. Subsequent applicants, who typically lack the resources to conduct expensive clinical trials, must refer to and rely upon the initial applicant's data in their applications to market a generic version of the drug. On June 17, 2006, the federal government of Canada published a proposed data protection regulation, which would provide an initial applicant with eight years of protection for clinical test results submitted in a new drug submission. This protection would lead to an eight year period of market exclusivity for the drug associated with the clinical test data, regardless of whether that drug was protected by a Canadian patent. In this thesis, the author first describes what data protection is on a practical level, and distinguishes data protection from other forms of intellectual property rights. Next, the author discusses how various jurisdictions choose to protect clinical test data submitted to their health authorities. Canada's international obligations pursuant to the NAFTA and the TRIPS Agreement are also examined. In this regard, the author argues that Canada is under no obligation to provide initial applicants with eight years of data protection. Furthermore, the author argues that exclusive time-limited property rights in clinical test data are difficult to justify from a theoretical perspective. Finally, the author prescribes certain legislative changes to Canada's proposed data protection regulation.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".