Orphan drugs approved in Canada: health technology assessment, price negotiation, and government formulary listing
Bibliographic record
Abstract
Background: The US Food and Drug Administration (FDA) and European Medicines Agency (EMA) have incentives to stimulate the development and marketing of orphan drugs.Health Canada has none.Methods: We identified 82 FDA and/or EMA-designated orphan drugs approved by one or both agencies between 2015 and 2020 that were also authorized in Canada.We tracked the drugs through health technology assessments (HTAs), price negotiations, and listing in government drug plans to assess the time required for these processes.Results: Median times for HTAs and price negotiations suggest a delay of around a year, but the median wait time between marketing authorization and price negotiation completion was over 18 months.Conclusions: Listing of orphan drugs in Canadian government drug plans is closely aligned with reimbursement recommendations and outcomes of price negotiations.Medicines with unsuccessful price negotiations are not listed.However, not all drugs with successful negotiations are listed by all provinces and listing does not guarantee patient access.Compared with Americans and some western Europeans, Canadians with rare disorders continue to suffer from a lack of timely and equitable access to innovative treatments.A comprehensive orphan drug policy would improve Canadians' access to the innovative treatments on the research horizon.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| 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) | 0.004 | 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".