Data from: Time to market for drugs approved in Canada between 2014-2018: an observational study
Bibliographic record
Abstract
Objectives: This study examines the length of time between when a patent application is filed in Canada for a new drug and when it is available for patients (time to market) and various components of that time. It also looks at whether various factors explain the time between patent application to New Drug Submission and compares Canadian and American times. Drugs approved between January 1, 2014 and December 31, 2018 are examined. Design: Descriptive study Data Sources: Websites from Health Canada, Food and Drug Administration, Merck Index, United States Patent and Trademark Office, World Health Organization and previously published articles. Interventions: None Primary and Secondary Outcomes: The primary outcomes are time to market, time from patent application to New Drug Submission (pre-NDS time), review time, time from approval to availability (postapproval time) and factors that may influence the pre-NDS time. The secondary outcome is a comparison of Canadian and American review times and times between patent application and approval. Results: There were 113 drugs available for analysis. The median time to market was 11.80 years (inter-quartile range (IQR) 9.40, 14.05). The component median times were pre-NDS 10.00 years (IQR 8.05, 12.80), review time 0.96 years (IQR 0.75, 1.15) and postapproval time 0.15 years (IQR 0.08, 0.28). Less than 8% of the pre-NDS time was explained by the factors that were analyzed in a multiple linear regression equation. There was no statistically significant difference between Canadian and American pre-NDS times. Conclusion: On average, once a drug reaches the market companies have a median of 8.2 years before the patent expires and generics can reach the market. Most of the time between the filing of a patent application and when a drug is marketed is determined by decisions that are largely under the control of the company.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".