How long do new medicines take to reach Canadian patients after companies file a submission: A cohort study
Bibliographic record
Abstract
INTRODUCTION: Studies of the delay between when companies file a New Drug Submission (NDS) and when drugs reach Canadian patients typically focus on the time in the regulatory review process and do not analyze the time between when approval is granted and the drug is available for purchase (company decision time). This study looks at the length of the two different time periods. Secondarily, it examines whether there is a difference in these time periods for drugs that received a standard review and those that received an expedited review. METHODS: A list of all New Active Substances approved in Canada between January 1, 2014 and December 31, 2018 was compiled and the dates when the companies applied for a NDS, the dates when the drugs received a market authorization (Notice of Compliance, NOC) and whether the drugs received a standard review or an expedited review were recorded. The date of original marketing comes from Health Canada's Drug Product Database. Times in days were calculated between NDS and NOC (review time), between NOC and the marketing date (company decision time) and between NDS and the marketing date (total time). The company decision time as a percent of the total time was calculated for all drugs. Times were compared between standard and expedited review drugs using a two-tailed t-test. RESULTS: One hundred and fifty-seven drugs were analyzed, 98 had a standard review and 59 had a priority review. Over 18% of the total time was due to company decisions. All three times were significantly lower for expedited review drugs versus standard review drugs as was the percent of total time due to company decision- 14.4% (95% CI 11.0, 17.8) versus 21.2% (95% CI 17.6, 24.8), p = 0.0102 (t-test). CONCLUSIONS: Over 18% of the total time between when companies file for drug approval until the drug is available is due to decisions made by companies. Company decision times are shorter for drugs with expedited approvals compared to drugs with standard approvals.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.005 | 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".