New drug submissions in Canada and a comparison with the Food and Drug Administration and the European Medicines Agency: Cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Health Canada posts the outcomes of all New Drug Submissions. In some cases, companies have withdrawn submissions or submissions have been rejected by Health Canada for new active substances (NAS). This study explores the reasons for those decisions and compares them with decisions made by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). METHODS: This is a cross-sectional analysis. Submissions for NAS between December 2015 and December 2022 were identified along with the original indications for the NAS, the information that Health Canada had available and the reasons for its decisions. Similar information was sourced from the FDA and the EMA. Their decisions were compared to those made by Health Canada. The time between decisions by Health Canada, the FDA and the EMA were calculated in months. RESULTS: Health Canada considered 272 NAS and approved 257. Sponsors withdrew 14 submissions for 13 NAS and Health Canada rejected submissions for 2 NAS. The FDA approved 7 of these NAS and the EMA approved 6, rejected 2 and submissions were withdrawn by 2 companies. Health Canada and the FDA considered similar information in 4 of 7 cases. Indications were the same except in one case. The FDA made decisions a mean of 15.5 months (interquartile range 11.4, 68.2) before companies withdrew their submissions from Health Canada. There were 5 cases where Health Canada and the EMA considered the same information and in 2 of those the outcome was different. Health Canada and EMA decisions were generally made within 1-2 months of each other. Indications were the same in all cases. CONCLUSIONS: Differences in decision making by regulators are due to more than the data which with they are presented, the timing of the presentations and the indications for the drugs. Regulatory culture may have influenced decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".