Cross-Comparison of Cancer Drug Approvals at Three International Regulatory Agencies
Bibliographic record
Abstract
Background: The primary objective of the present study was to examine the drug approval process and the time to approval (TTA) for cancer drugs by 3 major international regulatory bodies—Health Canada, the U.S. Food and Drug Administration (FDA), and the European Medicines Agency (EMA)—and to explore differences in the drug approval processes that might contribute to any disparities. Methods: The publicly available Health Canada Drug Product Database was surveyed for all marketed antineoplastic agents approved between 1 January 2005 and 1 June 2013. For the resulting set of cancer drugs, public records of sponsor submission and approval dates by Health Canada, the FDA, and the EMA were obtained. Results: Overall, the TTA for the 37 antineoplastic agents that met the study criteria was significantly less for the FDA than for the EMA (X̄ = 6.7 months, p < 0.001) or for Health Canada (X̄ = 6.4 months, p < 0.001). The TTA was not significantly different for Health Canada and the EMA (X̄ = 0.65 months, p = 0.89). An analysis of the review processes demonstrated that the primary reason for the identified discrepancies in TTA was the disparate use of accelerated approval mechanisms. Summary: In the present study, we systematically compared cancer drug approvals at 3 international regulatory bodies. The differences in TTA reflect several important considerations in the regulatory framework of cancer drug approvals. Those findings warrant an enhanced dialogue between clinicians and government agencies to understand opportunities and challenges in the current approval processes and to work toward balancing drug safety with timely access.
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 | 0.039 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".