Clinical Benefit and Expedited Approval of Cancer Drugs in the United States, European Union, Switzerland, Japan, Canada, and Australia
Bibliographic record
Abstract
PURPOSE: Regulatory agencies have sought to speed up the review of new cancer medicines and reduce delays in approval between countries. We examined trends in regulatory review times and association with clinical benefit for new cancer medicines in six jurisdictions: United States (Food and Drug Administration [FDA]), European Union (European Medicines Agency [EMA]), Switzerland (Swissmedic), Japan (Pharmaceuticals and Medical Devices Agency [PMDA]), Canada (Health Canada), and Australia (Therapeutic Goods Administration). METHODS: We studied all new cancer drugs approved in the six aforementioned jurisdictions from 2007 to 2020. We extracted all applicable expedited programs, total regulatory review times, and, for drugs first approved by the FDA, times to subsequent regulatory approval. Clinical benefit was assessed using the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale value framework and ASCO-Cancer Research Committee's targets. Nonparametric Kruskal-Wallis test was used to compare total review times for high versus low clinical benefit drugs. RESULTS: One hundred and twenty eight drugs received initial approval in at least one of the six included jurisdictions. Most drugs approved by the FDA (91%) and Health Canada (59%) qualified for at least one expedited program within those jurisdictions, compared with 46% of EMA approvals and 18% of PMDA approvals. The FDA was the first regulator to approve 102 (80%) drugs. Delays in submission accounted for a median of 20.2% (EMA) to 83.8% (PMDA) of the time to subsequent approval. There was no association between high clinical benefit and shorter total review times. CONCLUSION: Most new cancer therapies were approved first by the FDA, and delays in submission of regulatory applications accounted for substantial delays in approving cancer drugs in other countries. Regulators should prioritize faster review for drugs with high clinical benefit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".