Who goes first? Patterns of cancer drug approvals across four major regulatory authorities: EMA, FDA, Health Canada, and PMDA.
Bibliographic record
Abstract
149 Background: New anticancer therapies have led to substantial improvements in prognosis across many cancers. Commercial access to a drug is not possible until the drug has received regional regulatory authority market authorization. In prior work (1), we found that European Medicines Agency (EMA) drug approvals frequently lagged US Food and Drug Administration (FDA) approvals from 2010-2019. Here, we expand the analytic time period to 2004-2023 and include two additional regulatory agencies – Health Canada (HC) and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA). Methods: Drugs with an anticancer indication and first global approval from 2004-2023 were preliminarily identified. Only drugs with an approval by all 4 regulators were included. For each drug, the first and last regulator’s initial approval dates were determined, and the interval between first and last approval was calculated. For drugs approved by all 4 regulators within 1 year (y), it was further determined whether they were considered first-in-class. Results: 209 drugs met the preliminary criteria, and 98 (47%) had approvals by all 4 regulators. The FDA was most commonly the first to approve (84 drugs), followed by PMDA (9 drugs), then EMA (5 drugs); HC was never first to approve. 43 of the FDA-first approvals (51%) were by the accelerated approval (AA) pathway; none of the EMA-first approvals were on the conditional marketing pathway. PMDA was most commonly the last to approve (62 drugs), followed by HC (21 drugs), the EMA (14 drugs), then the FDA (1 drug). The median (IQR) time between first and last regulator’s first approval was 2.4 y (1.3-3.5 y). Some drugs had >5 y between first and last regulator’s approval (Table 1). Conversely, only 16 drugs were approved by all regulators within 1 y, and only 1 drug (isatuximab) was approved by all within 6 months. Of the 16 drugs approved within 1 y by all regulators, 6 (37.5%) were first-in-class: asciminib, elotuzumab, idecabtagene vicleucel, inotuzumab ozogamicin, sotorasib, and trastuzumab emtansine. Conclusions: This study shows that patients with cancer in the US usually have access to new cancer drugs earlier than those in Europe, Japan, and Canada, at least in part due to AA pathways. Less than 8% of newly approved cancer therapies are approved by all 4 regulatory agencies within 1 y of first approval, with a lengthy median first-to-last delay that could exceed the life expectancy of many patients with advanced cancer. Greater global regulatory collaboration in the approval of new anticancer drugs is essential to ensure patients have aligned and coordinated access. (1) Lythgoe et al. JAMA Network Open 2022.[Table: see text]
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".