Regulatory and clinical consequences of negative confirmatory trials of accelerated approval cancer drugs: retrospective observational study
Bibliographic record
Abstract
OBJECTIVES: To investigate the regulatory handling of cancer drugs that were granted accelerated approval by the US Food and Drug Administration (FDA) but failed to improve the primary endpoint in post-approval trials and to evaluate the extent to which negative post-approval trials changed the recommendations in treatment guidelines. DESIGN: Retrospective observational study. SETTING: FDA and National Comprehensive Cancer Network (NCCN) reports. INCLUDED DRUGS: Cancer drugs that received accelerated approval from the FDA and had negative post-approval trials. MAIN OUTCOME MEASURES: Regulatory outcomes, including withdrawal, conversion to regular approval, and no action. RESULTS: 18 indications for 10 cancer drugs that received accelerated approval but failed to improve the primary endpoint in post-approval trials were identified. Of these, 11 (61%) were voluntarily withdrawn by the manufacturer and one (bevacizumab for breast cancer) was revoked by the FDA. Of the 11 withdrawals, six occurred in 2021 alone. The remaining six (33%) indications remain on the label. The NCCN guidelines provide a high level of endorsement (category 1 endorsement for one and category 2A endorsement for seven) for accelerated approval drugs that have failed post-approval trials, sometimes even after the approval has been withdrawn or revoked. CONCLUSION: Cancer drug indications that received accelerated approval often remained on formal FDA approved drug labelling and continued to be recommended in clinical guidelines several years after statutorily required post-approval trials showed no improvement in the primary efficacy endpoint. Clinical guidelines should better align with the results of post-approval trials of cancer drugs that received accelerated approval.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".