Regulatory and clinical consequences of negative confirmatory trials of accelerated approval cancer drugs: retrospective observational study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 Of these, 11 (61%) were voluntarily withdrawn by the manufacturer and one (bevacizumab for breast cancer) was revoked by the FDA.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seven) for accelerated approval drugs that have failed post-approval trials, sometimes even after the approval has been withdrawn or revoked.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.
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.
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 | 0.019 | 0.315 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it