Predictors of withdrawal for FDA accelerated approvals of anticancer drugs, 1992-2022.
Bibliographic record
Abstract
11024 Background: The US Food and Drug Administration’s (FDA) accelerated approval pathway facilitates timely access to novel therapies based on surrogate measures that are supposed to be reasonably likely to predict clinical benefit. Post-approval, confirmatory studies are required to verify safety and efficacy, with approved indications subject to withdrawal from the labeling if these studies fail. While this pathway has been useful in some cases, concerns about delayed and an increasing number of withdrawals of anticancer indications highlight its associated risks to patients. This study identifies factors at the time of initial accelerated approval associated with subsequent withdrawal. Methods: In this retrospective cohort study, we analyzed FDA-approved drugs for solid and hematologic cancers receiving AA from 1992 to 2022. The analysis focused on key factors present at the time of accelerated approval, including the indication and pivotal trial characteristics, mechanisms of action and clinical outcomes. Clinical benefit was assessed using the European Society of Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS), categorizing benefits as high (A-B/4-5) or low (C/≤2). Multivariable logistic regression was used to identify associations between these factors and indication withdrawal. Results: Among 167 accelerated approvals for 113 anticancer drugs, by August 2024, 102 (61%) had been converted to regular approval, 31 (19%) were withdrawn, and 34 (20%) were still in the accelerated approval phase. Of the 133 indications either converted or withdrawn, 52 (39%) were approvals for hematologic cancer drugs, and 41 (31%) supported genome-targeted drug approvals. Among 83 eligible indications, 46 (55%) were granted Breakthrough Therapy designation. Of 133 indications analyzed, 106 (80%) were based on single-arm pivotal trials, and 112 (84%) used response rate as the primary endpoint. Most trials (66%) showed low clinical benefit (86/130) per the ESMO-MCBS framework. In multivariable analysis, indications associated with lower withdrawal risk were more likely to have Breakthrough Therapy designation (OR 0.26; 95% CI, 0.10-0.75; p = 0.01) and be genome-targeted (OR 0.26; 95% CI, 0.08-0.80; p = 0.02). Low ESMO-MCBS scores conversely increased the likelihood of withdrawal (OR, 4.63; 95% CI, 1.50-14.33; p = 0.008). Conclusions: Accelerated approvals based on pivotal trials demonstrating low clinical benefit have been at higher risk of subsequent withdrawal, whereas indications with Breakthrough Therapy designation or supporting genome-targeted therapies were more likely to achieve full approval. Patients and health care providers should consider these factors when evaluating therapies newly granted 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".