Predictors of withdrawal for FDA accelerated approvals of anticancer drugs, 1992-2022.
Notice bibliographique
Résumé
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approvals for 113 anticancer drugs, by August 2024, 102 (61%) had been converted to regular approval, 31 (19%) were withdrawn, and 34 (20%) were still in the accelerated approval phase. Of the 133 indications either converted or withdrawn, 52 (39%) were approvals for hematologic cancer drugs, and 41 (31%) supported genome-targeted drug approvals.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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».