Expediting treatments in the 21st century: orphan drugs and accelerated approvals
Notice bibliographique
Résumé
BACKGROUND: In response to activated patient communities' catalyzation, two significant efforts by the FDA to expedite treatments have now been in place for multiple decades. In 1983, the United States Congress passed the Orphan Drug Act to provide financial incentives for development of drugs for rare diseases. In 1992, partly in response to the HIV epidemic, the FDA implemented Accelerated Approval (AA) to expedite access to promising new therapies to treat serious conditions with unmet medical need based on surrogate marker efficacy while additional clinical data is confirmed. The uses of these regulatory approaches over time are assessed in this study. METHODS: The following U.S. FDA CDER published lists were used in this analysis: 1. all orphan designations and approvals; 2. all AA and their details updated through December 31, 2022; new molecular entities (NMEs). RESULTS: Orphan drug designations and approvals have increased several-fold over the past four decades. The largest increase recently has been in therapies targeting oncological diseases (comprised of both oncology and malignant hematology). Although orphan drug approvals based on NMEs are the minority of orphan drug designations, the count of approved orphan drug NMEs has increased in recent years. The characteristics of orphan drug approvals show notable differences by disease area with rare diseases and medical genetics (49%) having a relatively large fraction of orphan drug approvals with NMEs compared to the oncological diseases (32%). Similar to the use of orphan drug designation, oncological disease therapies have been the largest utilizers of AA. Many therapies targeting these diseases address unmet medical need and can leverage surrogate markers that have previously been used in similar trials. The timings of conversion of AA (confirmed or withdrawn) were assessed and found to be consistent across decades and to have some dependency upon the broad disease area (when assessed by three large groups: HIV conversions were fastest; followed by oncology; followed by all others). By the end of 2022, 98% of the first 105 (approved in 2010 or earlier) AA had been converted to confirmed or withdrawn. CONCLUSIONS: Although the typical timings for AA to be confirmed or withdrawn has not changed significantly over the decades, the disease areas utilizing orphan drug designation and AA have changed significantly over time. Both programs have had increases in their use for therapies targeting oncological diseases. The re-use of surrogate markers for oncological diseases has been an advantage in a way that may not be scientifically feasible in many other disease areas that have greater differentiation across disease etiology. For non-oncological diseases, applicability of AA is, in part, dependent upon greater focus on characterization and acceptance of novel surrogate markers.
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 | 0,021 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,003 |
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 ».