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Enregistrement W2593453782 · doi:10.1182/blood.v106.11.3120.3120

Trend of Drug Approval on Hematological Malignancies in the U.S. and Japan.

2005· article· en· W2593453782 sur OpenAlexaboutno aff
Fumitaka Nagamura, Arinobu Tojo, Tokiko Nagamura‐Inoue, Aikichi Iwamoto

Notice bibliographique

RevueBlood · 2005
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineClinical trialDrugDrug approvalApproved drugDrug developmentClinical researchInternal medicinePharmacology

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Characteristics on hematological malignancies, e.g., many of them arise from one chromosomal abnormality and there are many molecules discriminating malignancies from normal cells, have recently played very important role on the development of novel therapeutic options. Molecular-targeted therapies, such as antibodies and signal inhibitors, are good examples. On the other hand, drug evaluation and approval methods have been suffered from the difficulties in fastening approval periods and evaluating efficacies and safeties more precisely, especially in the case of these entirely new concepts of drugs. In this study, we clarified the trends of drug approval on hematological malignancies in the U.S. and Japan. By the comparison, the trends were made more clearly. Methods: Drugs for hematological malignancies, including CMPDs, which approved by December 2004 in the US or Japan were eligible. Supportive drugs, immunomodulators, biochemical modulators, and “off-label use” were excluded. Package inserts, reviews by agencies, publications on clinical trials were examined. The geographical analysis on clinical trials of oncologic drugs was based on the previous report (Proc ASCO2003; 22:534a). Results: Forty-six drugs were approved in the U.S., and 43 were in Japan. Twenty-seven drugs were approved in both countries. Twenty-two of 27 drugs were approved earlier in the U.S., and the dates of approval were considerably earlier in the U.S. (median: 46.0 Mo, mean: 54.7 Mo). These differences have not been shorten when compared in every 10-year period. Eight drugs were approved as “Accelerated Approval”, which stated in CFRs as “Subpart H”. Seven of eight “accelerated approval” drugs were approved only in the U.S. Furthermore, around one-thirds of drugs (7/19: 36.8%) approved only in the U.S. were based on “accelerated approval”. However, one drug approved as “accelerated approval” could have shown its clinical benefit in the designated clinical trial. Among the drugs approve only in the U.S., the number of drugs for “first line”, “second line or thereafter”, and “not specified” were 2, 13, 4, respectively. The geographical comparison of clinical trials was summarized in the Table below. The ratio of non-U.S. studies was considerably low in hematological malignancies. In Japan, the data on clinical trials exclusively performed in Japan was generally stated. Five drugs approved only in Japan were approved in the US for diseases other than hematological malignancies, while no drug was approved in the reverse case. Conclusion: “Accelerated approval” is useful for fastening the period until the approval, although the problem whether “surrogate markers” leads to “survival and/or QOL benefit” has not been clarified, yet. The outstanding result that most of pivotal/supportive studies were not “non-U.S.” studies may be caused by the superiority of drug development, especially in new concepts of drugs for hematological malignancies and the ability to conduct appropriate clinical trials in the U.S. On the contrary, the expansion of the indication would be the problem in the U.S. to be considered. Geographical Location of Studies U.S. only U.S. & Canada U.S. & Europe Non-U.S. Total All oncology drugs (1986.1–2002.9) 77 (43.5%) 23 (13.0%) 35 (19.8%) 42 (23.7%) 177 studies Hematological malignancies (1986.1–2004.12) 27 (62.8%) 4 (9.3%) 9 (20.9%) 3 (7.0%) 42 studies

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,024

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0040,007
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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.

Tête enseignante Opus0,022
Tête enseignante GPT0,282
Écart entre enseignants0,260 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2005
Routes d'admission1
Résumé présentoui

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