The Multiple Myeloma Research Consortium (MMRC): Accelerated Start up and Accrual Metrics Speeds Drug Development
Notice bibliographique
Résumé
Abstract Abstract 1024 Background: The MMRC is a non-profit, disease-focused consortium founded in 2004. Sixteen North American member institutions with expertise in multiple myeloma (MM) work collaboratively with the MMRC Inc. (Norwalk, CT) and numerous pharmaceutical partners to speed development of new treatment options to MM patients. In December 2007, MMRC Inc. implemented business solutions to address barriers to rapid activation of phase I-II trials and established benchmarks for initiating and conducting these studies. In December 2010, we reported significantly faster trial start up and accrual data from previous years1,2. Today, we update and expand on MMRC performance data and analysis of progress. Methods: Twenty-five (25) trials conducted within the Consortium from May 2006 to July 2011 had sufficient start up trial data for review. FPFD was defined as the time from the member institutions' receipt of the final protocol (FP) from the trial sponsor, to the time the first patient was dosed on the trial at any participating MMRC member institution. With respect to enrollment, pre-study enrollment commitment (EC) established between MMRC and the study sponsor was defined as the total number of subjects committed to receive at least one dose of study drug across all participating MMRC centers on a trial; baseline enrollment timeline (BET) was prospectively defined as the target time period to attain EC. Results: Mean time to FPFD in the recent group of trials (RG; n=18; Sept 08-Jul 11) held steady at 131 calendar days from receipt of FP as compared to 181 days for the early group of trials (EG; n=7; Jun06-Sept08) representing a 28% reduction in time to FPFD. More importantly, there was a 20% decrease in time to FPFD by all participating MMRC centers on any MMRC trial from 189 days in the RG compared to 236 days in the EG representing an important achievement especially in the Phase I/II arena. MMRC trial accrual data was available for 17/25 trials (2 EG trials were missing data and 6 RG trials continue enrolling). The pre-study mean MMRC EC was 44 subjects per trial (n=19 trials; 849 patients); the mean actual MMRC enrollment was 49 subjects per trial (n=19; 935 patients through July 11) representing a 10% over enrollment versus committed enrollment. A total of 17/19 evaluable trials (89%) met their EC; 12/19 trials met EC within BET (71%) of which 8/12 trials (67%) reached EC 34% faster than their BET (representing a mean reduction of 4.5 months). The overall pre-study mean BET for 19 trials was 13.6 months. MMRC's actual mean enrollment timeline was 12.8 months for the group of 17 evaluable trials representing improvement over the original BET by a mean of 10%. Discussion: MMRC's acceleration of clinical trials provides physicians and patients with rapid access to novel compounds; industry with data for important drug development decisions; and academic institutions with more trials of high scientific interest. Even so, our data uncovered opportunities for improvement. The submission time from receipt of the final protocol from the trial sponsor to Scientific Review Committee (SRC) took an average 30 calendar days across all trials. A reduction to half this time could make a difference to patients and therefore warrants further exploration. Conclusion: Today, drug development in multiple myeloma is fast-paced and highly competitive. MMRC's frequent review of trial metrics provides valuable insight to continually speed answers to physicians, patients and industry. Disclosures: Richardson: Multiple Myeloma Research Consortium: Annual grant in support Clinical Trial Project Management. Vij:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Lonial:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Siegel:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Jakubowiak:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Reece:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Jagannath:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Hofmeister:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Stewart:Multiple Myeloma Research Consortium: Annual Grant in support of clinical trial Project Management. Wolf:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Krishnan:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Zimmerman:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Kumar:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Roy:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Fay:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Anderson:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».