PDA Biosimilars Workshop Report (September 27—28, 2018)—Getting It Right the First Time for Biosimilar Marketing Applications
Notice bibliographique
Résumé
This workshop report summarizes the presentations, the breakout session outcomes, and the speaker panel discussions from the PDA Biosimilars Workshop held September 27–28, 2018, in Washington, DC. This format was deliberately selected for the workshop with the expectation of delivering a post-workshop paper on current best practices and existing challenges for sponsors. The event, co-chaired by Dr. Stephan Krause (AstraZeneca Biologics) and Dr. Emanuela Lacana (CDER/FDA), was attended by 140 agency and industry representatives. The workshop was separated into three major sessions P1: Regulatory Perspective, P2: Challenges in Biosimilar Development, and P3: Demonstrating Analytical Similarity. Each of the three sessions started with agency and industry presentations. Participants then split into two concurrent roundtable discussion groups to hear the answers to questions that had been provided to all participants one week prior to the event. The sessions were recorded. This paper provides consolidated answers to specific case studies for current challenges to sponsors and agencies. In addition, the panel discussion notes following each breakout roundtable session, as well as brief talk summaries of all speakers, are provided. The first session explored the challenges encountered with submission of biosimilar marketing applications from the perspectives of regulatory agencies. Expectations for a successful submission of the chemistry, manufacturing, and controls (CMC) information were described. The second session addressed high-level technical challenges and how to avoid pitfalls frequently encountered during biosimilar candidate development, including data quality expectations, creation of the final control strategy, and strategic choices necessary for candidate selection and development. Both regulatory perspectives and industry experience were shared. The last session explored the use of statistical tools to provide meaningful contributions to the demonstration of analytical similarity. The presentations highlighted common issues and practical challenges that arise during the application of statistical tools. LAY ABSTRACT: Significant challenges are still-remaining for sponsors and agencies to successfully develop and license Biosimilars. A Biosimilars Workshop was therefore held on 27-28 September 2018 in Washington, DC, to find practical solutions to the remaining challenges. The workshop planning committee with members from industry and agencies prepared specific case studies focused on some of most difficult situations. The workshop was separated into three major sessions (P1 - Regulatory Perspective; P2 - Challenges in Biosimilar Development; P3 - Demonstrating Analytical Similarity) and each session attempted to provide practical solutions to the relevant case studies. This first session explored the challenges encountered with submission of biosimilar marketing applications from the regulatory agencies9 perspectives. Expectations for a successful submission of the CMC information were described. The second session addressed high-level technical challenges frequently encountered during biosimilar candidate development, including data quality expectations, the creation of the final control strategy, and strategic choices necessary for candidate selection and development. The last session explored the use of statistical tools to provide meaningful contributions to the demonstration of analytical similarity and practical challenges that arise during the application of statistical tools.
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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 | 0,009 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,008 | 0,003 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,092 | 0,064 |
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 ».