Introducing the Molecular Pharmaceutics Special Issue on the 2023 PBBM Workshop for Drug Product Quality
Notice bibliographique
Résumé
RecommendationsT his Special Issue is dedicated to PBBM (Physiologically based biopharmaceutics modeling), its use in drug product quality and underlines the important strategic collaboration among academics, software companies, industry, and regulators in designing and delivering a 3-day workshop during August, 2023.The Special Issue brings together the combined workshop output including the summary, 1 details of all discussions and best practices on model parametrization, model verification, validation and application, current and future drug product quality applications of PBBM from the industry as well as the regulatory agencies perspectives, and to close, the development of a PBBM Report Template, which considers how to improve PBBM quality, with potential to promote increased utility of PBBM to support product understanding and life cycle management.Physiology Based Biopharmaceutics Modeling (PBBM) is a subset of PBPK and involves the application of PBPK for biopharmaceutics applications.PBBM is an evolving tool used in drug product development (Model Informed Drug Development), regulatory approval, and life cycle management.PBBMs are used to elevate drug product quality by providing a more accurate and holistic understanding of how drugs interact with the human body.These models are based on the integration of physiological, pharmacological and pharmaceutical data to simulate and predict drug behavior in vivo.Effective utilization of PBBM requires a consistent approach to model development, verification, validation and application.Currently, only one country has a draft guidance for PBBM whereas other major regulatory authorities have had limited experience with review of PBBMs.To address this gap, industry submitted confidential PBBM case studies for collaborative review by the regulatory agencies.Successful bioequivalence "safe space" industry case examples were also presented.Overall, six regulatory agencies were involved in the case study exercises, including ANVISA, FDA, Health Canada, MHRA, PMDA and EMA (experts from Belgium, Germany, Norway, Portugal, Spain, and Sweden), and we believe this is the first time such a collaboration has taken place.The outcomes were presented, together with a participant survey on the utility and experience with PBBM submissions, to discuss the best scientific practices for developing, validating and applying PBBM.The PBBM case studies enabled industry to receive constructive feedback from cross agency regulators and highlighted clear direction for future PBBM submissions for regulatory consideration.
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 | 0,008 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,003 |
| 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 ».