Understanding Microencapsulation and Performance of Composition-Equivalent PLGA Microspheres for 1-Month Controlled Release of Leuprolide
Notice bibliographique
Résumé
Poly(lactic-co-glycolic acid) (PLGA) microspheres are the most commonly used and commercially successful long-acting release depots (LARs) for delivery of peptide drugs. However, the approval of generic versions of these products are slow. Due to the complexity of the manufacturing process, concerns about bioequivalence of generic complex drug products have been raised by the U.S. Food and Drug Administration (FDA). In order to address these challenges, this work aims to help fill in the knowledge gap between: (a) raw materials and manufacturing parameters, (b) critical quality attributes, and (c) release performance and mechanisms for PLGA LARs encapsulating the model drug, leuprolide. The 1-month Lupron Depot® (LD) encapsulating water-soluble leuprolide in PLGA microspheres is the first injectable microsphere product launched in the US market. It is also a benchmark product upon which modern LAR products are often compared. Here, we describe the reverse engineering of the LD composition and important product attributes. Analyzed contents of the formulation and the determined PLGA characteristics matched well with the official values stated in the package insert and those found in the literature, respectively. The gelatin was identified as type B consistent with ~ 300 bloom. The 11-μm volume-median microspheres in the LD displayed very low content of residual moisture (< 0.5%) and methylene chloride (< 1 ppm). Composition-equivalent PLGA microsphere formulations to the LD were prepared as a function of raw material and manufacturing variables. The following variables were adjusted at constant theoretical loading of 16.4% leuprolide: polymer supplier/ polymerization type, gelatin supplier/ bloom number, polymer concentration, 1st homogenization speed and time, volume of primary water phase, 2nd homogenization time, volume of secondary water phase and stirring rate. The encapsulation efficiency (EE) of gelatin (101 ± 1%) was observed to be higher than the EE of leuprolide (42%- 63%). Desirable conditions of polymer concentration, homogenization time and volume of secondary water phase were critical to achieving high EE of leuprolide. The prepared formulations displayed a larger median particle size, a more porous surface, and higher residual solvents compared to the LD. The microspheres prepared with the identified LD raw materials possessed the same glass transition temperature as the LD. The leuprolide release kinetics of the formulations were also highly similar to the LD exhibiting zero-order kinetics after a ~20% initial burst release and displayed the same release versus mass loss kinetics. The correlations between the process variables and emulsion size were established. The dimensionless Sauter mean diameter of primary emulsion droplet was proportional to the product of key dimensionless groups raised to appropriate power indices. A new dimensionless group (total surface energy/total energy input to fluid) was used to rationalize insertion of a proportionate time dependence in the scaling of the Sauter mean diameter. The increased viscosity of primary emulsion inhibited drug loss during microencapsulation while increased droplet size enhanced drug leakage to outer water phase. The Sauter mean diameter of secondary emulsion was also found proportional to the product of three dimensionless groups raised to appropriate power indices. In summary, the rigorous approach of reverse engineering, characterization of composition-equivalent formulations and understanding of emulsion formation in the microencapsulation process described in this thesis could be useful for further development of generic or new peptide loaded PLGA microspheres, and for guiding decisions on the influence of process variables on product physicochemical attributes and release performance.
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 | 0,000 | 0,000 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».