Boundary convection during velocity sedimentation in the Optima analytical ultracentrifuge
Notice bibliographique
Résumé
Abstract Analytical ultracentrifugation (AUC) provides the most widely applicable, precise and accurate means for characterizing solution hydrodynamic and thermodynamic properties. In recent times AUC has found broad application in the biopharmaceutical industry as a first-principle means for quantitatively characterizing biopharmaceuticals. Boundary sedimentation velocity AUC (SV-AUC) analysis is widely used to assess protein aggregation, fragmentation and conformational variants in the same solvents used during drug development and production. SV-AUC is especially useful for the analysis of drug substance, drug product and dosing solution, where other techniques may exhibit solvent matrix issues or concentration limitations. Recently, the only manufacturer of the analytical ultracentrifuge, released its newest (third generation) analytical ultracentrifuge, the Optima, in early 2017 to replace its aging 2 nd generation XL series ultracentrifuges. However, SV-AUC data from four Optima units used in conducting characterization work on adeno-associated virus (AAV) has shown evidence of sample convection. Further investigation reveals that this problem arises from the temperature control system design, which is prone to producing destabilizing temperature induced density gradients that can lead to density inversions. The observed convection impacts both the qualitative and quantitative data generated by the Optima. The problem is intermittent and variable in severity within a given Optima unit and between Optima units. This convection appears to be mainly associated with low rotor speeds and dilute samples in dilute solvents, such as AAV samples in formulation buffers containing relatively low concentrations of salts, sugars, etc. Under these conditions it is found that a sufficiently robust stabilizing density gradient is not always present during sedimentation, making the sample susceptible to convection by localized density inversions. Because SV-AUC is used as an analytical tool in making critical decisions in the development and quality control of biotherapeutics, it is imperative to alert users about this potential problem. In general special attention to data quality needs to be made by those researchers working with very large biopharmaceutical particles (e.g. gene therapy products that involve viral vectors or nanoparticles), where the conditions leading to convection are most likely to occur. It is important to note that the XL series analytical ultracentrifuges do not suffer from this problem, indicating that this problem is unique to the Optima. Attributes that reveal the presence of this problem and strategies for its elimination or minimization are provided.
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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 | 0,001 | 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 | 0,001 |
| 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 ».