Boundary convection during velocity sedimentation in the Optima analytical ultracentrifuge
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".