Role of membrane structure on the filtrate flux during monoclonal antibody filtration through virus retentive membranes
Bibliographic record
Abstract
Virus removal filtration is a critical step in the manufacture of monoclonal antibody products, providing a robust size-based removal of both enveloped and non-enveloped viruses. Many monoclonal antibodies show very large reductions in filtrate flux during virus filtration, with the mechanisms governing this behavior and its dependence on the properties of the virus filter and antibody remaining largely unknown. Experiments were performed using the highly asymmetric Viresolve® Pro and the relatively homogeneous Pegasus™ SV4 virus filters using a highly purified monoclonal antibody. The filtrate flux for a 4 g/L antibody solution through the Viresolve® Pro decreased by about 10-fold when the filter was oriented with the skin side down but by more than 1000-fold when the asymmetric filter orientation was reversed and used with the skin side up. The very large flux decline observed with the skin side up could be eliminated by placing a large pore size prefilter directly on top of the virus filter; this improvement in filtrate flux was not seen when the prefilter was used inline or as a batch prefiltration step. The increase in flux due to the prefilter was not related to the removal of large protein aggregates or to an alteration in the extent of concentration polarization. Instead, the prefilter appears to transiently disrupt reversible associations of the antibodies caused by strong intermolecular attractions. These results provide important insights into the role of membrane morphology and antibody properties on the filtrate flux during virus filtration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".