Fouling of virus filtration membranes by monoclonal antibody feeds with low aggregate content
Bibliographic record
Abstract
Membrane fouling by monoclonal antibodies (mAbs) is one of the main challenges in virus-filtration processes. Previous publications attributed membrane fouling to the presence of mAb aggregates in the solution, which block the membrane pores. This fouling mechanism can be solved by a prefilter; however, it was shown that there are mAbs that severely foul the membranes (reduce permeability by 90% and more) even after prefiltering the aggregates, while other mAbs foul the membrane weakly (reduce permeability by ~10% and less). Unfortunately, the differences between the fouling- and the nonfouling mAbs have never been convincingly explained. To get a deeper insight on these differences, we measured the fouling of chemically modified Isoprene-Styrene-4-vinylpyridine (ISV) membranes (TeraPore Technologies) by 8 mAbs exhibiting different hydrophobicity and charge. The results show that mAb solutions with low concentration of aggregates foul ISV membranes via an adsorptive mechanism, and the adsorption is driven mainly by hydrophobic forces between the mAb and the membrane. The charge of the mAbs plays a secondary role in fouling. We want to emphasize that the conclusions pertain to ISV membranes; the insights presented in this paper can potentially be used to engineer new surface chemistries to mitigate fouling of other virus-filtration and/or ultrafiltration membranes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".