Non-Size-Based Membrane Chromatographic Separation and Analysis of Monoclonal Antibody Aggregates
Bibliographic record
Abstract
Humanized monoclonal antibody produced by mammalian cell culture may contain significant amounts of antibody dimers and smaller amounts of higher order aggregates. These are undesirable in therapeutic formulations, and their content should be lower than specific allowable limits. Quantitative analysis of aggregate content is usually carried out by size exclusion chromatography (SEC), which is slow and often gives poorly resolved peaks. We describe a novel hydrophobic interaction membrane chromatography-based technique for rapid, non-size-based separation and analysis of the aggregate content in monoclonal antibody samples. The typical sample analysis time using this technique is less than 3 min, this being significantly faster than SEC. The technique gives excellent resolution of the antibody, its dimer, and higher order aggregates and could potentially be scaled up for large-scale manufacture of aggregate-free monoclonal antibody. This work also clearly shows that monoclonal antibody aggregates are more hydrophobic than the monomer form, a fact that could have significant theoretical and practical implications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".