Ultrafast Separation and Analysis of Monoclonal Antibody Aggregates Using Membrane Chromatography
Bibliographic record
Abstract
We discuss a method for rapid and cost-effective analysis of monoclonal antibody (mAb) aggregates. Hydrophobic interaction membrane chromatography, which was previously shown to be highly suitable for such separation and analysis, was used in a recently developed format referred to as laterally fed membrane chromatography (or LFMC). A stack of rectangular polyvinylidene fluoride (or PVDF) membranes having 0.22 μm pores housed within a modified analytical-scale LFMC device was used for analyzing aggregate types and content in different monoclonal antibody samples. High-resolution separations could be achieved in less than 1.5 min, this being faster than other currently available techniques such as size exclusion ultraperformance liquid chromatography (SE-UPLC). Moreover, the operating pressure was less than 200 kPa, which eliminated the need for an expensive high-pressure pump and chromatography system. The resolution obtained using the LFMC was comparable to that obtained using SE-UPLC. The effect of design variations such as change in dead volume and pillar size within the lateral channels within the LFMC device was also examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".