Optimization of an IgG1 CIEF separation by using narrow‐range ampholytes and DMSO as protein solubilizer
Bibliographic record
Abstract
CIEF is a powerful separation tool utilized in the characterization and relative quantitation of therapeutic mAb charged isoforms. However, one CIEF method is not capable of separating all mAbs with high resolution and reproducibility. Optimization of sample composition and separation parameters is expected when developing a CIEF method for a specific mAb. This paper summarizes a root cause investigation into why a validated CIEF separation method for MAK33 (a type of IgG1) was no longer reproducible. In addition, this paper introduces the concept of sample focusing volume, which is defined as the actual capillary volume occupied by the sample after focusing and explains why there is less protein precipitation and aggregation when using narrow-range ampholytes than broad-range ampholytes. The use of DMSO as protein solubilizer and possible replacement of urea is also explored in this work. Finally, this paper demonstrates that a new optimized CIEF method can achieve over 100 reproducible high-resolution separations of MAK33 per neutral-coated capillary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".