High‐efficient characterization of complex protein drugs by imaged capillary isoelectric focusing with high‐resolution ampholytes
Bibliographic record
Abstract
Abstract A carrier ampholyte is a molecule containing both acid and base functionality that is critical for imaged capillary isoelectric focusing. The quality of an imaged capillary isoelectric focusing separation for protein charge variants’ characterization is highly dependent on attributes of the carrier ampholytes used including baseline signal, linearity of the pH gradient, pI discrimination, and consistency between manufactured lots. AESlytes are a high‐resolution carrier ampholyte series that have been developed for the high‐resolution and selective characterization of diverse and complex protein drugs including diverse fusion proteins, antibody‐drug‐conjugate, bi‐specific antibodies, and viral proteins. While routine commercial ampholytes usually cannot solve such challenges, AESlytes demonstrate a reduction in baseline noise and distinguishably increased consistency between lots as compared to other commercial ampholytes. Here we apply AESlytes for the imaged capillary isoelectric focusing separation of several commercial fusion proteins and biosimilars with excellent repeatability. In addition, AESlytes with narrow‐range pH were employed directly coupled to a mass spectrometer for optimizing the separation resolutions allowing more reliable and accurate protein charge variant identification. Our study demonstrates that innovative high‐resolution carrier ampholytes as critical reagents play an essential role in high‐performance imaged capillary isoelectric focussing and tandem mass spectrometry analysis the routinely commercial ampholytes cannot achieve, especially for extremely complex protein drugs.
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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 | 0.001 |
| 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".