Fractionation and online mass spectrometry based on imaged capillary isoelectric focusing (icIEF) for characterizing charge heterogeneity of therapeutic antibody
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Bibliographic record
Abstract
Imaged capillary isoelectric focusing (icIEF) technology has been proved to be robust for the characterization of protein charge heterogeneity due to its high-resolution pI discrimination and high-throughput. Although high performance liquid chromatography (HPLC) tandem mass spectrometry (MS) and offline fraction collection technologies including isoelectric focusing (IEF), ion exchange chromatography (IEX) and free flow electrophoresis (FFE) have been widely utilized for protein charge variant characterization, there are a few applications of MS coupling with icIEF as a front-separation technique and related fractionation technologies for protein charge heterogeneity. However, the application of icIEF-MS has been much less frequent due to difficulties in MS interface, compatible ampholyte and coated capillary cartridge designation, ultimately impeding the breadth of icIEF applications in protein charge heterogeneity. In this study, a therapeutic monoclonal antibody (mAb-M-AT) was used for its charge variant characterization on an integrated icIEF platform with functions including analytical profiling, MS online coupling and fraction collection for charge heterogeneities. The main protein component and its four charge variants were identified using direct icIEF-MS coupling. Additionally, the two major acidic and basic charge variants were collected using preparative fractionation after the protein focused in the separation capillary. The identity of the fractions was confirmed by LC-MS at intact protein level and the results were consistent with those using icIEF-MS online coupling. The multiple operation modes of the icIEF platform described above can be rapidly and flexibly switched just by changing customized capillary separation cartridges without drastically altering instrument configuration. The whole workflow of icIEF-based profiling, fractionation and MS online coupling for protein heterogeneity is straightforward, reliable, and accurate, thus providing comprehensive solutions for in-depth protein heterogeneity characterization.
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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 | 0.001 | 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 it