Imaged Capillary Isoelectric Focusing Coupled to High‐Resolution Mass Spectrometry (icIEF‐MS) for Cysteine‐Linked Antibody–Drug Conjugate (ADC) Heterogeneity Characterization Under Native Condition
Bibliographic record
Abstract
Native mass spectrometry (nMS) is a cutting-edge technique that leverages electrospray ionization MS (ESI-MS) to investigate large biomolecules and their complexes in solution. The goal of nMS is to retain the native structural features and interactions of the analytes during the transition to the gas phase, providing insights into their natural conformations. In biopharmaceutical development, nMS serves as a powerful tool for analyzing complex protein heterogeneity, allowing for the examination of non-covalently bonded assemblies in a state that closely resembles their natural folded form. Herein, we present an imaged capillary isoelectric focusing-MS (icIEF-MS) workflow to characterize cysteine-linked antibody-drug conjugate (ADC) under native conditions. Two ADCs were analyzed: a latest generation cysteine-linked ADC polatuzumab vedotin and the first FDA-approved cysteine-linked ADC brentuximab vedotin. This workflow benefits from a recently developed icIEF system that is MS-friendly and capable of directly coupling to a high-sensitivity MS instrument. Results show that the icIEF separation is influenced by both drug payloads and the post-translational modifications (PTMs), which are then promptly identified by MS. Overall, this native icIEF-MS method demonstrates the potential to understand and control the critical quality attributes (CQAs) that are essential for the safe and effective use of ADCs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".