Novel mass spectrometry methods for characterizing antibodies and antibody-drug conjugates
Bibliographic record
Abstract
Monoclonal antibodies (mAbs) and related products such as antibody-drug conjugates (ADCs) are the fastest evolving calss of therapeutic agents today. They are also amongst the most difficult to analyze effectively which can pose significant challenges during the the development process and when attempting to demonstrate regulatory compliance. Increasingly, the power of mass spectrometry (MS) is being exploited to create novel and effective solutions for characterizing biotherapeutics. "Middle-up" LC-MS is a simple technique that is effective for characterizing glycosylation and for mapping other modifications and sequence abnormalities to specific regions of the antibody. Antibodies are selectively cleaved into a handful of large polypeptides which are then analyzed by LC-MS. "Middle-up" LC-MS provided an elegant and rapid solution for separately profiling Fc and Fab N-glycosylation on anti-EGFR antibodies. Furthermore, the resulting glycan profiles are less likely to be biased by the presence of certain sugars such as sialic acid and are more representative of their true abundances. "Middle-up" LC-MS analysis has also been effective for monitoring the results of efforts to remodel Fc glycosylation through glycoengineering. An example will be presented here for Trastuzumab. LC-MS is also an effective method for measuring the drug-antibody ration (DAR) of ADCs provided that the additional molecular diversity due to PTMs can be eliminated. We developed a procedure that simplifies the molecular profiles of antibodies containing multiple sites of glycosylation using conditions that are compatible with LC-MS. The antibody is treated with a combination of exo- and endoglycosidases as well as a carboxypeptidase to remove C-terminal lysines. In this manner the complex mass profile of the antibody collapses into one major peak. Employing this approach for ADCs derived from these antibodies will enable their DAR to be determined.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".