Multilevel─Intact, Subunits, and Peptides─Characterization of Antibody-Based Therapeutics by a Single-Column LC–MS Setup
Bibliographic record
Abstract
A comprehensive characterization of biotherapeutics, mandated by regulatory authorities, requires analyses of a protein drug at multiple structure levels. Such multilevel characterization can be performed by mass spectrometry (MS), with established conventional MS-based assays of product quality attributes (PQAs) comprising intact protein and subunit middle-up MS with analytes resolved on a C4 column, and bottom-up peptide mapping with analytes resolved on a C18 column. Recent advances in MS have facilitated the increasing use of middle-down analysis, expanding the qualitative analytical capability of MS for protein characterization. Recent studies using less-retentive reversed-phase LC in bottom-up MS also offer an opportunity for streamlining equipment configuration to a single-column LC-MS setup for multilevel characterization of therapeutic proteins. In this study, we developed a robust middle-down LC-MS method on a ZenoTOF 7600 and evaluated a C4 LC-MS setup for the characterization of NISTmAb, RG7221 bispecific antibody (bsAb), and Fc-fusion etanercept by intact protein, subunit middle-up/down, and bottom-up analyses. Successful multilevel characterization of the analytes using C4 LC-MS was demonstrated; notably, high sequence coverage and comprehensive post-translational modification profiling, including the mapping of all 13 O- and 3 N-glycosylation sites on etanercept in a single run, were obtained by bottom-up C4 LC-MS. This is also the first report on middle-down analysis of the major etanercept TNFR and Fc subunit glycoforms. A streamlined single-column LC-MS setup will enable more robust and efficient MS workflows for PQA assessment and simplify the integration of an LC-MS analyzer as a process analytical technology instrument for biopharma applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".