LC-MS characterization and purity assessment of a prototype bispecific antibody
Bibliographic record
Abstract
Bispecific IgG asymmetric (heterodimeric) antibodies offer enhanced therapeutic efficacy, but present unique challenges for drug development. These challenges are related to the proper assembly of heavy and light chains. Impurities such as symmetric (homodimeric) antibodies can arise with improper assembly. A new method to assess heterodimer purity of such bispecific antibody products is needed because traditional separation-based purity assays are unable to separate or quantify homodimer impurities. This paper presents a liquid chromatography-mass spectrometry (LC-MS)-based method for evaluating heterodimeric purity of a prototype asymmetric antibody containing two different heavy chains and two identical light chains. The heterodimer and independently expressed homodimeric standards were characterized by two complementary LC-MS techniques: Intact protein mass measurement of deglycosylated antibody and peptide map analyses. Intact protein mass analysis was used to check molecular integrity and composition. LC-MS(E) peptide mapping of Lys-C digests was used to verify protein sequences and characterize post-translational modifications, including C-terminal truncation species. Guided by the characterization results, a heterodimer purity assay was demonstrated by intact protein mass analysis of pure deglycosylated heterodimer spiked with each deglycosylated homodimeric standard. The assay was capable of detecting low levels (2%) of spiked homodimers in conjunction with co-eluting half antibodies and multiple mass species present in the homodimer standards and providing relative purity differences between samples. Detection of minor homodimer and half-antibody C-terminal truncation species at levels as low as 0.6% demonstrates the sensitivity of the method. This method is suitable for purity assessment of heterodimer samples during process and purification development of bispecific antibodies, e.g., clone selection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".