A glyco-engineering approach for site-specific conjugation to Fab glycans
Bibliographic record
Abstract
Effective processes for synthesizing antibody-drug conjugates (ADCs) require: 1) site-specific incorporation of the payload to avoid interference with binding to the target epitope, 2) optimal drug/antibody ratio to achieve sufficient potency while avoiding aggregation or solubility problems, and 3) a homogeneous product to facilitate approval by regulatory agencies. In conventional ADCs, the drug molecules are chemically attached randomly to antibody surface residues (typically Lys or Cys), which can interfere with epitope binding and targeting, and lead to overall product heterogeneity, long-term colloidal instability and unfavorable pharmacokinetics. Here, we present a more controlled process for generating ADCs where drug is specifically conjugated to only Fab N-linked glycans in a narrow ratio range through functionalized sialic acids. Using a bacterial sialytransferase, we incorporated N-azidoacetylneuraminic acid (Neu5NAz) into the Fab glycan of cetuximab. Since only about 20% of human IgG1 have a Fab glycan, we extended the application of this approach by using molecular modeling to introduce N-glycosylation sites in the Fab constant region of other therapeutic monoclonal antibodies. We used trastuzumab as a model for the incorporation of Neu5NAz in the novel Fab glycans that we designed. ADCs were generated by clicking the incorporated Neu5NAz with monomethyl auristatin E (MMAE) attached to a self-immolative linker terminated with dibenzocyclooctyne (DBCO). Through this process, we obtained cetuximab-MMAE and trastuzumab-MMAE with drug/antibody ratios in the range of 1.3 to 2.5. We confirmed that these ADCs still bind their targets efficiently and are as potent in cytotoxicity assays as control ADCs obtained by standard conjugation protocols. The site-directed conjugation to Fab glycans has the additional benefit of avoiding potential interference with effector functions that depend on Fc glycan structure.
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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 | 0.000 | 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 | 0.001 |
| 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 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".