A gram-scale synthesis of β-L-carbafucose for engineering antibody glycans
Bibliographic record
Abstract
Afucosylated antibodies often exhibit superior properties compared to their fucosylated counterparts including, among others, enhanced antibody-dependent cell cytotoxicity (ADCC). While several recombinant and biochemical strategies have been identified for generating afucosylated antibodies, small molecule metabolic inhibitors provide a potentially more straightforward option. We recently reported that β-L-carbafucose is an inhibitor of antibody fucosylation and is not incorporated into the antibody glycans. To support the further study of β-L-carbafucose, a gram-scale synthesis was needed. Here, we report our investigation of three distinct synthetic routes, including a highly efficient chromatography-free synthesis. Further, we demonstrate multi-gram production of afucosylated Herceptin (Trastuzumab®) in 10 L bioreactors using β-L-carbafucose. We expect this new synthetic process will support the widespread adoption of β-L-carbafucose for producing afucosylated antibodies for discovery and development purposes. Afucosylated antibodies, known for enhanced antibody-dependent cell cytotoxicity, require efficient production methods for broader application. Here, the authors explore three synthetic routes for β-L-carbafucose as a metabolic inhibitor of antibody fucosylation, achieving a chromatography-free synthesis, and demonstrating a multi-gram production of afucosylated Herceptin using β-L-carbafucose.
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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) | 0.001 | 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) | 0.001 | 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".