Solid‐Phase Enzymatic Remodeling Produces High Yields of Single Glycoform Antibodies
Bibliographic record
Abstract
Antibodies are synthesized in mammalian cell culture as heterogeneous mixtures of glycoforms. Production of single glycoforms remains a challenge despite their value as therapeutics. The authors report a method of sequential enzymatic-based changes to antibodies while immobilized on an affinity column. Various antibodies (monoclonal and polyclonal) are isolated on Protein A or G columns and their glycans modified by sequential addition of enzymes for a desired transformation. Galactosylated antibodies (>90% yield) are produced by a one stage reaction process with sialidase to remove any sialic acid residues and addition of galactose with galactosyltransferase and UDP-Gal. Sialylated antibodies (>90%) are produced by a 2 stage conversion involving α(2,3) sialidase and galactosyltransferase followed by treatment with α(2,6) sialyltransferase in the presence of CMP-NANA. By this method, >90% of a disialylated human-llama antibody (EG2-hFc) and equimolar quantities of monosialylated and disialylated forms of human antibodies (αIL8-hFc and human polyclonal) are produced. Such high levels of sialylation are very difficult to obtain by typical cell culture methods. This method of transformation while the antibody is held on a solid-phase column is superior to previous methods because it allows a series of enzymatic steps without the need for intermediate purification. This is an efficient and rapid method to generate therapeutic antibodies with predefined glycosylation profiles. This should also assist in investigating the structure-function relationship of antibody glycans to find the desired glycosylation profile for high functional activity. With further optimization the method can be used to modify antibodies in large-scale manufacturing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.002 |
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".