Site-specific enzymatic polysialylation of therapeutic proteins using bacterial enzymes
Bibliographic record
Abstract
The posttranslational modification of therapeutic proteins with terminal sialic acids is one means of improving their circulating half-life, thereby improving their efficiency. We have developed a two-step in vitro enzymatic modification of glycoproteins, which has previously only been achieved by chemical means [Gregoriadis G, Jain S, Papaioannou I, Laing P (2005) Int J Pharm 300:125-130). This two-step procedure uses the Campylobacter jejuni Cst-II α2,8-sialyltransferase to provide a primer on N-linked glycans, followed by polysialylation using the Neisseria meningitidis α2,8-polysialyltransferase. Here, we have demonstrated the ability of this system to modify three glycoproteins with varying N-linked glycan compositions: the human therapeutic proteins alpha-1-antitrypsin (A1AT) and factor IX, as well as bovine fetuin. The chain length of the polysialic acid addition was optimized by controlling reaction conditions. After demonstrating the ability of this system to modify a variety of proteins, the effect of polysialylation on the activity and serum half-life of A1AT was examined. The polysialylation of A1AT did not adversely affect its in vitro inhibition activity against human neutrophil elastase. The polysialylation of A1AT resulted in a significantly improved pharmacokinetic profile when the modified proteins were injected into CD-1 mice. Together, these results suggest that polysialylated A1AT may be useful for improved augmentation therapy for patients with a deficiency in this protein and that this modification may be applied to other therapeutic proteins.
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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) | 0.000 | 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".