Fusion to Albumin as a Means to Slow the Clearance of Small Therapeutic Proteins Using the <I>Pichia pastoris</I> Expression System: A Case Study
Bibliographic record
Abstract
Of the numerous strategies that have been tested to slow the clearance of injected protein drugs from the body and circulation (), fusion to albumin offers several advantages. Albumin is the most abundant protein in mammalian plasma and one of the longest lived. It lacks posttranslational modifications, with the exception of extensive disulfide bonding (). If albumin can be fused in-frame with a therapeutic protein as a single-chain polypeptide, the novel protein may acquire the slow clearance profile of albumin, while retaining the activity important for clinical use. This acquisition derives primarily from an increase in the molecular volume of the therapeutic protein, such that it is no longer subject to loss via the kidneys. This approach has the potential to provide a more consistent and less heterogeneous product than one obtained, for instance, by chemical modification with polyethylene glycol. Whereas others have used Kluveromyces (,) and Saccharomyces () yeast species to produce human serum albumin (HSA) fusion proteins, we have used the methylotropic yeast, Pichia pastoris, to produce rabbit serum albumin (RSA) fusion proteins. This system is particularly well suited for albumin production (). This chapter summarizes our experience gained in expressing hirudin (), barbourin (), and reiterated RSA fusion proteins () in this system.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".