Modification of Clearance of Therapeutic and Potentially Therapeutic Proteins
Bibliographic record
Abstract
Advances in biochemistry, protein chemistry and molecular biology over the last twenty-five years have spurred the increased use and development of proteins as injectable therapeutic agents. Introduction of proteins into the circulation exposes them to numerous different cells, enzymes and routes of extravasation that contribute to their clearance and their catabolism. Overly rapid clearance, particularly of small proteins, can limit therapeutic efficacy. Many strategies have been devised to retard the clearance of therapeutic or potentially therapeutic proteins, but relatively few proteins with clearance-retarding modifications are in clinical use. Proteins have been chemically modified towards this end by covalent attachment of polyethylene glycol or dextran chains or by protein-protein cross-linking. Genetic modification has also been employed to fuse proteins of interest to long-lived plasma proteins like albumin or immunoglobulins, or portions of these proteins. While all modifications may reduce the biological activity of the protein of interest or elicit antibody formation in recipient animals or patients, there now exists sufficient experience in this area that an optimal clearance-extending strategy can often be designed and successfully executed. With the explosive growth of genomic and proteomic information, an exponentially increasing number of engineered proteins are likely to be developed, with a probable need for clearance-related modification.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".