Affinity-controlled capture and release of engineered monoclonal antibodies by macroporous dextran hydrogels using coiled-coil interactions
Bibliographic record
Abstract
Long-term delivery is a successful strategy used to reduce the adverse effects of monoclonal antibody (mAb)-based treatments. Macroporous hydrogels and affinity-based strategies have shown promising results in sustained and localized delivery of the mAbs. Among the potential tools for affinity-based delivery systems, the de novo designed Ecoil and Kcoil peptides are engineered to form a high-affinity, heterodimeric coiled-coil complex under physiological conditions. In this study, we created a set of trastuzumab molecules tagged with various Ecoil peptides and evaluated their manufacturability and characteristics. Our data show that addition of an Ecoil tag at the C-termini of the antibody chains (light chains, heavy chains, or both) does not hinder the production of chimeric trastuzumab in CHO cells or affect antibody binding to its antigen. We also evaluated the influence of the number, length, and position of the Ecoil tags on the capture and release of Ecoil-tagged trastuzumab from macroporous dextran hydrogels functionalized with Kcoil peptide (the Ecoil peptide-binding partner). Notably, our data show that antibodies are released from the macroporous hydrogels in a biphasic manner; the first phase corresponding to the rapid release of residual, unbound trastuzumab from the macropores, followed by the affinity-controlled, slow-rate release of antibodies from the Kcoil-functionalized macropore surface.
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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) | 0.000 | 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 | 0.000 |
| 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".