Engineering Polymer‐Binding Bispecific Antibodies for Enhanced Pretargeted Delivery of Nanoparticles to Mucus‐Covered Epithelium
Bibliographic record
Abstract
Mucus represents a major barrier to sustained and targeted drug delivery to mucosal epithelium. Ideal drug carriers should not only rapidly diffuse across mucus, but also bind the epithelium. Unfortunately, ligand-conjugated particles often exhibit poor penetration across mucus. In this work, we explored a two-step "pretargeting" approach through engineering a bispecific antibody that binds both cell-surface ICAM-1 and polyethylene glycol (PEG) on the surface of nanoparticles, thereby effectively decoupling cell targeting from particle design and formulation. When tested in a mucus-coated Caco-2 culture model that mimics the physiological process of mucus clearance, pretargeting increased the amount of PEGylated particles binding to cells by around 2-fold or more compared to either non-targeted or actively targeted PEGylated particles. Pretargeting also markedly enhanced particle retention in mouse intestinal tissues. Our work underscores pretargeting as a promising strategy to improve the delivery of therapeutics to mucosal surfaces.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".