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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".