Delivery of N-Cadherin Targeting Peptides to Vascular Tissues by Surface-Modified Polyurethane Nanoparticles via a Drug-Coated Balloon
Bibliographic record
Abstract
Restenosis remains a long-standing limitation to effectively maintain functional blood flow after percutaneous transluminal angioplasty (PTA). While the use of drug-coated balloons (DCBs) containing antiproliferative drugs has improved patient outcomes, limited tissue transfer and poor therapeutic targeting capabilities contribute to off-target cytotoxicity, precluding adequate endothelial repair. In this work, a DCB system was designed and tested to achieve defined arterial delivery of an antirestenosis therapeutic candidate, cadherin-2 (N-cadherin) mimetic peptides (NCad), shown to selectively inhibit smooth muscle cell migration in vitro and limit intimal thickening in early animal PTA models. To enable successful tissue transfer in the current work, a nanoparticle excipient system previously demonstrated to be an effective carrier of NCad in vitro was integrated with customized DCB coating methodologies designed to prevent therapeutic loss during delivery. DCB design took into consideration four components: (1) the angioplasty balloon; (2) a poly(ethylene oxide) (PEO) monolayer acting as a hydrophilic spacer between the balloon surface and the nanoparticles to assist with improved nanoparticle release; (3) surface-modified degradable polar hydrophobic ionic polyurethane (D-PHI) nanoparticles loaded with NCad to facilitate the transport of the therapeutic peptide into vascular tissue; and (4) a PEO sacrificial coating applied over the nanoparticle excipient layer to prevent premature losses during transit to the artery. The nanoparticle-DCB platform successfully delivered NCad to rat carotid tissue, with superior efficacy and increased permeation within the vessel wall compared with soluble NCad infusion alone. Nanoscale technologies in conjunction with enhanced DCB design properties hold promise in advancing the localized delivery of preventive restenosis therapies in vascular disease.
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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".