Thiol-Functionalized Polymeric Micelles: From Molecular Recognition to Improved Mucoadhesion
Bibliographic record
Abstract
Surface-modified colloids which can selectively interact with biological species or surfaces show promise as drug delivery systems. However, the preparation of such targeted devices remains challenging, especially when considering polyion complex micelles for which side reactions with the ionic core components (typically carboxylic acid or amino groups) can occur. To solve this issue, an innovative synthetic strategy is proposed and used to prepare an asymmetric poly(ethylene glycol)-block-poly(2-(N,N-dimethylamino)ethyl methacrylate) copolymer presenting a thiol group at the end of the poly(ethylene glycol) chain. Thiol groups are highly appealing given that they react almost exclusively and quantitatively with maleimides under physiological conditions, thereby facilitating the chemical functionalization of the copolymer. The simplicity of the derivatization procedure is illustrated by preparing model biotin-capped copolymers. The biotinylated copolymers are shown to self-assemble with an oligonucleotide in aqueous media to form polyion complex micelles with biotin groups at their outer surface. These micelles are capable of molecular recognition toward streptavidin. Alternatively, thiol-decorated (nonderivatized) micelles are prepared and show improved mucoadhesion through the formation of disulfide bonds with mucin. Finally, intermicellar disulfide bonds are generated under oxidative conditions to promote the formation of stimuli-responsive micellar networks.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".