Modulating the Mucosal Drug Delivery Efficiency of Polymeric Nanogels Tuning their Redox Response and Surface Charge
Bibliographic record
Abstract
Abstract Mucus is a hydrated, viscoelastic, and adhesive gel that lubricates and protects the body from pathogens; however, its protective function hinders drug/nanomedicine diffusion and treatment efficiency. Therefore, novel drug delivery strategies are required to overcome challenging mucosal barriers. Here, multi‐responsive nanogels (NGs) are developed and explored their interaction with mucus. Specific NG features (e.g., surface charge, temperature responsiveness, and redox response) are evaluated in a typical mucus‐associated environment (i.e., mucin proteins and high glutathione concentrations). The results demonstrate that biocompatibility and the capacity to deliver a protein through mucosal barriers in different in vitro and in vivo models highlight the importance of specific NG design elements. Disulfide bonds are highlighted as redox‐sensitive cross‐linkers within the NG structure as critical for drug delivery performance; they function as degradation points that enable NG degradation and subsequent drug release and anchoring points to adhere to mucin, thereby enhancing their residence time at the desired site of action. Additionally, it is confirmed that surface charges impact interactions with mucin; positively charged NGs exhibit improved interactions with mucin compared to negatively charged and neutral NGs. Overall, the findings underline the importance of redox response and surface charge in NG design for reaching efficient mucosal drug delivery.
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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 | 0.002 | 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".