Design and Testing of Efficient Mucus‐Penetrating Nanogels—Pitfalls of Preclinical Testing and Lessons Learned
Bibliographic record
Abstract
Mucosal surfaces pose a challenging environment for efficient drug delivery. Various delivery strategies such as nanoparticles have been employed so far; yet, still yielding limited success. To address the need of efficient transmucosal drug delivery, this report presents the synthesis of novel disulfide-containing dendritic polyglycerol (dPG)-based nanogels and their preclinical testing. A bifunctional disulfide-containing linker is coupled to dPG to act as a macromolecular crosslinker for poly-N-isopropylacrylamide (PNIPAM) and poly-N-isopropylmethacrylamide (PNIPMAM) in a precipitation polymerization process. A systematic analysis of the polymerization reveals the importance of a careful polymer choice to yield mucus-degradable nanogels with diameters between 100 and 200 nm, low polydispersity, and intact disulfide linkers. Absorption studies in porcine intestinal tissue and human bronchial epithelial models demonstrate that disulfide-containing nanogels are highly efficient in overcoming mucosal barriers. The nanogels efficiently degrade and deliver the anti-inflammatory biomacromolecule etanercept into epithelial tissues yielding local anti-inflammatory effects. Over the course of this work, several problems are encountered due to a limited availability of valid test systems for mucosal drug-delivery systems. Hence, this study also emphasizes how critical a combined and multifaceted approach is for the preclinical testing of mucosal drug-delivery systems, discusses potential pitfalls, and provides suggestions for solutions.
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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 | 0.001 | 0.004 |
| 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 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".