One-Pot Synthesis of Poly(glycerol-<i>co</i>-succinic acid) Nanogels for Dermal Delivery
Bibliographic record
Abstract
Polyglycerol nanogels are three-dimensional polymeric networks with a few hundred nanometer sizes and the ability to encapsulate and deliver cargos for a wide range of biomedical applications. However, time-consuming and multistep synthetic routes as well as milligram-scale production have hindered further development of these nanomaterials. In this work, we report on a straightforward synthetic method for the production of polyglycerol nanoarchitectures. Enzymatic ring-opening copolymerization of a mixture of glycidol and succinic anhydride resulted in polyglycerol nanogels with succinic acid segments in their backbone. Novozyme 435 was used as a dual catalytic agent to support ring-opening polymerization of the above-mentioned cyclic monomers as well as esterification of the produced oligomers to obtain nanogels. While succinic acid segments improved the biodegradability and loading capacity of nanogels, polyglycerol caused water solubility, high functionality, and biocompatibility. Nanogels were loaded with tacrolimus and photosensitizer 5,10,15,20-tetrakis(3-hydroxyphenyl)porphyrin (mTHPP)-a close congener of the approved photosensitizer temoporfin (mTHPC)-and their ability to improve the skin penetration of these therapeutic agents was investigated. mTHPP delivery experiments on human skin, which were quantified by fluorescence microscopy, showed that these nanogels deposit in the stratum corneum and release the loaded drug to viable epidermis of skin efficiently in comparison with commercially available base cream. Taking advantage of the straightforward synthesis as well as biodegradability, biocompatibility, high loading capacity, and efficient skin penetration, the synthesized nanogels could be used as future topical delivery systems.
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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) | 0.001 | 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".