Sulfoxide-functionalized nanogels inspired by the skin penetration properties of DMSO
Bibliographic record
Abstract
Among polymeric nanocarriers, nanogels are especially promising non-irritating delivery vehicles to increase dermal bioavailability of therapeutics. However, accurately tailoring defined interactions with the amphiphilic skin barrier is still challenging. To address this limited specificity, we herein present a new strategy to combine biocompatible nanogels with the outstanding skin interaction properties of sulfoxide moieties. These chemical motifs are known from dimethyl sulfoxide (DMSO), a potent chemical penetration enhancer, which can often cause undesired skin damage upon long-term usage. By covalently functionalizing the nanogels' polymer network with such methyl sulfoxide side groups, tailor-made dermal delivery vehicles are developed to circumvent the skin disrupting properties of the small molecules. Key to an effective nanogel-skin interaction is assumed to be the specific nanogel amphiphilicity. This is examined by comparing the delivery efficiency of sulfoxide-based nanogels (NG-SOMe) with their corresponding thioether (NG-SMe) and sulfone-functionalized (NG-SO2Me) analogues. We demonstrate that the amphiphilic sulfoxide-based NG-SOMe nanogels are superior in their interaction with the likewise amphipathic stratum corneum (SC) showing an increased topical delivery efficacy of Nile red (NR) to the viable epidermis (VE) of excised human skin. In addition, toxicological studies on keratinocytes and fibroblasts show good biocompatibility while no perturbation of the complex protein and lipid distribution is observed via stimulated Raman microscopy. Thus, our NG-SOMe nanogels show high potential to effectively emulate the skin penetration enhancing properties of DMSO without its negative side effects.
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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".