Local and Sustained Baricitinib Delivery to the Skin through Injectable Hydrogels Containing Reversible Thioimidate Adducts
Bibliographic record
Abstract
Abstract Janus kinase (JAK) inhibitors are approved for many dermatologic disorders, but their use is limited by systemic toxicities including serious cardiovascular events and malignancy. To overcome these limitations, injectable hydrogels are engineered for the local and sustained delivery of baricitinib, a representative JAK inhibitor. Hydrogels are formed via disulfide crosslinking of thiolated hyaluronic acid macromers. Dynamic thioimidate bonds are introduced between the thiolated hyaluronic acid and nitrile‐containing baricitinib for drug tethering, which is confirmed with 1H and 13C nuclear magnetic resonance (NMR). Release of baricitinib is tunable over six weeks in vitro and active in inhibiting JAK signaling in a cell line containing a luciferase reporter reflecting interferon signaling. For in vivo activity, baricitinib hydrogels or controls are injected intradermally into an imiquimod‐induced mouse model of psoriasis. Imiquimod increases epidermal thickness in mice, which is unaffected when treated with baricitinib or hydrogel alone. Treatment with baricitinib hydrogels suppresses the increased epidermal thickness in mice treated with imiquimod, suggesting that the sustained and local release of baricitinib is important for a therapeutic outcome. This study is the first to utilize a thioimidate chemistry to deliver JAK inhibitors to the skin through injectable hydrogels, which has translational potential for treating inflammatory disorders.
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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 | 0.000 |
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".