Release of delta-9-tetrahydrocannabinol from polyvinyl alcohol hydrogels and its safe interaction with human skin fibroblasts
Bibliographic record
Abstract
This study aimed to design a THC-rich hydrogel to deliver cannabis derivatives topically. We developed hydrogels using polyvinyl alcohol (PVA) mixed with propylene glycol (PG), vegetable glycerin (VG), or both to facilitate the dissolution of delta-9-tetrahydrocannabinol (THC). The hydrogels showed a brown color, confirming the presence of the cannabinoid. They exhibit a porous structure and better mechanical properties than PVA alone. Indeed, the hydrogel containing PG, VG, or both showed elastic deformation behaviors with lower water content. FTIR analysis demonstrated the presence of THC with two specific peaks at 1,575 and 1,619 cm−1, confirming the presence of THC in the hydrogels. Human dermal fibroblast cultures onto the surface of all hydrogels confirmed the safety of the THC-rich hydrogel as the cell adhesion was comparable to the control (no THC). Furthermore, cells adhering to the hydrogels could proliferate, showing increased cell viability at 48 and 72 h, with a higher proliferation obtained with the THC-rich PVA-PG-VG hydrogels. Such cell behavior could be due to the release of the THC in the culture medium, as demonstrated by ultra-high performance liquid chromatography (UPLC), showing the presence of THC in the culture medium, ranging from 203 to 290 μg after 24 h of incubation of the hydrogels containing PG and VG or both. In comparison, the released THC from the PVA hydrogel was higher, reaching 852 μg. It is interesting to note that the THC release at 24, 48, and 72 h was slower with the hydrogels containing PG, VG, and both, compared to PVA alone. Overall, the present study has designed safe THC-rich PVA-PG-VG hydrogels as a functional delivery system for the topical use of cannabinoids to control tissue diseases, such as inflammation.
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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".