In Vitro and Clinical Evaluation of Cannabigerol (CBG) Produced via Yeast Biosynthesis: A Cannabinoid with a Broad Range of Anti-Inflammatory and Skin Health-Boosting Properties
Bibliographic record
Abstract
Cannabigerol (CBG) is a minor non-psychoactive cannabinoid present in Cannabis sativa L. (C. sativa) at low levels (<1% per dry weight) that serves as the direct precursor to both cannabidiol (CBD) and tetrahydrocannabinol (THC). Consequently, efforts to extract and purify CBG from C. sativa is both challenging and expensive. However, utilizing a novel yeast fermentation technology platform, minor cannabinoids such as CBG can be produced in a more sustainable, cost-effective, and timely process as compared to plant-based production. While CBD has been studied extensively, demonstrating several beneficial skin properties, there are a paucity of studies characterizing the activity of CBG in human skin. Therefore, our aim was to characterize and compare the in vitro activity profile of non-psychoactive CBG and CBD in skin and be the first group to test CBG clinically on human skin. Gene microarray analysis conducted using 3D human skin equivalents demonstrates that CBG regulates more genes than CBD, including several key skin targets. Human dermal fibroblasts (HDFs) and normal human epidermal keratinocytes (NHEKs) were exposed in culture to pro-inflammatory inducers to trigger cytokine production and oxidative stress. Results demonstrate that CBG and CBD reduce reactive oxygen species levels in HDFs better than vitamin C. Moreover, CBG inhibits pro-inflammatory cytokine (Interleukin-1β, -6, -8, tumor necrosis factor α) release from several inflammatory inducers, such as ultraviolet A (UVA), ultraviolet B (UVB), chemical, C. acnes, and in several instances does so more potently than CBD. A 20-subject vehicle-controlled clinical study was performed with 0.1% CBG serum and placebo applied topically for 2 weeks after sodium lauryl sulfate (SLS)-induced irritation. CBG serum showed statistically significant improvement above placebo for transepidermal water loss (TEWL) and reduction in the appearance of redness. Altogether, CBG’s broad range of in vitro and clinical skin health-promoting activities demonstrates its strong potential as a safe, effective ingredient for topical use and suggests there are areas where it may be more effective than CBD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".