Development of Bioactive Cannabis Extracts Through Optimization of Green Supercritical Fluid Process
Bibliographic record
Abstract
RÉSUMÉ: En raison du potentiel médicinal des substances chimiques actives pour traiter les patients atteints de maladies telles que la maladie d'Alzheimer, les maladies auto-immunes et le cancer, la recherche sur l'huile de cannabis s'est orientée vers le marché médical. Le traitement avec des cannabinoïdes comme le tétrahydrocannabinol, le cannabigérol et le cannabinol pour réduire la douleur neuropathique et activer le système immunitaire est le moyen le plus efficace de traiter ces maladies mortelles. L'absence de traitement industriel standard de ces composés bioactifs est à l'origine de l'échec de l'introduction des produits à base de cannabis sur le marché pharmaceutique. La contribution de cette étude vise à pousser plus loin l'optimisation des conditions d'extraction de ces cannabinoïdes. Les cannabinoïdes étant des composés non polaires, ils ont une grande affinité avec le CO2 comme solvant, mais pas avec les autres composés polaires non nécessaires (polyphénols, terpènes). Nous suggérons l'extraction au CO2 supercritique comme traitement industriel afin de se rapprocher de la qualité pharmaceutique. Nous utilisons un plan d'expérience Box-Behnken à trois niveaux, et nous testons 27 expériences. Le débit de CO2, la pression, la température et le temps sont les principaux facteurs examinés. Pour le THC, le CBG et le CBN, les conditions optimales sont 15 g/min, 235 bar, 55 °C et 2 h, mais 4 h pour le CBN. Une comparaison rapide avec l'extraction à l'éthanol montre que les extraits au CO2 supercritique contiennent 24 % de cannabinoïdes de plus que l'extraction à l'éthanol. ABSTRACT: Due to the medicinal potential of active chemicals to treat patients with diseases including Alzheimer's, auto-immune illnesses, and cancer, research on cannabis oil has shifted toward the medical market. Treatment with cannabinoids such tetrahydrocannabinol, cannabigerol, and cannabinol to reduce neuropathic pain and activate the immune system is the most effective way to treat these fatal diseases. The lack of standard industrial processing of these bioactive compounds is at the origin of the failure to get cannabis-based products into the pharmaceutical market. The contribution of this study aims to push further the optimization of these cannabinoids’ extraction conditions. As CO2 is non polar, it has a great affinity to cannabinoids, but not for other polar compounds such as polyphenols, terpenes. We suggest supercritical CO2 extraction as an industrial processing in order to get closer to the pharmaceutical grade quality. We use a three-level Box-Behnken design of experiment, and we test 27 experiments. The CO2 flowrate, pressure, temperature, and time are the main factors examined. For THC, CBG, and CBN, the optimal conditions are 15 g/min, 235 bar, 55 °C, and 2 h but 4h for CBN. A quick comparison with ethanol extraction shows that the supercritical CO2 extracts contains 24 % more cannabinoids than conventional ethanol extraction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".