Dietary fats and pharmaceutical lipid excipients increase systemic exposure to orally administered cannabis and cannabis-based medicines.
Bibliographic record
Abstract
There has been an escalating interest in the medicinal use of Cannabis sativa in recent years. Cannabis is often administered orally with fat-containing foods, or in lipid-based pharmaceutical preparations. However, the impact of lipids on the exposure of patients to cannabis components has not been explored. Therefore, the aim of this study is to elucidate the effect of oral co-administration of lipids on the exposure to two main active cannabinoids, Δ(9)-tetrahydrocannabinol (THC) and cannabidiol (CBD). In this study, oral co-administration of lipids enhanced the systemic exposure of rats to THC and CBD by 2.5-fold and 3-fold, respectively, compared to lipid-free formulations. In vitro lipolysis was conducted to explore the effect of lipids on the intestinal solubilisation of cannabinoids. More than 30% of THC and CBD were distributed into micellar fraction following lipolysis, suggesting that at least one-third of the administered dose will be available for absorption following co-administration with lipids. Both cannabinoids showed very high affinity for artificial CM-like particles, as well as for rat and human CM, suggesting high potential for intestinal lymphatic transport. Moreover, comparable affinity of cannabinoids for rat and human CM suggests that similar increased exposure effects may be expected in humans. In conclusion, co-administration of dietary lipids or pharmaceutical lipid excipients has the potential to substantially increase the exposure to orally administered cannabis and cannabis-based medicines. The increase in patient exposure to cannabinoids is of high clinical importance as it could affect the therapeutic effect, but also toxicity, of orally administered cannabis or cannabis-based medicines.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".