Effects of cannabidiol and Δ <sup>9</sup> -tetrahydrocannabinol on cytochrome P450 enzymes: a systematic review
Bibliographic record
Abstract
-tetrahydrocannabinol (THC) inhibit and induce cytochrome P450 (CYP450) enzymes. The objective of this review is to evaluate the effect of CBD and THC on the activity of CYP450 enzymes and the implications for drug-drug interactions (DDIs) with psychotropic agents that are CYP substrates. A systematic search was conducted using PubMed, Scopus, Scientific Electronic Library Online (SciELO) and PsychINFO. Search terms included 'cannabidiol', 'tetrahydrocannabinol', and 'cytochrome P450'. A total of seven studies evaluating the interaction of THC and CBD with CYP450 enzymes and psychotropic drugs were included. Both preclinical and clinical studies were included. Results from the included studies indicate that both CBD and THC inhibit several CYP450 enzymes including, but not limited to, CYP1A2, CYP3C19, and CYP2B6. While there are a few known CYP450 enzymes that are induced by THC and CBD, the induction of CYP450 enzymes is an understudied area of research and lacks clinical data. The inhibitory effects observed by CBD and THC on CYP450 enzymes vary in magnitude and may decrease the metabolism of psychotropic agents, cause changes in plasma levels of psychotropic medications, and increase adverse effects. Our findings clearly present interactions between THC and CBD and several CYP450 enzymes, providing clinicians evidence of a high risk of DDIs for patients who consume both cannabis and psychotropic medication. However, more clinical research is necessary before results are applied to clinical settings.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".