Reduction in Tamoxifen Metabolites Endoxifen and N-desmethyltamoxifen With Chronic Administration of Low Dose Cannabidiol: A CYP3A4 and CYP2D6 Drug Interaction
Bibliographic record
Abstract
BACKGROUND: Cannabidiol (CBD) serves as a promising medicine, with few known adverse effects apart from the potential of drug interactions with the cytochrome P450 system. It has been hypothesized drug interactions may occur with chemotherapeutic agents, but no supporting evidence has been published to date. CASE: A 58-year-old female with a history of bilateral breast carcinoma in remission, was treated with tamoxifen for breast cancer prevention for over 6 years. CBD was instituted to treat persistent postsurgical pain, inadequately managed by alternate analgesics. It was postulated that CBD may diminish tamoxifen metabolism by CYP3A4 and 2D6 to form active metabolite endoxifen, which exerts the anticancer benefits. Endoxifen, tamoxifen, N-desmetyltamoxifen and 4-hydroxytamoxifen levels were collected while the patient chronically received CBD 40 mg/day, and after a 60-day washout. Upon discontinuation of CBD 40 mg/day, it was observed that endoxifen levels increased by 18.75% and N-desmethyltamoxifen by 9.24%, while 4-hydroxytamoxifen remained unchanged. CONCLUSION: CBD at a low dose of 40 mg/day resulted in the potential inhibition of CYP3A4 and/or CYP2D6. Patients receiving CBD and interacting chemotherapeutic drugs, such as tamoxifen, require monitoring to identify possible subtherapeutic response to treatment. Further pharmacokinetic studies are required to ascertain the dynamics of this drug interaction.
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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".