The Use of Medical Cannabis with Other Medications: A Review of Safety and Guidelines - An Update [Internet]
Bibliographic record
Abstract
According to Statistics Canada’s National Cannabis Survey, approximately 2.7 million Canadians (9%) reported using cannabis for medical reasons in the first half of 2019.For the purposes of this report, medical cannabis refers to use of the cannabis plant or its extracts or synthetic cannabinoids for medical purposes.The list of medical conditions for which cannabis may be of benefit is extensive and includes chemotherapy-induced nausea and vomiting, cachexia, anorexia nervosa, multiple sclerosis, amyotrophic lateral sclerosis, spinal cord injury and disease, epilepsy, pain, and many others.The cannabis plant contains hundreds of pharmacological components, not all of which are well-characterized. Presence and quantity of these components varies considerably between plants and products, and even within a single plant. Tetrahydrocannabinol (THC) is the most well-studied and is the primary pharmacological component responsible for the psychoactive and physical effects of cannabis. Cannabidiol (also commonly referred to as CBD) is the second most prevalent pharmacologically active compound. It does not have psychotropic properties, but is also proposed to be of value for treatment of a wide variety of medical conditions., Producers of cannabis and cannabis extracts for medical purposes are able to supply products with specific desired quantities and ratios of THC and cannabidiol. Two medical cannabis products are currently marketed for use in Canada. Nabiximols (Sativex) is a prescription product containing THC and cannabidiol. Nabilone (Cesamet) is a prescription synthetic cannabinoid product available in Canada. Synthetic cannabinoids mimic the effects of the active components of cannabis.Cytochrome P450 (CYP450) enzymes play a crucial role in the metabolism of many medications and are the main drivers of pharmacokinetic drug interactions. THC and cannabidiol are known substrates and modulators of the CYP450 enzyme system. THC and cannabidiol have shown to inhibit several CYP450 enzymes in vitro, whereas smoke from cannabis may induce one specific CYP450 enzyme. Along with a lack of robust clinical studies, inconsistency in chemical make-up and method of ingestion of various cannabis products makes it particularly difficult to clinically assess for and predict interactions between cannabis and other medications. In addition to the potential pharmacokinetic interactions due to changes in drug metabolism, the risk of pharmacodynamic interactions (e.g., adverse effects secondary to the use of cannabis with other psychoactive drugs) is also important to consider.,The aim of this report is to review the evidence surrounding safety of medical cannabis in combination with other medications, and relevant evidence-based guidelines.This is an update of a previous report published in April 2017, which found a single systematic review, and no evidence-based guidelines.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".