A CB1 PARTIAL agonist with a reported CB1 Ki near 40.7 nM, and a CYP2C9 and CYP3A4 substrate. Both halves matter: the partial agonism is why there is a ceiling on receptor activation, and the substrate status is why a CYP inhibitor in the same stack raises exposure without any change in dose.
Also known as: cannabis, marijuana, delta-9-THC, delta 9 thc, weed, dronabinol, THC. Category: plant.
What it does, mechanism by mechanism
CB1 agonism (partial) — agonist, moderate
Activates the CB1 receptor as a partial agonist, as the plant cannabinoids do. Partial agonism has a ceiling: beyond a point more drug does not produce more receptor activation. That ceiling is the reason cannabis does not depress respiration to the point of death.
Partial agonist. The ceiling on CB1 activation is the pharmacological reason acute cannabis does not produce the respiratory depression that defines opioid death, and it is exactly the property a synthetic full agonist does not have.
Sedation, impaired coordination, and at sufficient combined load impaired airway protection and breathing. The most common serious harm in this whole corpus and the least exotic. It does not need a metabolic interaction to happen: the agents simply add.
Sedation and impaired coordination, additive with alcohol and with the sedative botanicals. The additive impairment is documented even where the individual agents are mild.
Slows the enzyme handling S-warfarin, phenytoin and NSAIDs. S-warfarin is the clinically dominant enantiomer; small shifts move the INR.
Cleared substantially by CYP2C9, with CYP3A4 also contributing, so a CYP2C9 or CYP3A4 inhibitor raises exposure. CYP2C9 is polymorphic, which is one reason the same inhaled or oral amount produces very different blood levels between people.
Slows the enzyme that metabolises roughly half of all prescription drugs. A CYP3A4 substrate taken with a CYP3A4 inhibitor reaches higher blood levels than its dose implies.
Slows the phase-2 conjugation that makes a compound water-soluble enough to excrete. Phase 2 is the step most interaction checkers skip. Cannabinoids are heavily glucuronidated, so this is not a side issue in this corpus.
The 11-hydroxy and carboxy metabolites are glucuronidated before excretion, which is also why the carboxy-glucuronide is what a urine screen finds long after any effect has gone.
A clean result means NO DOCUMENTED INTERACTION IN THIS DATASET. It does not mean safe, and it is not a clearance. Most substances are not in this dataset at all, and for many pairs that are, nobody has ever studied the combination.
Serotonergic drugs and the serotonin-toxicity mechanism
Dietary tyramine and L-dopa loads
The major cytochrome P450 pathways: CYP3A4, CYP2D6, CYP1A2, CYP2C9, CYP2C19 — inhibition and induction
P-glycoprotein inhibition and induction
11β-HSD2 inhibition (the licorice mechanism) and the potassium consequences that follow it
QT prolongation as an additive pharmacodynamic axis
Culinary seasonings and common foods with documented pharmacological activity
A selected set of narrow-therapeutic-index drugs where those shifts matter most
The endocannabinoid enzymes and transport: FAAH, MAGL, endocannabinoid membrane transport, CB1 and CB2
Additive CNS depression and GABA-A positive modulation — the alcohol / benzodiazepine / opioid / kava axis
The phytocannabinoids delta-9-THC, cannabidiol and the converted cannabinoids, as both substrates and inhibitors
Synthetic full CB1 agonists as a class, and why they are pharmacologically unlike cannabis
CYP2E1, and phase-2 glucuronidation and sulfation where a specific entry names them
The sedative and potentiator botanicals of the kava literature, and dietary L-dopa from Mucuna
Not in this dataset
Any substance not named in this dataset — which is most substances. There are tens of thousands of marketed drugs and this table holds fewer than a hundred entries.
Phase-2 conjugation (UGT, SULT, NAT2, COMT) except where a specific entry names it. The oilahuasca corpus turns heavily on phase 2 and this engine models it only in passing.
Pharmacogenomics. CYP2D6 and CYP2C19 are strongly polymorphic; a poor metaboliser and an ultra-rapid metaboliser can have opposite outcomes from the same pair, and this engine does not know your genotype.
Dose, timing, duration, formulation and route — all of which change whether a documented interaction is clinically real for you.
Renal and hepatic impairment, age, pregnancy, and body composition.
Bleeding and antiplatelet risk, hypoglycaemia, anticholinergic load, and most other pharmacodynamic axes beyond the ones listed above. Additive CNS depression and GABA-A modulation ARE now modelled — see the covers list — but the absence of a sedation finding still only means the agents you named are not on that axis in this dataset.
Herb–herb interactions outside the named entries, and essentially the whole botanical world: most plants have no interaction literature at all.
Allergy, intolerance, and contamination or adulteration of unregulated products.
Anything published after the last-reviewed date below.
Bleeding and antiplatelet risk, which is the mechanism that matters most for garlic, ginkgo and several other common supplements. It is not modelled at all, so a clean result says nothing about it.
Whether any of the natural FAAH, MAGL or transport inhibition reported in vitro occurs at all at a dose a person would take. For most of these compounds nobody has measured it.
The actual contents of an unregulated cannabinoid product. This engine models named compounds; an unidentified isomer or side-product in a converted-cannabinoid product is outside it by construction.
Dose. Every cannabinoid interaction here scales with dose, and consumer product labelling for this category is repeatedly found inaccurate in published surveys.
Inhalation-specific hazards — thermal degradation products, diluents chosen for rheology rather than for inhalation toxicology, and carrier and adulterant contamination.
101 substances, 33 mechanisms,
101 citations. Last reviewed .
Primary literature (every DOI resolved against the Crossref API) and FDA drug labelling. There is no free, openly-licensed, comprehensive drug-interaction dataset to draw on; NLM retired its Drug Interaction API on 2024-01-02 and DrugBank's interaction set is a commercial licence.
References
Pertwee RG (2008). The diverse CB1 and CB2 receptor pharmacology of three plant cannabinoids: delta9-tetrahydrocannabinol, cannabidiol and delta9-tetrahydrocannabivarin. British Journal of Pharmacology. doi:10.1038/sj.bjp.0707442
Ligresti A, De Petrocellis L, Di Marzo V (2016). From Phytocannabinoids to Cannabinoid Receptors and Endocannabinoids: Pleiotropic Physiological and Pathological Roles Through Complex Pharmacology. Physiological Reviews. doi:10.1152/physrev.00002.2016
Chan LN, Anderson GD (2014). Pharmacokinetic and pharmacodynamic drug interactions with ethanol (alcohol). Clinical Pharmacokinetics. doi:10.1007/s40262-014-0155-0
Stott C, White L, Wright S, Wilbraham D, Guy G (2013). A phase I, open-label, randomized, crossover study in three parallel groups to evaluate the effect of Rifampicin, Ketoconazole, and Omeprazole on the pharmacokinetics of THC/CBD oromucosal spray in healthy volunteers. SpringerPlus. doi:10.1186/2193-1801-2-236
Brown JD, Winterstein AG (2019). Potential Adverse Drug Events and Drug-Drug Interactions with Medical and Consumer Cannabidiol (CBD) Use. Journal of Clinical Medicine. doi:10.3390/jcm8070989
Every DOI above was resolved against the Crossref API on 2026-09-09 and the returned
title checked against the one printed here. Three DOIs in the first draft resolved to real but different
papers and were corrected before publication.