Kaempferol-rich foods (broccoli, kale, tea) — interactions

Kaempferol is a competitive FAAH inhibitor with a reported Ki near 5 micromolar. It is in this table as the clearest illustration of why an in-vitro number is not a dose: nobody reaches 5 micromolar plasma kaempferol by eating broccoli.

Also known as: kaempferol, broccoli, kale, green tea. Category: food.

What it does, mechanism by mechanism

FAAH inhibition — inhibits, weak

Slows fatty acid amide hydrolase, the enzyme that breaks down anandamide and the related fatty-acid amides. Raises anandamide tone rather than adding an outside agonist. Additive with anything else acting on the same system, and the clinical FAAH-inhibitor programmes are a reminder that an enzyme inhibitor is not inherently mild.

Competitive inhibition, reported Ki near 5 micromolar. Reported from in-vitro enzyme assays. A micromolar IC50 in a dish does not establish that a dietary, tea or capsule dose reaches that concentration at the enzyme in a person, and for most of these compounds no human pharmacokinetic study exists. Treated here as a mechanism worth knowing about, not as an established clinical effect. Cruciferous vegetables separately induce CYP1A2, which is the interaction from this food group that actually shows up in people.

Sources: Thors L 2008 · more on FAAH inhibition

Specific combinations

What a clean result means here

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.

In this dataset

  • Monoamine oxidase inhibition (prescription MAOIs, RIMAs, linezolid, methylene blue, harmala alkaloids)
  • 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

  1. Thors L, Belghiti M, Fowler CJ (2008). Inhibition of fatty acid amide hydrolase by kaempferol and related naturally occurring flavonoids. British Journal of Pharmacology. doi:10.1038/bjp.2008.237

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.

Last reviewed . All interaction pages.