Drug, food and seasoning interactions

Most interaction checkers are pair lookup tables, and the pairs nobody typed in are invisible. This one is built the other way round: on mechanisms. CYP3A4 inhibition is one rule and it covers grapefruit, ketoconazole, ritonavir, clarithromycin and the piperine in black pepper. A mechanism generalises to a substance that is not in the table yet. A pair list cannot.

It is also why the seasoning shelf is here rather than as a curiosity. Nutmeg, grapefruit, black pepper, licorice, cinnamon and star anise are not folk warnings — they are documented pharmacology, and the enzymes they move are the same ones your prescriptions are cleared by.

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.

By mechanism

By substance

Specific combinations

157 combination pages exist because there is documented, cited, substance-specific material to put on them. 4893 further combinations were deliberately not given a page: a page with nothing on it is worse than no page, both for the reader and for the site.

Last reviewed . All interaction pages.