Grapefruit and Opioids

Yes — there is a documented interaction between these two, and it has a named mechanism. CYP3A4 inhibition.

Major Grapefruit inhibits CYP3A4; Opioids is cleared by it

Mechanism: CYP3A4 inhibition

Grapefruit slows the enzyme that clears Opioids, so Opioids reaches higher blood levels than its dose implies. A CYP3A4 substrate taken with a CYP3A4 inhibitor reaches higher blood levels than its dose implies.

Mechanism-based (irreversible) inactivation of enterocyte CYP3A4. A single 200–300 mL glass is enough. Recovery of CYP3A activity takes roughly 3 days, and separating the juice from the dose by a few hours does NOT avoid it — which is the part almost every patient gets wrong.

Oxycodone, fentanyl and methadone are CYP3A4-dependent, so a CYP3A4 inhibitor raises exposure at an unchanged dose.

What to watch for. The exaggerated version of that drug's own dose-related effects.

Sources: Bailey DG 2013, Bailey DG 1991, Paine MF 2006, Lown KS 1997, Lundahl J 1995, Greenblatt DJ 2003, Edwards DJ 1996, U.S. Food and Drug Administration 2023

The mechanism, generalised

Read the mechanism page and you can apply this to substances that are not on it: CYP3A4 inhibition.

Substance pages: Grapefruit · Opioids.

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. Bailey DG, Dresser G, Arnold JMO (2013). Grapefruit–medication interactions: Forbidden fruit or avoidable consequences?. CMAJ (published online 2012-11-26). doi:10.1503/cmaj.120951
  2. Bailey DG, Spence JD, Munoz C, Arnold JMO (1991). Interaction of citrus juices with felodipine and nifedipine. The Lancet. doi:10.1016/0140-6736(91)90872-M
  3. Paine MF, Widmer WW, Hart HL, et al. (2006). A furanocoumarin-free grapefruit juice establishes furanocoumarins as the mediators of the grapefruit juice-felodipine interaction. The American Journal of Clinical Nutrition. doi:10.1093/ajcn/83.5.1097
  4. Lown KS, Bailey DG, Fontana RJ, et al. (1997). Grapefruit juice increases felodipine oral availability in humans by decreasing intestinal CYP3A protein expression. Journal of Clinical Investigation. doi:10.1172/JCI119439
  5. Lundahl J, Regårdh CG, Edgar B, Johnsson G (1995). Relationship between time of intake of grapefruit juice and its effect on pharmacokinetics and pharmacodynamics of felodipine in healthy subjects. European Journal of Clinical Pharmacology. doi:10.1007/BF00192360
  6. Greenblatt DJ, von Moltke LL, Harmatz JS, et al. (2003). Time course of recovery of cytochrome p450 3A function after single doses of grapefruit juice. Clinical Pharmacology & Therapeutics. doi:10.1016/S0009-9236(03)00118-8
  7. Edwards DJ, Bellevue FH 3rd, Woster PM (1996). Identification of 6',7'-dihydroxybergamottin, a cytochrome P450 inhibitor, in grapefruit juice. Drug Metabolism and Disposition. doi:10.1016/s0090-9556(25)08464-8
  8. U.S. Food and Drug Administration (2023). Drug Development and Drug Interactions: Table of Substrates, Inhibitors and Inducers. FDA. https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-table-substrates-inhibitors-and-inducers

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.