Mucuna pruriens (L-dopa) — interactions

Seeds carry roughly 3-6% L-dopa by weight, which makes this a dietary levodopa source rather than a herb with an incidental amine content. That is the whole interaction story.

Also known as: Mucuna pruriens, velvet bean, L-DOPA, levodopa, kapikachhu. Category: supplement.

What it does, mechanism by mechanism

Dietary L-dopa load — provides, strong

Food carrying levodopa, which is converted to dopamine and noradrenaline. This is why broad-bean PODS are on the MAOI list — the mechanism is L-dopa, not tyramine, and the distinction matters because it changes which part of the plant is the problem.

L-dopa is decarboxylated to dopamine and onward to noradrenaline. With a monoamine oxidase inhibitor present the pressor response is not destroyed, and that is a documented hypertensive hazard — the same mechanism that puts broad-bean pods rather than broad beans on the MAOI list. Mucuna delivers far more L-dopa than any food does.

Sources: Katzenschlager R 2004, Gardner DM 1996 · more on Dietary L-dopa load

Toxicity in its own right

Two further considerations. Dopamine antagonists, including most antipsychotics and metoclopramide, oppose this pharmacology directly. And L-dopa is a catechol that oxidises readily in light, warmth and at neutral to alkaline pH, so the amount actually present in an aged or poorly stored preparation is not the amount on the label.

Sources: Katzenschlager R 2004

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. Katzenschlager R, Evans A, Manson A, et al. (2004). Mucuna pruriens in Parkinson's disease: a double blind clinical and pharmacological study. Journal of Neurology, Neurosurgery & Psychiatry. doi:10.1136/jnnp.2003.028761
  2. Gardner DM, Shulman KI, Walker SE, Tailor SAN (1996). The making of a user friendly MAOI diet. The Journal of Clinical Psychiatry 57(3):99-104. PMID 8617704

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.