An opioid that is also a serotonin and noradrenaline reuptake inhibitor, and lowers the seizure threshold. Three mechanisms in one tablet, which is why it appears in so many case reports.
Blocks the serotonin transporter (SERT), raising synaptic serotonin. Additive with anything else that raises serotonin; combined with MAO inhibition it is the classic lethal pairing.
Tramadol is the opioid most often named in serotonin-toxicity case reports, and the reason is structural rather than incidental: the molecule was designed with monoamine reuptake activity, so the analgesia and the serotonergic load are not separable by dose adjustment. Gillman's classification puts it with meperidine, methadone and the fentanyls among the SRI opioids, apart from morphine, codeine and oxycodone which are not.
Agents that make a seizure more likely, additively. A CYP interaction that raises the blood level of a seizure-threshold-lowering drug converts a pharmacokinetic problem into a neurological one.
Slows the enzyme handling many antidepressants, antipsychotics, opioids and psilocin. For a prodrug like codeine the effect inverts — less active drug, not more.
CYP2D6 converts tramadol to its far more potent O-desmethyl metabolite, so a CYP2D6 inhibitor reduces ANALGESIA while leaving the serotonergic parent drug in place.
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
Beakley BD, Kaye AM, Kaye AD (2015). Tramadol, Pharmacology, Side Effects, and Serotonin Syndrome: A Review. Pain Physician. doi:10.36076/ppj.2015/18/395
Gillman PK (2005). Monoamine oxidase inhibitors, opioid analgesics and serotonin toxicity. British Journal of Anaesthesia. doi:10.1093/bja/aei210
Flockhart DA, Thacker D, McDonald C, Desta Z (2021). The Flockhart Cytochrome P450 Drug-Drug Interaction Table. Division of Clinical Pharmacology, Indiana University School of Medicine. https://drug-interactions.medicine.iu.edu
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.