Metabolomics predicts the pharmacological profile of new psychoactive substances

Background: The unprecedented proliferation of new psychoactive substances (NPS) threatens public health and challenges drug policy. Information on NPS pharmacology and toxicity is, in most cases, unavailable or very limited and, given the large number of new compounds released on the market each ye...

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Bibliographic Details
Published in:Journal of psychopharmacology (Oxford) 2019-03, Vol.33 (3), p.347-354
Main Authors: Olesti, Eulàlia, De Toma, Ilario, Ramaekers, Johannes G, Brunt, Tibor M, Carbó, Marcel·lí, Fernández-Avilés, Cristina, Robledo, Patricia, Farré, Magí, Dierssen, Mara, Pozo, Óscar J, de la Torre, Rafael
Format: Article
Language:English
Subjects:
Algorithms
Animals
Brain - drug effects
Brain - metabolism
Cannabinoids
Cannabinoids - pharmacology
Cocaine
Drug abuse
Evaluation
Hallucinogens - pharmacology
Health risks
Heroin
Learning algorithms
Machine Learning
Male
MDMA
Metabolomics
Metabolomics - methods
Methamphetamine
Methamphetamine - analogs & derivatives
Methamphetamine - pharmacology
Monoamines
N-Methyl-3,4-methylenedioxyamphetamine - pharmacology
Narcotics
Neurotransmitters
New psychoactive substances
Pharmacology
Predicted pharmacology
Psychotropic drugs
Psychotropic Drugs - pharmacology
Public health
Rats
Rats, Wistar
Steroid hormones
Steroids
Targeted metabolomics
Tetrahydrocannabinol
Toxicity
Urine
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Summary:Background: The unprecedented proliferation of new psychoactive substances (NPS) threatens public health and challenges drug policy. Information on NPS pharmacology and toxicity is, in most cases, unavailable or very limited and, given the large number of new compounds released on the market each year, their timely evaluation by current standards is certainly challenging. Aims: We present here a metabolomics-targeted approach to predict the pharmacological profile of NPS. Methods: We have created a machine learning algorithm employing the quantification of monoamine neurotransmitters and steroid hormones in rats to predict the similarity of new drugs to classical ones of abuse (MDMA (3,4-methyl enedioxy methamphetamine), methamphetamine, cocaine, heroin and Δ9-tetrahydrocannabinol). Results: We have characterized each classical drug of abuse and two examples of NPS (mephedrone and JWH-018) following alterations observed in the targeted metabolome profile (monoamine neurotransmitters and steroid hormones) in different brain areas, plasma and urine at 1 h and 4 h post drug/vehicle administration. As proof of concept, our model successfully predicted the pharmacological profile of a synthetic cannabinoid (JWH-018) as a cannabinoid-like drug and synthetic cathinone (mephedrone) as a MDMA-like psychostimulant. Conclusion: Our approach allows a fast NPS pharmacological classification which will benefit both drug risk evaluation policies and public health.
ISSN:0269-8811
1461-7285
DOI:10.1177/0269881118812103