Metabolomics predicts the pharmacological profile of new psychoactive substances

Eulàlia Olesti, Ilario De Toma, Johannes G Ramaekers, Tibor M Brunt, Marcel Lí Carbó, Cristina Fernández-Avilés, Patricia Robledo, Magí Farré, Mara Dierssen, Óscar J Pozo, Rafael de la Torre

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)347-354
Number of pages8
JournalJournal of Psychopharmacology
Volume33
Issue number3
Early online date19 Nov 2018
DOIs
Publication statusPublished - Mar 2019

Keywords

  • CANNABINOIDS
  • DOPAMINE
  • DRUG DISCOVERY
  • JWH-018
  • MEPHEDRONE
  • METABOLISM
  • PHARMACOKINETICS
  • SEROTONIN
  • TRYPTOPHAN
  • Targeted metabolomics
  • URINE
  • new psychoactive substances
  • predicted pharmacology

Cite this

Olesti, E., De Toma, I., Ramaekers, J. G., Brunt, T. M., Carbó, M. L., Fernández-Avilés, C., ... de la Torre, R. (2019). Metabolomics predicts the pharmacological profile of new psychoactive substances. Journal of Psychopharmacology, 33(3), 347-354. https://doi.org/10.1177/0269881118812103
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. / Metabolomics predicts the pharmacological profile of new psychoactive substances. In: Journal of Psychopharmacology. 2019 ; Vol. 33, No. 3. pp. 347-354.
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abstract = "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.",
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author = "Eul{\`a}lia Olesti and {De Toma}, Ilario and Ramaekers, {Johannes G} and Brunt, {Tibor M} and Carb{\'o}, {Marcel L{\'i}} and Cristina Fern{\'a}ndez-Avil{\'e}s and Patricia Robledo and Mag{\'i} Farr{\'e} and Mara Dierssen and Pozo, {{\'O}scar J} and {de la Torre}, Rafael",
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Olesti, E, De Toma, I, Ramaekers, JG, Brunt, TM, Carbó, ML, Fernández-Avilés, C, Robledo, P, Farré, M, Dierssen, M, Pozo, ÓJ & de la Torre, R 2019, 'Metabolomics predicts the pharmacological profile of new psychoactive substances', Journal of Psychopharmacology, vol. 33, no. 3, pp. 347-354. https://doi.org/10.1177/0269881118812103

Metabolomics predicts the pharmacological profile of new psychoactive substances. / 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.

In: Journal of Psychopharmacology, Vol. 33, No. 3, 03.2019, p. 347-354.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Metabolomics predicts the pharmacological profile of new psychoactive substances

AU - Olesti, Eulàlia

AU - De Toma, Ilario

AU - Ramaekers, Johannes G

AU - Brunt, Tibor M

AU - Carbó, Marcel Lí

AU - Fernández-Avilés, Cristina

AU - Robledo, Patricia

AU - Farré, Magí

AU - Dierssen, Mara

AU - Pozo, Óscar J

AU - de la Torre, Rafael

PY - 2019/3

Y1 - 2019/3

N2 - 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.

AB - 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.

KW - CANNABINOIDS

KW - DOPAMINE

KW - DRUG DISCOVERY

KW - JWH-018

KW - MEPHEDRONE

KW - METABOLISM

KW - PHARMACOKINETICS

KW - SEROTONIN

KW - TRYPTOPHAN

KW - Targeted metabolomics

KW - URINE

KW - new psychoactive substances

KW - predicted pharmacology

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DO - 10.1177/0269881118812103

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JF - Journal of Psychopharmacology

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