Eulàlia Olesti1,2, Ilario De Toma2,3, Johannes G Ramaekers4, Tibor M Brunt5,6, Marcel Lí Carbó2,7,8, Cristina Fernández-Avilés1, Patricia Robledo1,2, Magí Farré8,9, Mara Dierssen1,2,3,9,10, Óscar J Pozo1, Rafael de la Torre1,2,9,10. 1. 1 Integrative Pharmacology and Systems Neuroscience Research Group, Neurosciences Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. 2. 2 Department of Experimental and Health Sciences, Pompeu Fabra University (CEXS-UPF), Barcelona, Spain. 3. 3 Cellular & Systems Neurobiology, Systems Biology Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain. 4. 4 Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands. 5. 5 Amsterdam Institute for Addiction Research, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. 6. 6 Department of Drug Monitoring & Policy, Netherlands Institute of Mental Health and Addiction (Trimbos Institute), Utrecht, The Netherlands. 7. 7 Biomedical Research, Prous Institute, Barcelona, Spain. 8. 8 Department of Pharmacology, Toxicology and Therapeutic Chemistry. Faculty of Pharmacy and Food Sciences, University of Barcelona. Av. Joan XXIII 27-31, Barcelona, Spain. 9. 10 School of Medicine, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain. 10. 11 CIBER de Fisiopatología de la Obesidad y Nutrición (CB06/03), CIBEROBN, Madrid, Spain.
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.
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.
Entities:
Keywords:
Targeted metabolomics; new psychoactive substances; predicted pharmacology
Authors: Andrea E Steuer; Daria Kaelin; Martina I Boxler; Lisa Eisenbeiss; Friederike Holze; Patrick Vizeli; Joanna Czerwinska; Paul I Dargan; Vincenzo Abbate; Matthias E Liechti; Thomas Kraemer Journal: Metabolites Date: 2020-07-27
Authors: Nieves Pizarro; Elk Kossatz; Pedro González; Alba Gamero; Emma Veza; Cristina Fernández; Toni Gabaldón; Rafael de la Torre; Patricia Robledo Journal: Front Nutr Date: 2021-11-26