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Workflow to facilitate the detection of new psychoactive substances and drugs of abuse in influent urban wastewater

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Version 2 2024-10-15, 21:44
Version 1 2024-09-10, 23:03
journal contribution
posted on 2024-09-10, 23:03 authored by Richard Bade, Denice van HerwerdenDenice van Herwerden, Nikolaos Rousis, Sangeet Adhikari, Darren Allen, Christine Baduel, Lubertus Bijlsma, Tim BoogaertsTim Boogaerts, Dan Burgard, Andrew Chappell, Erin M. Driver, Fernando Fabriz Sodre, Despo Fatta-Kassinos, Emma Gracia-Lor, Elisa Gracia-Marín, Rolf U. Halden, Ester Heath, Emma Jaunay, Alex Krotulski, Foon Yin Lai, Arndís Sue Ching Löve, Jake W. O’Brien, Jeong-Eun Oh, Daniel Pasin, Marco Pineda Castro, Magda Psichoudaki, Noelia Salgueiro-Gonzalez, Cezar Silvino Gomes, Bikram Subedi, Kevin V. Thomas, Nikolaos Thomaidis, Degao Wang, Viviane Yargeau, Saer Samanipour

The complexity around the dynamic markets for new psychoactive substances (NPS) forces researchers to develop and apply innovative analytical strategies to detect and identify them in influent urban wastewater. In this work a comprehensive suspect screening workflow following liquid chromatographyhigh resolution mass spectrometry analysis was established utilising the open-source InSpectra data processing platform and the HighResNPS library. In total, 278 urban influent wastewater samples from 47 sites in 16 countries were collected to investigate the presence of NPS and other drugs of abuse. A total of 50 compounds were detected in samples from at least one site. Most compounds found were prescription drugs such as gabapentin (detection frequency 79%), codeine (40%) and pregabalin (15%). However, cocaine was the most found illicit drug (83%), in all countries where samples were collected apart from the Republic of Korea and China. Eight NPS were also identified with this protocol: 3-methylmethcathinone 11%), eutylone (6%), etizolam (2%), 3-chloromethcathinone (4%), mitragynine (6%), phenibut (2%), 25I-NBOH (2%) and trimethoxyamphetamine (2%). The latter three have not previously been reported in municipal wastewater samples. The workflow employed allowed the prioritisation of features to be further investigated, reducing processing time and gaining in confidence in their identification.

Funding

Australian Research Council Australian Laureate Fellowship (project number FL200100028)

History

Submitter

Salila Bryant