Investigation of Biotransformation Products of p-Methoxymethylamphetamine and Dihydromephedrone in Wastewater by High-Resolution Mass Spectrometry

Abstract

There is a paucity of information on biotransformation and stability of new psychoactive substances (NPS) in wastewater. Moreover, the fate of NPS and their transformation products (TPs) in wastewater treatment plants is not well understood. In this study, batch reactors seeded with activated sludge were set up to evaluate biotic, abiotic, and sorption losses of p-methoxymethylamphetamine (PMMA) and dihydromephedrone (DHM) and identify TPs formed during these processes. Detection and identification of all compounds was performed with target and suspect screening approaches using liquid chromatography quadrupole-time-of-flight mass spectrometry. Influent and effluent 24 h composite wastewater samples were collected from Athens from 2014 to 2020. High elimination rates were found for PMMA (80%) and DHM (97%) after a seven-day experiment and degradation appeared to be related to biological activity in the active bioreactor. Ten TPs were identified and the main reactions were O- and N-demethylation, oxidation, and hydroxylation. Some TPs were reported for the first time and some were confirmed by reference standards. Identification of some TPs was enhanced by the use of an in-house retention time prediction model. Mephedrone and some of its previously reported human metabolites were formed from DHM incubation. Retrospective analysis showed that PMMA was the most frequently detected compound.

Identification of biomarkers in wastewater-based epidemiology: Main approaches and analytical methods

“…4 ). Similar suspect screening has been reported for NPSs and their metabolites [ [91] , [92] , [93] , [94] , [95] ]. Interestingly, Kinyua et al.…”

“…Nowadays, the possibilities of the bottom-up approach to detect different biomarkers have already been quite explored. Several bottom-up studies are involved in the estimation of community-wide exposure to bisphenol A [ 88 ], searching of proteins [ 89 ], cancer [ 65 ] and multi-chemical exposure [ 90 ] markers, as well as investigation of (bio)transformation products [ 91 , 92 ] and new psychoactive substances [ 93 , 94 ]. All these studies highlight the difficulty of using this system in wastewater, mainly because many of the biomarkers are not major compounds in the matrix.…”

“…The SPE is performed mostly as in the previous approach with sorbents based on HLB [ 88 , 90 , 94 ] and mixed-mode cartridges containing strong or weak cationic exchangers [ 95 ]. Interestingly, a home-made cartridge that mixes several sorbents (Oasis HLB, Isolute ENV+, Strata-X-AW and Strata-X-CV) have also been proposed [ 91 , 99 ]. The aim was to broaden as much as possible the spectrum of compounds that can be retained to be able to identify as many compounds as possible.…”

“…FEV: 0.5 μL LC-QqTOF-MS/MS ESI +/− (DDA: isolation m/z values) Acclaim RSLC C18 column (2.1 × 100 mm, 2.2 μm) and H 2 O–MeOH with NH 4 CO 2 +0.1% FA (+) and NH 4 C 2 H 3 O 2 (−) Suspected database of TPs was built using two in silico prediction tools, the Eawag-Biocatalysis/Biodegradation Database Pathway Prediction System and the MetabolitePredict software, and a list of reported metabolites and TPs from the literature. In silico HPLC retention times estimation is used for identification [ 91 ] Illicit drugs and other biomarkers specially amphetamine and its TPs 100 mL adjusted to pH 7 HLB SPE. FEV: 0.5 μL UHPLC-Q Orbitrap-MS/MS ESI 50–1300 Da (DDA: most intense m/z ) XBridge BEH C18 XP column (150 mm × 2.1 mm, 2.5 μm) and H 2 O–MeOH 0.1% FA Suspected screening against a homemade list of compounds Non-target screening searching major peaks in libraries available through internet.…”

“…Interestingly, Kinyua et al. [ 91 ] built a suspect database of potential TPs using two in silico prediction tools, the Eawag-Biocatalysis/Biodegradation Database Pathway Prediction System and the Metabolite Predict software, and a list of reported metabolites and TPs from the literature. Samples were screened using not only filters but also an in-house retention time prediction model.…”