Untargeted metabolomics analysis assisted by signal selection for comprehensively identifying metabolites of new psychoactive substances: 4-MeO-α-PVP as an example

Hsin-Yi Wu, Instrumentation Center, National Taiwan University, Taipei 10617, Taiwan, R.O.C.Follow
Yuan-Chih Chen, Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 70428, Taiwan, R.O.C.
Jing-Fang Hsu, National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 350, Taiwan, R.O.C.
Hsiang-Ting Lu, Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan, R.O.C.
Yu-Yi Pan, Department of Statistics, National Cheng Kung University, Tainan 701, Taiwan, R.O.C.
Mi-Chia Ma, Department of Statistics, National Cheng Kung University, Tainan 701, Taiwan, R.O.C.
Pao-Chi Liao, Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 70428, Taiwan, R.O.C.Follow

Keywords

4-methoxy-α-pyrrolidinovalerophenone (4-MeO-α-PVP); LC-MS/MS; Metabolome; Metabolomics; New psychoactive substances

Abstract

New psychoactive substances (NPS) have been rapidly emerged as legal alternatives to controlled drugs, which raised severe public health issue. The detection and monitoring of its intake by complete metabolic profiling is an urgent and vital task. Untargeted metabolomics approach has been applied for several NPS metabolites studies. Although the number of such works is relatively limited but with a rapidly growing need. The present study aimed to propose a procedure that includes liquid chromatography high-resolution mass spectrometry (LC-HRMS) analysis and a signal selection software, MetaboFinder, programed as a web tool. The comprehensive metabolites profile of one kind of NPS, 4-methoxy-α-pyrrolidinovalerophenone (4-MeO-α-PVP), was studied by using this workflow. In this study, two different concentrations of 4-MeO-α-PVP along with as blank sample were incubated with human liver S9 fraction for the conversion to their metabolites and followed by LC-MS analysis. After retention time alignment and feature identification, 4640 features were obtained and submitted to statistical analysis for signal selection by using MetaboFinder. A total of 50 features were considered as 4-MeO-α-PVP metabolite candidates showing significant changes (p < 0.00001 and fold change >2) between the two investigated groups. Targeted LC-MS/MS analysis was conducted focusing on these significantly expressed features. Assisted by chemical formula determination according to high mass accuracy and in-silico MS² fragmentation prediction, 19 chemical structure identifications were achieved. Among which, 8 metabolites have been reported derived from 4-MeO-α-PVP in a previous literature while 11 novel 4-MeO-α-PVP metabolites were identified by using our strategy. Further in vivo animal experiment confirmed that 18 compounds were 4-MeO-α-PVP metabolites, which demonstrated the feasibility of our strategy for screening the 4-MeO-α-PVP metabolites. We anticipate that this procedure may support and facilitate traditional metabolism studies and potentially being applied for routine NPS metabolites screening.

Abstract Image

Recommended Citation

Wu, Hsin-Yi; Chen, Yuan-Chih; Hsu, Jing-Fang; Lu, Hsiang-Ting; Pan, Yu-Yi; Ma, Mi-Chia; and Liao, Pao-Chi (2023) "Untargeted metabolomics analysis assisted by signal selection for comprehensively identifying metabolites of new psychoactive substances: 4-MeO-α-PVP as an example," Journal of Food and Drug Analysis: Vol. 31 : Iss. 1 , Article 9.
Available at: https://doi.org/10.38212/2224-6614.3447