Application of a molecular networking approach for clinical and forensic toxicology exemplified in three cases involving 3‐MeO‐PCP, doxylamine, and chlormequat

Abstract

Untargeted toxicological screening is an analytical challenge, given the high number of molecules and metabolites to be detected and the constant appearance of new psychoactive substances (NPS). The combination of liquid chromatography with high-resolution tandem mass spectrometry (HRMS/MS) in a data-dependent acquisition mode generates a large volume of high quality spectral data. Commercial software for processing MS data acquired during untargeted screening experiments usually compare measured features (mass, retention time, and fragmentation spectra) against a predefined list of analytes. However, there is a lack of tools for visualizing and organizing MS data of unknown compounds. Here, we applied molecular networking to untargeted toxicological screening. This bioinformatic tool allows the exploration and organization of MS/MS data without prior knowledge of the sample's chemical composition. The organization of spectral data is based on spectral similarity.Hence, important information can be obtained even before the annotation step. The link established between molecules enables the propagation of structural information.We applied this approach to three clinical and forensic cases with various matrices: (a) blood and a syringe content in a forensic case of death by self-injection, (b) hair segments in a case of drug-facilitated assault, and (c) urine and blood samples in a case of 3-methoxyphencyclidine intoxication. Data preprocessing with MZmine allows sample-to-sample comparison and generation of multisample molecular networks.Our present study shows that molecular networking can be a useful complement to conventional approaches for untargeted screening interpretation, for example for xenobiotics identification or NPS metabolism elucidation.

New insights into quetiapine metabolism using molecular networking

“…Identification means were also of major importance in this study. For this purpose, we used validated process based on exact mass, mass spectra, retention times, fragmentation pattern analysis with SIRIUS 4.0.1 software, www.nature.com/scientificreports/ information propagation, and mass shifts corresponding to biotransformation reactions as already described 9,13,25 .…”

“…Each node represents an ion and its associated fragmentation spectrum, the links between the nodes indicating similarities between spectra. By allowing to propagate structural information within the network and facilitating sample-to-sample comparison, the MN approach offers valuable insights into drug metabolism 9 . Thereby, a multi-matrix approach provides a semi-quantitative visualization of molecule repartition in different matrix samples.…”

“…3 School of Pharmaceutical Sciences, and Institute of Pharmaceutical Sciences of Western Switzerland (ISPSW), University of Geneva, CMU, Rue Michel Servet 1, 1211 Geneva 4, Switzerland. * email: brendan.le.dare@ chu-rennes.fr clinical or forensic purposes 9,13 . Particularly, previous works focused on in vivo new psychoactive substances metabolite discovery 9 , multi-matrix post-mortem samples in case of drug intoxication 13 , and toxic plant samples in case of intoxication 14 .…”

“…* email: brendan.le.dare@ chu-rennes.fr clinical or forensic purposes 9,13 . Particularly, previous works focused on in vivo new psychoactive substances metabolite discovery 9 , multi-matrix post-mortem samples in case of drug intoxication 13 , and toxic plant samples in case of intoxication 14 . However, to our knowlegde, MN applied to in vitro metabolism exploration has not already been reported.…”

Molecular Networking: A Useful Tool for the Identification of New Psychoactive Substances in Seizures by LC–HRMS

“…MN has been implemented in different fields, particularly metabolomics and drug discovery (Quinn et al, 2017); MN in forensic toxicology was previously used by Allard et al (2019) for the retrospective analysis of routine cases involving biological sample analysis. Yu et al (2019) also used MN analysis for the detection of designer drugs such as NBOMe derivatives and they showed that unknown compounds could be recognized as NBOMe-related substances by MN.…”