“Zombie” Outbreak Caused by Synthetic Cannabinoid

Gerona, Roy; Adams, Axel; Banister, Samuel D.

doi:10.1056/nejmc1701936

Cited by 2 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The requirement for the expertise of a synthetic organic chemist and the generation of numerous potential derivatives for each illicit drug make the comprehensive characterization of these substances in clinical laboratories highly impractical. 15 In fact, recent advancements in machine learning have enabled the in silico prediction of EI mass spectra. For example, neural electron−ionization mass spectrometry (NEIMS) 16 utilizes neural networks to convert chemical structure representations (i.e., extended circular fingerprints; ECFPs 17 ) into mass spectra, enabling researchers to expand the libraries with compounds with known structures.…”

Section: Introductionmentioning

confidence: 99%

“…While this approach effectively addresses a cluster of mass intoxication cases, it has inherent limitations. The requirement for the expertise of a synthetic organic chemist and the generation of numerous potential derivatives for each illicit drug make the comprehensive characterization of these substances in clinical laboratories highly impractical …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PS²MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

Lin,

Chien,

Wang

et al. 2024

Anal. Chem.

View full text Add to dashboard Cite

The rapid proliferation of new psychoactive substances (NPS) poses significant challenges to conventional mass-spectrometry-based identification methods due to the absence of reference spectra for these emerging substances. This paper introduces PS 2 MS, an AI-powered predictive system designed specifically to address the limitations of identifying the emergence of unidentified novel illicit drugs. PS 2 MS builds a synthetic NPS database by enumerating feasible derivatives of known substances and uses deep learning to generate mass spectra and chemical fingerprints. When the mass spectrum of an analyte does not match any known reference, PS 2 MS simultaneously examines the chemical fingerprint and mass spectrum against the putative NPS database using integrated metrics to deduce possible identities. Experimental results affirm the effectiveness of PS 2 MS in identifying cathinone derivatives within real evidence specimens, signifying its potential for practical use in identifying emerging drugs of abuse for researchers and forensic experts.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PS²MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

Lin,

Chien,

Wang

et al. 2024

Anal. Chem.

View full text Add to dashboard Cite

show abstract

Analysis of AMB-FUBINACA Biotransformation Pathways in Human Liver Microsome and Zebrafish Systems by Liquid Chromatography-High Resolution Mass Spectrometry

Zhang

et al. 2019

Front. Chem.

View full text Add to dashboard Cite

In this study, the metabolic profiles of a new illicit drug AMB-FUBINACA were investigated using both human liver microsome and zebrafish models. Liquid chromatography Q Extractive HF Hybrid Quadrupole-Orbitrap mass spectrometry (LC-QE-HF-MS) was employed to analyze the metabolic sites and pathways. AMB-FUBINACA was added to the in vitro liver microsome incubation model to simulate the metabolic processes in human body. The results showed that a total of 17 metabolites were generated in the human liver microsome model; the main metabolic pathways of the phase I metabolism included ester hydrolysis, methylation, ester hydrolysis combined with decarboxylation, hydroxylation, ester hydrolysis combined with indazole ring hydroxylation, etc. while glucuronidation served as the main metabolic pathway of the phase II metabolism. The zebrafish system produced a similar result with 16 of the same 17 metabolites identified. The phase I metabolites M3.1 (ester hydrolysis), M1.2 (alkyl chain hydrolysis) and the phase II metabolite M3.2 (M3.1 glucuronide) were recommended to be the potential poisoning markers.

show abstract

“Zombie” Outbreak Caused by Synthetic Cannabinoid

Cited by 2 publications

References 10 publications

PS²MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

PS²MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

Analysis of AMB-FUBINACA Biotransformation Pathways in Human Liver Microsome and Zebrafish Systems by Liquid Chromatography-High Resolution Mass Spectrometry

Contact Info

Product

Resources

About

“Zombie” Outbreak Caused by Synthetic Cannabinoid

Cited by 2 publications

References 10 publications

PS2MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

PS2MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

Analysis of AMB-FUBINACA Biotransformation Pathways in Human Liver Microsome and Zebrafish Systems by Liquid Chromatography-High Resolution Mass Spectrometry

Contact Info

Product

Resources

About

PS²MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

PS²MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry