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Comparison of Illicit Drug Seizures Products of Natural Origin Using a Molecular Networking Approach.
- Source :
-
International Journal of Toxicology (Sage) . Mar2022, Vol. 41 Issue 2, p108-114. 7p. - Publication Year :
- 2022
-
Abstract
- Drug powder composition analysis is of particular interest in forensic investigations to identify illicit substance content, cutting agents and impurities. Powder profiling is difficult to implement due to multiple analytical methods requirement and remains a challenge for forensic toxicology laboratories. Furthermore, visualization tools allowing seizure products identification appear to be under-used to date. The aim of this study is to present the utility of molecular networking for the composition establishment of natural origin drugs. A powder suspected to contain heroin and three powders suspected to contain cocaine obtained from law enforcement agency seizures were analyzed using untargeted screening by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS/MS). Molecular networking and metabolite annotation applied to suspected heroin sample allowed rapid confirmation of its illicit content (heroin), the identification of structurally related major impurities (6-monoacetylmorphine, 6-monoacetylcodeine, noscapine, and papaverine), as well as cutting agents (acetaminophen and caffeine). The cocaine powder profiling allowed the comparison of its constituents in a semi-quantitative manner (cocaine, benzoylecgonine, trans/cis-cinnamoylcocaine, trimethoxycocaine, hexanoylecgonine methylester, caffeine, hydroxyzine, levamisole, and phenacetin), bringing additional information for their identification, including geographically sourcing of natural product and their putative place in the supply chain. Although this approach does not replace the profiling techniques used by forensic laboratories, the use of molecular networks provides a visual overview of structurally related constituents which aids the comparison and investigation of seizure powders. Molecular networks offers here an ideal way to depict structurally related and unrelated compounds in these often complex mixtures of chemicals. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10915818
- Volume :
- 41
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- International Journal of Toxicology (Sage)
- Publication Type :
- Academic Journal
- Accession number :
- 156121440
- Full Text :
- https://doi.org/10.1177/10915818211065161