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Anal Bioanal Chem. 2012 Feb;402(6):2141-51. doi: 10.1007/s00216-011-5678-8. Epub 2012 Jan 10.

Generation of metabolites by an automated online metabolism method using human liver microsomes with subsequent identification by LC-MS(n), and metabolism of 11 cathinones.

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  • 1Institute for Clinical Chemistry, University Hospital Zurich, Zurich, Switzerland.

Abstract

Human liver microsomes (HLMs) are used to simulate human xenobiotic metabolism in vitro. In forensic and clinical toxicology, HLMs are popularly used to study the metabolism of new designer drugs for example. In this work, we present an automated online extraction system we developed for HLM experiments, which was compared to a classical offline approach. Furthermore, we present studies on the metabolism of 11 cathinones; for eight of these, the metabolism has not previously been reported. Metabolites were identified based on MS(2) and MS(3) scans. Fifty-three substances encompassing various classes of drugs were employed to compare the established offline and the new online methods. The metabolism of each of the following 11 cathinones was studied using the new method: 3,4-methylenedioxy-N-benzylcathinone, benzedrone, butylone, dimethylcathinone, ethylone, flephedrone, methedrone, methylone, methylethylcathinone, naphyrone, and pentylone. The agreement between the offline and the online methods was good; a total of 158 metabolites were identified. Using only the offline method, 156 (98.7%) metabolites were identified, while 151 (95.6%) were identified using only the online method. The metabolic pathways identified for the 11 cathinones included the reduction of the keto group, desalkylation, hydroxylation, and desmethylenation in cathinones containing a methylenedioxy moiety. Our method provides a straightforward approach to identifying metabolites which can then be added to the library utilized by our clinical toxicological screening method. The performance of our method compares well with that of an established offline HLM procedure, but is as automated as possible.

PMID:
22231510
[PubMed - indexed for MEDLINE]
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