Since the late 1990s, many derivatives of the α-pyrrolidinophenone (PPP) drug class appeared on the drugs of abuse market. The latest compound was described in 2009 to be a classic PPP carrying a methylenedioxy moiety remembering the classic entactogens (ecstasy). Besides Germany, 3,4-methylene-dioxypyrovalerone (MDPV) has appeared in many countries in Europe and Asia, indicating its worldwide importance for forensic and clinical toxicology. The aim of the presented work was to identify the phase I and II metabolites of MDPV and the human cytochrome-P450 (CYP) isoenzymes responsible for its main metabolic step(s). Finally, the detectability of MDPV in urine by the authors' systematic toxicological analysis (STA) should be studied. The urine samples were extracted after and without enzymatic cleavage of conjugates. The metabolites were separated and identified after work-up by GC-MS and liquid chromatography (LC)-high-resolution MS (LC-HR-MS). The studies revealed the following phase I main metabolic steps in rat and human: demethylenation followed by methylation, aromatic and side chain hydroxylation and oxidation of the pyrrolidine ring to the corresponding lactam as well as ring opening to the corresponding carboxylic acid. Using LC-HR-MS, most metabolite structures postulated according to GC-MS fragmentation could be confirmed and the phase II metabolites were identified. Finally, the formation of the initial metabolite demethylenyl-MDPV could be confirmed using incubation of human liver microsomes. Using recombinant human CYPs, CYP 2C19, CYP 2D6 and CYP 1A2 were found to catalyze this initial step. Finally, the STA allowed the detection of MDPV metabolites in the human urine samples.
In recent years, a new class of designer drugs has appeared on the drugs of abuse market in many countries, namely, the so-called beta-keto (bk) designer drugs such as mephedrone (bk-4-methylmethamphetamine), butylone (bk-MBDB), and methylone (bk-MDMA). The aim of the present study was to identify the metabolites of mephedrone in rat and human urine using GC-MS techniques and to include mephedrone, butylone, and methylone within the authors' systematic toxicological analysis (STA) procedure. Six phase I metabolites of mephedrone were detected in rat urine and seven in human urine suggesting the following metabolic steps: N-demethylation to the primary amine, reduction of the keto moiety to the respective alcohol, and oxidation of the tolyl moiety to the corresponding alcohols and carboxylic acid. The STA procedure allowed the detection of mephedrone, butylone, methylone, and their metabolites in urine of rats treated with doses corresponding to those reported for abuse of amphetamines. Besides macro-based data evaluation, an automated evaluation using the automated mass spectral deconvolution and identification system was performed. Mephedrone and butylone could be detected also in human urine samples submitted for drug testing. Assuming similar kinetics in humans, the described STA procedure should be suitable for proof of an intake of the bk-designer drugs in human urine.
Gracia-Lor, E. et al. (2017) Measuring biomarkers in wastewater as a new source of epidemiological information: current state and future perspectives. Environment International, 99, pp. 131-150. (doi:10.1016International, 99, pp. 131-150. (doi:10. /j.envint.2016 This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/133949/
Today, immunoassays and several chromatographic methods are in use for drug screening in clinical and forensic toxicology and in doping control. For further proof of the authors' new metabolite-based liquid chromatography-mass spectrometry (LC-MS(n)) screening concept, the detectability of drugs of abuse and their metabolites using this screening approach was studied. As previously reported, the corresponding reference library was built up with MS(2) and MS(3) wideband spectra using a LXQ linear ion trap with electrospray ionization in the positive mode and full scan information-dependent acquisition. In addition to the parent drug spectra recorded in methanolic solution, metabolite spectra were identified after protein precipitation of urine from rats after administration of the corresponding drugs and added to the library. This consists now of data of over 900 parent compounds, including 87 drugs of abuse, and of over 2,300 metabolites and artifacts, among them 436 of drugs of abuse. Recovery, process efficiency, matrix effects, and limits of detection for selected drugs of abuse were determined using spiked human urine, and the resulting data have been acceptable. Using two automatic data evaluation tools (ToxID and SmileMS), the intake of 54 of the studied drugs of abuse could be confirmed in urine samples of drug users after protein precipitation and LC separation. The following drugs classes were covered: stimulants, designer drugs, hallucinogens, (synthetic) cannabinoids, opioids, and selected benzodiazepines. The presented LC-MS(n) method complements the well-established gas chromatography-mass spectroscopy procedure in the authors' laboratory.
In contrast to GC-MS libraries, currently available LC-MS libraries for toxicological detection contain besides parent drugs only some main metabolites limiting their applicability for urine screening. Therefore, a metabolite-based LC-MS(n) screening procedure was developed and exemplified for antidepressants. The library was built up with MS(2) and MS(3) wideband spectra using an LXQ linear ion trap with electrospray ionization in the positive mode and full-scan information-dependent acquisition. Pure substance spectra were recorded in methanolic solution and metabolite spectra in urine from rats after administration of the corresponding drugs. After identification, the metabolite spectra were added to the library. Various drugs and metabolites could be sufficiently separated. Recovery, process efficiency, matrix effects, and limits of detection for selected drugs were determined using protein precipitation. Automatic data evaluation was performed using ToxID and SmileMS software. The library consists of over 700 parent compounds including 45 antidepressants, over 1,600 metabolites, and artifacts. Protein precipitation led to sufficient results for sample preparation. ToxID and SmileMS were both suitable for target screening with some pros and cons. In our study, only SmileMS was suitable for untargeted screening being not limited to precursor selection. The LC-MS(n) method was suitable for urine screening as exemplified for antidepressants. It also allowed detecting unknown compounds based on known fragment structures. As ion suppression can never be excluded, it is advantageous to have several targets per drug. Furthermore, the detection of metabolites confirms the body passage. The presented LC-MS(n) method complements established GC-MS or LC-MS procedures in the authors' lab.
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