1,731,598 research outputs found
In vitro and in vivo human metabolism of a new synthetic cannabinoid NM-2201 (CBL-2201)
No full textIn 2014, NM-2201 (CBL-2201), a novel synthetic cannabinoid (SC), was detected by scientists at Russian and US laboratories. It has been already added to the list of scheduled drugs in Japan, Sweden and Germany. Unfortunately, no human metabolism data are currently available, which makes it challenging to confirm its intake, especially given that all SCs investigated thus far have been found to be extensively metabolized. The present study aims to recommend appropriate marker metabolites by investigating NM-2201 metabolism in human hepatocytes, and to confirm the results in authentic human urine specimens. For the metabolic stability assay, 1 µM NM-2201 was incubated in human liver microsomes (HLMs) for up to 1 h; for metabolite profiling, 10 µM of NM-2201 was incubated in human hepatocytes for 3 h. Two authentic urine specimens from NM-2201-positive cases were subjected to β-glucuronidase hydrolysis prior to analysis. The identification of metabolites in hepatocyte samples and urine specimens was achieved with high-resolution mass spectrometry via information-dependent acquisition. NM-2201 was quickly metabolized in HLMs, with an 8.0-min half-life. In human hepatocyte incubation samples, a total of 13 NM-2201 metabolites were identified, generated mainly from ester hydrolysis and further hydroxylation, oxidative defluorination and subsequent glucuronidation. M13 (5-fluoro PB-22 3-carboxyindole) was found to be the major metabolite. In the urine specimens, the parent drug NM-2201 was not detected; M13 was the predominant metabolite after β-glucuronidase hydrolysis. Therefore, based on the results of our study, we recommend M13 as a suitable urinary marker metabolite for confirming NM-2201 and/or 5F-PB-22 intake
In vitro and in vivo human metabolism of a new synthetic cannabinoid NM-2201 (CBL-2201)
No full textIn 2014, NM-2201 (CBL-2201), a novel synthetic cannabinoid (SC), was detected by scientists at Russian and US laboratories. It has been already added to the list of scheduled drugs in Japan, Sweden and Germany. Unfortunately, no human metabolism data are currently available, which makes it challenging to confirm its intake, especially given that all SCs investigated thus far have been found to be extensively metabolized. The present study aims to recommend appropriate marker metabolites by investigating NM-2201 metabolism in human hepatocytes, and to confirm the results in authentic human urine specimens. For the metabolic stability assay, 1 μM NM-2201 was incubated in human liver microsomes (HLMs) for up to 1 h; for metabolite profiling, 10 μM of NM-2201 was incubated in human hepatocytes for 3 h. Two authentic urine specimens from NM-2201-positive cases were subjected to β-glucuronidase hydrolysis prior to analysis. The identification of metabolites in hepatocyte samples and urine specimens was achieved with high-resolution mass spectrometry via information-dependent acquisition. NM-2201 was quickly metabolized in HLMs, with an 8.0-min half-life. In human hepatocyte incubation samples, a total of 13 NM-2201 metabolites were identified, generated mainly from ester hydrolysis and further hydroxylation, oxidative defluorination and subsequent glucuronidation. M13 (5-fluoro PB-22 3-carboxyindole) was found to be the major metabolite. In the urine specimens, the parent drug NM-2201 was not detected; M13 was the predominant metabolite after β-glucuronidase hydrolysis. Therefore, based on the results of our study, we recommend M13 as a suitable urinary marker metabolite for confirming NM-2201 and/or 5F-PB-22 intake
Also By The Same Author: AKTiveAuthor, a Citation Graph Approach to Name Disambiguation
No full textThe desire for definitive data and the semantic web drive for inference over heterogeneous data sources requires co-reference resolution to be performed on those data. In particular, name disambiguation is required to allow accurate publication lists, citation counts and impact measures to be determined. This paper describes a graph-based approach to author disambiguation on large-scale citation networks. Using self-citation, co-authorship and document source analyses, AKTiveAuthor clusters papers, achieving precision of 0.997 and recall of 0.818 over a test group of eight surname clusters
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