Using Isotopic Fractionation to Link Precursor to Product in the Synthesis of (±)-Mephedrone: A New Tool for Combating “Legal High” Drugs

Several recent deaths in the U.K. have been attributed to “legal high” drugs and in particular to (±)-4-methylmethcathinone ((±)-mephedrone). Recent literature has begun to focus on the chemical analysis of mephedrone and related substituted cathinones and methcathinones; however, no studies involving the application of stable isotope analysis to these compounds has yet emerged. Such studies have, for example, the potential to provide information linking the final products to a particular precursor by the manufacturer. In this study, the use of stable isotope profiling was explored to provide a possible connection between product and precursor chemicals. Six samples each of mephedrone were prepared using precursor chemicals from two different manufacturers, providing 12 samples in total. Synthesis was via a stable intermediat

Mean and standard deviation of <i>δ</i>¹⁵N and <i>δ</i>¹³C values (‰) for Eastern European wolves from 15 geographical regions.

<p>Mit pop: four subpopulations delimited based on mitochondrial DNA data.</p><p>Nuc pop: two subpopulations delimited based on 14 microsatellite loci.</p><p>BIAL region included individuals assigned to either subpopulations NUC1 or NUC2, and the region as a whole was assigned to subpopulation NUC1, where the majority of individuals were assigned.</p

IsoSource dietary mixing polygon for Eastern European grey wolves.

<p>The wolf <i>δ</i>¹³C and <i>δ</i>¹⁵N values are plotted with potential prey. Trophic enrichment values of 1.3‰ for <i>δ</i>¹³C and 4.6‰ for <i>δ</i>¹⁵N <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039341#pone.0039341-FoxDobbs1" target="_blank">[17]</a> were added to the mean <i>δ</i>¹³C and <i>δ</i>¹⁵N values of potential prey. Stable isotope profiles are presented as mean and standard deviation for: (A) The entire wolf population. Contribution of each prey species to the diet is reported as the 25th to 75th percentile ranges of the estimated feasible distributions; (B) Subpopulations delimited based on microsatellite loci (NUC 1 and 2); (C) Subpopulations delimited based on mtDNA (MIT 1-4); (D) All analyzed individuals. Subpopulation MIT4 was represented by only one individual, and it was excluded from DISTLM analysis (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039341#s2" target="_blank">Materials and Methods</a>). For standard deviation of prey stable isotope profiles, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039341#pone.0039341.s002" target="_blank">Figure S2</a>.</p

Effects of dietary differentiation and geographic distance on genetic differentiation of Eastern European wolves.

<p>Marginal and conditional tests of individual variable sets as well as sequential tests of the forward selection procedure are reported (see Methods for the description of the tests). “Pseudo-F” indicates test statistics, <i>P</i> probability values and “%var” the percentage of the genetic variation explained by the particular variable. In the case of sequential tests, “%var” indicates the percentage of the genetic variation explained by a cumulative effect of variables. The top-down sequence of variables corresponds to the sequence that was indicated by the forward selection procedure. The variable set “coordinates” included latitude and longitude, and “stable isotope composition” included <i>δ</i>¹⁵N and <i>δ</i>¹³C values. Genetic distances were calculated based on the data on variability at 14 microsatellite loci obtained in an earlier study <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039341#pone.0039341-Pilot1" target="_blank">[7]</a>.</p

Mean values and standard deviation of <i>δ</i>¹⁵N and d15N values (‰) for Eastern European wolves and their prey.

<p>MIT 1-MIT 4: For the wolves, the average for all individuals is reported, as well as for four subpopulations delimited based on mtDNA data (MIT 1–4) and two subpopulations delimited based on microsatellite loci (NUC 1, NUC 2).</p

Diet composition of wolves inferred from the stable isotope data using IsoSource for (A) subpopulations delimited based on mtDNA variability (MIT 1-MIT 4), (B) subpopulations delimited based on microsatellite variability (NUC 1, NUC 2), and (C) all individuals at average.

<p>We report mean, standard deviation (SD) and 25th–75th percentile (25 and 75%ile) ranges. The mean values are given for comparative purposes only and should be treated with caution because of the lack of uniqueness of the mixing model results <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039341#pone.0039341-Andersone1" target="_blank">[24]</a>. The result for the subpopulation MIT 4 is based on one individual only and therefore is biased. This individual has not been considered in any population-based analyses.</p

Graphical illustration of correlations between genetic and dietary differentiation and geographic distance.

<p>Correlations are presented at a population (left) and individual (right) level. (A) The correlation between genetic and stable isotope differentiation. Genetic distances between populations were represented by pairwise <i>F</i>_ST values. Genetic distances between individuals were calculated using a method implemented in GenAlEx. Isotopic distances between populations and individuals were calculated by treating <i>δ</i>¹³C and <i>δ</i>¹⁵N values as two-dimensional Cartesian coordinates. Both correlations are significant (see Results). (B) The correlation between genetic and geographic distances. Only the correlation at individual level is significant (<i>P</i> = 0.04). (C) Correlation between stable isotope differentiation and geographic distances. Subpopulation MIT4 was represented by only one individual, and it was excluded from the population-level analysis.</p

Organic Reference Materials for Hydrogen, Carbon, and Nitrogen Stable Isotope-Ratio Measurements: Caffeines, <i>n</i>‑Alkanes, Fatty Acid Methyl Esters, Glycines, l‑Valines, Polyethylenes, and Oils

An international project developed, quality-tested, and determined isotope−δ values of 19 new organic reference materials (RMs) for hydrogen, carbon, and nitrogen stable isotope-ratio measurements, in addition to analyzing pre-existing RMs NBS 22 (oil), IAEA-CH-7 (polyethylene foil), and IAEA-600 (caffeine). These new RMs enable users to normalize measurements of samples to isotope−δ scales. The RMs span a range of δ²H_VSMOW‑SLAP values from −210.8 to +397.0 mUr or ‰, for δ¹³C_VPDB‑LSVEC from −40.81 to +0.49 mUr and for δ¹⁵N_Air from −5.21 to +61.53 mUr. Many of the new RMs are amenable to gas and liquid chromatography. The RMs include triads of isotopically contrasting caffeines, C₁₆ <i>n</i>-alkanes, <i>n</i>-C₂₀-fatty acid methyl esters (FAMEs), glycines, and l-valines, together with polyethylene powder and string, one <i>n</i>-C₁₇-FAME, a vacuum oil (NBS 22a) to replace NBS 22 oil, and a ²H-enriched vacuum oil. A total of 11 laboratories from 7 countries used multiple analytical approaches and instrumentation for 2-point isotopic normalization against international primary measurement standards. The use of reference waters in silver tubes allowed direct normalization of δ²H values of organic materials against isotopic reference waters following the principle of identical treatment. Bayesian statistical analysis yielded the mean values reported here. New RMs are numbered from USGS61 through USGS78, in addition to NBS 22a. Because of exchangeable hydrogen, amino acid RMs currently are recommended only for carbon- and nitrogen-isotope measurements. Some amino acids contain ¹³C and carbon-bound organic ²H-enrichments at different molecular sites to provide RMs for potential site-specific isotopic analysis in future studies