Zaluski D.,Jagiellonian University |
Mendyk E.,Analytical Laboratory |
Smolarz H.D.,Medical University of Lublin
Natural Product Research | Year: 2016
The purpose of this study was the isolation of metalloproteinases MMP-1 and MMP-9 inhibitors from the chloroform extract of the Eleutherococcus divaricatus roots. Using GC-MS, 1H and 13C NMR, HMQC, HMBC, COSY and DEPT, (+)-sesamin has been identified as a new anti-MMP inhibitor. We report for the first time that (+)-sesamin inhibited MMP-1 and MMP-9 activity in 40% and 17%, respectively. The high inhibitory potential has been shown by ursolic acid (90.9% and 89.8% for MMP-1 and MMP-9). In the PAMPA test, the Pe value for sesamin was established as 17.4 × 10-6 cm/s, that for ursolic acid as 30.0 × 10-6 cm/s. Verapamil and theophylline were used as a positive and negative control (Pe 42.1 and 2.9 × 10-6 cm/s). To our best knowledge, no information was available on this activity of sesamin and other compounds. These studies provide a biochemical basis for the regulation of MMP-1 and MMP-9 by E. divaricatus compounds. © 2015 Taylor & Francis.
New cathinone-derived designer drugs 3-bromomethcathinone and 3-fluoromethcathinone: Studies on their metabolism in rat urine and human liver microsomes using GC-MS and LC-high-resolution MS and their detectability in urine
Meyer M.R.,Saarland University |
Vollmar C.,Saarland University |
Schwaninger A.E.,Saarland University |
Wolf E.U.,Analytical Laboratory |
Maurer H.H.,Saarland University
Journal of Mass Spectrometry | Year: 2012
3-Bromomethcathinone (3-BMC) and 3-Fluoromethcathinone (3-FMC) are two new designer drugs, which were seized in Israel during 2009 and had also appeared on the illicit drug market in Germany. These two compounds were sold via the Internet as so-called "bath salts" or "plant feeders." The aim of the present study was to identify for the first time the 3-BMC and 3-FMC Phase I and II metabolites in rat urine and human liver microsomes using GC-MS and LC-high-resolution MS (HR-MS) and to test for their detectability by established urine screening approaches using GC-MS or LC-MS. Furthermore, the human cytochrome-P450 (CYP) isoenzymes responsible for the main metabolic steps were studied to highlight possible risks of consumption due to drug-drug interaction or genetic variations. For the first aim, rat urine samples were extracted after and without enzymatic cleavage of conjugates. The metabolites were separated and identified by GC-MS and by LC-HR-MS. The main metabolic steps were N-demethylation, reduction of the keto group to the corresponding alcohol, hydroxylation of the aromatic system and combinations of these steps. The elemental composition of the metabolites identified by GC-MS could be confirmed by LC-HR-MS. Furthermore, corresponding Phase II metabolites were identified using the LC-HR-MS approach. For both compounds, detection in rat urine was possible within the authors' systematic toxicological analysis using both GC-MS and LC-MS n after a suspected recreational users dose. Following CYP enzyme kinetic studies, CYP2B6 was the most relevant enzyme for both the N-demethylation of 3-BMC and 3-FMC after in vitro-in vivo extrapolation. © 2012 John Wiley & Sons, Ltd.
Strempel S.,ETH Zurich |
Nendza M.,Analytical Laboratory |
Scheringer M.,ETH Zurich |
Hungerbuhler K.,ETH Zurich
Environmental Toxicology and Chemistry | Year: 2013
The present study presents a data-oriented, tiered approach to assessing the bioaccumulation potential of chemicals according to the European chemicals regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). The authors compiled data for eight physicochemical descriptors (partition coefficients, degradation half-lives, polarity, and so forth) for a set of 713 organic chemicals for which experimental values of the bioconcentration factor (BCF) are available. The authors employed supervised machine learning methods (conditional inference trees and random forests) to derive relationships between the physicochemical descriptors and the BCF values. In a first tier, the authors established rules for classifying a chemical as bioaccumulative (B) or nonbioaccumulative (non-B). In a second tier, the authors developed a new tool for estimating numerical BCF values. For both cases the optimal set of relevant descriptors was determined; these are biotransformation half-life and octanol-water distribution coefficient (log D) for the classification rules and log D, biotransformation half-life, and topological polar surface area for the BCF estimation tool. The uncertainty of the BCF estimates obtained with the new estimation tool was quantified by comparing the estimated and experimental BCF values of the 713 chemicals. Comparison with existing BCF estimation methods indicates that the performance of this new BCF estimation tool is at least as high as that of existing methods. The authors recommend the present study's classification rules and BCF estimation tool for a consensus application in combination with existing BCF estimation methods. © 2013 SETAC.
Welter J.,Saarland University |
Meyer M.R.,Saarland University |
Wolf E.,Analytical Laboratory |
Weinmann W.,University of Bern |
And 2 more authors.
Analytical and Bioanalytical Chemistry | Year: 2013
2-Methiopropamine [1-(thiophen-2-yl)-2-methylaminopropane, 2-MPA], a thiophene analogue of methamphetamine, is available from online vendors selling "research chemicals." The first samples were seized by the German police in 2011. As it is a recreational stimulant, its inclusion in routine drug screening protocols should be required. The aims of this study were to identify the phase I and II metabolites of 2-MPA in rat and human urine and to identify the human cytochrome-P450 (CYP) isoenzymes involved in its phase I metabolism. In addition, the detectability of 2-MPA in urine samples using the authors' well-established gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-linear ion trap-mass spectrometry (LC-MSn) screening protocols was also evaluated. The metabolites were isolated from rat and human urine samples by solid-phase extraction without or following enzymatic cleavage of conjugates. The phase I metabolites, following acetylation, were separated and identified by GC-MS and/or liquid chromatography-high-resolution linear ion trap mass spectrometry (LC-HR-MSn) and the phase II metabolites by LC-HR-MSn. The following major metabolic pathways were proposed: N-demethylation, hydroxylation at the side chain and at the thiophene ring, and combination of these transformations followed by glucuronidation and/or sulfation. CYP1A2, CYP2C19, CYP2D6, and CYP3A4 were identified as the major phase I metabolizing enzymes. They were also involved in the N-demethylation of the analogue methamphetamine and CYP2C19, CYP2D6, and CYP3A4 in its ring hydroxylation. Following the administration of a typical user's dose, 2-MPA and its metabolites were identified in rat urine using the authors' GC-MS and the LC-MSn screening approaches. Ingestion of 2-MPA could also be detected by both protocols in an authentic human urine sample. © 2013 Springer-Verlag Berlin Heidelberg.
A review and accounting of the history of claims and disputed points in the published literature was developed before construction of the meta-model that guided this analysis (Extended Data Fig. 1 and Supplementary Information). During this review, attention was paid to the theoretical constructs invoked by various authors, since our goal was to provide a framework that had the potential to clarify and resolve disputed points. Attention was also paid to types of variable measured by different authors, as the relationship between constructs and measurements constitutes one of the several sources of ambiguity and confusion31, 32. An in-depth description of the literature synthesized to generate the meta-model is presented in the Supplementary Information. Data collected by the Nutrient Network Cooperative33 was used to design and evaluate a structural equation model based on the meta-model presented. The Nutrient Network is a distributed, coordinated research cooperative. Sites in the Network are dominated primarily by herbaceous vegetation and intended to represent natural/semi-natural grasslands and related ecosystems worldwide. Individual sites were selected to accommodate at least a 1,000 m2 study design footprint. Most sites sampled vegetation in 2007, although 12 sites sampled in 2008 or 2009. No statistical methods were used to predetermine sample size. Samples were collected using a completely randomized block design. The standard design has three blocks and ten plots per block at each site, although some sites deviate slightly from this design. A few sites are grazed or burned before sampling, consistent with their traditional management. Further details on site selection and design can be found at http://www.nutnet.org/exp_protocol. In this study, we analysed data from 39 of the 45 sites considered in ref. 2 possessing a complete set of covariates (Extended Data Table 3). While ref. 2 only examined bivariate relations between productivity and richness, our analyses brought in many additional variables (Extended Data Table 1) so that we could address the many hypotheses embodied in the meta-model. Individual plots with greater than 10% woody plant cover were omitted from consideration to maintain comparability in total biomass across plots. This step resulted in the removal of 73 plots, leaving 1,126 plots in the data set analysed. Four plots were omitted owing to incomplete plant data and one for incomplete light data. For two of the sites, live mass was estimated from total mass using available information on the proportion of live to total. One apparent measurement error was detected for light data and the associated plot removed from the analysed sample. Random imputation methods34 were used for cases where there were missing soil measurements at a site. The decision to use this approach was based on weighing the demerits of deleting nearly complete multivariate data records versus introducing a modest amount of random error through the imputation process. Study plots in this investigation had a perimeter of 5 m × 5 m and were separated by 1 m walkways. A single 1 m × 1 m subplot within each plot was permanently marked and sampled for species richness during the season of peak biomass. Sites with strong seasonal variation in composition were sampled twice during the season to assemble a complete list of species. To obtain an estimate of site-level richness, we used a jack-knife procedure35. (Because there have been some recent advances in the reduction of certain sources of bias in richness estimation36, we checked our original results by computing site-level richness using the new iNEXT R package. The correlation between the two estimates of richness was found to be 0.972.) Productivity and total above-ground biomass were sampled immediately adjacent to the permanent vegetation subplot. Vegetation was sampled destructively by clipping at ground level all above-ground biomass of individual plants rooted within two 0.1-m2 (10 cm × 100 cm) strips. Harvested plant material was sorted into the current year’s live and recently senescent material, and into previous year’s growth (including litter). For shrubs and sub-shrubs, the current year’s leaves and stems were collected. Plant material was dried at 60 °C to a constant mass and weighed to the nearest 0.01 g. We used the current year’s biomass increment as our estimate of annual above-ground productivity, which commonly serves as a measurable surrogate for total productivity37, 38. All sites used this protocol to estimate productivity (except for the Sevilleta, New Mexico, site which relied on species-specific allometric relationships39). Total above-ground biomass was computed as the sum of the current year’s biomass and that from previous years and included remaining dead material (litter). Photosynthetically active radiation was measured at the time of peak biomass, both above the vegetation and at the ground surface, the ratio representing the proportion of available light reaching the ground. Degree of shading was computed as 1.0 minus the proportion of light reaching the ground. Within each plot, 250 g of soil were collected and air dried for processing and soil archiving. Total soil %C and %N were measured using dry combustion gas chromatography analysis (COSTECH ESC 4010 Element Analyzer) at the University of Nebraska. All other soil analyses were performed at A&L Analytical Laboratory, Memphis, Tennessee, USA; these included the following: extractable soil phosphorus and potassium were quantified using the Mehlich-3 extraction method, and parts per million concentration estimated using inductively coupled plasma-emission spectrometry. Soil pH was quantified with a pH probe (Fisher Scientific) in a slurry made from 10 g dry soil and 25 ml of deionized water. Soil texture, expressed as the percentage sand, percentage silt, and percentage clay, was measured on 100 g dry soil using the Buoycous method. Further details on sampling methodology are at http://www.nutnet.org/exp_protocol. Climatic characteristics were obtained for each site from version 1.4 of BioClim, which is part of the WorldClim40 set of global climate layers at 1 km2 spatial resolution. To represent measures of temperature and precipitation with meaningful relationships to plant growth in global grasslands, we selected mean temperature of the wettest quarter of the year (BIO8) and total precipitation of the warmest quarter of the year (BIO18). Climate values were extracted using universal transverse Mercator (UTM) coordinates collected near the centre of each site. Several derived variables were developed to include in the modelling effort. To represent within-site heterogeneity, coefficients of variation were computed for the site-level model based on plot-to-plot variation in plot-level measures. This allowed us to examine the explanatory value of heterogeneity in soil nitrogen, phosphorus, potassium, and pH, as well as heterogeneity in biomass and light interception. Indices of total resource supply and resource imbalance were also calculated using the method of ref. 27 and evaluated for inclusion in our models. Disturbance history information for the sites was converted into four binary (0,1) variables for analyses; information available included pretreatment history of (1) substantial anthropogenic alteration (for example, conversion to pasture), (2) grazing history, by wild or domestic animals, (3) active management (typically haying or mowing), and (4) fire. Current levels of herbivory were estimated by comparing biomass inside and outside exclosure plots located at each site. Certain variables were constructed within the structural equation modeling process using the composite index development methods of ref. 41. Consideration of the ideas conveyed by the meta-model (Extended Data Fig. 1) and the specific situation being modelled suggested the need to develop index variables for soil fertility and soil suitability. Soil fertility indices were developed using all measured soil properties and were operationally defined as the drivers of productivity, controlling for all other effects on productivity in the model. Two indices were developed, one for site-to-site variations and another for plot-to-plot variations. Similarly, soil suitability indices were developed for the site- and plot-level data using all measured soil properties as potential contributors and operationally defined as the drivers of richness, controlling for all other effects on richness in the model. Modelling with composites in structural equation models involved a two-step process. First, we constructed a fully specified structural equation model (as represented in Fig. 2), but providing a specific set of soil properties to serve as formative indicators for soil fertility and soil suitability. Variables that did not contribute to the total model (on the basis of model fit indices) were eliminated individually for the two composites being formed. The resulting prediction equations were used to compute index scores. Then, the model was reconstructed, substituting the indices in place of the collection of individual soil properties. Documentation of this process is provided in the Supplementary Information computer code (R script). A structural equation model was developed based on the ideas embodied in the meta-model, available data, and the principles and procedures laid out in ref. 42. Indicators for constructs were chosen from the set of variables available and quantities that could be computed from them (Extended Data Table 1). The modelling approach used was semi-exploratory in that while we worked to address the general hypothesis embodied in the meta-model, the precise variables (for example, mean annual precipitation versus mean annual precipitation in the warmest quarter of the year) to use for certain constructs (specifically, resource supplies and regulators) were determined empirically. Compositing techniques were used to estimate construct-level effects41. For comparative purposes, we analysed the bivariate pattern in Fig. 1A using a variety of regression models, including Ricker-type nonlinear models as well as second- and third-order polynomials. A three-parameter Ricker-type model provided the best fit for the data. Data were screened for distributional properties and nonlinear relations. Several variables were log-transformed as a result of evaluations (Extended Data Table 1). We used the R software platform43 and the lavaan package44 along with the lavaan.survey45 package for our structural equation model analyses. For the plot-scale model, robust χ2 tests, as implemented in the lavaan.survey package, were used to judge variable inclusion and model adequacy because of the nested nature of the plot-level data. Each link in the final model was evaluated for significant contribution to the model. Final model fit to data was very good for both submodels. Model fit indices were supplemented by using additional diagnostic evaluations that involve visualizing residual relationships to evaluate conditional independence29. These residual visualizations allowed, among other things, an ability to evaluate linearity assumptions and implement curve-fitting procedures if needed (which was only the case for the composite relationships in this case). Our structural equation model in this case is non-recursive and includes a causal loop. Models of this form are commonplace in structural equation model applications, although they come with some additional assumptions and requirements. Specifically, there is a requirement for unique predictors for the elements involved in loops, a requirement that was met in this case. Additional analysis details are documented in the R script used for the analysis (Supplementary Information). Multi-level relations were incorporated into the architecture of our model. Several ways to incorporate both site- and plot-level variations in the model were considered and multiple approaches evaluated to ensure results are general. In the model form presented, we chose to follow modern hierarchical modelling principles and allow plot-level observations to depend on site-level parameters, since plots were nested within sites. The result of choosing this approach means site-level explanatory effects can filter down to the plot level while plot-level explanatory variables (for example, pathways from edaphic conditions to plot richness) explain additional plot-to-plot variations in responses that are not predicted from site-level (mean) conditions. Consistent with the capabilities of the structural equation model software used in our analyses (described below), we estimated site- and plot-level submodels using a two-stage approach, first estimating parameters for the site-level component and then using site productivity, biomass, and richness as exogenous predictors in the plot-level component. Comparisons with results from separate site- and plot-level models led to very similar conclusions, although the hierarchical approach used allowed a better integration of processes and greater variance explanation. One of our objectives in this study was to assess the model dimensionality needed to detect the hypothesized signals in the data. To do this, we started with the most complete model (Fig. 2) and eliminated variables from the model (always retaining richness and some measure of biomass production, either productivity or total biomass). We then made any modifications needed to ensure adequate model-data fit for these reduced-form models. The consequences of model simplification was judged on the basis of signal retention, in particular a loss of capacity to detect signals associated with the remaining parts of the model. The computer script associated with the analyses in this paper is available as part of the Supplementary Information.