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Eingeladener Vortrag
- nein (46)
Herein, we present the development of a gas chromatographic method for Determination of warfarin, its TPs, and selected metabolites.
TPs were formed by applying UV-irradiation, ozonation, and an electrochemical cell coupled to mass spectrometry to mimic the oxidative phase I metabolism. The further aim is to use this method for detection and quantification under environmentally relevant conditions, as well as, toxicological assessment.
We report the first results for the immunochemical determination of the β-lactam antibiotic drug amoxicillin (AMX) in water samples by ELISA (enzyme-linked immunosorbent assay). The applied indirect competitive ELISA using a commercially available mouse anti-AMX antibody enables testing for AMX with a limit of detection of around 1 µg/L. Calibration curves of calibrators prepared in different water samples showed equal shape. This underlines the suitability of this assay for the AMX determination in these samples. Furthermore, cross-reactivities of structurally similar β-lactam antibiotics were examined and found to be low for ampicillin, penicillin G and penicillin V. In contrast, carbenicillin was recognized by the antibody even better than AMX indicating a positive effect of an additional carboxyl group on antibody binding. This is promising for further application of this assay, e.g. in the determination of the most relevant hydrolysis product of AMX, amoxicilloic acid.
Comparing Nontargeted LC-MS Methods by Co-visualizing Linear Dynamic Range and Chemical Coverage
(2019)
INTRODUCTION
Biological and environmental samples contain thousands of small molecule species that all vary in chemical properties and concentration range. Identifying and quantifying all these chemical entities remains a long-term goal in metabolomics and related systems approaches. Due to its broad selectivity, nontargeted LC-MS is usually the method of choice for broad chemical screening. Optimizing nontargeted LC-MS methods, however, is less straightforward than for targeted methods where sensitivity, specificity, linearity etc. serve as well-established performance criteria. We therefore investigated linear dynamic range (LDR) and chemical classification as alternative performance criteria to guide nontargeted method development.
EXPERIMENTAL METHODS
LDR was defined as the linear portion of a feature’s response curve over multiple concentration levels. Comparing the LDR of features across methods can be expected to be significantly more robust than comparing signal intensities for a single concentration. To determine LDR for all features, a computational workflow was implemented in the R programming language. For estimating the linear portion of a curve, several mathematical approaches including linear, non-linear and piecewise linear regression were evaluated. Chemical classification was based on ClassyFire, which computes chemical classes for a given structure. To avoid false classifications for incorrectly annotated compounds, we took the following statistical approach. For each compound, multiple likely annotation hypotheses were derived using a recently described workflow[2]. All annotation hypotheses were submitted to ClassyFire and obtained classifications were ranked by frequency. The most frequently suggested class was kept for further analysis. Finally, LDR and chemical classes were visualized together on a molecular network, which was constructed using the well-established MS/MS similarity approach.
RESULTS AND DISCUSSION
For technical validation of the workflow, several hundred curve fits obtained from the different regression models were reviewed visually. Piecewise linear regression performed the most reliably with respect to the heterogeneous curve shapes of ‘real-life’ features. Validation of chemical classification was performed against a compound library, which showed that 90% of ~450 library compounds were correctly classified using the described approach. Two liquid chromatography methods (HILIC, RPC) as well as two electrospray ionization variants (low/high-temperature ESI) applied to urinary metabolomics were exemplarily studied to test the workflow. Molecular network visualization indicated that of all analytical setups, HILIC/high temperature ESI performed best in terms of high LDR achieved over a wide range of compound classes. Despite one order of magnitude lower sensitivity, HILIC/low temperature ESI showed similar chemical coverage, except for organic nitrogen compounds that were underrepresented compared to high-temperature ESI. Both RPC setups were inferior to the HILIC setups in terms of high-LDR features, supporting previous findings for the given matrix. The higher relative representation of benzenoids and lipids in RPC demonstrated that the workflow successfully captured expected selectivity differences between chromatographies.
CONCLUSION
When comparing nontargeted LC-MS methods for optimization purposes, ideally all available quantitative and qualitative information should be integrated. The present workflow follows this idea. Visualizing LDR and chemical classes of all features on a molecular network quickly indicated differences in method selectivity that were otherwise difficult to spot. As an automated approach, it is easily applied to repeated optimization steps, enabling effective optimization strategies.
Nontargeted high-resolution mass spectrometry (HRMS) is widely used for small molecule screening in biotic or abiotic samples. However, HRMS approaches like metabolomics or environmental nontarget screening currently still lack confidence in chemical annotation, i.e. computational structure assignment to all measured mass signals. As a crucial step within the annotation pipeline, molecular weight inference (MWI) deduces a compound’s intact mass from diagnostic mass differences between MS1 peaks, allowing precise database queries in subsequent steps. As the common practice of considering all possible ionization products such as adducts, multimers, multiple charges etc. in MWI suffers from high false positive rates, we aimed at selecting candidate ionization products in a chemically sensitive way. Generally, electrospray ionization produces different types of adducts depending on chromatographic system and sample matrix, necessitating application-specific optimization for optimum MWI performance. To avoid, however, the tedious and potentially biased manual data curation connected to optimization, we established an R-based workflow for automating this task. The workflow consists of two parts. Part 1 creates an MS1 spectral library by performing peak detection, spectral deconvolution and target peak assignment based on density estimation. Part 2 analyzes ion relationships within the library and returns a list of detected ionization products ranked by their frequency. We applied the workflow to a commercial 634-compound library that was acquired for two chromatographic methods (reverse phase, RP; hydrophilic liquid interaction chromatographic, HILIC) and the two ESI modes (positive, negative). As expected, different frequency distributions of ionization products were found for the two chromatographies. Interestingly, however, some of the differences were expected in terms of solvent chemistries (e.g. [M+NH4]+ in ammonium formate-buffered HILIC) while others indicated more complex ion competition (e.g. abundant [M+K]+, [M+2K-H]+ in HILIC). This demonstrated the relevance of this empirical approach. We further show that MWI accuracy clearly benefitted from derived optimized adduct lists – by adding filters or weighting terms – and present FDR calculations supporting this observation. We conclude that chemistry-aware compound annotation based on the combination of high-throughput library acquisition and statistical analysis holds significant potential for further improvements in nontargeted small molecule HRMS.
Bisphenol A (BPA) is a chemical that has been used in the production of plastics for more than 40 years. BPA released from bottles made of polycarbonates has been identified as a potential endocrine disrupting substance. Comprehensive research has been undertaken to test and verify its effect on animals and human beings.
Many methods have been proposed for the determination of BPA, in particular, gas and liquid chromatography. The main disadvantages of such approaches are the high cost of the equipment, the significant duration of an analysis, and difficulties in screening a large number of samples. Lateral Flow ImmunoAssay (LFIA) attributes are high throughput, high specificity and sensitivity, as well as low cost and simplicity.
An LFIA test strip was developed. This LFIA is based on anti-BPA antibodies (from mouse) conjugated with gold nanoparticles as the marker by passive absorption and covalent coupling, deposited in an elaborate pad. To form control and test zones, anti-mouse antibody and a BVA-BSA conjugate were spotted on the nitrocellulose membrane, respectively.
Negative samples are revealed by red lines both in the test and control zones, respectively, whereas positive samples produce a single red line only in the control zone. The measurement range is 0.05 – 23 μg/L, visually detected by the naked eye within 5 minutes. In addition, the intensities at the test line can be read by dedicated lateral flow readers (opTrilyzer® and Cube by opTricon, Berlin) for a more precise and documented determination.
Gegenstand der vorzustellenden Arbeiten ist die Prüfung der Umweltbeständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polystyrol (PS) und Polypropylen (PP) zum Einsatz, die jeweils mit polybromierten Flammschutzmitteln (PBFSM) versehen sind. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Auch soll mit Hilfe der RFA und der LA-ICP-MS die Abreicherung der PBFSM in den Modellmaterialien beschrieben werden. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden.
Gegenstand der vorzustellenden Arbeiten ist die Prüfung der Umweltbeständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polystyrol (PS) und Polypropylen (PP) zum Einsatz, die jeweils mit polybromierten Flammschutzmitteln (PBFSM) versehen sind. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Auch soll mit Hilfe der RFA und der LA-ICP-MS die Abreicherung der PBFSM in den Modellmaterialien beschrieben werden. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden.
SRT-n11: Metrology for monitoring endocrine disrupting compounds under the Water Framework Directive
(2018)
The Water Framework Directive (WFD) establishes a strategy for the protection and improvement of the aquatic environment and water quality in Europe.
Among chemical pollutants of particular concern are endocrine disrupting compounds (EDCs) due to their environmental and public health effects. In 2013, a class of EDCs - natural and synthetic estrogens - were introduced in the first WFD watch list of priority substances together with their possible methods of analysis and performance requirements.
However, reliable harmonised methods that meet these requirements are still missing and urgently needed.
The project plan to develop traceable measurement methods for the determination of the targeted EDCs and to ensure their transfer to normative bodies.