Improving wastewater-based epidemiology for new psychoactive substance surveillance by combining a high-throughput in vitro metabolism assay and LC-HRMS metabolite identification.

One of the primary criteria for a suitable drug biomarker for wastewater-based epidemiology (WBE) is having a unique source representing human metabolism. For WBE studies, this means it is important to identify and monitor metabolites rather than parent drugs, to capture consumption of drugs and not fractions that could be directly disposed. In this study, a high-throughput workflow based on a human liver S9 fraction in vitro metabolism assay was developed to identify human transformation products of new chemicals, using α-pyrrolidino-2-phenylacetophenone (α-D2PV) as a case study. Analysis by liquid chromatography coupled to high resolution mass spectrometry identified four metabolites. Subsequently, a targeted liquid chromatography - tandem mass spectrometry method was developed for their analysis in wastewater samples collected from a music festival in Australia. The successful application of this workflow opens the door for future work to better understand the metabolism of chemicals and their detection and application for wastewater-based epidemiology.

Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography-high resolution mass spectrometry.

Controlling and minimising background contamination is crucial for maintaining a high quality of samples in human biomonitoring targeting organic chemicals. We assessed the contamination of three previous types and one newly introduced medical-grade type of sample tubes used for storing human body fluids at the German Environmental Specimen Bank. Aqueous extracts from these tubes were analysed by non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) before and after a dedicated cleaning procedure. After peak detection using MZmine, Bayesian hypothesis testing was used to group peaks into those originating either from instrumental and laboratory background contamination, or actual tube contaminants, based on if their peak height was reduced, increased or not affected by the cleaning procedure. For all four tube types 80-90% of the 2475 peaks (1549 in positive and 926 in negative mode) were assigned to laboratory/instrumental background, which we have to consider as potential sample tube contaminants. Among the tube contaminants, results suggest a considerable difference in the contaminant peak inventory and the absolute level of contamination among the different sample tube types. The cleaning procedure did not affect the largest fraction of peaks (50-70%). For the medical grade tubes, the removal of contaminants by the cleaning procedure was strongest compared to the previous tubes, but in all cases a small fraction increased in intensity after cleaning, probably due to a release of oligomers or additives. The identified laboratory background contaminants were mainly semi-volatile polymer additives such as phthalates and phosphate esters. A few compounds could be assigned solely as tube-specific contaminants, such as N,N-dibutylformamide and several constituents of the oligomeric light stabiliser Tinuvin-622. A cleaning procedure before use is an effective way to standardise the used sample tubes and minimises the background contamination, and therefore increases sample quality and therewith analytical results.

Assessment of exposure to pesticide mixtures in five European countries by a harmonized urinary suspect screening approach.

Humans are exposed to a mixture of pesticides through diet as well as through the environment. We conducted a suspect-screening based study to describe the probability of (concomitant) exposure to a set of pesticide profiles in five European countries (Latvia, Hungary, Czech Republic, Spain and the Netherlands). We explored whether living in an agricultural area (compared to living in a peri-urban area), being a a child (compared to being an adult), and the season in which the urine sample was collected had an impact on the probability of detection of pesticides (-metabolites). In total 2088 urine samples were collected from 1050 participants (525 parent-child pairs) and analyzed through harmonized suspect screening by five different laboratories. Fourty pesticide biomarkers (either pesticide metabolites or the parent pesticides as such) relating to 29 pesticides were identified at high levels of confidence in samples across all study sites. Most frequently detected were biomarkers related to the parent pesticides acetamiprid and chlorpropham. Other biomarkers with high detection rates in at least four countries related to the parent pesticides boscalid, fludioxonil, pirimiphos-methyl, pyrimethanil, clothianidin, fluazifop and propamocarb. In 84% of the samples at least two different pesticides were detected. The median number of detected pesticides in the urine samples was 3, and the maximum was 13 pesticides detected in a single sample. The most frequently co-occurring substances were acetamiprid with chlorpropham (in 62 urine samples), and acetamiprid with tebuconazole (30 samples). Some variation in the probability of detection of pesticides (-metabolites) was observed with living in an agricultural area or season of urine sampling, though no consistent patterns were observed. We did observe differences in the probability of detection of a pesticide (metabolite) among children compared to adults, suggesting a different exposure and/or elimination patterns between adults and children. This survey demonstrates the feasibility of conducting a harmonized pan-European sample collection, combined with suspect screening to provide insight in the presence of exposure to pesticide mixtures in the European population, including agricultural areas. Future improvements could come from improved (harmonized) quantification of pesticide levels.

A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences.

Within the Human Biomonitoring for Europe initiative (HBM4EU), a study to determine new biomarkers of exposure to pesticides and to assess exposure patterns was conducted. Human urine samples (N = 2,088) were collected from five European regions in two different seasons. The objective of the study was to identify pesticides and their metabolites in collected urine samples with a harmonized suspect screening approach based on liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) applied in five laboratories. A combined data processing workflow included comprehensive data reduction, correction of mass error and retention time (RT) drifts, isotopic pattern analysis, adduct and elemental composition annotation, finalized by a mining of the elemental compositions for possible annotations of pesticide metabolites. The obtained tentative annotations (n = 498) were used for acquiring representative data-dependent tandem mass spectra (MS2) and verified by spectral comparison to reference spectra generated from commercially available reference standards or produced through human liver S9 in vitro incubation experiments. 14 parent pesticides and 71 metabolites (including 16 glucuronide and 11 sulfate conjugates) were detected. Collectively these related to 46 unique pesticides. For the remaining tentative annotations either (i) no data-dependent MS2 spectra could be acquired, (ii) the spectral purity was too low for sufficient matching, or (iii) RTs indicated a wrong annotation, leaving potential for more pesticides and/or their metabolites being confirmed in further studies. Thus, the reported results are reflecting only a part of the possible pesticide exposure.

Harmonized Quality Assurance/Quality Control Provisions for Nontargeted Measurement of Urinary Pesticide Biomarkers in the HBM4EU Multisite SPECIMEn Study.

A set of quality assurance/quality control (QA/QC) criteria for nontargeted measurement of pesticide exposure markers in a large-scale study of human urine has been proposed and applied across five laboratories within the HBM4EU project. Quality control material, including reference standards and fortified pooled urine samples (QC urine) were prepared in a centralized way and distributed across participants to monitor analytical performance and consistency of the liquid chromatography coupled to high-resolution mass spectrometry data generated with a harmonized workflow. Signal intensities, mass accuracy, and retention times of selected QA/QC markers covering a broad range of physicochemical properties were monitored across QC solvent standards, QC urine samples, study urine samples, and procedural blanks, setting acceptance thresholds for repeatability and accuracy. Overall, results showed high repeatability of the collected data. The RSDs of the signal intensities were typically below 20-30% in QC and study samples, with good stability of the chromatographic separation (retention time drift within 2-4 s intrabatch and 5 s interbatch) and excellent mass accuracy (average error < 2 ppm). The use of the proposed criteria allowed for the identification of handling errors, instrumental issues, and potential batch effects. This is the first elaboration of harmonized QA/QC criteria applied across multiple laboratories to assess the quality of data generated by nontargeted analysis of human samples.

In silico deconjugation of glucuronide conjugates enhances tandem mass spectra library annotation of human samples.

Mass spectral library annotation of liquid chromatography-high resolution tandem mass spectrometry (LC-HRMS/MS) data is a reliable approach for fast identification of organic contaminants and toxicants in complex environmental and biological matrices. While determining the exposure of humans or mammals, it is indispensable to include phase I and phase II metabolites (conjugates) along with the parent compounds, but often, tandem mass spectra for these are unavailable. In this study, we present and evaluate a strategy for annotating glucuronide conjugates in LC-HRMS/MS scans by applying a neutral loss search for detection, then truncating the spectra which we refer to as in silico deconjugation, and finally searching these against mass spectral libraries of the aglycones. The workflow was tested on a dataset of in vitro-generated glucuronides of reference standard mixtures and a dataset of 51 authentic urine samples collected from patients with known medication status, acquired on different instrumentations. A total number of 75 different glucuronidated molecular structures were identified by in silico deconjugation and spectral library annotation. We also identified specific molecular structures (sulfonamides, ether bonds, di-glucuronides), which resulted in slightly different fragmentation patterns between the glucuronide and the unconjugated compound. This led to a decreased spectral matching score and in some cases to a false-negative identification. Still, by applying this method, we revealed a reliable annotation of most common glucuronides, leading to a new strategy reducing the need for deconjugation steps or for recording many reference glucuronide spectra for screening approaches.

Improving the Screening Analysis of Pesticide Metabolites in Human Biomonitoring by Combining High-Throughput In Vitro Incubation and Automated LC-HRMS Data Processing.

There is a current need to monitor human exposure to a large number of pesticides and other chemicals of emerging concern (CECs). This requires screening analysis with high confidence for these compounds and their metabolites in complex matrices, which is hampered by the fact that no reference standards are available for most metabolites. We address this challenge by a high-throughput workflow based on incubation of pesticides (or other CECs) with human liver S9, followed by solid-phase extraction, liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analysis, and automated data processing to generate a database (retention time, precursor m/z, and MS2 spectral library) for the annotation in human samples. The metabolite prioritization consists of statistical comparisons and mass defect and m/z range filtering to obtain a subset of probable phase I metabolites, for which molecular formulas and likely metabolic transformation are retrieved. We tested the workflow on 22 pesticides, for which we could determine 91 metabolite molecular formulas which are only partly covered by the literature and/or predicted by in silico metabolization. Our workflow allows for an efficient generation of metabolite reference information, which can be used directly for annotating LC-HRMS data from human samples. A full structure elucidation of individual metabolites can be limited to those being actually present in human samples.

An annotation database for chemicals of emerging concern in exposome research.

BACKGROUND: Chemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans. OBJECTIVES: This study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples. METHODS: The approach consists of three main parts. First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create "MS-ready" and "QSAR-ready" SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard. RESULTS: All CECscreen database and property files are publicly available (DOI: https://doi.org/10.5281/zenodo.3956586). In total, 145,284 entries were aggregated from various CECs data sources. After elimination of duplicates and curation, the pipeline produced 70,397 unique "MS-ready" structures and 66,071 unique QSAR-ready structures, corresponding with 69,526 CAS numbers. Simulation of Phase I metabolites resulted in 306,279 unique metabolites. QSAR predictions could be performed for 64,684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59,739 CAS numbers out of 69,526 inquiries. CECscreen is incorporated in the in silico fragmentation approach MetFrag. DISCUSSION: The CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towards in silico and experimental (e.g., MassBank) generation of MS/MS spectra, and development of bioinformatics approaches capable of using correlation patterns in the measured chemical features.

Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing.

Nontargeted mass spectrometry (MS) is widely used in life sciences and environmental chemistry to investigate large sets of samples. A major problem for larger-scale MS studies is data gaps or missing values in aligned data sets. The main causes for these data gaps are the absence of the compound from the sample, issues related to chromatography or mass spectrometry (for example, broad peaks, early eluting peaks, ion suppression, low ionization efficiency), and issues related to software (mainly limitations of peak detection algorithms). While those algorithms are heuristic by necessity and should be used with strict settings to minimize the number of false positive and negative peaks in a data set, gap filling may be used to reduce missing data in single samples remaining after peak detection. In this study, we present a new gap filling algorithm. The method is based on the symbolic aggregation approximation (SAX) algorithm that was developed for the evaluation and classification of time series in data mining studies. We adopted SAX for liquid chromatography high-resolution MS nontarget screening to support the detection of missing peaks in aligned mass spectral data sets. The SAX-based algorithm improves the detection efficiency considerably compared to existing gap filling methods including the Peak Finder algorithm provided in MZmine.

A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods.

Non-targeted mass spectrometry (MS) has become an important method over recent years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of non-targeted data sets, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection and refinement of it is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap-filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of the converted mass spectrometry files, intermediate processing files and the central file containing a table with all important information for the classified peaks.

Trace elements and polychlorinated biphenyls (PCBs) in terrestrial compartments of Svalbard, Norwegian Arctic.

Despite being a remote location, the Arctic is a major receptor for anthropogenic pollution transported from the mid-latitudes. Vegetation and underlying organic soils in the Norwegian Arctic, Svalbard were used to study the occurrences of polychlorinated biphenyls (PCBs) and trace elements. In this study, current concentrations of PCBs and trace elements, namely, Al, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, S, Sb, U and Zn in the terrestrial compartments of Svalbard are presented. Samples were collected from Adventdalen near Longyearbyen and from areas in proximity to Ny-Ålesund. There was significant variability in soil organic matter (SOM) among the soils analysed (5.0%-72.1%), with the highest values detected in Ny-Ålesund. The concentrations of Al, As, Cr Cu, Fe, Pb and Ni were associated with the geology of the local bedrock. The concentrations of all elements, except for Cd, Hg and Zn, were higher in soils than those in the overlying vegetation layers. Mean concentrations of ∑PCBs were significantly higher in vegetation (6.90 ±â€¯0.81 ng g-1 dw) than the underlying organic soils (3.70 ±â€¯0.36 ng g-1 dw). An inverse correlation of PCBs with the elements originating from the local bedrock indicated that their concentrations were potentially impacted by atmospheric deposition. PCBs and Cd were strongly associated, proposing a potential concomitant source of origin in Svalbard. Concentrations of PCBs and trace elements measured herein were below the proposed guidelines for Norwegian soil quality.

Suspect and untargeted screening of bisphenol S metabolites produced by in vitro human liver metabolism.

Bisphenol S (BPS) is increasingly used as substitute for bisphenol A, resulting in higher potential of human exposure to this compound. Yet, information on the human metabolism of BPS is limited. Hence, current biomonitoring studies rely only on the measurement of BPS itself, leading to a potential underestimation of assessing human exposure to this emerging contaminant. The aims of this study were to investigate the in vitro metabolic pathways of BPS using human liver microsomes and cytosol fractions and propose in vitro metabolites for evaluation in pharmacokinetics studies. Liquid chromatography coupled to quadrupole time-of-flight high-resolution mass spectrometry was used for the screening, identification, and structural elucidation of Phase I and II metabolites of BPS for the first time. Metabolite identification was performed using two complementary workflows: suspect and untargeted screening. Two Phase I metabolites were formed through hydroxylation of the phenolic rings. Four Phase II metabolites were formed through conjugation with glucuronic acid or sulfate. Three of these metabolites, namely dihydroxy-BPS, hydroxy-BPS-glucuronide and hydroxy-BPS-sulfate were identified and structurally elucidated for the first time. As such, we provide an expanded set of in vitro biotransformation products of BPS, which can potentially support a reliable assessment of BPS exposure in future biomonitoring studies.