High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis.

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion.

Data-independent acquisition-all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples.

Non-targeted analysis based on quantitative prediction and toxicity assessment for emerging contaminants in tire particle leachates.

Non-targeted analysis (NTA) has great potential to screen emerging contaminants in the environment, and some studies have conducted in-depth investigation on environmental samples. Here, we used a NTA workflow to identify emerging contaminants in used tire particle (TP) leachates, followed by quantitative prediction and toxicity assessment based on hazard scores. Tire particles were obtained from four different types of automobiles, representing the most common tires during daily transportation. With the instrumental analysis of TP leachates, a total of 244 positive and 104 negative molecular features were extracted from the mass data. After filtering by a specialized emerging contaminants list and matching by spectral databases, a total of 51 molecular features were tentatively identified as contaminants, including benzothiazole, hexaethylene glycol, 2-hydroxybenzaldehyde, etc. Given that these contaminants have different mass spectral responses in the mass spectrometry, models for predicting the response of contaminants were constructed based on machine learning algorithms, in this case random forest and artificial neural networks. After five-fold cross-validation, the random forest algorithm model had better prediction performance (MAECV = 0.12, Q2 = 0.90), and thus it was chosen to predict the contaminant concentrations. The prediction results showed that the contaminant at the highest concentration was benzothiazole, with 4,875 µg/L in the winter tire sample. In addition, the joint toxicity assessment of four types of tires was conducted in this study. According to different hazard levels, hazard scores increasing by a factor 10 were developed, and hazard scores of all the contaminants identified in each TP leachate were summed to obtain the total hazard score. All four tires were calculated to have relatively high risks, with winter tires having the highest total hazard score of 40,751. This study extended the application of NTA research and led to the direction of subsequent targeting studies on highly concentrated and toxic contaminants.

Size-Resolved Molecular Characterization of Water-Soluble Organic Matter in Atmospheric Particulate Matter from Northern China.

Atmospheric particulate matter (PM) affects visibility, climate, biogeochemical cycles and human health. Water-soluble organic matter (WSOM) is an important component of PM. In this study, PM samples with size-resolved measurements at aerodynamic cut-point diameters (Dp) of 0.01-18µm were collected in the rural area of Baoding and the urban area of Dalian, Northern China. Non-targeted analysis was adopted for the characterization of the molecule constitutes of WSOM in different sized particles using Fourier transform-ion cyclotron resonance mass spectrometry. Regardless of the location, the composition of WSOM in Aitken mode particles (aerodynamic diameter < 0.05 µm) was similar. The WSOM in accumulation mode particles (0.05-2 µm) in Baoding was predominantly composed of CHO compounds (84.9%), which were mainly recognized as lignins and lipids species. However, S-containing compounds (64.2%), especially protein and carbohydrates species, accounted for most of the WSOM in the accumulation mode particles in Dalian. The CHO compounds (67.6%-79.7%) contributed the most to the WSOM in coarse mode particles (> 2 µm) from both sites. Potential sources analysis indicated the WSOM in Baoding were mainly derived from biomass burning and oxidation reactions, while the WSOM in Dalian arose from coal combustion, oxidation reactions, and regional transport.

High-Resolution Mass Spectrometry Non-Targeted Detection of Per- and Polyfluoroalkyl Substances in Roe Deer (Capreolus capreolus).

Among wildlife species, roe deer stands out as a valuable indicator of environmental pollution due to its ecological significance and role as a game animal. The assessment of poly- and perfluoro substances (PFASs) bioaccumulation is of the utmost importance, relying on the liver and muscles as the main organs of interest. The study concerned the identification of 60 PFAS through a non-target workflow analysis based on HPLC Q-Exactive Orbitrap High-Resolution Mass Spectrometry in a homogeneous group of 18 female roe deer species. The developed strategy allowed us to individuate the 60 PFAS compounds with different levels of confirmation. Apart from seven PFASs identified via analytical standards, the remaining fifty-three features were identified with CL 2 or 3. Moreover, by applying a differential statistic approach, it was possible to distinguish the bioaccumulation patterns in the liver and muscle, identifying 12 PFAS upregulated in the muscle and 20 in the liver. The analysis reveals that specific PFAS compounds present exclusively in either the muscle or in the liver. The study emphasises the specificity of the liver and muscle as significant bioaccumulation sites for PFAS, raising questions about the underlying mechanisms of this process. In conclusion, the presented non-targeted PFAS analysis workflow evidenced promising and reliable results, successfully demonstrating its feasibility in the field of environmental research.

Demonstrating the Use of Non-targeted Analysis for Identification of Unknown Chemicals in Rapid Response Scenarios.

Several thousand intentional and unintentional chemical releases occur annually in the U.S., with the contents of almost 30% being of unknown composition. When targeted methods are unable to identify the chemicals present, alternative approaches, including non-targeted analysis (NTA) methods, can be used to identify unknown analytes. With new and efficient data processing workflows, it is becoming possible to achieve confident chemical identifications via NTA in a timescale useful for rapid response (typically 24-72 h after sample receipt). To demonstrate the potential usefulness of NTA in rapid response situations, we have designed three mock scenarios that mimic real-world events, including a chemical warfare agent attack, the contamination of a home with illicit drugs, and an accidental industrial spill. Using a novel, focused NTA method that utilizes both existing and new data processing/analysis methods, we have identified the most important chemicals of interest in each of these designed mock scenarios in a rapid manner, correctly assigning structures to more than half of the 17 total features investigated. We have also identified four metrics (speed, confidence, hazard information, and transferability) that successful rapid response analytical methods should address and have discussed our performance for each metric. The results reveal the usefulness of NTA in rapid response scenarios, especially when unknown stressors need timely and confident identification.

Extracting Structural Information from Physicochemical Property Measurements Using Machine Learning─A New Approach for Structure Elucidation in Non-targeted Analysis.

Non-targeted analysis (NTA) has made critical contributions in the fields of environmental chemistry and environmental health. One critical bottleneck is the lack of available analytical standards for most chemicals in the environment. Our study aims to explore a novel approach that integrates measurements of equilibrium partition ratios between organic solvents and water (KSW) to predictions of molecular structures. These properties can be used as a fingerprint, which with the help of a machine learning algorithm can be converted into a series of functional groups (RDKit fragments), which can be used to search chemical databases. We conducted partitioning experiments using a chemical mixture containing 185 chemicals in 10 different organic solvents and water. Both a liquid chromatography quadrupole time-of-flight mass spectrometer (LC-QTOF MS) and a LC-Orbitrap MS were used to assess the feasibility of the experimental method and the accuracy of the algorithm at predicting the correct functional groups. The two methods showed differences in log KSW with the QTOF method showing a mean absolute error (MAE) of 0.22 and the Orbitrap method 0.33. The differences also culminated into errors in the predictions of RDKit fragments with the MAE for the QTOF method being 0.23 and for the Orbitrap method being 0.31. Our approach presents a new angle in structure elucidation for NTA and showed promise in assisting with compound identification.

Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis.

Non-targeted analysis (NTA) has emerged as a valuable approach for the comprehensive monitoring of chemicals of emerging concern (CECs) in the exposome. The NTA approach can theoretically identify compounds with diverse physicochemical properties and sources. Even though they are generic and have a wide scope, non-targeted analysis methods have been shown to have limitations in terms of their coverage of the chemical space, as the number of identified chemicals in each sample is very low (e.g., ≤5%). Investigating the chemical space that is covered by each NTA assay is crucial for understanding the limitations and challenges associated with the workflow, from the experimental methods to the data acquisition and data processing techniques. In this review, we examined recent NTA studies published between 2017 and 2023 that employed liquid chromatography-high-resolution mass spectrometry. The parameters used in each study were documented, and the reported chemicals at confidence levels 1 and 2 were retrieved. The chosen experimental setups and the quality of the reporting were critically evaluated and discussed. Our findings reveal that only around 2% of the estimated chemical space was covered by the NTA studies investigated for this review. Little to no trend was found between the experimental setup and the observed coverage due to the generic and wide scope of the NTA studies. The limited coverage of the chemical space by the reviewed NTA studies highlights the necessity for a more comprehensive approach in the experimental and data processing setups in order to enable the exploration of a broader range of chemical space, with the ultimate goal of protecting human and environmental health. Recommendations for further exploring a wider range of the chemical space are given.

Exploring chemical space in non-targeted analysis: a proposed ChemSpace tool.

Non-targeted analysis (NTA) using high-resolution mass spectrometry allows scientists to detect and identify a broad range of compounds in diverse matrices for monitoring exposure and toxicological evaluation without a priori chemical knowledge. NTA methods present an opportunity to describe the constituents of a sample across a multidimensional swath of chemical properties, referred to as "chemical space." Understanding and communicating which region of chemical space is extractable and detectable by an NTA workflow, however, remains challenging and non-standardized. For example, many sample processing and data analysis steps influence the types of chemicals that can be detected and identified. Accordingly, it is challenging to assess whether analyte non-detection in an NTA study indicates true absence in a sample (above a detection limit) or is a false negative driven by workflow limitations. Here, we describe the need for accessible approaches that enable chemical space mapping in NTA studies, propose a tool to address this need, and highlight the different ways in which it could be implemented in NTA workflows. We identify a suite of existing predictive and analytical tools that can be used in combination to generate scores that describe the likelihood a compound will be detected and identified by a given NTA workflow based on the predicted chemical space of that workflow. Higher scores correspond to a higher likelihood of compound detection and identification in a given workflow (based on sample extraction, data acquisition, and data analysis parameters). Lower scores indicate a lower probability of detection, even if the compound is truly present in the samples of interest. Understanding the constraints of NTA workflows can be useful for stakeholders when results from NTA studies are used in real-world applications and for NTA researchers working to improve their workflow performance. The hypothetical ChemSpaceTool suggested herein could be used in both a prospective and retrospective sense. Prospectively, the tool can be used to further curate screening libraries and set identification thresholds. Retrospectively, false detections can be filtered by the plausibility of the compound identification by the selected NTA method, increasing the confidence of unknown identifications. Lastly, this work highlights the chemometric needs to make such a tool robust and usable across a wide range of NTA disciplines and invites others who are working on various models to participate in the development of the ChemSpaceTool. Ultimately, the development of a chemical space mapping tool strives to enable further standardization of NTA by improving method transparency and communication around false detection rates, thus allowing for more direct method comparisons between studies and improved reproducibility. This, in turn, is expected to promote further widespread applications of NTA beyond research-oriented settings.

Critical evaluation of MS acquisition conditions and identification process in LC-Q-Orbitrap-MS for non-targeted analysis: pesticide residues as case of study.

In routine measurements, the length of analysis time and the number of samples analysed during a given time unit are crucial. Additionally, the analytical method used has to provide reliable results and be able to identify and quantify any compound present in the matrix. High-resolution equipment, including Orbitrap analysers, is commonly used for non-targeted determinations. However, researchers still rely on trial and error to achieve the best acquisition conditions on the mass spectrometer, which is a tedious and time-consuming process that can lead to errors. Moreover, tentative compound identification, particularly when using a non-targeted approach, heavily depends on commercial databases. All of these issues can ultimately result in incomplete identification of compounds in the study matrix. In this framework, the study presented here has a dual objective: to use the experimental design tool to optimise critical parameters in mass spectrometry using LC-Q-Orbitrap-MS equipment when working in a non-targeted approach and to compare the mzCloud™ and ChemSpider™ commercial databases included in Compound Discoverer software with TraceFinder home-made databases generated to evaluate the ability to identify compounds. The study's noteworthy findings reveal that employing an experimental design has facilitated rapid optimisation of the mass spectrometer's multiplexing and loop parameters. Furthermore, the study highlights that the lack of harmonisation in commercial databases poses a disadvantage in the identification of compounds, leading to superior results when using home-made databases. In the latter databases, around 80% of the compounds were identified, which is approximately twice the number identified in commercial databases (around 40% in the best case with ChemSpider™).

Complementary methods for structural assignment of isomeric candidate structures in non-target liquid chromatography ion mobility high-resolution mass spectrometric analysis.

Non-target screening with LC/IMS/HRMS is increasingly employed for detecting and identifying the structure of potentially hazardous chemicals in the environment and food. Structural assignment relies on a combination of multidimensional instrumental methods and computational methods. The candidate structures are often isomeric, and unfortunately, assigning the correct structure among a number of isomeric candidate structures still is a key challenge both instrumentally and computationally. While practicing non-target screening, it is usually impossible to evaluate separately the limitations arising from (1) the inability of LC/IMS/HRMS to resolve the isomeric candidate structures and (2) the uncertainty of in silico methods in predicting the analytical information of isomeric candidate structures due to the lack of analytical standards for all candidate structures. Here we evaluate the feasibility of structural assignment of isomeric candidate structures based on in silico-predicted retention time and database collision cross-section (CCS) values as well as based on matching the empirical analytical properties of the detected feature with those of the analytical standards. For this, we investigated 14 candidate structures corresponding to five features detected with LC/HRMS in a spiked surface water sample. Considering the predicted retention times and database CCS values with the accompanying uncertainty, only one of the isomeric candidate structures could be deemed as unlikely; therefore, the annotation of the LC/IMS/HRMS features remained ambiguous. To further investigate if unequivocal annotation is possible via analytical standards, the reversed-phase LC retention times and low- and high-resolution ion mobility spectrometry separation, as well as high-resolution MS2 spectra of analytical standards were studied. Reversed-phase LC separated the highest number of candidate structures while low-resolution ion mobility and high-resolution MS2 spectra provided little means for pinpointing the correct structure among the isomeric candidate structures even if analytical standards were available for comparison. Furthermore, the question arises which prediction accuracy is required from the in silico methods to par the analytical separation. Based on the experimental data of the isomeric candidate structures studied here and previously published in the literature (516 retention time and 569 CCS values), we estimate that to reduce the candidate list by 95% of the structures, the confidence interval of the predicted retention times would need to decrease to below 0.05 min for a 15-min gradient while that of CCS values would need to decrease to 0.15%. Hereby, we set a clear goal to the in silico methods for retention time and CCS prediction.

Fisher ratio feature selection by manual peak area calculations on comprehensive two-dimensional gas chromatography data using standard mixtures with variable composition, storage, and interferences.

Comprehensive two-dimensional gas chromatography (GC×GC) is becoming increasingly more common for non-targeted characterization of complex volatile mixtures. The information gained with higher peak capacity and sensitivity provides additional sample composition information when one-dimensional GC is not adequate. GC×GC generates complex multivariate data sets when using non-targeted analysis to discover analytes. Fisher ratio (FR) analysis is applied to discern class markers, limiting complex GC×GC profiles to the most discriminating compounds between classes. While many approaches for feature selection using FR analysis exist, FR can be calculated relatively easily directly on peak areas after any native software has performed peak detection. This study evaluated the success rates of manual FR calculation and comparison to a critical F-value for samples analyzed by GC×GC with defined concentration differences. Long-term storage of samples and other spiked interferences were also investigated to examine their impact on analyzing mixtures using this FR feature selection strategy. Success rates were generally high with mostly 90-100% success rates and some instances of percentages between 80 and 90%. There were rare cases of false positives present and a low occurrence of false negatives. When errors were made in the selection of a compound, it was typically due to chromatographic artifacts present in chromatograms and not from the FR approach itself. This work provides foundational experimental data on the use of manual FR calculations for feature selection from GC×GC data.

Influence of data acquisition modes and data analysis approaches on non-targeted analysis of phthalate metabolites in human urine.

Humans are often exposed to phthalates and their alternatives, on account of their widespread use in PVC as plasticizers, which are associated with harmful human effects. While targeted biomonitoring provides quantitative information for exposure assessment, only a small portion of phthalate metabolites has been targeted. This results in a knowledge gap in human exposure to other unknown phthalate compounds and their metabolites. Although the non-targeted analysis (NTA) approach is capable of screening a broad spectrum of chemicals, there is a lack of harmonized workflow in NTA to generate reproducible data within and between different laboratories. The objective of this study was to compare two different NTA data acquisition modes, the data-dependent (DDA) and independent (DIA) acquisition (DDA), as well as two data analysis approaches, based on diagnostic ions and Compound Discoverer software for the prioritization of candidate precursors and identification of unknown compounds in human urine. Liquid chromatography coupled to high-resolution mass spectrometry was used for sample analysis. The combination of three-diagnostic-ion extraction and DDA data acquisition was able to improve data filtering and data analysis for prioritizing phthalate metabolites. With DIA, 25 molecular features were identified in human urine, while 32 molecular features were identified in the same urine samples using DDA data. The number of molecular features identified with level 1 confidence was 11 and 9 using DIA and DDA data, respectively. The study demonstrated that besides sample preparation, the impact of data acquisition must be taken into account when developing a NTA method and a consistent protocol for evaluating such an impact is necessary.

Dispersive magnetic solid-phase extraction for capsaicinoid compounds in human serum using LC-HRMS: targeted and non-targeted approaches.

A new analytical method based on the use of dispersive magnetic solid-phase extraction (DMSPE) is described for the preconcentration of capsaicin (CAP), dihydrocapsaicin (DCAP), and N-vanillylnonanamide (PCAP) from human serum samples. The influence of several experimental factors affecting the adsorption (nature and amount of magnetic material, adsorption time, and pH) and desorption (nature of solvent, its volume and desorption time) steps was studied. Among seven different nanomaterials studied, the best results were obtained using magnetic multiwalled carbon nanotubes, which were characterized by means of spectrometry- and microscopy-based techniques. Analyses were performed by ultra-high-performance liquid chromatography with quadrupole-time-of-flight mass spectrometry using electrospray ionization in positive mode (UHPLC-ESI-Q-TOF-MS). The developed method was validated by obtaining several parameters, including linearity (0.3-300 µg L-1 range), and limits of detection which were 0.1, 0.15, and 0.17 µg L-1 for CAP, DCAP, and PCAP, respectively. The repeatability of the method, expressed as relative standard deviation (RSD, n = 7), varied from 3.4 to 11%. The serum samples were also studied through a non-targeted approach in a search for capsaicinoid metabolites and related compounds. With this objective, the fragmentation pathway of this family of compounds was initially studied and a strategy was established for the identification of novel or less studied capsaicinoid-derived compounds.

Investigation on molecular characteristics of organic compounds during a full-scale landfill leachate treatment process based on non-targeted analysis.

In this study, a new methodology for evaluating full-scale landfill leachate treatment processes by non-targeted analysis using comprehensive two-dimensional gas chromatography quadrupole time-of-flight mass spectrometry (GC × GC-QTOF-MS) was proposed. The method revealed the chemical complexity of organic compounds in landfill leachate samples at the molecular level and evaluated the removal efficiency of the anaerobic-anoxic-oxic (A2O) - membrane bioreactor (MBR) - nanofiltration (NF) treatment process in conjunction with multi-level classification of organic compounds. Results showed that the results of non-targeted analysis combined with multi-level classification of organic compounds had a significant correlation with the conventional water quality parameters and can be used to evaluate the treatment process. A total of 2508 organic compounds were detected in 6 samples. 17 emerging contaminants (ECs) with known potentially hazards were detected, including Diisobutyl Phthalate (DIBP), which is toxic to male reproduction and development, and 4-Tert-Butylphenol, which causes endocrine disruption in animals. The removal rate of organic compounds by this full-scale landfill leachate treatment processes reached 79.14%. The anaerobic tank played a crucial role with 64.98% contribution. For compounds, the removal rate of heterocyclics was as high as 94.67%, and the removal rate of aliphatics was poor, only 63.49%. This treatment process had almost perfect removal effect on the steroids in alicyclics and phenols in aromatics, but poor treatment effect on saturated alkanes in aliphatics and naphthenes in alicyclics. This study provides a methodology for accurate assessment of the molecular level of treatment processes, new insights for process optimization in waste treatment plants, and data support for the detection of emerging contaminants. The environmental hazards of landfill leachate can be further evaluated in the future in conjunction with ecotoxicity assessment studies.

Quantitative Structure Use Relationships: Highlights from a technical summit meeting.

The Quantitative Structure Use Relationship (QSUR) Summit, held on November 2-4, 2022, focused on advancing the development, refinement, and use of QSURs to support chemical substance prioritization and risk assessment and mitigation. QSURs utilize chemical structures to predict the function of a chemical within a formulated product or an industrial process. This presumed function can then be used to develop chemical use categories or other information necessary to refine exposure assessments. The invited expert meeting was attended by 38 scientists from Canada, Finland, France, the UK, and the USA, representing government, business, and academia, with expertise in exposure science, chemical engineering, risk assessment, formulation chemistry, and machine learning. Workshop discussions emphasized the importance of collection and sharing of data and quantification of relative chemical quantities to progress QSUR development. Participants proposed collaborative approaches to address key challenges, including mechanisms for aggregating information while still protecting proprietary product composition and other confidential business information. Discussions also led to proposals for applications beyond exposure and risk modeling, including sustainable formulation discovery. In addition, discussions continue to construct, conduct, and circulate case studies tied to various specific problem formulations in which QSURs supply or derive information on chemical functions, concentrations, and exposures.

FluoroMatch 2.0-making automated and comprehensive non-targeted PFAS annotation a reality.

Because of the pervasiveness, persistence, and toxicity of per- and polyfluoroalkyl substances (PFAS), there is growing concern over PFAS contamination, exposures, and health effects. The diversity of potential PFAS is astounding, with nearly 10,000 PFAS catalogued in databases to date (and growing). The ability to detect the thousands of known PFAS, and discover previously uncatalogued PFAS, is necessary to understand the scope of PFAS contamination and to identify appropriate remediation and regulatory solutions. Current non-targeted methods for PFAS analysis require manual curation and are time-consuming, prone to error, and not comprehensive. FluoroMatch Flow 2.0 is the first software to cover all steps of data processing for PFAS discovery in liquid chromatography-high-resolution tandem mass spectrometry samples. These steps include feature detection, feature blank filtering, exact mass matching to catalogued PFAS, mass defect filtering, homologous series detection, retention time pattern analysis, class-based MS/MS screening, fragment screening, and predicted MS/MS from SMILES structures. In addition, a comprehensive confidence level criterion is implemented to help users understand annotation certainty and integrate various layers of evidence to reduce overreporting. Applying the software to aqueous film forming foam analysis, we discovered over one thousand likely PFAS including previously unreported species. Furthermore, we were able to filter out 96% of features which were likely not PFAS. FluoroMatch Flow 2 increased coverage of likely PFAS by over tenfold compared to the previous release. This software will enable researchers to better characterize PFAS in the environment and in biological systems.

Recent Trends in Multiclass Analysis of Emerging Endocrine Disrupting Contaminants (EDCs) in Drinking Water.

Ingestion of water is a major route of human exposure to environmental contaminants. There have been numerous studies exploring the different compounds present in drinking water, with recent attention drawn to a new class of emerging contaminants: endocrine-disrupting compounds (EDCs). EDCs encompass a broad range of physio-chemically diverse compounds; from naturally occurring to manmade. Environmentally, EDCs are found as mixtures containing multiple classes at trace amounts. Human exposure to EDCs, even at low concentrations, is known to lead to adverse health effects. Therefore, the ability to evaluate EDC contamination with a high degree of sensitivity and accuracy is of the utmost importance. This review includes (i) discussion on the perceived and actual risks associated with EDC exposure (ii) regulatory actions that look to limit EDC contamination (iii) analytical methods, including sample preparation, instrumentation and bioassays that have been advanced and employed for multiclass EDC identification and quantitation.

A simple and fast method for metabolomic analysis by gas liquid chromatography-mass spectrometry.

INTRODUCTION: The metabolomic profile is an essential tool for understanding the physiological processes of biological samples and their changes. In addition, it makes it possible to find new substances with industrial applications or use as drugs. As GC-MS is a very common tool for obtaining the metabolomic profile, a simple and fast method for sample preparation is required. OBJECTIVES: The aim of this research was to develop a direct derivatization method for GC-MS to simplify the sample preparation process and apply it to a wide range of samples for non-targeted metabolomic analysis purposes. METHODS: One pot combined esterification of carboxylic acids with methanol and silylation of the hydroxyl groups was achieved using a molar excess of chlorotrimethylsilane with respect to methanol in the presence of pyridine. RESULTS: The metabolome profile obtained from different samples, such as bilberry and cherry cuticles, olive leaves, P. aeruginosa and E. coli bacteria, A. niger fungi and human sebum from the ceruminous gland, shows that the procedure allows the identification of a wide variety of metabolites. Aliphatic fatty acids, hydroxyfatty acids, phenolic and other aromatic compounds, fatty alcohols, fatty aldehydes dimethylacetals, hydrocarbons, terpenoids, sterols and carbohydrates were identified at different MSI levels using their mass spectra. CONCLUSION: The metabolomic profile of different biological samples can be easily obtained by GC-MS using an efficient simultaneous esterification-silylation reaction. The derivatization method can be carried out in a short time in the same injection vial with a small amount of reagents.