Linear Solvation Energy Relationships as classifiers in non-target analysis--a capillary liquid chromatography approach

Reactivity-based identification of oxygen containing functional groups of chemicals applied as potential classifier in non-target analysis

In this work, we developed a reactivity-based strategy to identify functional groups of unknown analytes, which can be applied as classifier in non-target analysis with gas chromatography. The aim of this strategy is to reduce the number of potential candidate structures generated for a molecular formula determined by high resolution mass spectrometry. We selected an example of 18 isomers with the molecular formula C12H10O2 to test the performance of different derivatization reagents, whereas our aim was to select mild and fast reaction conditions. Based on the results for the isomers, we developed a four-step workflow for the identification of functional groups containing oxygen.

Photo-assisted transformation of furosemide: Exploring transformation pathways, structure database and suspect and non-target workflows for comprehensive screening of unknown transformation products in wastewaters and landfill leachates

In recent years, transformation products-(TPs) of pharmaceuticals in the environment have received considerable attention. In this context, here, a customized overview of transformation of Furosemide-(FRS) in aqueous matrices treated by photo-oxidation is provided as a proof of concept. Hence, the primary goal of the study was to display an integrated strategy by combining the target (parent-molecule) and suspect screening-(SS) approaches (TPs) in order to build an in-house High-Resolution mass spectrometry (HRMS) database able to provide reference information (chromatographic/spectral) for environmental investigations in complex matrices (wastewaters/landfill leachates). Data analysis was performed by optimizing a SS workflow. Additional confirmation for the proposed structural elucidation was provided by correlating retention time to the proposed structure employing three prediction models. This approach was applied for the tentative identification of 35 TPs of FRS, 28 of which are reported herein for the first time. Finally, SS and non-target analysis (NTA) have been successfully applied for retrospective screening of FRS and its TPs in real samples. The findings demonstrated that SS allows the proper identification of TPs of FRS in complex matrices proving its outstanding importance compared to NTA. In total, six TPs were identified by SS with potential ecotoxicological implications for two of them according to in silico risk assessment.

Development of a Retention Time Interpolation scale (RTi) for liquid chromatography coupled to mass spectrometry in both positive and negative ionization modes

The accuracy and sensitivity of high resolution mass spectrometry (HRMS) enables the identification of candidate compounds with the use of mass spectrometric databases among other tools. However, retention time (RT) data in identification workflows has been sparingly used since it could be strongly affected by matrix or chromatographic performance. Retention Time Interpolation scaling (RTi) strategies can provide a more robust and valuable information than RT, gaining more confidence in the identification of candidate compounds in comparison to an analytical standard. Up to our knowledge, no RTi has been developed for LC-HRMS systems providing information when acquiring in either positive or negative ionization modes. In this work, an RTi strategy was developed by means of the use of 16 isotopically labelled reference standards, which can be spiked into a real sample without resulting in possible false positives or negatives. For testing the RTi performance, a mixture of several reference standards, emulating suspect analytes, were used. RTi values for these compounds were calculated both in solvent and spiked in a real matrix to assess the effect of either chromatographic parameters or matrix in different scenarios. It has been demonstrated that the variation of injection volume, chromatographic gradient and initial percentage of organic solvent injected does not considerably affect RTi calculation. Column aging and solid support of the stationary phase of the column, however, showed strong effects on the elution of several test compounds. Yet, RTi permitted the correction of elution shifts of most compounds. Furthermore, RTi was tested in 47 different matrices from food, biological, animal feeding and environmental origin. The application of RTi in both positive and negative ionization modes showed in general satisfactory results for most matrices studied. The RTi developed can be used in future LC-HRMS screening analysis giving an additional parameter, which facilitates tedious processing tasks and gain more confidence in the identification of (non)-suspect analytes.

A comparison of three liquid chromatography (LC) retention time prediction models

High-resolution mass spectrometry (HRMS) data has revolutionized the identification of environmental contaminants through non-targeted analysis (NTA). However, chemical identification remains challenging due to the vast number of unknown molecular features typically observed in environmental samples. Advanced data processing techniques are required to improve chemical identification workflows. The ideal workflow brings together a variety of data and tools to increase the certainty of identification. One such tool is chromatographic retention time (RT) prediction, which can be used to reduce the number of possible suspect chemicals within an observed RT window. This paper compares the relative predictive ability and applicability to NTA workflows of three RT prediction models: (1) a logP (octanol-water partition coefficient)-based model using EPI Suite™ logP predictions; (2) a commercially available ACD/ChromGenius model; and, (3) a newly developed Quantitative Structure Retention Relationship model called OPERA-RT. Models were developed using the same training set of 78 compounds with experimental RT data and evaluated for external predictivity on an identical test set of 19 compounds. Both the ACD/ChromGenius and OPERA-RT models outperformed the EPI Suite™ logP-based RT model (R² = 0.81-0.92, 0.86-0.83, 0.66-0.69 for training-test sets, respectively). Further, both OPERA-RT and ACD/ChromGenius predicted 95% of RTs within a ± 15% chromatographic time window of experimental RTs. Based on these results, we simulated an NTA workflow with a ten-fold larger list of candidate structures generated for formulae of the known test set chemicals using the U.S. EPA's CompTox Chemistry Dashboard (https://comptox.epa.gov/dashboard), RTs for all candidates were predicted using both ACD/ChromGenius and OPERA-RT, and RT screening windows were assessed for their ability to filter out unlikely candidate chemicals and enhance potential identification. Compared to ACD/ChromGenius, OPERA-RT screened out a greater percentage of candidate structures within a 3-min RT window (60% vs. 40%) but retained fewer of the known chemicals (42% vs. 83%). By several metrics, the OPERA-RT model, generated as a proof-of-concept using a limited set of open source data, performed as well as the commercial tool ACD/ChromGenius when constrained to the same small training and test sets. As the availability of RT data increases, we expect the OPERA-RT model's predictive ability will increase.

Integrated Framework for Identifying Toxic Transformation Products in Complex Environmental Mixtures

Complex environmental mixtures consist of hundreds to thousands of unknown and unregulated organic compounds that may have toxicological relevance, including transformation products (TPs) of anthropogenic organic pollutants. Non-targeted analysis and suspect screening analysis offer analytical approaches for potentially identifying these toxic transformation products. However, additional tools and strategies are needed in order to reduce the number of chemicals of interest and focus analytical efforts on chemicals that may pose risks to humans and the environment. This brief review highlights recent developments in this field and suggests an integrated framework that incorporates complementary instrumental techniques, computational chemistry, and toxicity analysis, for prioritizing and identifying toxic TPs in the environment.

Effect-directed analysis supporting monitoring of aquatic environments--An in-depth overview

Aquatic environments are often contaminated with complex mixtures of chemicals that may pose a risk to ecosystems and human health. This contamination cannot be addressed with target analysis alone but tools are required to reduce this complexity and identify those chemicals that might cause adverse effects. Effect-directed analysis (EDA) is designed to meet this challenge and faces increasing interest in water and sediment quality monitoring. Thus, the present paper summarizes current experience with the EDA approach and the tools required, and provides practical advice on their application. The paper highlights the need for proper problem formulation and gives general advice for study design. As the EDA approach is directed by toxicity, basic principles for the selection of bioassays are given as well as a comprehensive compilation of appropriate assays, including their strengths and weaknesses. A specific focus is given to strategies for sampling, extraction and bioassay dosing since they strongly impact prioritization of toxicants in EDA. Reduction of sample complexity mainly relies on fractionation procedures, which are discussed in this paper, including quality assurance and quality control. Automated combinations of fractionation, biotesting and chemical analysis using so-called hyphenated tools can enhance the throughput and might reduce the risk of artifacts in laboratory work. The key to determining the chemical structures causing effects is analytical toxicant identification. The latest approaches, tools, software and databases for target-, suspect and non-target screening as well as unknown identification are discussed together with analytical and toxicological confirmation approaches. A better understanding of optimal use and combination of EDA tools will help to design efficient and successful toxicant identification studies in the context of quality monitoring in multiply stressed environments.

MetFrag relaunched: incorporating strategies beyond in silico fragmentation

BACKGROUND

The in silico fragmenter MetFrag, launched in 2010, was one of the first approaches combining compound database searching and fragmentation prediction for small molecule identification from tandem mass spectrometry data. Since then many new approaches have evolved, as has MetFrag itself. This article details the latest developments to MetFrag and its use in small molecule identification since the original publication.

RESULTS

MetFrag has gone through algorithmic and scoring refinements. New features include the retrieval of reference, data source and patent information via ChemSpider and PubChem web services, as well as InChIKey filtering to reduce candidate redundancy due to stereoisomerism. Candidates can be filtered or scored differently based on criteria like occurence of certain elements and/or substructures prior to fragmentation, or presence in so-called "suspect lists". Retention time information can now be calculated either within MetFrag with a sufficient amount of user-provided retention times, or incorporated separately as "user-defined scores" to be included in candidate ranking. The changes to MetFrag were evaluated on the original dataset as well as a dataset of 473 merged high resolution tandem mass spectra (HR-MS/MS) and compared with another open source in silico fragmenter, CFM-ID. Using HR-MS/MS information only, MetFrag2.2 and CFM-ID had 30 and 43 Top 1 ranks, respectively, using PubChem as a database. Including reference and retention information in MetFrag2.2 improved this to 420 and 336 Top 1 ranks with ChemSpider and PubChem (89 and 71 %), respectively, and even up to 343 Top 1 ranks (PubChem) when combining with CFM-ID. The optimal parameters and weights were verified using three additional datasets of 824 merged HR-MS/MS spectra in total. Further examples are given to demonstrate flexibility of the enhanced features.

CONCLUSIONS

In many cases additional information is available from the experimental context to add to small molecule identification, which is especially useful where the mass spectrum alone is not sufficient for candidate selection from a large number of candidates. The results achieved with MetFrag2.2 clearly show the benefit of considering this additional information. The new functions greatly enhance the chance of identification success and have been incorporated into a command line interface in a flexible way designed to be integrated into high throughput workflows. Feedback on the command line version of MetFrag2.2 available at http://c-ruttkies.github.io/MetFrag/ is welcome.

Linking mutagenic activity to micropollutant concentrations in wastewater samples by partial least square regression and subsequent identification of variables

We deployed multivariate regression to identify compounds co-varying with the mutagenic activity of complex environmental samples. Wastewater treatment plant (WWTP) effluents with a large share of industrial input of different sampling dates were evaluated for mutagenic activity by the Ames Fluctuation Test and chemically characterized by a screening for suspected pro-mutagens and non-targeted software-based peak detection in full scan data. Areas of automatically detected peaks were used as predictor matrix for partial least squares projections to latent structures (PLS) in combination with measured mutagenic activity. Detected peaks were successively reduced by the exclusion of all peaks with lowest variable importance until the best model (high R(2) and Q(2)) was reached. Peaks in the best model co-varying with the observed mutagenicity showed increased chlorine, bromine, sulfur, and nitrogen abundance compared to original peak set indicating a preferential selection of anthropogenic compounds. The PLS regression revealed four tentatively identified compounds, newly identified 4-(dimethylamino)-pyridine, and three known micropollutants present in domestic wastewater as co-varying with the mutagenic activity. Co-variance between compounds stemming from industrial wastewater and mutagenic activity supported the application of "virtual" EDA as a statistical tool to separate toxicologically relevant from less relevant compounds.

Decoding and Discrimination of Chemical Cues and Signals: Avoidance of Predation and Competition during Parental Care Behavior in Sympatric Poison Frogs

The evolution of chemical communication and the discrimination between evolved functions (signals) and unintentional releases (cues) are among the most challenging issues in chemical ecology. The accurate classification of inter- or intraspecific chemical communication is often puzzling. Here we report on two different communication systems triggering the same parental care behavior in the poison frog Ranitomeya variabilis. This species deposits its tadpoles and egg clutches in phytotelmata and chemically recognizes and avoids sites with both predatory conspecific and non-predatory heterospecific tadpoles (of the species Hyloxalus azureiventris). Combining chemical analyses with in-situ bioassays, we identified the molecular formulas of the chemical compounds triggering this behavior. We found that both species produce distinct chemical compound combinations, suggesting two separate communication systems. Bringing these results into an ecological context, we classify the conspecific R. variabilis compounds as chemical cues, advantageous only to the receivers (the adult frogs), not the emitters (the tadpoles). The heterospecific compounds, however, are suggested to be chemical signals (or cues evolving into signals), being advantageous to the emitters (the heterospecific tadpoles) and likely also to the receivers (the adult frogs). Due to these assumed receiver benefits, the heterospecific compounds are possibly synomones which are advantageous to both emitter and receiver ‒ a very rare communication system between animal species, especially vertebrates.

Multicriteria approach to select polyaromatic river mutagen candidates

The identification of unknown compounds remains one of the most challenging tasks to link observed toxic effects in complex environmental mixtures to responsible toxicants in effect-directed analysis (EDA). Here, a workflow is presented based on nontarget liquid chromatography-high resolution mass spectrometry (LC-HRMS) starting with molecular formulas determined in a previous study. A compound database search (ChemSpider) was performed to retrieve candidates for each formula. Subsequently, the number of candidates was reduced by applying MS-, physical-chemical, and chromatography-based selection criteria including HRMS/MS fragmentation and plausibility, ionization efficiency with different ion sources and detection modes, acid/base behavior, octanol/water partitioning, retention time prediction and finally toxic effects (mutagenicity caused by aromatic amines). The workflow strongly decreased the number of possible candidates and resulted in the tentative identification of possible mutagens and the positive identification of the nonmutagen benzyl(diphenyl) phosphine oxide in a mutagenic fraction. The positive identification of mutagens was hampered by a lack of commercially available standards. The workflow is an innovative and promising approach and forms an excellent basis for possible further advancements.

Metabolic transformation as a diagnostic tool for the selection of candidate promutagens in effect-directed analysis

To implement metabolic activation by S9 rat liver homogenate in the selection of candidate promutagens in effect-directed analysis, we critically assessed the capability of LC-HRMS measurements to detect depletion and formation of metabolites by S9 exposure. The exposure of a reference mixture to S9 led to a depletion by >70% for most compounds. Other processes than metabolism were excluded as significant contribution to compound depletion. Metabolites formed by S9 exposure were identified and S9 metabolism was incorporated in the identification of candidate promutagens in a wastewater treatment plant (WWTP) effluent with mutagenic activity only after metabolic activation by S9. The metabolism by S9 in the WWTP effluent was confirmed. Based on a candidate exclusion of all peaks not depleted, thus not activated by the S9 mix, the number of candidate promutagens was reduced by 40%. Selected remaining candidates were evaluated and identified, but could not be confirmed as promutagens.

Identification of novel micropollutants in wastewater by a combination of suspect and nontarget screening

To detect site-specific, suspected and formerly unknown contaminants in a wastewater treatment plant effluent, we established a screening procedure based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) with stepwise identification schemes. Based on automated substructure searches a list of 2160 suspected site-specific and documented water contaminants was reduced to those amenable to LC-HRMS. After searching chromatograms for exact masses of suspects, presumably false positive detections were stepwise excluded by retention time prediction, the evaluation of isotope patterns, ionization behavior, and HRMS/MS spectra. In nontarget analysis, peaks for identification were selected based on distinctive isotope patterns and intensity. The stepwise identification of nontarget compounds was automated by a plausibility check of molecular formulas using the Seven Golden Rules, an exclusion of compounds with presumably low commercial importance and an automated HRMS/MS evaluation. Six suspected and five nontarget chemicals were identified, of which two have not been previously reported as environmental pollutants.

Predicting hexadecane-air equilibrium partition coefficients (L) using a group contribution approach constructed from high quality data

A group contribution-based quantitative structure-property relationship (QSPR) for the hexadecane-air equilibrium partition coefficients (L) of organic chemicals is developed using the iterative fragment selection (IFS) approach. This new QSPR includes in its training and external validation data sets L values for a large number of structurally complex chemicals measured by the same group using consistent methods. The resulting QSPR has better predictive power than other prediction methods trained primarily using data for chemicals of simpler structures, and measurements of L values from diverse sources. For a subset of chemicals in which the L values have non-additive effects caused by intramolecular hydrogen bonds, the new QSPR gives much better performance in comparison to the most commonly used prediction method.

Is nontarget screening of emerging contaminants by LC-HRMS successful? A plea for compound libraries and computer tools

This review focuses on the possibilities and limits of nontarget screening of emerging contaminants, with emphasis on recent applications and developments in data evaluation and compound identification by liquid chromatography-high-resolution mass spectrometry (HRMS). The general workflow includes determination of the elemental composition from accurate mass, a further search for the molecular formula in compound libraries or general chemical databases, and a ranking of the proposed structures using further information, e.g., from mass spectrometry (MS) fragmentation and retention times. The success of nontarget screening is in some way limited to the preselection of relevant compounds from a large data set. Recently developed approaches show that statistical analysis in combination with suspect and nontarget screening are useful methods to preselect relevant compounds. Currently, the unequivocal identification of unknowns still requires information from an authentic standard which has to be measured or is already available in user-defined MS/MS reference databases or libraries containing HRMS spectral information and retention times. In this context, we discuss the advantages and future needs of publicly available MS and MS/MS reference databases and libraries which have mostly been created for the metabolomic field. A big step forward has been achieved with computer-based tools when no MS library or MS database entry is found for a compound. The numerous search results from a large chemical database can be condensed to only a few by in silico fragmentation. This has been demonstrated for selected compounds and metabolites in recent publications. Still, only very few compounds have been identified or tentatively identified in environmental samples by nontarget screening. The availability of comprehensive MS libraries with a focus on environmental contaminants would tremendously improve the situation.