Suspect screening and non-targeted analysis of drinking water using point-of-use filters

Newton, Seth; McMahen, Rebecca L.; Sobus, Jon R.; Mansouri, Kamel; Williams, Antony J.; McEachran, Andrew D.; Strynar, Mark J.

doi:10.1016/j.envpol.2017.11.033

Cited by 108 publications

(82 citation statements)

References 21 publications

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“…Additional parameters to assist in the identification of chemicals based on molecular formula search hit lists is required, and predicted RTs can be a valuable parameter for this purpose. The OPERA RT model has already been used in a non-targeted screening analysis of drinking water conducted at the EPA [ 147 ]. OPERA logP, MP, VP and WS models were used in a recent environmental fate assessment study at the EPA showing good performance and room for improvement as well [ 122 ].…”

Section: Discussionmentioning

confidence: 99%

OPERA models for predicting physicochemical properties and environmental fate endpoints

et al. 2018

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The collection of chemical structure information and associated experimental data for quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2–15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission’s Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure–activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency’s CompTox Chemistry Dashboard.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0263-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

OPERA models for predicting physicochemical properties and environmental fate endpoints

et al. 2018

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“…Additional work will further incorporate data from a collaborative NTA trial to work towards a global RT prediction system [53]. Future implementation of the OPERA-RT model [54] and associated data through the U.S. EPA's CompTox Chemistry Dashboard [15] will allow analysts to incorporate the OPERA-RT model in chemical identification workflows in NTA.…”

Section: Future Workmentioning

confidence: 99%

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

McEachran

Mansouri

Newton

et al. 2018

Talanta

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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 (R = 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.

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“…Perfluorinated chemicals also present a challenge for NTA, as the presence of monoisotopic fluorine renders calculation of possible molecular formulae very challenging [5, 72]. As a result, SSA and compound database searching is advantageous to finding these compounds.…”

Section: Resultsmentioning

confidence: 99%

“MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies

et al. 2018

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Chemical database searching has become a fixture in many non-targeted identification workflows based on high-resolution mass spectrometry (HRMS). However, the form of a chemical structure observed in HRMS does not always match the form stored in a database (e.g., the neutral form versus a salt; one component of a mixture rather than the mixture form used in a consumer product). Linking the form of a structure observed via HRMS to its related form(s) within a database will enable the return of all relevant variants of a structure, as well as the related metadata, in a single query. A Konstanz Information Miner (KNIME) workflow has been developed to produce structural representations observed using HRMS (“MS-Ready structures”) and links them to those stored in a database. These MS-Ready structures, and associated mappings to the full chemical representations, are surfaced via the US EPA’s Chemistry Dashboard (https://comptox.epa.gov/dashboard/). This article describes the workflow for the generation and linking of ~ 700,000 MS-Ready structures (derived from ~ 760,000 original structures) as well as download, search and export capabilities to serve structure identification using HRMS. The importance of this form of structural representation for HRMS is demonstrated with several examples, including integration with the in silico fragmentation software application MetFrag. The structures, search, download and export functionality are all available through the CompTox Chemistry Dashboard, while the MetFrag implementation can be viewed at https://msbi.ipb-halle.de/MetFragBeta/.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0299-2) contains supplementary material, which is available to authorized users.

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Suspect screening and non-targeted analysis of drinking water using point-of-use filters

Cited by 108 publications

References 21 publications

OPERA models for predicting physicochemical properties and environmental fate endpoints

OPERA models for predicting physicochemical properties and environmental fate endpoints

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

“MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies

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