EPA CompTox Chemicals Dashboard Research Articles

Exploring applications of non-targeted analysis in the characterization of the prenatal exposome

Capturing the breadth of chemical exposures in utero is critical in understanding their long-term health effects for mother and child. We explored methodological adaptations in a Non-Targeted Analysis (NTA) pipeline and evaluated the effects on chemical annotation and discovery for maternal and infant exposure. We focus on lesser-known/underreported chemicals in maternal and umbilical cord serum analyzed with liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). The samples were collected from a demographically diverse cohort of 296 maternal-cord pairs (n = 592) recruited in San Francisco Bay area. We developed and evaluated two data processing pipelines, primarily differing by detection frequency cut-off, to extract chemical features from non-targeted analysis (NTA). We annotated the detected chemical features by matching with EPA CompTox Chemicals Dashboard (n = 860,000 chemicals) and Human Metabolome Database (n = 3140 chemicals) and applied a Kendrick Mass Defect filter to detect homologous series. We collected fragmentation spectra (MS/MS) on a subset of serum samples and matched to an experimental MS/MS database within the MS-Dial website and other experimental MS/MS spectra collected from standards in our lab. We annotated ~72 % of the features (total features = 32,197, levels 1–4). We confirmed 22 compounds with analytical standards, tentatively identified 88 compounds with MS/MS spectra, and annotated 4862 exogenous chemicals with an in-house developed annotation algorithm. We detected 36 chemicals that appear to not have been previously reported in human blood and 9 chemicals that were reported in less than five studies. Our findings underline the importance of NTA in the discovery of lesser-known/unreported chemicals important to characterize human exposures.

A New CSRML Structure-Based Fingerprint Method for Profiling and Categorizing Per- and Polyfluoroalkyl Substances (PFAS).

The term PFAS encompasses diverse per- and polyfluorinated alkyl (and increasingly aromatic) chemicals spanning industrial processes, commercial uses, environmental occurrence, and potential concerns. With increased chemical curation, currently exceeding 14,000 structures in the PFASSTRUCTV5 inventory on EPA's CompTox Chemicals Dashboard, has come increased motivation to profile, categorize, and analyze the PFAS structure space using modern cheminformatics approaches. Making use of the publicly available ToxPrint chemotypes and ChemoTyper application, we have developed a new PFAS-specific fingerprint set consisting of 129 TxP_PFAS chemotypes coded in CSRML, a chemical-based XML-query language. These are split into two groups, the first containing 56 mostly bond-type ToxPrints modified to incorporate attachment to either a CF group or F atom to enforce proximity to the fluorinated portion of the chemical. This focus resulted in a dramatic reduction in TxP_PFAS chemotype counts relative to the corresponding ToxPrint counts (averaging 54%). The remaining TxP_PFAS chemotypes consist of various lengths and types of fluorinated chains, rings, and bonding patterns covering indications of branching, alternate halogenation, and fluorotelomers. Both groups of chemotypes are well represented across the PFASSTRUCT inventory. Using the ChemoTyper application, we show how the TxP_PFAS chemotypes can be visualized, filtered, and used to profile the PFASSTRUCT inventory, as well as to construct chemically intuitive, structure-based PFAS categories. Lastly, we used a selection of expert-based PFAS categories from the OECD Global PFAS list to evaluate a small set of analogous structure-based TxP_PFAS categories. TxP_PFAS chemotypes were able to recapitulate the expert-based PFAS category concepts based on clearly defined structure rules that can be computationally implemented and reproducibly applied to process large PFAS inventories without need to consult an expert. The TxP_PFAS chemotypes have the potential to support computational modeling, harmonize PFAS structure-based categories, facilitate communication, and allow for more efficient and chemically informed exploration of PFAS chemicals moving forward.

Wikipedia on the CompTox Chemicals Dashboard: Connecting Resources to Enrich Public Chemical Data.

The online encyclopedia Wikipedia aggregates a large amount of data on chemistry, encompassing well over 20,000 individual Wikipedia pages and serves the general public as well as the chemistry community. Many other chemical databases and services utilize these data, and previous projects have focused on methods to index, search, and extract it for review and use. We present a comprehensive effort that combines bulk automated data extraction over tens of thousands of pages, semiautomated data extraction over hundreds of pages, and fine-grained manual extraction of individual lists and compounds of interest. We then correlate these data with the existing contents of the U.S. Environmental Protection Agency’s (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database. This was performed with a number of intentions including ensuring as complete a mapping as possible between the Dashboard and Wikipedia so that relevant snippets of the article are loaded for the user to review. Conflicts between Dashboard content and Wikipedia in terms of, for example, identifiers such as chemical registry numbers, names, and InChIs and structure-based collisions such as SMILES were identified and used as the basis of curation of both DSSTox and Wikipedia. This work also allowed us to evaluate available data for sets of chemicals of interest to the Agency, such as synthetic cannabinoids, and expand the content in DSSTox as appropriate. This work also led to improved bidirectional linkage of the detailed chemistry and usage information from Wikipedia with expert-curated structure and identifier data from DSSTox for a new list of nearly 20,000 chemicals. All of this work ultimately enhances the data mappings that allow for the display of the introduction of the Wikipedia article in the community-accessible web-based EPA Comptox Chemicals Dashboard, enhancing the user experience for the thousands of users per day accessing the resource.

Integrating publicly available information to screen potential candidates for chemical prioritization under the Toxic Substances Control Act: A proof of concept case study using genotoxicity and carcinogenicity

The Toxic Substances Control Act (TSCA) became law in the U.S. in 1976 and was amended in 2016. The amended law requires the U.S. EPA to perform risk-based evaluations of existing chemicals. Here, we developed a tiered approach to screen potential candidates based on their genotoxicity and carcinogenicity information to inform the selection of candidate chemicals for prioritization under TSCA. The approach was underpinned by a large database of carcinogenicity and genotoxicity information that had been compiled from various public sources. Carcinogenicity data included weight-of-evidence human carcinogenicity evaluations and animal cancer data. Genotoxicity data included bacterial gene mutation data from the Salmonella (Ames) and Escherichia coli WP2 assays and chromosomal mutation (clastogenicity) data. Additionally, Ames and clastogenicity outcomes were predicted using the alert schemes within the OECD QSAR Toolbox and the Toxicity Estimation Software Tool (TEST).The evaluation workflows for carcinogenicity and genotoxicity were developed along with associated scoring schemes to make an overall outcome determination. For this case study, two sets of chemicals, the TSCA Active Inventory non-confidential portion list available on the EPA CompTox Chemicals Dashboard (33,364 chemicals, ‘TSCA Active List’) and a representative proof-of-concept (POC) set of 238 chemicals were profiled through the two workflows to make determinations of carcinogenicity and genotoxicity potential. Of the 33,364 substances on the ‘TSCA Active List’, overall calls could be made for 20,371 substances. Here 46.67%% (9507) of substances were non-genotoxic, 0.5% (103) were scored as inconclusive, 43.93% (8949) were predicted genotoxic and 8.9% (1812) were genotoxic. Overall calls for genotoxicity could be made for 225 of the 238 POC chemicals. Of these, 40.44% (91) were non-genotoxic, 2.67% (6) were inconclusive, 6.22% (14) were predicted genotoxic, and 50.67% (114) genotoxic. The approach shows promise as a means to identify potential candidates for prioritization from a genotoxicity and carcinogenicity perspective.

Personal External Exposomes from Around the World

BACKGROUND AND AIM: Industrial advances have led to a plethora of anthropogenic chemicals in our air, water, and soil. Exposures to a subset of these chemicals can lead to illnesses including respiratory illnesses, auto-immune disorders, inhibited immune response, and various cancers. Most studies focus on particulate matter or a select set of chemicals, missing chemicals of potential concern. Therefore, we have developed a personal exposure monitoring technology and workflow to comprehensively determine airborne chemical exposures of concern in populations across the globe. METHODS: Polydimethylsiloxane (PDMS) passive samplers placed in wristbands were deployed in South Africa, China, United States, Canada, and India, both in rural and urban regions. Samplers were shipped to Yale University and analyzed by direct thermal desorption gas-chromatography high-resolution mass spectrometry using a Q-Exactive orbitrap mass spectrometer. A software was developed to rank suspect screening annotations in terms of confidence, normalize for batch effects, filter out chemicals from background contamination, and remove redundant chemical annotations. Furthermore, a workflow implementing tools on the US EPA CompTox Chemicals Dashboard and prototype Hazard Comparison Dashboard was developed to automatically determine chemicals of most concern from a health perspective. RESULTS:Unique chemicals of concern, including pesticides used for malaria control in South Africa and for household pest control in China, fungicides deployed in the United States, and anti-microbial agents found in all populations, were determined. Participants were likely inhaling some of these chemicals, which poses health concerns. Chemical exposures were dynamic, with seasonal variation predominating any other factor among studies in different locations. CONCLUSIONS:We are finding that people around the world, from children in rural South African villages to the elderly in Chinese metropolis, to college students at Yale, are inhaling chemicals of concern, including biocides. These chemical exposures are dynamic, with season seeming to have the greatest influence on exposure profiles. KEYWORDS: Air pollution, external exposome, exposure assessment, omics technologies, mixtures, pesticides

Large-Scale Implementation and Flaw Investigation of Human Serum Suspect Screening Analysis for Industrial Chemicals.

Non-targeted analysis (NTA), including both suspect screening analysis (SSA) and unknown compound analysis, has gained increasing popularity in various fields for its capability in identifying new compounds of interests. Current major challenges for NTA SSA are that (1) tremendous effort and resources are needed for large-scale identification and confirmation of suspect chemicals and (2) suspect chemicals generally show low matching rates during identification and confirmation processes. To narrow the gap between these challenges and smooth implementation of NTA SSA methodology in the biomonitoring field, we present a thorough SSA workflow for the large-scale screen, identification, and confirmation of industrial chemicals that may pose adverse health effects in pregnant women and newborns. The workflow was established in a study of 30 paired maternal and umbilical cord serum samples collected at delivery in the San Francisco Bay area. By analyzing LC-HRMS and MS/MS data, together with the assistance of a combination of resources including online MS/MS spectra libraries, online in silico fragmentation tools, and the EPA CompTox Chemicals Dashboard, we confirmed the identities of 17 chemicals, among which monoethylhexyl phthalate, 4-nitrophenol, tridecanedioic acid, and octadecanedioic acid are especially interesting due to possible toxicities and their high-volume use in industrial manufacturing. Similar to other previous studies in the SSA field, the suspect compounds show relatively low MS/MS identification (16%) and standard confirmation (8%) rates. Therefore, we also investigated origins of false positive features and unidentifiable suspected features, as well as technical obstacles encountered during the confirmation process, which would promote a better understanding of the flaw of low confirmation rate and encourage gaining more effective tools for tackling this issue in NTA SSA.

An annotation database for chemicals of emerging concern in exposome research

BackgroundChemicals 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. ObjectivesThis study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples. MethodsThe 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. ResultsAll 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. DiscussionThe 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.

Identification of Three Novel Chloroalkyl Organophosphate Triesters in House Dust Using Halogenation-Guided Nontarget Screening Combined with Suspect Screening

Several haloalkyl organophosphate triester (OPTE) flame retardants have been restricted in some countries due to their potential health risks, but the usage of alternative haloalkyl OPTEs is of concern. In this study, we comprehensively screened for haloalkyl OPTEs in house dust using high-resolution mass spectrometry. Through halogenation-guided nontarget screening, a rare chloroalkyl OPTE, diethylene glycol bis(bis(2-chloroisopropyl)phosphate) (DEGBBCPP), was unequivocally identified (Level 1) in house dust of Beijing, North China. In addition, by screening a suspect list of 61 haloalkyl OPTEs from the EPA's CompTox Chemicals Dashboard, we tentatively identified diethylene glycol bis(bis(2-chloroethyl)phosphate) (DEGBBCEP) and ethylene bis[bis(2-chloroethyl)phosphate] (EBBCEP) (Level 2). DEGBBCPP was detected in all 45 house dust samples, and the median concentration was 98.4 ng/g (13.6-6217 ng/g), that is, approximately one-half that of tris(1,3-dichloro-2-propyl) phosphate, a traditional high-production chloroalkyl OPTE. The detection frequencies of DEGBBCEP and EBBCEP were 96% and 98%, respectively, but at relatively low median concentrations of 10.6 ng/g (from not detected to 152 ng/g) and 3.79 ng/g (from not detected to 130 ng/g), respectively. In standard house dust SRM2585, DEGBBCEP and EBBCEP were detected at 160 ± 15.7 and 1897 ± 38.8 ng/g, respectively, but DEGBBCPP was not detected. Future studies should evaluate the potential adverse health effects of these emerging flame retardants.

Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard_suppl

Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard_suppl

Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard.

Generalized Read-Across (GenRA) is a data driven approach which makes read-across predictions on the basis of a similarity weighted activity of source analogues (nearest neighbors). GenRA has been described in more detail in the literature (Shah et al., 2016; Helman et al., 2018). Here we present its implementation within the EPA's CompTox Chemicals Dashboard to provide public access to a GenRA module structured as a read-across workflow. GenRA assists researchers in identifying source analogues, evaluating their validity and making predictions of in vivo toxicity effects for a target substance. Predictions are presented as binary outcomes reflecting presence or absence of toxicity together with quantitative measures of uncertainty. The approach allows users to identify analogues in different ways, quickly assess the availability of relevant in vivo data for those analogues and visualize these in a data matrix to evaluate the consistency and concordance of the available experimental data for those analogues before making a GenRA prediction. Predictions can be exported into a tab-separated value (TSV) or Excel file for additional review and analysis (e.g., doses of analogues associated with production of toxic effects). GenRA offers a new capability of making reproducible read-across predictions in an easy-to use-interface.