Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity

Per- and polyfluoroalkyl substances (PFAS) are a diverse set of commercial chemicals widely detected in humans and the environment. However, only a limited number of PFAS are associated with epidemiological or experimental data for hazard identification. To provide developmental neurotoxicity (DNT) hazard information, the work herein employed DNT new approach methods (NAMs) to generate in vitro screening data for a set of 160 PFAS. The DNT NAMs battery was comprised of the microelectrode array neuronal network formation assay (NFA) and high-content imaging (HCI) assays to evaluate proliferation, apoptosis, and neurite outgrowth. The majority of PFAS (118/160) were inactive or equivocal in the DNT NAMs, leaving 42 active PFAS that decreased measures of neural network connectivity and neurite length. Analytical quality control indicated 43/118 inactive PFAS samples and 10/42 active PFAS samples were degraded; as such, careful interpretation is required as some negatives may have been due to loss of the parent PFAS, and some actives may have resulted from a mixture of parent and/or degradants of PFAS. PFAS containing a perfluorinated carbon (C) chain length ≥8, a high C:fluorine ratio, or a carboxylic acid moiety were more likely to be bioactive in the DNT NAMs. Of the PFAS positives in DNT NAMs, 85% were also active in other EPA ToxCast assays, whereas 79% of PFAS inactives in the DNT NAMs were active in other assays. These data demonstrate that a subset of PFAS perturb neurodevelopmental processes in vitro and suggest focusing future studies of DNT on PFAS with certain structural feature descriptors.

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.

Development of a CSRML version of the Analog Identification Methodology (AIM) fragments and their evaluation within the Generalised Read-Across (GenRA) approach

The Analog Identification Methodology (AIM) was developed over 20 years ago to identify analogues to support read-across at the US Environmental Protection Agency. However, the current public version of the standalone tool, released in 2012, is no longer usable on Windows operating systems supported by Microsoft. Additionally, the structural logic for analogue selection is based on older, customised Simplified molecular-input-line-entry system (SMILES)-type features that are incompatible with modern cheminformatics tools. Given these limitations, a case study was undertaken to explore a more transparent, extensible method of implementing the AIM fragments using Chemical Subgraphs and Reactions Mark-up Language (CSRML). A CSRML file was developed to codify the original AIM fragments, and the extent to which AIM fragments were faithfully replicated was assessed using the AIM Database. The overall mean performance of the CSRML-AIM across all fragments in terms of sensitivity, specificity, and Jaccard similarity was 89.5%, 99.9%, and 82.2%, respectively. Comparing the AIM fragments with public ToxPrints using a large set of ~25,000 substances of regulatory interest to EPA found them to be dissimilar, with an average maximum Jaccard score of 0.24 for AIM and 0.29 for ToxPrint fingerprints. Both fragment sets were then used as inputs in the automated read-across approach, Generalised Read-Across (GenRA), to evaluate the quality of fit in predicting rat acute oral toxicity LD50 values with the coefficient of determination (R2) and root mean squared error (RMSE). The performance of AIM fragments was R2=0.434 and RMSE=0.663 whereas that of ToxPrints was R2=0.477 and RMSE=0.638. A bootstrap resampling using 100 iterations found the mean and the 95th confidence interval of R2 to be 0.349 [0.319, 0.379] for AIM fragments and 0.377 [0.338, 0.412] for ToxPrints. Although AIM and ToxPrints performed similarly in predicting LD50, they differed in their performance at a local level, revealing that their features can offer complementary insights.

Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing

Per- and Polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterised for their hazard profiles, the vast majority of PFAS have not been studied. The US Environmental Protection Agency (EPA) undertook a research project to screen ~150 PFAS through an array of different in vitro high throughput toxicity and toxicokinetic tests in order to inform chemical category and read-across approaches. A previous publication described the rationale behind the selection of an initial set of 75 PFAS, whereas herein, we describe how various category approaches were applied and extended to inform the selection of a second set of 75 PFAS from our library of approximately 430 commercially procured PFAS. In particular, we focus on the challenges in grouping PFAS for prospective analysis and how we have sought to develop and apply objective structure-based categories to profile the testing library and other PFAS inventories. We additionally illustrate how these categories can be enriched with other information to facilitate read-across inferences once experimental data become available. The availability of flexible, objective, reproducible and chemically intuitive categories to explore PFAS constitutes an important step forward in prioritising PFAS for further testing and assessment.

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.

Spatial-temporal lipidomics profile of acute myocardial injury

Background

Lipidome disturbance has long been recognized to occur after myocardial infarction (MI). Accumulation of excessive fatty acids induces production of reactive oxygen species and consequently deteriorates cardiac injury in MI. However, the spatial and temporal lipid profile in the heart following ischemic injury remains unknown.

Purpose

We aim to uncover the temporal-spatial lipidome profile of the heart following ischemia reperfusion (I/R) injury and identify circulating lipids released from injured myocardium that are potentially useful for diagnosis of ischemic heart disease.

Methods

C57/BL6 mice were subjected to 30 min myocardial ischemia followed by removal of the ligature to establish reperfusion injury. Porcine I/R injury was induced by 105 min myocardial ischemia followed by reperfusion. Human plasma was obtained from 143 post-MI patients. Myocardial lipid profiles were generated by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MALDI-MSI) in different regions (infarct, remote and peri-infarct) at different time points. Moreover, the lipids in the heart and plasma were analysed by LC-MS/MS.

Results

We observed a drastic alteration in the lipidome with distinct spatial-temporal features in the injured heart by both MALDI-MSI and LC-MS/MS. In the infarct heart tissue, as revealed by LC-MS/MS, we observed an elevation of glycerolipids that peaked at 3 hours after I/R, and a sustained elevation of phospholipids and sphingolipids up to 3 days. Similar alternations in lipid profile was observed but much weaker in the remote and peri-infarct heart tissue compared to the infarct tissue. Among those lipids, PC 32:0 detected by MALDI-MSI highly overlapped CD68 staining at a single-cell level, showing a strong correlation of PC 32:0 with macrophage infiltration in mouse hearts (R2=0.93, p<0.0001). A similar increase of PC 32:0 in the infarct area was also observed in porcine hearts following I/R injury. Surprisingly, plasma levels of PC 32:0 in the mice decreased after I/R injury. In humans, plasma levels of PC 32:0 in post-MI patients were lower than that in healthy individuals (p=0.03). Further analysis demonstrated that plasma levels of PC 32:0 determined within 72 hours after percutaneous coronary intervention were negatively correlated with the 6-month post-MI cardiac ejection fraction in patients (R2=0.08, p<0.001).

Conclusions

A temporal-spatial lipidome profile was established in heart injury by synergizing LC-MS/MS and mass spectrometry imaging. PC 32:0 levels are positively correlated with myocardial macrophage infiltration but negatively correlated with cardiac function in cardiac I/R injury. Our findings indicate that PC 32:0 is a potential biomarker for cardiac injury and the inflammatory status in the injured heart.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Singapore Ministry of Health's National Medical Research Council

Assembly and Curation of Lists of Per- and Polyfluoroalkyl Substances (PFAS) to Support Environmental Science Research

Per- and polyfluoroalkyl substances (PFAS) are a class of man-made chemicals of global concern for many health and regulatory agencies due to their widespread use and persistence in the environment (in soil, air, and water), bioaccumulation, and toxicity. This concern has catalyzed a need to aggregate data to support research efforts that can, in turn, inform regulatory and statutory actions. An ongoing challenge regarding PFAS has been the shifting definition of what qualifies a substance to be a member of the PFAS class. There is no single definition for a PFAS, but various attempts have been made to utilize substructural definitions that either encompass broad working scopes or satisfy narrower regulatory guidelines. Depending on the size and specificity of PFAS substructural filters applied to the U.S. Environmental Protection Agency (EPA) DSSTox database, currently exceeding 900,000 unique substances, PFAS substructure-defined space can span hundreds to tens of thousands of compounds. This manuscript reports on the curation of PFAS chemicals and assembly of lists that have been made publicly available to the community via the EPA's CompTox Chemicals Dashboard. Creation of these PFAS lists required the harvesting of data from EPA and online databases, peer-reviewed publications, and regulatory documents. These data have been extracted and manually curated, annotated with structures, and made available to the community in the form of lists defined by structure filters, as well as lists comprising non-structurable PFAS, such as polymers and complex mixtures. These lists, along with their associated linkages to predicted and measured data, are fueling PFAS research efforts within the EPA and are serving as a valuable resource to the international scientific community.

Identification of Branched and Linear Forms of PFOA and Potential Precursors: A User-Friendly SMILES Structure-based Approach

Perfluorooctanoic acid (PFOA) and related compounds are per- and polyfluorinated alkyl substances (PFASs) of concern from toxicological, environmental, and regulatory perspectives. In 2019, the Conference of the Parties to the Stockholm Convention on Persistent Organic Pollutants listed PFOA, its salts, and PFOA-related compounds in Annex A to the Convention. Additionally, the listing specifically included PFOA branched isomers and compounds containing a perfluoroheptyl (C7F15)C moiety, with some noted exclusions. A draft updated "Indicative List" of 393 PFASs (335 with defined structures), each specified as falling within or outside the listing, was released for comment in 2021. The U.S. Environmental Protection Agency's CompTox Chemicals Dashboard has published a curated PFAS list containing more than 10,700 structures. Applying the PFOA and related compounds listing definition to screen this list required a structure-based approach capable of discerning salts and branched or linear forms of the (C7F15)C moiety. A PFOA SMILES workflow and associated Excel macro file, developed to address this need, applies a series of text substitution rules to a set of canonicalized SMILES structure representations to convert branched forms of the (C7F15)C moiety to linear forms to aid their detection. The approach correctly classified each Stockholm Convention draft Indicative List structure relative to the PFOA and related compounds definition, and accurately discerned branched and linear forms of the (C7F15)C moiety in over 10,700 PFAS structures with 100% sensitivity (no false negatives) and 99.7% accuracy (35 false positives). Approximately 20% of structures in the large PFAS list fell within the PFOA and related compounds definition, and 10% of those were branched. The present work highlights the need to computationally detect branched forms of PFASs and promotes the use of unambiguous, structure-based definitions, along with tools that are publicly available and easy to use, to support clear communication and regulatory action within the PFAS community.

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization

Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.

The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology

Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology

Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

Concentration-response evaluation of ToxCast compounds for multivariate activity patterns of neural network function

The US Environmental Protection Agency's ToxCast program has generated toxicity data for thousands of chemicals but does not adequately assess potential neurotoxicity. Networks of neurons grown on microelectrode arrays (MEAs) offer an efficient approach to screen compounds for neuroactivity and distinguish between compound effects on firing, bursting, and connectivity patterns. Previously, single concentrations of the ToxCast Phase II library were screened for effects on mean firing rate (MFR) in rat primary cortical networks. Here, we expand this approach by retesting 384 of those compounds (including 222 active in the previous screen) in concentration-response across 43 network activity parameters to evaluate neural network function. Using hierarchical clustering and machine learning methods on the full suite of chemical-parameter response data, we identified 15 network activity parameters crucial in characterizing activity of 237 compounds that were response actives ("hits"). Recognized neurotoxic compounds in this network function assay were often more potent compared to other ToxCast assays. Of these chemical-parameter responses, we identified three k-means clusters of chemical-parameter activity (i.e., multivariate MEA response patterns). Next, we evaluated the MEA clusters for enrichment of chemical features using a subset of ToxPrint chemotypes, revealing chemical structural features that distinguished the MEA clusters. Finally, we assessed distribution of neurotoxicants with known pharmacology within the clusters and found that compounds segregated differentially. Collectively, these results demonstrate that multivariate MEA activity patterns can efficiently screen for diverse chemical activities relevant to neurotoxicity, and that response patterns may have predictive value related to chemical structural features.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

EPA's DSSTox database: History of development of a curated chemistry resource supporting computational toxicology research

The US Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database, launched publicly in 2004, currently exceeds 875 K substances spanning hundreds of lists of interest to EPA and environmental researchers. From its inception, DSSTox has focused curation efforts on resolving chemical identifier errors and conflicts in the public domain towards the goal of assigning accurate chemical structures to data and lists of importance to the environmental research and regulatory community. Accurate structure-data associations, in turn, are necessary inputs to structure-based predictive models supporting hazard and risk assessments. In 2014, the legacy, manually curated DSSTox_V1 content was migrated to a MySQL data model, with modern cheminformatics tools supporting both manual and automated curation processes to increase efficiencies. This was followed by sequential auto-loads of filtered portions of three public datasets: EPA's Substance Registry Services (SRS), the National Library of Medicine's ChemID, and PubChem. This process was constrained by a key requirement of uniquely mapped identifiers (i.e., CAS RN, name and structure) for each substance, rejecting content where any two identifiers were conflicted either within or across datasets. This rejected content highlighted the degree of conflicting, inaccurate substance-structure ID mappings in the public domain, ranging from 12% (within EPA SRS) to 49% (across ChemID and PubChem). Substances successfully added to DSSTox from each auto-load were assigned to one of five qc_levels, conveying curator confidence in each dataset. This process enabled a significant expansion of DSSTox content to provide better coverage of the chemical landscape of interest to environmental scientists, while retaining focus on the accuracy of substance-structure-data associations. Currently, DSSTox serves as the core foundation of EPA's CompTox Chemicals Dashboard [https://comptox.epa.gov/dashboard], which provides public access to DSSTox content in support of a broad range of modeling and research activities within EPA and, increasingly, across the field of computational toxicology.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

High-throughput screening and chemotype-enrichment analysis of ToxCast phase II chemicals evaluated for human sodium-iodide symporter (NIS) inhibition

In support of the Endocrine Disruptor Screening Program (EDSP), the U.S.EPA's Office of Research and Development (ORD) is developing high-throughput screening (HTS) approaches to identify chemicals that alter target sites in the thyroid hormone (TH) pathway. The sodium iodide symporter (NIS) is a transmembrane glycoprotein that mediates iodide uptake into the thyroid as the initial step of TH biosynthesis. Previously, we screened 293 ToxCast chemicals (ph1v2) using a HEK293T cell line expressing human NIS in parallel radioactive iodide uptake (RAIU) and cell viability assays to identify potential environmental NIS inhibitors. Here, we expanded NIS inhibitor screening for a set of 768 ToxCast Phase II (ph2) chemicals, and applied a novel computational toxicology approach based on the ToxPrint chemotype to identify chemical substructures associated with NIS inhibition. Following single-concentration screening (at 1 × 10-4 M with a 20% inhibition cutoff), 235 samples (228 chemicals) were further tested in multiple-concentration (1 × 10-9 - 1 × 10-4 M) format in both RAIU and cell viability assays. The 167 chemicals that exhibited significant RAIU inhibition were then prioritized using combined RAIU and cell viability responses that were normalized relative to the known NIS inhibitor sodium perchlorate. Some of the highest ranked chemicals, such as PFOS, tributyltin chloride, and triclocarban, have been previously reported to be thyroid disruptors. In addition, several novel chemicals were identified as potent NIS inhibitors. The present results were combined with the previous ph1v2 screening results to produce two sets of binary hit-calls for 1028 unique chemicals, consisting of 273 positives exhibiting significant RAIU inhibition, and 63 positives following application of a cell viability filter. A ToxPrint chemotype-enrichment analysis identified >20 distinct chemical substructural features, represented in >60% of the active chemicals, as significantly enriched in each NIS inhibition hit-call space. A shared set of 9 chemotypes enriched in both hit-call sets indicates stable chemotype signals (insensitive to cytotoxicity filters) that can help guide structure-activity relationship (SAR) investigations and inform future research.

Using prepared mixtures of ToxCast chemicals to evaluate non-targeted analysis (NTA) method performance

Non-targeted analysis (NTA) methods are increasingly used to discover contaminants of emerging concern (CECs), but the extent to which these methods can support exposure and health studies remains to be determined. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) was launched in 2016 to address this need. As part of ENTACT, 1269 unique substances from EPA's ToxCast library were combined to make ten synthetic mixtures, with each mixture containing between 95 and 365 substances. As a participant in the trial, we first performed blinded NTA on each mixture using liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS). We then performed an unblinded evaluation to identify limitations of our NTA method. Overall, at least 60% of spiked substances could be observed using selected methods. Discounting spiked isomers, true positive rates from the blinded and unblinded analyses reached a maximum of 46% and 65%, respectively. An overall reproducibility rate of 75% was observed for substances spiked into more than one mixture and observed at least once. Considerable discordance in substance identification was observed when comparing a subset of our results derived from two separate reversed-phase chromatography methods. We conclude that a single NTA method, even when optimized, can likely characterize only a subset of ToxCast substances (and, by extension, other CECs). Rigorous quality control and self-evaluation practices should be required of labs generating NTA data to support exposure and health studies. Accurate and transparent communication of performance results will best enable meaningful interpretations and defensible use of NTA data. Graphical abstract ᅟ.

A Chemical Category-Based Prioritization Approach for Selecting 75 Per- and Polyfluoroalkyl Substances (PFAS) for Tiered Toxicity and Toxicokinetic Testing

Per- and polyfluoroalkyl substances (PFASs) are a group of fluorinated substances of interest to researchers, regulators, and the public due to their widespread presence in the environment. A few PFASs have comparatively extensive amounts of human epidemiological, exposure, and experimental animal toxicity data (e.g., perfluorooctanoic acid), whereas little toxicity and exposure information exists for much of the broader set of PFASs. Given that traditional approaches to generate toxicity information are resource intensive, new approach methods, including in vitro high-throughput toxicity (HTT) testing, are being employed to inform PFAS hazard characterization and further (in vivo) testing. The U.S. Environmental Protection Agency (EPA) and the National Toxicology Program (NTP) are collaborating to develop a risk-based approach for conducting PFAS toxicity testing to facilitate PFAS human health assessments. This article describes the construction of a PFAS screening library and the process by which a targeted subset of 75 PFASs were selected. Multiple factors were considered, including interest to the U.S. EPA, compounds within targeted categories, structural diversity, exposure considerations, procurability and testability, and availability of existing toxicity data. Generating targeted HTT data for PFASs represents a new frontier for informing priority setting. https://doi.org/10.1289/EHP4555.

A Mechanistic Framework for Integrating Chemical Structure and High-Throughput Screening Results to Improve Toxicity Predictions

Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. Detailed understanding of the MIE provides the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program, coupled with chemical structural information, to generate chemical clusters using three similarity methods pertaining to nine MIEs within an AOP network for hepatic steatosis. Three case studies demonstrate the utility of the mechanistic information held by the MIE for integrating biological and chemical data. Evaluation of the chemical clusters activating the glucocorticoid receptor identified activity differences in chemicals within a cluster. Comparison of the estrogen receptor results with previous work showed that bioactivity data and structural alerts can be combined to improve predictions in a customizable way where bioactivity data are limited. The aryl hydrocarbon receptor (AHR) highlighted that while structural data can be used to offset limited data for new screening efforts, not all ToxCast targets have sufficient data to define robust chemical clusters. In this context, an alternative to additional receptor assays is proposed where assays for proximal key events downstream of AHR activation could be used to enhance confidence in active calls. These case studies illustrate how the AOP framework can support an iterative process whereby in vitro toxicity testing and chemical structure can be combined to improve toxicity predictions. In vitro assays can inform the development of structural alerts linking chemical structure to toxicity. Consequently, structurally related chemical groups can facilitate identification of assays that would be informative for a specific MIE. Together, these activities form a virtuous cycle where the mechanistic basis for the in vitro results and the breadth of the structural alerts continually improve over time to better predict activity of chemicals for which limited toxicity data exist.