Enabling High-Throughput Searches for Multiple Chemical Data Using the U.S.-EPA CompTox Chemicals Dashboard

An automated and high-throughput data processing workflow for PFAS identification in biota by direct infusion ultra-high resolution mass spectrometry

The increasing recognition of the health impacts from human exposure to per- and polyfluorinated alkyl substances (PFAS) has surged the need for sophisticated analytical techniques and advanced data analyses, especially for assessing exposure by food of animal origin. Despite the existence of nearly 15,000 PFAS listed in the CompTox chemicals dashboard by the US Environmental Protection Agency, conventional monitoring and suspect screening methods often fall short, covering only a fraction of these substances. This study introduces an innovative automated data processing workflow, named PFlow, for identifying PFAS in environmental samples using direct infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FT-ICR MS). PFlow's validation on a bream liver sample, representative of low-concentration biota, involves data pre-processing, annotation of PFAS based on their precursor masses, and verification through isotopologues. Notably, PFlow annotated 17 PFAS absent in the comprehensive targeted approach and tentatively identified an additional 53 compounds, thereby demonstrating its efficiency in enhancing PFAS detection coverage. From an initial dataset of 30,332 distinct m/z values, PFlow thoroughly narrowed down the candidates to 84 potential PFAS compounds, utilizing precise mass measurements and chemical logic criteria, underscoring its potential in advancing our understanding of PFAS prevalence and of human exposure.

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Where the rubber meets the road: Emerging environmental impacts of tire wear particles and their chemical cocktails

About 3 billion new tires are produced each year and about 800 million tires become waste annually. Global dependence upon tires produced from natural rubber and petroleum-based compounds represents a persistent and complex environmental problem with only partial and often-times, ineffective solutions. Tire emissions may be in the form of whole tires, tire particles, and chemical compounds, each of which is transported through various atmospheric, terrestrial, and aquatic routes in the natural and built environments. Production and use of tires generates multiple heavy metals, plastics, PAH's, and other compounds that can be toxic alone or as chemical cocktails. Used tires require storage space, are energy intensive to recycle, and generally have few post-wear uses that are not also potential sources of pollutants (e.g., crumb rubber, pavements, burning). Tire particles emitted during use are a major component of microplastics in urban runoff and a source of unique and highly potent toxic substances. Thus, tires represent a ubiquitous and complex pollutant that requires a comprehensive examination to develop effective management and remediation. We approach the issue of tire pollution holistically by examining the life cycle of tires across production, emissions, recycling, and disposal. In this paper, we synthesize recent research and data about the environmental and human health risks associated with the production, use, and disposal of tires and discuss gaps in our knowledge about fate and transport, as well as the toxicology of tire particles and chemical leachates. We examine potential management and remediation approaches for addressing exposure risks across the life cycle of tires. We consider tires as pollutants across three levels: tires in their whole state, as particulates, and as a mixture of chemical cocktails. Finally, we discuss information gaps in our understanding of tires as a pollutant and outline key questions to improve our knowledge and ability to manage and remediate tire pollution.

MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators

Xenobiotic metabolism is a key consideration in evaluating the hazards and risks posed by environmental chemicals. A number of software tools exist that are capable of simulating metabolites, but each reports its predictions in a different format and with varying levels of detail. This makes comparing the performance and coverage of the tools a practical challenge. To address this shortcoming, we developed a metabolic simulation framework called MetSim, which comprises three main components. A graph-based schema was developed to allow metabolism information to be harmonized. The schema was implemented in MongoDB to store and retrieve metabolic graphs for subsequent analysis. MetSim currently includes an application programming interface for four metabolic simulators: BioTransformer, the OECD Toolbox, EPA's chemical transformation simulator (CTS), and tissue metabolism simulator (TIMES). Lastly, MetSim provides functions to help evaluate simulator performance for specific data sets. In this study, a set of 112 drugs with 432 reported metabolites were compiled, and predictions were made using the 4 simulators. Fifty-nine of the 112 drugs were taken from the Small Molecule Pathway Database, with the remainder sourced from the literature. The human models within BioTransformer and CTS (Phase I only) and the rat models within TIMES and the OECD Toolbox (Phase I only) were used to make predictions for the chemicals in the data set. The recall and precision (recall, precision) ranked in order of highest recall for each individual tool were CTS (0.54, 0.017), BioTransformer (0.50, 0.008), Toolbox in vitro (0.40, 0.144), TIMES in vivo (0.40, 0.133), Toolbox in vivo (0.40, 0.118), and TIMES in vitro (0.39, 0.128). Combining all of the model predictions together increased the overall recall (0.73, 0.008). MetSim enabled insights into the performance and coverage of in silico metabolic simulators to be more efficiently derived, which in turn should aid future efforts to evaluate other data sets.

Translating nanoEHS data using EPA NaKnowBase and the resource description framework

Background

The U.S. Federal Government has supported the generation of extensive amounts of nanomaterials and related nano Environmental Health and Safety (nanoEHS) data, there is a need to make these data available to stakeholders. With recent efforts, a need for improved interoperability, translation, and sustainability of Federal nanoEHS data in the United States has been realized. The NaKnowBase (NKB) is a relational database containing experimental results generated by the EPA Office of Research and Development (ORD) regarding the actions of engineered nanomaterials on environmental and biological systems. Through the interaction of the National Nanotechnology Initiative's Nanotechnology Environmental Health Implications (NEHI) Working Group, and the Database and Informatics Interest Group (DIIG), a U.S. Federal nanoEHS Consortium has been formed.

Methods

The primary goal of this consortium is to establish a "common language" for nanoEHS data that aligns with FAIR data standards. A second goal is to overcome nomenclature issues inherent to nanomaterials data, ultimately allowing data sharing and interoperability across the diverse U.S. Federal nanoEHS data compendium, but also in keeping a level of consistency that will allow interoperability with U.S. and European partners. The most recent version of the EPA NaKnowBase (NKB) has been implemented for semantic integration. Computational code has been developed to use each NKB record as input, modify and filter table data, and subsequently output each modified record to a Research Description Framework (RDF). To improve the accuracy and efficiency of this process the EPA has created the OntoSearcher tool. This tool partially automates the ontology mapping process, thereby reducing onerous manual curation.

Conclusions

Here we describe the efforts of the US EPA in promoting FAIR data standards for Federal nanoEHS data through semantic integration, as well as in the development of NAMs (computational tools) to facilitate these improvements for nanoEHS data at the Federal partner level.

Evaluating scientific confidence in the concordance of in vitro and in vivo protective points of departure

To fulfil the promise of reducing reliance on mammalian in vivo laboratory animal studies, new approach methods (NAMs) need to provide a confident basis for regulatory decision-making. However, previous attempts to develop in vitro NAMs-based points of departure (PODs) have yielded mixed results, with PODs from U.S. EPA's ToxCast, for instance, appearing more conservative (protective) but poorly correlated with traditional in vivo studies. Here, we aimed to address this discordance by reducing the heterogeneity of in vivo PODs, accounting for species differences, and enhancing the biological relevance of in vitro PODs. However, we only found improved in vitro-to-in vivo concordance when combining the use of Bayesian model averaging-based benchmark dose modeling for in vivo PODs, allometric scaling for interspecies adjustments, and human-relevant in vitro assays with multiple induced pluripotent stem cell-derived models. Moreover, the available sample size was only 15 chemicals, and the resulting level of concordance was only fair, with correlation coefficients <0.5 and prediction intervals spanning several orders of magnitude. Overall, while this study suggests several ways to enhance concordance and thereby increase scientific confidence in vitro NAMs-based PODs, it also highlights challenges in their predictive accuracy and precision for use in regulatory decision making.

Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional "QSAR-ready" forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the "QSAR-ready" workflow to generate "MS-ready structures" to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.

Perfluorooctanoic acid induces transcriptomic alterations in second trimester human cytotrophoblasts

Poly- and perfluroroalkylated substances (PFAS) are a major class of surfactants used in industry applications and consumer products. Despite efforts to reduce the usage of PFAS due to their environmental persistence, compounds such as perfluorooctanoic acid (PFOA) are widely detected in human blood and tissue. Although growing evidence supports that prenatal exposures to PFOA and other PFAS are linked to adverse pregnancy outcomes, the target organs and pathways remain unclear. Recent investigations in mouse and human cell lines suggest that PFAS may impact the placenta and impair trophoblast function. In this study, we investigated the effects of PFOA on cytotoxicity and the transcriptome in cultured second trimester human cytotrophoblasts (CTBs). We show that PFOA significantly reduces viability and induces cell death at 24 h, in a concentration-dependent manner. At subcytotoxic concentrations, PFOA impacted expression of hundreds of genes, including several molecules (CRH, IFIT1, and TNFSF10) linked with lipid metabolism and innate immune response pathways. Furthermore, in silico analyses suggested that regulatory factors such as peroxisome proliferator-activated receptor-mediated pathways may be especially important in response to PFOA. In summary, this study provides evidence that PFOA alters primary human CTB viability and gene pathways that could contribute to placental dysfunction and disease.

Seeing Beyond the Smoke: Selecting Waterpipe Wastewater Chemicals for Risk Assessments

Background

Increasing use prevalence of waterpipe tobacco smoking raises concerns about environmental impacts from waterpipe waste disposal. The U.S. Food and Drug Administration (FDA) is required to assess the environmental impact of its tobacco regulatory actions per the National Environmental Policy Act. This study builds on FDA's efforts characterizing the aquatic toxicity of waterpipe wastewater chemicals.

Methods

We compiled a comprehensive list of waterpipe wastewater chemical concentrations from literature. We then selected chemicals for risk assessment by estimating persistence, bioaccumulation, and aquatic toxicity characteristics (PBT; U.S. Environmental Protection Agency), and hazardous concentration values (concentration affecting specific proportion of species).

Results

Of 38 chemicals in waterpipe wastewater with concentration data, 20 are listed as harmful or potentially harmful constituents (HPHCs) in tobacco smoke and tobacco products by FDA, and 15 are hazardous waste per U. S. Environmental Protection Agency. Among metals, six (cadmium, chromium, lead, mercury, nickel and selenium) are included in both HPHC and hazardous waste lists and were selected for future risk assessments. Among non-metals, nicotine, and 4-methylnitrosamino-1-(3-pyridyl)-1-butanone (NNK) were shortlisted, as they are classified as persistent and toxic. Further, N-nitrosonornicotine (NNN), with a low HC50 value for chronic aquatic toxicity, had high aquatic toxicity concern and is selected.

Conclusions

The presence of multiple hazardous compounds in waterpipe wastewater highlights the importance of awareness on the proper disposal of waterpipe wastewater in residential and retail settings. Future studies can build on the hazard characterization provided in this study through fate and transport modeling, exposure characterization and risk assessments of waterpipe wastewater chemicals.

Estimation of the Half-Lives of Recently Detected Per- and Polyfluorinated Alkyl Ethers in an Exposed Community

To estimate half-lives for novel fluoroethers, the GenX Exposure Study obtained two serum measurements for per- and polyfluoroalkyl substances (PFAS) for 44 participants of age 12-86 years from North Carolina, collected 5 and 11 months after fluoroether discharges into the drinking water source were controlled. The estimated half-lives for these compounds were 127 days (95% confidence interval (95% CI) = 86, 243 days) for perfluorotetraoxadecanoic acid (PFO4DA), 296 days for Nafion byproduct 2 (95% CI = 176, 924 days), and 379 days (95% CI = 199, 3870 days) for perfluoro-3,5,7,9,11-pentaoxadodecanoic acid (PFO5DoA). Using these estimates and the literature values, a model was built that predicted PFAS half-lives using structural properties. Three chemical properties predicted 55% of the variance of PFAS half-lives based on 15 PFAS. A model with only molecular weight predicted 69% of the variance. Some properties can predict the half-lives of PFAS, but a deeper understanding is needed. These fluoroethers had biological half-lives longer than published half-lives for PFHxA and PFHpA (30-60 days) but shorter than those for PFOA and PFOS (800-1200 days). These are the first and possibly only estimates of human elimination half-lives of these fluoroethers.

Environmental persistence, bioaccumulation, and hazards of chemicals in e-cigarette e-liquids: short-listing chemicals for risk assessments

BACKGROUND/METHODS

Increased use and sales of e-cigarettes raises concerns about the potential environmental impacts throughout their life-cycle. However, few available research studies focus on the environmental impacts and ecotoxicity of e-cigarettes. In this study, we short-list e-liquid chemicals from published literature that should be considered in future environmental impact and risk assessments. We used a combination of available laboratory bioassays-based data and predictive methods (eg, Structure-Activity Relationships) to characterise the hazards of the e-liquid chemicals (environmental persistence, bioaccumulation, and aquatic toxicity including hazardous concentration values (concentration affecting specific proportion of species)) for short-listing.

RESULTS

Of the 421 unique e-liquid chemicals compiled from literature, 35 are US Environmental Protection Agency's hazardous constituents, 42 are US Food and Drug Administration's harmful or potentially harmful constituents in tobacco products and smoke, and 20 are listed as both. Per hazard characteristics, we short-listed 81 chemicals that should be considered for future environmental impact and risk assessments, including tobacco-specific compounds (eg, nicotine, N'-nitrosonornicotine), polycyclic aromatic hydrocarbons (eg, chrysene), flavours (eg, (-)caryophyllene oxide), metals (eg, lead), phthalates (eg, di(2-ethylhexyl)phthalate) and flame retardants (eg, tris(4-methylphenyl)phosphate).

IMPLICATIONS

Our findings documenting various hazardous chemicals in the e-liquids underscore the importance of awareness and education when handling or disposing of e-liquids/e-cigarettes and aim to inform strategies to prevent and reduce hazards from e-cigarettes. This includes any scenario where e-liquids can come into contact with people or the environment during e-liquid storage, manufacturing, use, and disposal practices. Overall, our study characterises the environmental hazards of e-liquid chemicals and provides regulators and researchers a readily available list for future ecological and health risk assessments.

Computational prediction of the metabolites of agrochemicals formed in rats

Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the availability of measured data, these in silico tools are trained and validated primarily on drug metabolism data. In this work, we assessed the capacity of five leading metabolite structure predictors to represent the metabolism of agrochemicals observed in rats. More specifically, we tested the ability of SyGMa, GLORY, GLORYx, BioTransformer 3.0, and MetaTrans to correctly predict and rank the experimentally observed metabolites of a set of 85 parent compounds. We found that the models were able to recover about one to two-thirds of the experimentally observed first-generation, second-generation and third-generation metabolites, confirming their value in applications such as metabolite identification. However, precision was low for all investigated tools and did not exceed approximately 18 % for the pool of first-generation metabolites and 2 % for the pool of compounds representing the first three generations of metabolites. The variance in prediction success rates was high across the individual metabolic maps, meaning that outcomes depend strongly on the specific compound under investigation. We also found that the predictions for individual parent compounds differed strongly between the tools, particularly between those built on orthogonal technologies (e.g., rule-based and end-to-end machine learning approaches). This renders ensemble model strategies promising for improving success rates. Overall, the results of this benchmark study show that there is still considerable room for the improvement of metabolite structure predictors left. Our discussion points out several avenues to progress. The bottleneck in method development certainly has been, and will remain, for the foreseeable future, the limited quantity and quality of available measured data on small-molecule metabolism.

A proposed approach to defining per- and polyfluoroalkyl substances (PFAS) based on molecular structure and formula

Various groups and researchers, including the authors of this work, have proposed different definitions of what constitutes per- and polyfluoroalkyl substances (PFAS). The different definitions are all based on a structural definition. Although a structural definition is reasonable, such an approach is difficult to execute if the intent is to narrow or refine the definition. This approach can also lead to inexplicable demarcations of what are and what are not PFAS. Our objective was to create a narrow, simple PFAS definition that allows interested groups to communicate with a common understanding and will also serve as a starting point to focus on substances that are of real environmental concern. Our studies have demonstrated that numerous highly fluorinated complex structures exist that make a structure-based definition difficult. We suggest that a definition based on fractional fluorination expressed as the percentage of fluorine of a molecular formula using atom counting offers advantages. Using a combination of a structure-based definition and a definition based on the fractional percentage of the molecular formula that is fluorine can provide a more inclusive and succinct definition. Thus, we propose a new definition based on four substructures along with any structures where the molecular formula is 30% fluorine by atom count. Integr Environ Assess Manag 2023;19:1333-1347. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A weight of evidence approach for bioaccumulation assessment

Bioaccumulation assessments conducted by regulatory agencies worldwide use a variety of methods, types of data, metrics, and categorization criteria. Lines of evidence (LoE) for bioaccumulation assessment can include bioaccumulation metrics such as in vivo bioconcentration factor (BCF) and biomagnification factor (BMF) data measured from standardized laboratory experiments, and field (monitoring) data such as BMFs, bioaccumulation factors (BAFs), and trophic magnification factors (TMFs). In silico predictions from mass-balance models and quantitative structure-activity relationships (QSARs) and a combination of in vitro biotransformation rates and in vitro-in vivo extrapolation (IVIVE) models can also be used. The myriad bioaccumulation metrics and categorization criteria and underlying uncertainty in measured or modeled data can make decision-making challenging. A weight of evidence (WoE) approach is recommended to address uncertainty. The Bioaccumulation Assessment Tool (BAT) guides a user through the process of collecting and generating various LoE required for assessing the bioaccumulation of neutral and ionizable organic chemicals in aquatic (water-respiring) and air-breathing organisms. The BAT includes data evaluation templates (DETs) to critically evaluate the reliability of the LoE used in the assessment. The DETs were developed from standardized testing guidance. The approach used in the BAT is consistent with OECD and SETAC WoE principles and facilitates the implementation of chemical policy objectives in chemical assessment and management. The recommended methods are also iterative and tiered, providing pragmatic methods to reduce unnecessary animal testing. General concepts of the BAT are presented and case study applications of the tool for hexachlorobenzene (HCB) and β-hexachlorocyclohexane (β-HCH) are demonstrated. The BAT provides a consistent and transparent WoE framework to address uncertainty in bioaccumulation assessment and is envisaged to evolve with scientific and regulatory developments. Integr Environ Assess Manag 2023;19:1235-1253. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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.

Implementing a suspect screening method to assess occupational chemical exposures among US-based hairdressers serving an ethnically diverse clientele: a pilot study

BACKGROUND

There are over 700,000 hairdressers in the United States, and it is estimated that >90% are female and 31% are Black or Hispanic/Latina. Racial and ethnic minorities in this workforce may be exposed to a unique mixture of potentially hazardous chemicals from products used and services provided. However, previous biomonitoring studies of hairdressers target a narrow list of compounds and few studies have investigated exposures among minority hairdressers.

OBJECTIVE

To assess occupational chemical exposures in a sample of US-based Black and Latina hairdressers serving an ethnically diverse clientele by analyzing urine specimens with a suspect screening method.

METHODS

Post-shift urine samples were collected from a sample of US female hairdressers (n = 23) and office workers (n = 17) and analyzed via reverse-phase liquid chromatography coupled to high-resolution mass spectrometry. Detected compounds were filtered based on peak area differences between groups and matching with a suspect screening list. When possible, compound identities were confirmed with reference standards. Possible exposure sources were evaluated for detected compounds.

RESULTS

The developed workflow allowed for the detection of 24 compounds with median peak areas ≥2x greater among hairdressers compared to office workers. Product use categories (PUCs) and harmonized functional uses were searched for these compounds, including confirmed compounds methylparaben, ethylparaben, propylparaben, and 2-naphthol. Most product use categories were associated with "personal use" and included 11 different "hair styling and care" product types (e.g., hair conditioner, hair relaxer). Functional uses for compounds without associated PUCs included fragrance, hair and skin conditioning, hair dyeing, and UV stabilizer.

SIGNIFICANCE

Our suspect screening approach detected several compounds not previously reported in biomonitoring studies of hairdressers. These results will help guide future studies to improve characterization of occupational chemical exposures in this workforce and inform exposure and risk mitigation strategies to reduce potential associated work-related health disparities.

A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data

A growing list of chemicals are approved for production and use in the United States and elsewhere, and new approaches are needed to rapidly assess the potential exposure and health hazard posed by these substances. Here, we present a high-throughput, data-driven approach that will aid in estimating occupational exposure using a database of over 1.5 million observations of chemical concentrations in U.S. workplace air samples. We fit a Bayesian hierarchical model that uses industry type and the physicochemical properties of a substance to predict the distribution of workplace air concentrations. This model substantially outperforms a null model when predicting whether a substance will be detected in an air sample, and if so at what concentration, with 75.9% classification accuracy and a root-mean-square error (RMSE) of 1.00 log10 mg m-3 when applied to a held-out test set of substances. This modeling framework can be used to predict air concentration distributions for new substances, which we demonstrate by making predictions for 5587 new substance-by-workplace-type pairs reported in the US EPA's Toxic Substances Control Act (TSCA) Chemical Data Reporting (CDR) industrial use database. It also allows for improved consideration of occupational exposure within the context of high-throughput, risk-based chemical prioritization efforts.

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.

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.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.

Results

The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).

Conclusions

The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12302-022-00680-6.

Identifying the landscape of developmental toxicity new approach methodologies

BACKGROUND

The dynamics and complexities of in utero fetal development create significant challenges in transitioning from lab animal-centric developmental toxicity testing methods to assessment strategies based on new approach methodologies (NAMs). Nevertheless, considerable progress is being made, stimulated by increased research investments and scientific advances, such as induced pluripotent stem cell-derived models. To help identify developmental toxicity NAMs for toxicity screening and potential funding through the American Chemistry Council's Long-Range Research Initiative, a systematic literature review was conducted to better understand the current landscape of developmental toxicity NAMs.

METHODS

Scoping review tools were used to systematically survey the literature (2010-2021; ~18,000 references identified), results and metadata were then extracted, and a user-friendly interactive dashboard was created.

RESULTS

The data visualization dashboard, developed using Tableau® software, is provided as a free, open-access web tool. This dashboard enables straightforward interactive queries and visualizations to identify trends and to distinguish and understand areas or NAMs where research has been most, or least focused.

CONCLUSIONS

Herein, we describe the approach and methods used, summarize the benefits and challenges of applying the systematic-review techniques, and highlight the types of questions and answers for which the dashboard can be used to explore the many different facets of developmental toxicity NAMs.

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.

A Framework for Utilizing High-Resolution Mass Spectrometry and Nontargeted Analysis in Rapid Response and Emergency Situations

Unknown chemical releases constitute a large portion of the rapid response situations to which the US Environmental Protection Agency is called on to respond. Workflows used to address unknown chemical releases currently involve screening for a large array of known compounds using many different targeted methods. When matches are not found, expert analytical chemistry knowledge is used to propose possible candidates from the available data, which generally includes low-resolution mass spectra and situational clues such as the location of the release, nearby industrial operations, and other field-reported facts. The past decade has witnessed dramatic improvements in capabilities for identifying unknown compounds using high-resolution mass spectrometry (HRMS) and nontargeted analysis (NTA) approaches. Complementary developments in cheminformatics tools have further enabled an increase in NTA throughput and identification confidence. Together with the expanding availability of HRMS instrumentation in monitoring laboratories, these advancements make NTA highly relevant to rapid response scenarios. In this article, we introduce the concept of NTA as it relates to rapid response needs and describe how it can be applied to address unknown chemical releases. We advocate for the consideration of HRMS-based NTA approaches to support future rapid response scenarios. Environ Toxicol Chem 2022;41:1117-1130. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

Curation of a list of chemicals in biosolids from EPA National Sewage Sludge Surveys & Biennial Review Reports

Section 405(d) of the Clean Water Act requires the US Environmental Protection Agency to review sewage sludge regulations every two years to identify any additional pollutants that may occur in biosolids and to set regulations for pollutants identified in biosolids if sufficient scientific evidence shows they may harm human health or the environment. To date, EPA has conducted eight biennial reviews to identify chemical and microbial pollutants and three national sewage sludge surveys to identify pollutants and obtain concentration data for chemicals found in biosolids. Prior to 2021, there was inconsistent reporting of chemicals identified and EPA did not cumulatively track chemicals in biosolids. Through the efforts presented here, EPA produced a list of 726 chemicals and structure-based classes found in biosolids based on biennial reviews and national sewage sludge surveys. Summary statistics of concentration data are also reported for the 484 chemicals found in the three national sewage sludge surveys. The creation of the Biosolids List supports EPA in assessing the potential risk of chemical pollutants found in biosolids.

Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR

The pregnane X receptor (PXR) regulates the metabolism of many xenobiotic and endobiotic substances. In consequence, PXR decreases the efficacy of many small-molecule drugs and induces drug-drug interactions. The prediction of PXR activators with theoretical approaches such as machine learning (ML) proves challenging due to the ligand promiscuity of PXR, which is related to its large and flexible binding pocket. In this work we demonstrate, by the example of random forest models and support vector machines, that classifiers generated following classical training procedures often fail to predict PXR activity for compounds that are dissimilar from those in the training set. We present a novel regularization technique that penalizes the gap between a model’s training and validation performance. On a challenging test set, this technique led to improvements in Matthew correlation coefficients (MCCs) by up to 0.21. Using these regularized ML models, we selected 31 compounds that are structurally distinct from known PXR ligands for experimental validation. Twelve of them were confirmed as active in the cellular PXR ligand-binding domain assembly assay and more hits were identified during follow-up studies. Comprehensive analysis of key features of PXR biology conducted for three representative hits confirmed their ability to activate the PXR.

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.

Machine learning models on chemical inhibitors of mitochondrial electron transport chain

Chemicals can induce adverse effects in humans by inhibiting mitochondrial electron transport chain (ETC) such as disrupting mitochondrial membrane potential, enhancing oxidative stress and causing some diseases. Thus, identifying ETC inhibitors (ETCi) is important to chemical risk assessment and protecting the public health. However, it is not feasible to identify all ETCi with experimental methods. Quantitative structure-activity relationship (QSAR) modeling is a promising method to rapidly and effectively identify ETCi. In this study, QSAR models for predicting ETCi were developed using machine learning methods. A clustering-based under-sampling (CBUS) method was developed to handle the imbalance issue in training sets. Structure-activity landscapes were generated and analyzed for training sets generated by the CBUS method. The consensus QSAR models constructed with CBUS achieved satisfactory performances (balanced accuracy = 0.852) in 100 iterations of five-fold cross validations, indicating the models can effectively classify ETCi. The classification model was further employed to screen chemicals in the Inventory of Existing Chemical Substances of China and 13 chemicals were identified as ETCi. Fifteen structural alerts for ETCi were identified in this study. These results demonstrated that the model and structural alerts are useful to screen ETCi.

Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis

With the increasing availability of high-resolution mass spectrometers, suspect screening and non-targeted analysis are becoming popular compound identification tools for environmental researchers. Samples of interest often contain a large (unknown) number of chemicals spanning the detectable mass range of the instrument. In an effort to separate these chemicals prior to injection into the mass spectrometer, a chromatography method is often utilized. There are numerous types of gas and liquid chromatographs that can be coupled to commercially available mass spectrometers. Depending on the type of instrument used for analysis, the researcher is likely to observe a different subset of compounds based on the amenability of those chemicals to the selected experimental techniques and equipment. It would be advantageous if this subset of chemicals could be predicted prior to conducting the experiment, in order to minimize potential false-positive and false-negative identifications. In this work, we utilize experimental datasets to predict the amenability of chemical compounds to detection with liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). The assembled dataset totals 5517 unique chemicals either explicitly detected or not detected with LC-ESI-MS. The resulting detected/not-detected matrix has been modeled using specific molecular descriptors to predict which chemicals are amenable to LC-ESI-MS, and to which form(s) of ionization. Random forest models, including a measure of the applicability domain of the model for both positive and negative modes of the electrospray ionization source, were successfully developed. The outcome of this work will help to inform future suspect screening and non-targeted analyses of chemicals by better defining the potential LC-ESI-MS detectable chemical landscape of interest.

Sourcing data on chemical properties and hazard data from the US-EPA CompTox Chemicals Dashboard: A practical guide for human risk assessment

For the past six decades, human health risk assessment of chemicals has relied on in vivo data from human epidemiological and experimental animal toxicological studies to inform the derivation of non-cancer toxicity values. The ongoing evolution of this risk assessment paradigm in an environmental landscape of data-poor chemicals has highlighted the need to develop and implement non-testing methods, so-called New Approach Methodologies (NAMs). NAMs include a growing number of in silico and in vitro data streams designed to inform hazard properties of chemicals, including kinetics and dynamics at different levels of biological organization, environmental fate and transport, and exposure. NAMs provide a fit-for-purpose science-basis for human hazard and risk characterization of chemicals ranging from data-gap filling applications to broad evidence-based decision-making. Systematic assembly and delivery of empirical and predicted data for chemicals are paramount to advancing chemical evaluation, and software tools serve an essential role in delivering these data to the scientific community. The CompTox Chemicals Dashboard (from here on referred to as the "Dashboard") is one such tool and is a publicly available web-based application developed by the US Environmental Protection Agency to provide access to chemistry, toxicity and exposure information for ~900,000 chemicals. The Dashboard is increasingly becoming a valuable resource for assessors tasked with the evaluation of potential human health risks associated with chemical exposures. In this context, the significant amount of information present in the Dashboard facilitates: 1) assembly of information on physicochemical properties and environmental fate and transport and exposure parameters and metrics; 2) identification of cancer and non-cancer health effects from extant human and experimental animal studies in the public domain and/or information not available in the public domain (i.e., "grey literature"); 3) systematic literature searching and review for developing cancer and non-cancer hazard evidence bases; and 4) access to mechanistic information that can aid or augment the analysis of traditional toxicology evidence bases, or potentially, serve as the primary basis for informing hazard identification and dose-response when traditional bioassay data are lacking. Finally, in silico predictive tools developed to conduct structure-activity or read-across analyses are also available within the Dashboard. This practical tutorial is intended to address key questions from the human health risk assessment community dealing with chemicals in both food and in the environment. Perspectives for future development or refinement of the Dashboard highlight foreseen activities to further support the research and risk assessment community in cancer and non-cancer chemical evaluations.

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates

The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the classification of xenobiotics into substrates and non-substrates of nine human CYP isozymes: CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4. The models are trained on an extended, high-quality collection of known substrates and non-substrates and have been subjected to thorough validation. Our results show that the models yield competitive performance and are favorable for the detection of CYP substrates. In particular, a new consensus model reached high performance, with Matthews correlation coefficients (MCCs) between 0.45 (CYP2C8) and 0.85 (CYP3A4), although at the cost of coverage. The best models presented in this work are accessible free of charge via the "CYPstrate" module of the New E-Resource for Drug Discovery (NERDD).