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.

Systematic Approaches for the Encoding of Chemical Groups: A Case Study.

Regulatory authorities aim to organize substances into groups to facilitate prioritization within hazard and risk assessment processes. Often, such chemical groupings are not explicitly defined by structural rules or physicochemical property information. This is largely due to how these groupings are developed, namely, a manual expert curation process, which in turn makes updating and refining groupings, as new substances are evaluated, a practical challenge. Herein, machine learning methods were leveraged to build models that could preliminarily assign substances to predefined groups. A set of 86 groupings containing 2,184 substances as published on the European Chemicals Agency (ECHA) website were mapped to the U.S. Environmental Protection Agency (EPA) Distributed Toxicity Structure Database (DSSTox) content to extract chemical and structural information. Substances were represented using Morgan fingerprints, and two machine learning approaches were used to classify test substances into 56 groups containing at least 10 substances with a structural representation in the data set: k-nearest neighbor (kNN) and random forest (RF), that led to mean 5-fold cross-validation test accuracies (average F1 scores) of 0.781 and 0.853, respectively. With a 9% improvement, the RF classifier was significantly more accurate than KNN (p-value = 0.001). The approach offers promise as a means of the initial profiling of new substances into predefined groups to facilitate prioritization efforts and streamline the assessment of new substances when earlier groupings are available. The algorithm to fit and use these models has been made available in the accompanying repository, thereby enabling both use of the produced models and refitting of these models, as new groupings become available by regulatory authorities or industry.

Expanded Systematic Evidence Map for Hundreds of Per- and Polyfluoroalkyl Substances (PFAS) and Comprehensive PFAS Human Health Dashboard.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) encompass a class of chemically and structurally diverse compounds that are extensively used in industry and detected in the environment. The US Environmental Protection Agency (US EPA) 2021 PFAS Strategic Roadmap describes national research plans to address the challenge of PFAS. OBJECTIVES: Systematic Evidence Map (SEM) methods were used to survey and summarize available epidemiological and mammalian bioassay evidence that could inform human health hazard identification for a set of 345 PFAS that were identified by the US EPA's Center for Computational Toxicology and Exposure (CCTE) for in vitro toxicity and toxicokinetic assay testing and through interagency discussions on PFAS of interest. This work builds from the 2022 evidence map that collated evidence on a separate set of ∼150 PFAS. Like our previous work, this SEM does not include PFAS that are the subject of ongoing or completed assessments at the US EPA. METHODS: SEM methods were used to search, screen, and inventory mammalian bioassay and epidemiological literature from peer-reviewed and gray literature sources using manual review and machine-learning software. For each included study, study design details and health end points examined were summarized in interactive web-based literature inventories. Some included studies also underwent study evaluation and detailed extraction of health end point data. All underlying data is publicly available online as interactive visuals with downloadable metadata. RESULTS: More than 13,000 studies were identified from scientific databases. Screening processes identified 121 mammalian bioassay and 111 epidemiological studies that met screening criteria. Epidemiological evidence (available for 12 PFAS) mostly assessed the reproductive, endocrine, developmental, metabolic, cardiovascular, and immune systems. Mammalian bioassay evidence (available for 30 PFAS) commonly assessed effects in the reproductive, whole-body, nervous, and hepatic systems. Overall, 41 PFAS had evidence across mammalian bioassay and epidemiology data streams (roughly 11% of searched chemicals). DISCUSSION: No epidemiological and/or mammalian bioassay evidence were identified for most of the PFAS included in our search. Results from this SEM, our 2022 SEM on ∼150 PFAS, and other PFAS assessment products from the US EPA are compiled into a comprehensive PFAS dashboard that provides researchers and regulators an overview of the current PFAS human health landscape including data gaps and can serve as a scoping tool to facilitate prioritization of PFAS-related research and/or risk assessment activities. https://doi.org/10.1289/EHP13423.

Towards systematic read-across using Generalised Read-Across (GenRA).

Read-across continues to be a popular data gap filling technique within category and analogue approaches. One of the main issues hindering read-across acceptance is the notion of addressing and reducing uncertainties. Frameworks and formats have been created to help facilitate read-across development, evaluation, and residual uncertainties. However, read-across remains an expert-driven approach with each assessment decided on its own merits with no objective means of evaluating performance or quantifying uncertainties. Here, the underlying motivation of creating an algorithmic approach to read-across, namely the Generalised Read-Across (GenRA) approach, is described. The overall objectives of the approach were to quantify performance and uncertainty. Progress made in quantifying the impact of each similarity context commonly relied upon as part of read-across assessment are discussed. The framework underpinning the approach, the software tools developed to date and how GenRA can be used to make and interpret predictions as part of a screening level hazard assessment decision context are illustrated. Future directions and some of the overarching issues still needed in this field and the extent to which GenRA might facilitate those needs are discussed.

Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library.

High-throughput screening (HTS) assays for bioactivity in the Tox21 program aim to evaluate an array of different biological targets and pathways, but a significant barrier to interpretation of these data is the lack of high-throughput screening (HTS) assays intended to identify non-specific reactive chemicals. This is an important aspect for prioritising chemicals to test in specific assays, identifying promiscuous chemicals based on their reactivity, as well as addressing hazards such as skin sensitisation which are not necessarily initiated by a receptor-mediated effect but act through a non-specific mechanism. Herein, a fluorescence-based HTS assay that allows the identification of thiol-reactive compounds was used to screen 7,872 unique chemicals in the Tox21 10K chemical library. Active chemicals were compared with profiling outcomes using structural alerts encoding electrophilic information. Random Forest classification models based on chemical fingerprints were developed to predict assay outcomes and evaluated through 10-fold stratified cross validation (CV). The mean CV Balanced Accuracy of the validation set was 0.648. The model developed shows promise as a tool to screen untested chemicals for their potential electrophilic reactivity based solely on chemical structural features.

Workshop report: Challenges faced in developing inhalation thresholds of Toxicological Concern (TTC) - State of the science and next steps.

A challenging step in human risk assessment of chemicals is the derivation of safe thresholds. The Threshold of Toxicological Concern (TTC) concept is one option which can be used for the safety evaluation of substances with a limited toxicity dataset, but for which exposure is sufficiently low. The application of the TTC is generally accepted for orally or dermally exposed cosmetic ingredients; however, these values cannot directly be applied to the inhalation route because of differences in exposure route versus oral and dermal. Various approaches of an inhalation TTC concept have been developed over recent years to address this. A virtual workshop organized by Cosmetics Europe, held in November 2020, shared the current state of the science regarding the applicability of existing inhalation TTC approaches to cosmetic ingredients. Key discussion points included the need for an inhalation TTC for local respiratory tract effects in addition to a systemic inhalation TTC, dose metrics, database building and quality of studies, definition of the chemical space and applicability domain, and classification of chemicals with different potencies. The progress made to date in deriving inhalation TTCs was highlighted, as well as the next steps envisaged to develop them further for regulatory acceptance and use.

Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments.

'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ∼100 µM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

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.

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.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis.

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

Evaluation of Complex Mixture Toxicity in the Milwaukee Estuary (WI, USA) Using Whole-Mixture and Component-Based Evaluation Methods.

Anthropogenic activities introduce complex mixtures into aquatic environments, necessitating mixture toxicity evaluation during risk assessment. There are many alternative approaches that can be used to complement traditional techniques for mixture assessment. Our study aimed to demonstrate how these approaches could be employed for mixture evaluation in a target watershed. Evaluations were carried out over 2 years (2017-2018) across 8-11 study sites in the Milwaukee Estuary (WI, USA). Whole mixtures were evaluated on a site-specific basis by deploying caged fathead minnows (Pimephales promelas) alongside composite samplers for 96 h and characterizing chemical composition, in vitro bioactivity of collected water samples, and in vivo effects in whole organisms. Chemicals were grouped based on structure/mode of action, bioactivity, and pharmacological activity. Priority chemicals and mixtures were identified based on their relative contributions to estimated mixture pressure (based on cumulative toxic units) and via predictive assessments (random forest regression). Whole mixture assessments identified target sites for further evaluation including two sites targeted for industrial/urban chemical mixture effects assessment; three target sites for pharmaceutical mixture effects assessment; three target sites for further mixture characterization; and three low-priority sites. Analyses identified 14 mixtures and 16 chemicals that significantly contributed to cumulative effects, representing high or medium priority targets for further ecotoxicological evaluation, monitoring, or regulatory assessment. Overall, our study represents an important complement to single-chemical prioritizations, providing a comprehensive evaluation of the cumulative effects of mixtures detected in a target watershed. Furthermore, it demonstrates how different tools and techniques can be used to identify diverse facets of mixture risk and highlights strategies that can be considered in future complex mixture assessments. Environ Toxicol Chem 2023;42:1229-1256. © 2023 SETAC.

Advancing the science of a read-across framework for evaluation of data-poor chemicals incorporating systematic and new approach methods.

The assessment of human health hazards posed by chemicals traditionally relies on toxicity studies in experimental animals. However, most chemicals currently in commerce do not meet the minimum data requirements for hazard identification and dose-response analysis in human health risk assessment. Previously, we introduced a read-across framework designed to address data gaps for screening-level assessment of chemicals with insufficient in vivo toxicity information (Wang et al., 2012). It relies on inference by analogy from suitably tested source analogues to a target chemical, based on structural, toxicokinetic, and toxicodynamic similarity. This approach has been used for dose-response assessment of data-poor chemicals relevant to the U.S. EPA's Superfund program. We present herein, case studies of the application of this framework, highlighting specific examples of the use of biological similarity for chemical grouping and quantitative read-across. Based on practical knowledge and technological advances in the fields of read-across and predictive toxicology, we propose a revised framework. It includes important considerations for problem formulation, systematic review, target chemical analysis, analogue identification, analogue evaluation, and incorporation of new approach methods. This work emphasizes the integration of systematic methods and alternative toxicity testing data and tools in chemical risk assessment to inform regulatory decision-making.

Evaluation of 147 perfluoroalkyl substances for immunotoxic and other (patho)physiological activities through phenotypic screening of human primary cells.

A structurally diverse set of 147 per- and polyfluoroalkyl substances (PFAS) was screened in a panel of 12 human primary cell systems by measuring 148 biomarkers relevant to (patho)physiological pathways to inform hypotheses about potential mechanistic effects of data-poor PFAS in human model systems. This analysis focused on immunosuppressive activity, which was previously reported as an in vivo effect of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), by comparing PFAS responses to four pharmacological immunosuppressants. The PFOS response profile had little correlation with reference immunosuppressants, suggesting in vivo activity does not occur by similar mechanisms. The PFOA response profile did share features with the profile of dexamethasone, although some distinct features were lacking. Other PFAS, including 2,2,3,3-tetrafluoropropyl acrylate, demonstrated more similarity to the reference immunosuppressants but with additional activities not found in the reference immunosuppressive drugs. Correlation of PFAS profiles with a database of environmental chemical responses and pharmacological probes identified potential mechanisms of bioactivity for some PFAS, including responses similar to ubiquitin ligase inhibitors, deubiquitylating enzyme (DUB) inhibitors, and thioredoxin reductase inhibitors. Approximately 21% of the 147 PFAS with confirmed sample quality were bioactive at nominal testing concentrations in the 1-60 micromolar range in these human primary cell systems. These data provide new hypotheses for mechanisms of action for a subset of PFAS and may further aid in development of a PFAS categorization strategy useful in safety assessment.

Evaluating the utility of the Threshold of Toxicological Concern (TTC) and its exclusions in the biocompatibility assessment of extractable chemical substances from medical devices.

The Threshold of Toxicological Concern (TTC) is a pragmatic approach used to establish safe thresholds below which there can be no appreciable risk to human health. Here, a large inventory of ~45,000 substances (referred to as the LRI dataset) was profiled through the Kroes TTC decision module within Toxtree v3.1 to assign substances into their respective TTC categories. Four thousand and two substances were found to be not applicable for the TTC approach. However, closer examination of these substances uncovered several implementation issues: substances represented in their salt forms were automatically assigned as not appropriate for TTC when many of these contained essential metals as counter ions which would render them TTC applicable. High Potency Carcinogens and dioxin-like substances were not fully captured based on the rules currently implemented in the software. Phosphorus containing substances were considered exclusions when many of them would be appropriate for TTC. Refinements were proposed to address the limitations in the current software implementation. A second component of the study explored a set of substances representative of those released from medical devices and compared them to the LRI dataset as well as other toxicity datasets to investigate their structural similarity. A third component of the study sought to extend the exclusion rules to address application to substances released from medical devices that lack toxicity data. The refined rules were then applied to this dataset and the TTC assignments were compared. This case study demonstrated the importance of evaluating the software implementation of an established TTC workflow, identified certain limitations and explored potential refinements when applying these concepts to medical devices.

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods.

Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA's ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA's ToxCast high-throughput screening program, together with in vitro to in vivo extrapolation and 2) thresholds of toxicological concern (TTCs) determined using a structure-based decision-tree using the Toxtree open source software. To ground-truth these computational approaches, we compared the MoEs based on predicted noncancer TTC and OED values to those derived using the traditional method of deriving points of departure from no-observed adverse effect levels (NOAELs) from in vivo oral exposures in rodents. TTC-based MoEs were lower than NOAEL-based MoEs for 520 out of 522 (99.6%) compounds in this smaller overlapping dataset, but were relatively well correlated with the same (r 2 = 0.59). TTC-based MoEs were also lower than OED-based MoEs for 590 (83.2%) of the 709 evaluated chemicals, indicating that TTCs may serve as a conservative surrogate in the absence of chemical-specific experimental data. The TTC-based MoE prioritization process was then applied to over 45,000 curated environmental chemical structures as a proof-of-concept for high-throughput prioritization using TTC-based MoEs. This study demonstrates the utility of exploiting existing computational methods at the pre-assessment phase of a tiered risk-based approach to quickly, and conservatively, prioritize thousands of untested chemicals for further study.

"In Litero" Screening: Retrospective Evaluation of Clinical Evidence to Establish a Reference List of Human Chemical Respiratory Sensitizers.

Despite decades of investigation, test methods to identify respiratory sensitizers remain an unmet regulatory need. In order to support the evaluation of New Approach Methodologies in development, we sought to establish a reference set of low molecular weight respiratory sensitizers based on case reports of occupational asthma. In this context, we have developed an "in litero" approach to identify cases of low molecular weight chemical exposures leading to respiratory sensitization in clinical literature. We utilized the EPA-developed Abstract Sifter literature review tool to maximize the retrieval of publications relevant to respiratory effects in humans for each chemical in a list of chemicals suspected of inducing respiratory sensitization. The literature retrieved for each of these candidate chemicals was sifted to identify relevant case reports and studies, and then evaluated by applying defined selection criteria. Clinical diagnostic criteria were defined around exposure history, respiratory effects, and specific immune response to conclusively demonstrate occupational asthma as a result of sensitization, rather than irritation. This approach successfully identified 28 chemicals that can be considered as human respiratory sensitizers and used to evaluate the performance of NAMs as part of a weight of evidence approach to identify novel respiratory sensitizers. Further, these results have immediate implications for the development and refinement of predictive tools to distinguish between skin and respiratory sensitizers. A comparison of the protein binding mechanisms of our identified "in litero" clinical respiratory sensitizers shows that acylation is a prevalent protein binding mechanism, in contrast to Michael addition and Schiff base formation common to skin sensitizers. Overall, this approach provides an exemplary method to evaluate and apply human data as part of the weight of evidence when establishing reference chemical lists.

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.