In vitro screening of environmental chemicals for targeted testing prioritization: the ToxCast project

Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity

Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we aimed to create a hybrid model to predict hepatotoxicants. We first curated a reference dataset of 869 compounds for hepatotoxicity modeling. Then, we profiled them against PubChem for existing in vitro toxicity data. Of the 2560 resulting assays, we selected the mitochondrial membrane potential (MMP) assay, a high-throughput screening (HTS) tool that can test chemical disruptors for mitochondrial function. Machine learning was applied to develop quantitative structure-activity relationship (QSAR) models with 2536 compounds tested in the MMP assay for screening new compounds. The MMP assay results, including QSAR model outputs, yielded hepatotoxicity predictions for reference set compounds with a Correct Classification Ratio (CCR) of 0.59. The predictivity improved by including 37 structural alerts (CCR = 0.8). We validated our model by testing 37 reference set compounds in human HepG2 hepatoma cells, and reliably predicting them for hepatotoxicity (CCR = 0.79). This study introduces a novel AOP modeling strategy that combines public HTS data, computational modeling, and experimental testing to predict chemical hepatotoxicity.

Health of wild fish exposed to contaminants of emerging concern in freshwater ecosystems utilized by a Minnesota Tribal community

Fish serve as indicators of exposure to contaminants of emerging concern (CECs)-chemicals such as pharmaceuticals, hormones, and personal care products-which are often designed to impact vertebrates. To investigate fish health and CECs in situ, we evaluated the health of wild fish exposed to CECs in waterbodies across northeastern Minnesota with varying anthropogenic pressures and CEC exposures: waterbodies with no human development along their shorelines, those with development, and those directly receiving treated wastewater effluent. Then, we compared three approaches to evaluate the health of fish exposed to CECs in their natural environment: a refined fish health assessment index, a histopathological index, and high-throughput (ToxCast) in vitro assays. Lastly, we mapped adverse outcome pathways (AOPs) associated with identified ToxCast assays to determine potential impacts across levels of biological organization within the aquatic system. These approaches were applied to subsistence fish collected from the Grand Portage Indian Reservation and 1854 Ceded Territory in 2017 and 2019. Overall, 24 CECs were detected in fish tissues, with all but one of the sites having at least one detection. The combined implementation of these tools revealed that subsistence fish exposed to CECs had histological and macroscopic tissue and organ abnormalities, although a direct causal link could not be established. The health of fish in undeveloped sites was as poor, or sometimes poorer, than fish in developed and wastewater effluent-impacted sites based on gross and histologic tissue lesions. Adverse outcome pathways revealed potential hazardous pathways of individual CECs to fish. A better understanding of how the health of wild fish harvested for consumption is affected by CECs may help prioritize risk management research efforts and can ultimately be used to guide fishery management and public health decisions. Integr Environ Assess Manag 2024;20:846-863. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

Per- and Polyfluoroalkyl Substances in Food Packaging: Migration, Toxicity, and Management Strategies

PFASs are linked to serious health and environmental concerns. Among their widespread applications, PFASs are known to be used in food packaging and directly contribute to human exposure. However, information about PFASs in food packaging is scattered. Therefore, we systematically map the evidence on PFASs detected in migrates and extracts of food contact materials and provide an overview of available hazard and biomonitoring data. Based on the FCCmigex database, 68 PFASs have been identified in various food contact materials, including paper, plastic, and coated metal, by targeted and untargeted analyses. 87% of these PFASs belong to the perfluorocarboxylic acids and fluorotelomer-based compounds. Trends in chain length demonstrate that long-chain perfluoroalkyl acids continue to be found, despite years of global efforts to reduce the use of these substances. We utilized ToxPi to illustrate that hazard data are available for only 57% of the PFASs that have been detected in food packaging. For those PFASs for which toxicity testing has been performed, many adverse outcomes have been reported. The data and knowledge gaps presented here support international proposals to restrict PFASs as a group, including their use in food contact materials, to protect human and environmental health.

Low levels of nicotine and cotinine but not benzo[a]pyrene induce human trophoblast cell proliferation

E-cigarettes use constitutes a source of thirdhand nicotine exposure. The increasing use of electronic cigarettes in homes and public places increases the risk of exposure of pregnant women to thirdhand nicotine. The effects of exposure of pregnant women to very low levels of nicotine have not been studied in humans but detrimental in experimental animals. The objective of this study is to investigate the effect of nanomolar concentrations of nicotine and its metabolite cotinine on the proliferation of JEG-3, a human trophoblast cell line. We also studied the proliferative effect of nanomolar concentrations of benzo[a]pyrene (B[a]P), a polycyclic hydrocarbon in tobacco smoke, for comparison. We treated JEG-3 cells in culture with nanomolar concentrations of nicotine, cotinine, and B[a]P. Their effect on cell proliferation was determined, relative to untreated cells, by MTT assay. Western blotting was used to assess the mitogenic signaling pathways affected by nicotine and cotinine. In contrast to the inhibitory effects reported with higher concentrations, we showed that nanomolar concentrations of nicotine and cotinine resulted in significant JEG-3 cell proliferation and a rapid but transient increase in levels of phosphorylated ERK and AKT, but not STAT3. Biphasic, non-monotonic effect on cell growth is characteristic of endocrine disruptive chemicals like nicotine. The mitogenic effects of nicotine and cotinine potentially contribute to increased villous epithelial thickness, seen in placentas of some smoking mothers. This increases the diffusion distance for oxygen and nutrients between mother and fetus, contributing to intrauterine growth restriction in infants of smoking mothers.

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs. We introduced a cross-attention network framework, "CatNet", innovatively integrating compound and protein representations through cross-attention mechanisms. The results showed that CatNet model achieved excellent performance with an area under the receiver operating characteristic curve (AUCROC) = 0.916 on the test set, and exhibited reliable generalization on unseen compound-NR pairs. A distinguishing feature of our research is its capacity to expand to novel targets. Beyond its predictive accuracy, CatNet offers a valuable mechanistic perspective on compound-NR interactions through feature visualization. Augmenting the utility of our research, we have also developed a graphical user interface, empowering researchers to predict chemical binding to diverse NRs. Our model enables the prediction of human NR-related EDCs and shows the potential to identify EDCs related to other targets.

Predictive value of the ToxCast/Tox21 high throughput toxicity screening data for approximating in vivo ecotoxicity endpoints and ecotoxicological risk in eco- surveillance applications

Ecotoxicology has long relied on assessing the hazard potential of chemicals through traditional in vivo testing methods to understand the possible risk exposure could pose to ecological taxa. In the past decade, the development of non-animal new approach methods (NAMs) for assessing chemical hazard and risk has quickly grown. These methods are often cheaper and faster than traditional toxicity testing, and thus are amenable to high-throughput toxicity testing (HTT), resulting in large datasets. The ToxCast/Tox21 HTT programs have produced in vitro data for thousands of chemicals covering a large space of biological activity. The relevance of these data to in vivo mammalian toxicity has been much explored. Interest has also grown in using these data to evaluate the risk of environmental exposures to taxa of ecological importance such as fish, aquatic invertebrates, etc.; particularly for the purpose of estimating the risk of exposure from real-world complex mixtures. Understanding the relationship and relative sensitivity of NAMs versus standardized ecotoxicological whole organism models is a key component of performing reliable read-across from mammalian in vitro data to ecotoxicological in vivo data. In this work, we explore the relationship between in vivo ecotoxicity data from several publicly available databases and the ToxCast/Tox21 data. We also performed several case studies in which we compare how using different ecotoxicity datasets, whether traditional or ToxCast-based, affects risk conclusions based on exposure to complex mixtures derived from existing large-scale chemical monitoring data. Generally, predictive value of ToxCast data for traditional in vivo endpoints (EPs) was poor (r ≤ 0.3). Risk conclusions, including identification of different chemical risk drivers and prioritized monitoring sites, were different when using HTT data vs. traditional in vivo data.

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.

Regulation of PXR in drug metabolism: chemical and structural perspectives

INTRODUCTION

Pregnane X receptor (PXR) is a master xenobiotic sensor that transcriptionally controls drug metabolism and disposition pathways. PXR activation by pharmaceutical drugs, natural products, environmental toxins, etc. may decrease drug efficacy and increase drug-drug interactions and drug toxicity, indicating a therapeutic value for PXR antagonists. However, PXR's functions in physiological events, such as intestinal inflammation, indicate that PXR activators may be useful in certain disease contexts.

AREAS COVERED

We review the reported roles of PXR in various physiological and pathological processes including drug metabolism, cancer, inflammation, energy metabolism, and endobiotic homeostasis. We then highlight specific cellular and chemical routes that modulate PXR activity and discuss the functional consequences. Databases searched and inclusive dates: PubMed, 1 January 1980 to 10 January 2024.

EXPERT OPINION

Knowledge of PXR's drug metabolism function has helped drug developers produce small molecules without PXR-mediated metabolic liabilities, and further understanding of PXR's cellular functions may offer drug development opportunities in multiple disease settings.

Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates

Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.

Interpretable machine learning for the identification of estrogen receptor agonists, antagonists, and binders

An abnormal hormonal activity or exposure to endocrine-disrupting chemicals (EDCs) can cause endocrine system malfunction. Among the many interactions EDCs can affect is the disruption of estrogen signalling, which can lead to adverse health effects such as cancer, osteoporosis, neurodegenerative diseases, cardiovascular disease, insulin resistance, and obesity. Knowing which chemical can act as an EDC is a significant advantage and a practical necessity. New Approach Methodologies (NAM) computational models offer a quick and cost-effective solution for preliminary hazard assessment of chemicals without animal testing. Therefore, a machine learning approach was used to investigate the relationships between estrogen receptor (ER) activity and chemical structure to identify chemicals that can interact with ER. For this purpose, the consolidated in vitro assay data from ToxCast/Tox21 projects was used for developing Random Forest classification models for ER binding, agonists, and antagonists. The overall classification prediction accuracy reaches up to 82%, depending on whether the model predicted agonists, antagonists, or compounds that bind to the active site. Given the imbalance in endocrine disruption data, the derived models are good candidates for deprioritising chemicals and reducing animal testing. The interpretation of theoretical molecular descriptors of the models was consistent with the molecular interactions known in the ligand binding pocket. The estimated class probabilities enabled the analysis of the applicability domain of the developed models and the assessment of the predictions' reliability, followed by the guidelines for interpreting prediction results. The models are openly accessible and useable at QsarDB.org (http://dx.doi.org/10.15152/QDB.259) according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles.

Changes in chemical occurrence, concentration, and bioactivity in the Colorado River before and after replacement of the Moab, Utah wastewater treatment plant

Long-term (2010-19) water-quality monitoring on the Colorado River downstream from Moab Utah indicated the persistent presence of Bioactive Chemicals (BC), such as pesticides and pharmaceuticals. This stream reach near Canyonlands National Park provides critical habitat for federally endangered species. The Moab wastewater treatment plant (WWTP) outfall discharges to the Colorado River and is the nearest potential point-source to this reach. The original WWTP was replaced in 2018. In 2016-19, a study was completed to determine if the new plant reduced BC input to the Colorado River at, and downstream from, the outfall. Water samples were collected before and after the plant replacement at sites upstream and downstream from the outfall. Samples were analyzed for as many as 243 pesticides, 109 pharmaceuticals, 20 hormones, 51 wastewater indicator chemicals, 20 metals, and 8 nutrients. BC concentrations, hazard quotients (HQs), and exposure activity ratios (EARs) were used to identify and prioritize contaminants for their potential to have adverse biological effects on the health of native and endangered wildlife. There were 22 BC with HQs >1, mostly metals and hormones; and 23 BC with EARs >0.1, mostly hormones and pharmaceuticals. Most high HQs or EARs were associated with samples collected at the WWTP outfall site prior to its replacement. Discharge from the new plant had reduced concentrations of nutrients, hormones, pharmaceuticals, and other BC. For example, all 16 of the hormones detected at the WWTP outfall site had maximum concentrations in samples collected prior to the WWTP replacement. The WWTP replacement had less effect on instream concentrations of metals and pesticides, BC whose sources are less directly tied to domestic wastewater. Study results indicate that improved WWTP technology can create substantial reductions in concentrations of non-regulated BC such as pharmaceuticals, in addition to regulated contaminants such as nutrients.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies

Zebrafish have become an essential tool in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential "normal" behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems.

Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes

In silico (quantitative) structure-activity relationship modeling is an approach that provides a fast and cost-effective alternative to assess the genotoxic potential of chemicals. However, one of the limiting factors for model development is the availability of consolidated experimental datasets. In the present study, we collected experimental data on micronuclei in vitro and in vivo, utilizing databases and conducting a PubMed search, aided by text mining using the BioBERT large language model. Chemotype enrichment analysis on the updated datasets was performed to identify enriched substructures. Additionally, chemotypes common for both endpoints were found. Five machine learning models in combination with molecular descriptors, twelve fingerprints and two data balancing techniques were applied to construct individual models. The best-performing individual models were selected for the ensemble construction. The curated final dataset consists of 981 chemicals for micronuclei in vitro and 1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. The ensemble model exhibited high accuracy and sensitivity when applied to an external test set of in vitro data. A good balanced predictive performance was also achieved for the micronucleus in vivo endpoint.

Selecting the appropriate indirect viability assay for 3D paper-based cultures: a data-driven study

Cellular viability measurements quantify decreased proliferation or increased cytotoxicity caused by drug candidates or potential environmental toxins. Direct viability measures count each cell to provide an accurate readout. This approach can prove analytically challenging and time-consuming when cells are maintained in 3D structures akin to tissues or solid tumors. While less labor-intensive, indirect viability measures can be less accurate due to the heterogeneous structural and chemical microenvironment that arises when cells are maintained in tissue-like architectures and in contact with extracellular matrices. Here we determine the analytical figures of merit of five indirect viability assays in the paper-based cell culture platform we continue to develop in our laboratory: calcein-AM staining, the CellTiter-Glo assay, imaging fluorescent protein expression, propidium iodide staining, and the resazurin assay. We also determined the compatibility of each indirect assay with hypoxic conditions, intra-experimental repeatability, inter-experimental reproducibility, and ability to predict a potency value for a known antineoplastic drug. Our results show that each assay has benefits and drawbacks to consider when choosing the appropriate readout to answer a particular research question. We also highlight that only one indirect readout is unaffected by hypoxia, a commonly overlooked variable in cell culture that likely yields inaccurate viability measures.

Bayesian matrix completion for hypothesis testing

We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity.

Systematic Evaluation of the Application of Zebrafish in Toxicology (SEAZIT): Developing a Data Analysis Pipeline for the Assessment of Developmental Toxicity with an Interlaboratory Study

The embryonic zebrafish is a useful vertebrate model for assessing the effects of substances on growth and development. However, cross-laboratory developmental toxicity outcomes can vary and reported developmental defects in zebrafish may not be directly comparable between laboratories. To address these limitations for gaining broader adoption of the zebrafish model for toxicological screening, we established the Systematic Evaluation of the Application of Zebrafish in Toxicology (SEAZIT) program to investigate how experimental protocol differences can influence chemical-mediated effects on developmental toxicity (i.e., mortality and the incidence of altered phenotypes). As part of SEAZIT, three laboratories were provided a common and blinded dataset (42 substances) to evaluate substance-mediated effects on developmental toxicity in the embryonic zebrafish model. To facilitate cross-laboratory comparisons, all the raw experimental data were collected, stored in a relational database, and analyzed with a uniform data analysis pipeline. Due to variances in laboratory-specific terminology for altered phenotypes, we utilized ontology terms available from the Ontology Lookup Service (OLS) for Zebrafish Phenotype to enable additional cross-laboratory comparisons. In this manuscript, we utilized data from the first phase of screening (dose range finding, DRF) to highlight the methodology associated with the development of the database and data analysis pipeline, as well as zebrafish phenotype ontology mapping.

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.

Prioritizing Pesticides of Potential Concern and Identifying Potential Mixture Effects in Great Lakes Tributaries Using Passive Samplers

To help meet the objectives of the Great Lakes Restoration Initiative with regard to increasing knowledge about toxic substances, 223 pesticides and pesticide transformation products were monitored in 15 Great Lakes tributaries using polar organic chemical integrative samplers. A screening-level assessment of their potential for biological effects was conducted by computing toxicity quotients (TQs) for chemicals with available US Environmental Protection Agency (USEPA) Aquatic Life Benchmark values. In addition, exposure activity ratios (EAR) were calculated using information from the USEPA ToxCast database. Between 16 and 81 chemicals were detected per site, with 97 unique compounds detected overall, for which 64 could be assessed using TQs or EARs. Ten chemicals exceeded TQ or EAR levels of concern at two or more sites. Chemicals exceeding thresholds included seven herbicides (2,4-dichlorophenoxyacetic acid, diuron, metolachlor, acetochlor, atrazine, simazine, and sulfentrazone), a transformation product (deisopropylatrazine), and two insecticides (fipronil and imidacloprid). Watersheds draining agricultural and urban areas had more detections and higher concentrations of pesticides compared with other land uses. Chemical mixtures analysis for ToxCast assays associated with common modes of action defined by gene targets and adverse outcome pathways (AOP) indicated potential activity on biological pathways related to a range of cellular processes, including xenobiotic metabolism, extracellular signaling, endocrine function, and protection against oxidative stress. Use of gene ontology databases and the AOP knowledgebase within the R-package ToxMixtures highlighted the utility of ToxCast data for identifying and evaluating potential biological effects and adverse outcomes of chemicals and mixtures. Results have provided a list of high-priority chemicals for future monitoring and potential biological effects warranting further evaluation in laboratory and field environments. Environ Toxicol Chem 2023;42:340-366. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Regulation of Nuclear Receptors PXR and CAR by Small Molecules and Signal Crosstalk: Roles in Drug Metabolism and Beyond

Pregnane X receptor (PXR) and constitutive androstane receptor (CAR) are ligand-activated transcription factors that regulate the expression of drug metabolizing enzymes and drug transporters. Since their discoveries, they have been studied as important factors for regulating processes related to drug efficacy, drug toxicity, and drug-drug interactions. However, their vast ligand-binding profiles extend into additional spaces, such as endogenously produced chemicals, microbiome metabolites, dietary compounds, and environmental pollutants. Therefore, PXR and CAR can respond to an enormous abundance of stimuli, resulting in significant shifts in metabolic programs and physiologic homeostasis. Naturally, PXR and CAR have been implicated in various diseases related to homeostatic perturbations, such as inflammatory bowel disorders, diabetes, and certain cancers. Recent findings have injected the field with new signaling mechanisms and tools to dissect the complex PXR and CAR biology and have strengthened the potential for future PXR and CAR modulators in the clinic. Here, we describe the historical and ongoing importance of PXR and CAR in drug metabolism pathways and how this history has evolved into new mechanisms that regulate and are regulated by these xenobiotic receptors, with a specific focus on small molecule ligands. To effectively convey the impact of newly emerging research, we have arranged five diverse and representative key recent advances, four specific challenges, and four perspectives on future directions. SIGNIFICANCE STATEMENT: PXR and CAR are key transcription factors that regulate homeostatic detoxification of the liver and intestines. Diverse chemicals bind to these nuclear receptors, triggering their transcriptional tuning of the cellular metabolic response. This minireview revisits the importance of PXR and CAR in pharmaceutical drug responses and highlights recent results with implications beyond drug metabolism.

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework

Spiking neural networks are biologically inspired machine learning algorithms attracting researchers' attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural networks have been tested in a quantitative structure-activity analysis targeting the toxicity of molecules. Multiple public-domain databases of compounds have been evaluated with spiking neural networks, achieving accuracies compatible with high-quality frameworks presented in the previous literature. The numerical experiments also included an analysis of hyperparameters and tested the spiking neural networks on molecular fingerprints of different lengths. Proposing alternatives to traditional software and hardware for time- and resource-consuming tasks, such as those found in chemoinformatics, may open the door to new research and improvements in the field.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction

BACKGROUND

Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods.

OBJECTIVES

This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets.

METHODS

Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts.

RESULTS

Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10^-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts.

CONCLUSION

TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

Priority screening of contaminant of emerging concern (CECs) in surface water from drinking water sources in the lower reaches of the Yangtze River based on exposure-activity ratios (EARs)

Surface water provides ecological services such as drinking water supply. However, contaminants of emerging concern (CECs) are rising concerns because they are ubiquitously detected in surface water and pose potential risks to the aquatic environment and human health. This study investigated the occurrence of 165 CECs in surface water from drinking water source areas along the lower reaches of the Yangtze River to prioritize the CECs and to estimate potential biological activity based on exposure-activity ratio (EAR). A total of 70 CECs were detected in the surface water at least once at the selected 17 sampling sites, and their concentrations ranged from 0.592 to 4650 ng/L. Twenty-four CECs were detected at each site, and these were mostly pharmaceutical and personal care products and pesticides. Sucralose, 1H-benzotriazole and carbendazim were the most common CECs with high median concentrations in the study area. Specifically, sucralose, an artificial sweetener, was presented at each site with the highest median concentration (3010 ng/L), which indicated that anthropogenic inputs are an important source of contaminants. Medroxyprogesterone and trenbolone were identified as the priority contaminants of interest, with maximum EAR_chemical values of 0.389 and 0.183, respectively. Among all the sites, the higher cumulative EAR_mixture value was found from Nantong City (0.765), which indicated that this site could have a relatively greater potential for biological effects, and these effects were mainly due to medroxyprogesterone and trenbolone. In regard to the bioactivity of all detected CECs, nuclear receptors showed the greatest potential bioactivity in this region, particularly androgen receptor-mediated bioactivity, which is most likely affected organisms residing in the source water area. These results suggest that the drinking water sources from the studied region are contaminated with CECs, and highlight the prioritization of future monitoring and research to protect source waters.

Papyrus: a large-scale curated dataset aimed at bioactivity predictions

With the ongoing rapid growth of publicly available ligand-protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers' time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges, we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million data points. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some examples of quantitative structure-activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing an accessible data source for research.

Artificial neural networks in contemporary toxicology research

Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be used to predict toxicity of various chemical compounds or classify the compounds based on their toxic effects. Today, numerous ANN models have been developed, some of which may be used to detect and possibly explain complex chemico-biological interactions. Fully connected multilayer perceptrons may in some circumstances have high classification accuracy and discriminatory power in separating damaged from intact cells after exposure to a toxic substance. Regularized and not fully connected convolutional neural networks can detect and identify discrete changes in patterns of two-dimensional data associated with toxicity. Bayesian neural networks with weight marginalization sometimes may have better prediction performance when compared to traditional approaches. With the further development of artificial intelligence, it is expected that ANNs will in the future become important parts of various accurate and affordable biosensors for detection of various toxic substances and evaluation of their biochemical properties. In this concise review article, we discuss the recent research focused on the scientific value of ANNs in evaluation and prediction of toxicity of chemical compounds.

Widespread formation of toxic nitrated bisphenols indoors by heterogeneous reactions with HONO

With numerous structurally diverse indoor contaminants, indoor transformation chemistry has been largely unexplored. Here, by integrating protein affinity purification and nontargeted mass spectrometry analysis (PUCA), we identified a substantial class of previously unrecognized indoor transformation products formed through gas-surface reactions with nitrous acid (HONO). Through the PUCA, we identified a noncommercial compound, nitrated bisphenol A (BPA), from house dust extracts strongly binding to estrogen-related receptor γ. The compound was detected in 28 of 31 house dust samples with comparable concentrations (ND to 0.30 μg/g) to BPA. Via exposing gaseous HONO to surface-bound BPA, we demonstrated it likely forms via a heterogeneous indoor chemical transformation that is highly selective toward bisphenols with electron-rich aromatic rings. We used 15N-nitrite for in situ labeling and found 110 nitration products formed from indoor contaminants with distinct aromatic moieties. This study demonstrates a previously unidentified class of chemical reactions involving indoor HONO, which should be incorporated into the risk evaluation of indoor contaminants, particularly bisphenols.

Cellular and molecular responses to ethyl-parathion in undifferentiated SH-SY5Y cells provide neurotoxicity pathway indicators for organophosphorus impacts

High-fidelity nonanimal screening methods are needed that can rapidly and accurately characterize organophosphorus compound (OP)-induced neurotoxicity. Herein, the efficacy of human neuroblastoma cell line (SH-SY5Y) to provide molecular and cellular responses characteristic of the OP neurotoxicity pathway was investigated in response to the OP-model compound, ethyl-parathion. Undifferentiated SH-SY5Y cells were exposed to ethyl-parathion for 30 min at 0 (control), 0.5, 2.5, 5, 10, and 25 µg/ml. Dose-responsive reductions in cell viability were observed with significant reductions at ≥10 µg/ml. From these results, ethyl-parathion exposures of 0 (control), 5, and 10 µg/ml were selected to examine bioindicators underlying the OP neurotoxicity pathway including: reactive oxygen species (ROS), cell membrane peroxidation, mitochondrial membrane potential (MMP), and apoptosis. Ethyl-parathion elicited highly significant increases in ROS relative to controls (p < .01) at both exposure concentrations, confirmed using N-acetyl cysteine (NAC) as a ROS quencher which alleviated ROS increases. A response characteristic of increased ROS exposure, cell membrane-lipid peroxidation, significantly increased (p < .05) at the highest ethyl-parathion exposure (10 µg/ml). As a likely consequence of membrane-lipid peroxidation, ethyl-parathion-induced reductions in MMP were observed with significant effects at 10 µg/ml, reducing MMP by 58.2%. As a culmination of these cellular-damage indicators, apoptosis progression was investigated by phosphatidylserine translocation where ethyl-parathion-induced dose-responsive, highly significant (p < .01) increases at both 5 and 10 µg/ml. Overall, the mechanistic responses observed in undifferentiated SH-SY5Y cells corresponded with in vivo mammalian results demonstrating potential for this nonanimal model to provide accurate OP neurotoxicology screening.

High-throughput screening paradigms in ecotoxicity testing: Emerging prospects and ongoing challenges

The rapidly increasing number of new production chemicals coupled with stringent implementation of global chemical management programs necessities a paradigm shift towards boarder uses of low-cost and high-throughput ecotoxicity testing strategies as well as deeper understanding of cellular and sub-cellular mechanisms of ecotoxicity that can be used in effective risk assessment. The latter will require automated acquisition of biological data, new capabilities for big data analysis as well as computational simulations capable of translating new data into in vivo relevance. However, very few efforts have been so far devoted into the development of automated bioanalytical systems in ecotoxicology. This is in stark contrast to standardized and high-throughput chemical screening and prioritization routines found in modern drug discovery pipelines. As a result, the high-throughput and high-content data acquisition in ecotoxicology is still in its infancy with limited examples focused on cell-free and cell-based assays. In this work we outline recent developments and emerging prospects of high-throughput bioanalytical approaches in ecotoxicology that reach beyond in vitro biotests. We discuss future importance of automated quantitative data acquisition for cell-free, cell-based as well as developments in phytotoxicity and in vivo biotests utilizing small aquatic model organisms. We also discuss recent innovations such as organs-on-a-chip technologies and existing challenges for emerging high-throughput ecotoxicity testing strategies. Lastly, we provide seminal examples of the small number of successful high-throughput implementations that have been employed in prioritization of chemicals and accelerated environmental risk assessment.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

Prioritization of chemicals in food for risk assessment by integrating exposure estimates and new approach methodologies: A next generation risk assessment case study

Next generation risk assessment is defined as a knowledge-driven system that allows for cost-efficient assessment of human health risk related to chemical exposure, without animal experimentation. One of the key features of next generation risk assessment is to facilitate prioritization of chemical substances that need a more extensive toxicological evaluation, in order to address the need to assess an increasing number of substances. In this case study focusing on chemicals in food, we explored how exposure data combined with the Threshold of Toxicological Concern (TTC) concept could be used to prioritize chemicals, both for existing substances and new substances entering the market. Using a database of existing chemicals relevant for dietary exposure we calculated exposure estimates, followed by application of the TTC concept to identify substances of higher concern. Subsequently, a selected set of these priority substances was screened for toxicological potential using high-throughput screening (HTS) approaches. Remarkably, this approach resulted in alerts for a selection of substances that are already on the market and represent relevant exposure in consumers. Taken together, the case study provides proof-of-principle for the approach taken to identify substances of concern, and this approach can therefore be considered a supportive element to a next generation risk assessment strategy.

Identifying candidate reference chemicals for in vitro testing of the retinoid pathway for predictive developmental toxicity

Evaluating chemicals for potential in vivo toxicity based on their in vitro bioactivity profile is an important step toward animal- free testing. A compendium of reference chemicals and data describing their bioactivity on specific molecular targets, cellular pathways, and biological processes is needed to bolster confidence in the predictive value of in vitro hazard detection. Endogenous signaling by all-trans retinoic acid (ATRA) is an important pathway in developmental processes and toxicities. Employing data extraction methods and advanced literature extraction tools, we assembled a set of candidate reference chemicals with demonstrated activity on ten protein family targets in the retinoid system. The compendium was culled from Protein Data Bank, ChEMBL, ToxCast/Tox21, and the biomedical literature in PubMed. Finally, we performed a case study on one chemical in our collection, citral, an inhibitor of endogenous ATRA production, to determine whether the literature supports an adverse outcome pathway explaining the compound’s developmental toxicity initiated by disruption of the retinoid pathway. We also deliver an updated Abstract Sifter tool populated with these reference compounds and complex search terms designed to query the literature for the downstream consequences to support concordance with targeted retinoid pathway disruption.

Effect-Directed Analysis Based on the Reduced Human Transcriptome (RHT) to Identify Organic Contaminants in Source and Tap Waters along the Yangtze River

Since a large number of contaminants are detected in source waters (SWs) and tap waters (TWs), it is important to perform a comprehensive effect evaluation and key contributor identification. A reduced human transcriptome (RHT)-based effect-directed analysis, which consisted of a concentration-dependent RHT to reveal the comprehensive effects and noteworthy pathways and systematic identification of key contributors based on the interactions between compounds and pathway effects, was developed and applied to typical SWs and TWs along the Yangtze River. By RHT, 42% more differentially expressed genes and 33% more pathways were identified in the middle and lower reaches, indicating heavier pollution. Hormone and immune pathways were prioritized based on the detection frequency, sensitivity, and removal efficiency, among which the estrogen receptor pathway was the most noteworthy. Consistent with RHT, estrogenic effects were widespread along the Yangtze River based on in vitro evaluations. Furthermore, 38 of 100 targets, 39 pathway-related suspects, and 16 estrogenic nontargets were systematically identified. Among them, diethylstilbestrol was the dominant contributor, with the estradiol equivalent (EEQ) significantly correlated with EEQ_water. In addition, zearalenone and niclosamide explained up to 54% of the EEQ_water. The RHT-based EDA method could support the effect evaluation, contributor identification, and risk management of micropolluted waters.

Chemical and biological assessments of environmental mixtures: A review of current trends, advances, and future perspectives

Considering the chemical complexity and toxicity data gaps of environmental mixtures, most studies evaluate the chemical risk individually. However, humans are usually exposed to a cocktail of chemicals in real life. Mixture health assessment remains to be a research area having significant knowledge gaps. Characterization of chemical composition and bioactivity/toxicity are the two critical aspects of mixture health assessments. This review seeks to introduce the recent progress and tools for the chemical and biological characterization of environmental mixtures. The state-of-the-art techniques include the sampling, extraction, rapid detection methods, and the in vitro, in vivo, and in silico approaches to generate the toxicity data of an environmental mixture. Application of these novel methods, or new approach methodologies (NAMs), has increased the throughput of generating chemical and toxicity data for mixtures and thus refined the mixture health assessment. Combined with computational methods, the chemical and biological information would shed light on identifying the bioactive/toxic components in an environmental mixture.

Screening of ToxCast Chemicals Responsible for Human Adverse Outcomes with Exposure to Ambient Air

Air pollution poses a major threat to global public health. Although there have been a few investigations into the relationships between organic pollutants and adverse outcomes, the responsible components and molecular mechanisms may be ignored. In this study, a suspect screening method combining comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS) with the Toxicity Forecaster (ToxCast) database was applied to analyze complex hydrophobic compounds in ambient air and prospectively figure out toxicologically significant compounds. Seventy-six ToxCast compounds were screened, including seven pollutants receiving less attention and five chemicals never published in the air previously. Given the concentrations, bioactivities, as well as absorption, distribution, metabolism, and excretion properties in vivo, 29 contaminants were assigned high priority since they had active biological effects in the vascular, lung, liver, kidney, prostate, and bone tissues. Phenotypic linkages of key pollutants to potential mechanistic pathways were explored by systems toxicology. A total of 267 chemical-effect pathways involving 29 toxicants and 31 molecular targets were mapped in bipartite network, in which 12 key pathogenic pathways were clarified, which not only provided evidence supporting the previous hypothesis but also provided new insights into the molecular targets. The results would facilitate the development of pollutant priority control, population intervention, and clinical therapeutic strategies so as to substantially reduce human health hazards induced by urban air.

Exploring the influence of experimental design on toxicity outcomes in zebrafish embryo tests

Compound toxicity data obtained from independent zebrafish laboratories can vary vastly, complicating the use of zebrafish screening for regulatory decisions. Differences in the assay protocol parameters are the primary source of variability. We investigated this issue by utilizing data from the NTP DNT-DIVER database (https://doi.org/10.22427/NTP-DATA-002-00062-0001-0000-1), which consists of data from zebrafish developmental toxicity (devtox) and locomotor response (designated as 'neurotox') screens from three independent laboratories, using the same set of 87 compounds. The data were analyzed using the benchmark concentration (BMC) modeling approach, which estimates the concentration of interest based on a predetermined response threshold. We compared the BMC results from three laboratories (A, B, C) in three toxicity outcome categories: mortality, cumulative devtox, and neurotox, in terms of activity calls and potency values. We found that for devtox screening, laboratories with similar/same protocol parameters (B vs C) had an active call concordance as high as 86% with negligible potency difference. For neurotox screening, active call concordances between paired laboratories are lower than devtox screening (highest 68%). When protocols with different protocol parameters were compared, the concordance dropped, and the potency shift was on average about 3.8-fold for the cumulative devtox outcome and 5.8-fold for the neurotox outcome. The potential contributing protocol parameters for potency shift are listed or ranked. This study provides a quantitative assessment of the source of variability in zebrafish screening protocols and sets the groundwork for the ongoing Systematic Evaluation of the Application of Zebrafish in Toxicology (SEAZIT) effort at the National Toxicology Program (NTP).

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data

For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxicity studies are costly, time-consuming, and require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, and cost-effective method for evaluating the developmental toxicity potential of chemicals, such as endocrine disruptors, without the use of animals. We aimed to develop a predictive and explainable computational model for developmental toxicants. To this end, a comprehensive dataset of 1244 chemicals with developmental toxicity classifications was curated from public repositories and literature sources. Data from 2140 toxicological high-throughput screening assays were extracted from PubChem and the ToxCast program for this dataset and combined with information about 834 chemical fragments to group assays based on their chemical-mechanistic relationships. This effort revealed two assay clusters containing 83 and 76 assays, respectively, with high positive predictive rates for developmental toxicants identified with animal testing guidelines (PPV = 72.4 and 77.3% during cross-validation). These two assay clusters can be used as developmental toxicity models and were applied to predict new chemicals for external validation. This study provides a new strategy for constructing alternative chemical developmental toxicity evaluations that can be replicated for other toxicity modeling studies.

Pluripotent Stem Cell Assays: Modalities and Applications For Predictive Developmental Toxicity

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A systematic scoping review of the literature evaluated the embryonic stem cell test (EST).

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1533 publications included 18 publications testing 10 or more compounds in human or mouse EST.

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Selected case examples included 5-fluorouracil, thalidomide, and caffeine.

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Applicability, limitations, and recommendations for further work are discussed.

This manuscript provides a review focused on embryonic stem cell-based models and their place within the landscape of alternative developmental toxicity assays. Against the background of the principles of developmental toxicology, the wide diversity of alternative methods using pluripotent stem cells developed in this area over the past half century is reviewed. In order to provide an overview of available models, a systematic scoping review was conducted following a published protocol with inclusion criteria, which were applied to select the assays. Critical aspects including biological domain, readout endpoint, availability of standardized protocols, chemical domain, reproducibility and predictive power of each assay are described in detail, in order to review the applicability and limitations of the platform in general and progress moving forward to implementation. The horizon of innovative routes of promoting regulatory implementation of alternative methods is scanned, and recommendations for further work are given.

Benchmark Dose Modeling Approaches for Volatile Organic Chemicals using a Novel Air-Liquid Interface In Vitro Exposure System

Inhalation is the most relevant route of volatile organic chemical (VOC) exposure; however, due to unique challenges posed by their chemical properties and poor solubility in aqueous solutions, in vitro chemical safety testing is predominantly performed using direct application dosing/submerged exposures. To address the difficulties in screening toxic effects of VOCs, our cell culture exposure system permits cells to be exposed to multiple concentrations at air-liquid interface (ALI) in a 24-well format. ALI exposure methods permit direct chemical-to-cell interaction with the test article at physiological conditions. In the present study, BEAS-2B and primary normal human bronchial epithelial cells (pHBEC) are used to assess gene expression, cytotoxicity, and cell viability responses to a variety of volatile chemicals including acrolein, formaldehyde, 1,3-butadiene, acetaldehyde, 1-bromopropane, carbon tetrachloride, dichloromethane, and trichloroethylene. BEAS-2B cells were exposed to all the test agents, while pHBECs were only exposed to the latter four listed above. The VOC concentrations tested elicited only slight cell viability changes in both cell types. Gene expression changes were analyzed using benchmark dose (BMD) modeling. The BMD for the most sensitive gene set was within one order of magnitude of the threshold-limit value reported by the American Conference of Governmental Industrial Hygienists, and the most sensitive gene sets impacted by exposure correlate to known adverse health effects recorded in epidemiologic and in vivo exposure studies. Overall, our study outlines a novel in vitro approach for evaluating molecular-based points-of-departure in human airway epithelial cell exposure to volatile chemicals.

Towards regulation of Endocrine Disrupting chemicals (EDCs) in water resources using bioassays - A guide to developing a testing strategy

Endocrine disrupting chemicals (EDCs) are found in every environmental medium and are chemically diverse. Their presence in water resources can negatively impact the health of both human and wildlife. Currently, there are no mandatory screening mandates or regulations for EDC levels in complex water samples globally. Bioassays, which allow quantifying in vivo or in vitro biological effects of chemicals are used commonly to assess acute toxicity in water. The existing OECD framework to identify single-compound EDCs offers a set of bioassays that are validated for the Estrogen-, Androgen-, and Thyroid hormones, and for Steroidogenesis pathways (EATS). In this review, we discussed bioassays that could be potentially used to screen EDCs in water resources, including in vivo and in vitro bioassays using invertebrates, fish, amphibians, and/or mammalians species. Strengths and weaknesses of samples preparation for complex water samples are discussed. We also review how to calculate the Effect-Based Trigger values, which could serve as thresholds to determine if a given water sample poses a risk based on existing quality standards. This work aims to assist governments and regulatory agencies in developing a testing strategy towards regulation of EDCs in water resources worldwide. The main recommendations include 1) opting for internationally validated cell reporter in vitro bioassays to reduce animal use & cost; 2) testing for cell viability (a critical parameter) when using in vitro bioassays; and 3) evaluating the recovery of the water sample preparation method selected. This review also highlights future research avenues for the EDC screening revolution (e.g., 3D tissue culture, transgenic animals, OMICs, and Adverse Outcome Pathways (AOPs)).

A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity Profiling

The U.S. Tox21 program has developed in vitro assays to test large collections of environmental chemicals in a quantitative high-throughput screening (qHTS) format, using triplicate 15-dose titrations to generate over 100 million data points to date. Counterscreens are also employed to minimize interferences from non-target-specific assay artifacts, such as compound autofluorescence and cytotoxicity. New data analysis approaches are needed to integrate these data and characterize the activities observed from these assays. Here, we describe a complete analysis pipeline that evaluates these qHTS data for technical quality in terms of signal reproducibility. We integrate signals from repeated assay runs, primary readouts and counterscreens to produce a final call on on-target compound activity.

Enzyme-Mediated Reactions of Phenolic Pollutants and Endogenous Metabolites as an Overlooked Metabolic Disruption Pathway

It is generally recognized that phenol-containing molecules mainly undergo phase II metabolic reactions, whereas glucuronide and sulfate are conjugated to form water-soluble products. Here, we report direct reactions of phenolic pollutants (triclosan, alkylphenol, bisphenol A [BPA], and its analogues) and some endogenous metabolites (vitamin E [VE] and estradiol) to generate new lipophilic ether products (e.g., BPA-O-VEs and alkylphenol-O-estradiol). A nontargeted screening strategy was used to identify the products in human liver microsome incubations, and the most abundant products (BPA-O-VEs) were confirmed via in vivo exposure in mice. BPA-O-VEs were frequently detected in sera from the general population at levels comparable to those of glucuronide/sulfate-conjugated BPA. Recombinant human cytochrome P450s were applied to find that CYP3A4 catalyzed the formation of these newly discovered ether metabolites by linking the VE hydroxyl group to the BPA phenolic ring, leading to the significantly reduced antioxidative activities of BPA-O-VEs compared to VEs. The effects of the reaction on the homeostasis of reacted biomolecules were finally assessed by in vitro assay and in vivo mice exposures. The generation of BPA-O-VEs decreased the VE concentrations and increased the reactive oxygen species generation after exposure to BPA at environmentally relevant concentrations. The identified reactions provided an overlooked metabolic disruption pathway for phenolic pollutants.

Assessing Fish Immunotoxicity by Means of In Vitro Assays: Are We There Yet?

There is growing awareness that a range of environmental chemicals target the immune system of fish and may compromise the resistance towards infectious pathogens. Existing concepts to assess chemical hazards to fish, however, do not consider immunotoxicity. Over recent years, the application of in vitro assays for ecotoxicological hazard assessment has gained momentum, what leads to the question whether in vitro assays using piscine immune cells might be suitable to evaluate immunotoxic potentials of environmental chemicals to fish. In vitro systems using primary immune cells or immune cells lines have been established from a wide array of fish species and basically from all immune tissues, and in principal these assays should be able to detect chemical impacts on diverse immune functions. In fact, in vitro assays were found to be a valuable tool in investigating the mechanisms and modes of action through which environmental agents interfere with immune cell functions. However, at the current state of knowledge the usefulness of these assays for immunotoxicity screening in the context of chemical hazard assessment appears questionable. This is mainly due to a lack of assay standardization, and an insufficient knowledge of assay performance with respect to false positive or false negative signals for the different toxicant groups and different immune functions. Also the predictivity of the in vitro immunotoxicity assays for the in vivo immunotoxic response of fishes is uncertain. In conclusion, the currently available database is too limited to support the routine application of piscine in vitro assays as screening tool for assessing immunotoxic potentials of environmental chemicals to fish.

ER/AR Multi-Conformational Docking Server: A Tool for Discovering and Studying Estrogen and Androgen Receptor Modulators

The prediction of the estrogen receptor (ER) and androgen receptor (AR) activity of a compound is quite important to avoid the environmental exposures of endocrine-disrupting chemicals. The Estrogen and Androgen Receptor Database (EARDB, http://eardb.schanglab.org.cn/) provides a unique collection of reported ERα, ERβ, or AR protein structures and known small molecule modulators. With the user-uploaded query molecules, molecular docking based on multi-conformations of a single target will be performed. Moreover, the 2D similarity search against known modulators is also provided. Molecules predicted with a low binding energy or high similarity to known ERα, ERβ, or AR modulators may be potential endocrine-disrupting chemicals or new modulators. The server provides a tool to predict the endocrine activity for compounds of interests, benefiting for the ER and AR drug design and endocrine-disrupting chemical identification.

Accounting for Artifacts in High-Throughput Toxicity Assays

Compound activity identification is the primary goal in high throughput screening (HTS) assays. However, assay artifacts including both systematic (e.g., compound autofluorescence) and nonsystematic (e.g., noise) complicate activity interpretation. In addition, other than the traditional potency parameter, half-maximal effect concentration [EC₅₀], additional activity parameters (e.g., point-of-departure [POD] and weighted area-under-the-curve [wAUC]) could be derived from HTS data for activity profiling. A data analysis pipeline has been developed to handle the artifacts, and to provide compound activity characterization with either binary or continuous metrics. This chapter outlines the steps in the pipeline using Tox21 estrogen receptor (ER) β-lactamase assays, including the formats to identify either agonists or antagonists, as well as the counterscreen assays for identifying artifacts as examples. The steps can be applied to other lower throughput assays with concentration-response data.

Quantitative high-throughput phenotypic screening for environmental estrogens using the E-Morph Screening Assay in combination with in silico predictions

BACKGROUND

Exposure to environmental chemicals that interfere with normal estrogen function can lead to adverse health effects, including cancer. High-throughput screening (HTS) approaches facilitate the efficient identification and characterization of such substances.

OBJECTIVES

We recently described the development of the E-Morph Assay, which measures changes at adherens junctions as a clinically-relevant phenotypic readout for estrogen receptor (ER) alpha signaling activity. Here, we describe its further development and application for automated robotic HTS.

METHODS

Using the advanced E-Morph Screening Assay, we screened a substance library comprising 430 toxicologically-relevant industrial chemicals, biocides, and plant protection products to identify novel substances with estrogenic activities. Based on the primary screening data and the publicly available ToxCast dataset, we performed an insilico similarity search to identify further substances with potential estrogenic activity for follow-up hit expansion screening, and built seven insilico ER models using the conformal prediction (CP) framework to evaluate the HTS results.

RESULTS

The primary and hit confirmation screens identified 27 'known' estrogenic substances with potencies correlating very well with the published ToxCast ER Agonist Score (r=+0.95). We additionally detected potential 'novel' estrogenic activities for 10 primary hit substances and for another nine out of 20 structurally similar substances from insilico predictions and follow-up hit expansion screening. The concordance of the E-Morph Screening Assay with the ToxCast ER reference data and the generated CP ER models was 71% and 73%, respectively, with a high predictivity for ER active substances of up to 87%, which is particularly important for regulatory purposes.

DISCUSSION

These data provide a proof-of-concept for the combination of in vitro HTS approaches with insilico methods (similarity search, CP models) for efficient analysis of large substance libraries in order to prioritize substances with potential estrogenic activity for subsequent testing against higher tier human endpoints.

Machine Learning Models for Predicting Liver Toxicity

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk

BACKGROUND

Large numbers of chemicals require evaluation to determine if their production and use pose potential risks to ecological and human health. For most chemicals, the inadequacy and uncertainty of chemical-specific data severely limit the application of exposure- and risk-based methods for screening-level assessments, priority setting, and effective management.

OBJECTIVE

We developed and evaluated a holistic, mechanistic modeling framework for ecological and human health assessments to support the safe and sustainable production, use, and disposal of organic chemicals.

METHODS

We consolidated various models for simulating the PROduction-To-EXposure (PROTEX) continuum with empirical data sets and models for predicting chemical property and use function information to enable high-throughput (HT) exposure and risk estimation. The new PROTEX-HT framework calculates exposure and risk by integrating mechanistic computational modules describing chemical behavior and fate in the socioeconomic system (i.e., life cycle emissions), natural and indoor environments, various ecological receptors, and humans. PROTEX-HT requires only molecular structure and chemical tonnage (i.e., annual production or consumption volume) as input information. We evaluated the PROTEX-HT framework using 95 organic chemicals commercialized in the United States and demonstrated its application in various exposure and risk assessment contexts.

RESULTS

Seventy-nine percent and 97% of the PROTEX-HT human exposure predictions were within one and two orders of magnitude, respectively, of independent human exposure estimates inferred from biomonitoring data. PROTEX-HT supported screening and ranking chemicals based on various exposure and risk metrics, setting chemical-specific maximum allowable tonnage based on user-defined toxicological thresholds, and identifying the most relevant emission sources, environmental media, and exposure routes of concern in the PROTEX continuum. The case study shows that high chemical tonnage did not necessarily result in high exposure or health risks.

CONCLUSION

Requiring only two chemical-specific pieces of information, PROTEX-HT enables efficient screening-level evaluations of existing and premanufacture chemicals in various exposure- and risk-based contexts. https://doi.org/10.1289/EHP9372.

Update and Evaluation of a High-Throughput In Vitro Mass Balance Distribution Model: IV-MBM EQP v2.0

This study demonstrates the utility of an updated mass balance model for predicting the distribution of organic chemicals in in vitro test systems (IV-MBM EQP v2.0) and evaluates its performance with empirical data. The IV-MBM EQP v2.0 tool was parameterized and applied to four independent data sets with measured ratios of bulk medium or freely-dissolved to initial nominal concentrations (e.g., C24/C0 where C24 is the measured concentration after 24 h of exposure and C0 is the initial nominal concentration). Model performance varied depending on the data set, chemical properties (e.g., "volatiles" vs. "non-volatiles", neutral vs. ionizable organics), and model assumptions but overall is deemed acceptable. For example, the r² was greater than 0.8 and the mean absolute error (MAE) in the predictions was less than a factor of two for most neutral organics included. Model performance was not as good for the ionizable organic chemicals included but the r² was still greater than 0.7 and the MAE less than a factor of three. The IV-MBM EQP v2.0 model was subsequently applied to several hundred chemicals on Canada's Domestic Substances List (DSL) with nominal effects data (AC50s) reported for two in vitro assays. We report the frequency of chemicals with AC50s corresponding to predicted cell membrane concentrations in the baseline toxicity range (i.e., >20-60 mM) and tabulate the number of chemicals with "volatility issues" (majority of chemical in headspace) and "solubility issues" (freely-dissolved concentration greater than water solubility after distribution). In addition, the predicted "equivalent EQP blood concentrations" (i.e., blood concentration at equilibrium with predicted cellular concentration) were compared to the AC50s as a function of hydrophobicity (log octanol-water partition or distribution ratio). The predicted equivalent EQP blood concentrations exceed the AC50 by up to a factor of 100 depending on hydrophobicity and assay conditions. The implications of using AC50s as direct surrogates for human blood concentrations when estimating the oral equivalent doses using a toxicokinetic model (i.e., reverse dosimetry) are then briefly discussed.