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Background: The TGx-DDI biomarker identifies transcripts specifically induced by primary DNA damage. Profiling similarity of TGx-DDI signatures can allow clustering compounds by genotoxic mechanism. This transcriptomics-based approach complements conventional toxicology testing by enhancing mechanistic resolution. Methods: Unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding (tSNE) were utilized to assess similarity of publicly-available per- and polyfluoroalkyl substances (PFAS) and ToxCast chemicals based on TGx-DDI modulation. TempO-seq transcriptomic data after highest chemical concentrations were analyzed. Results: Clustering discriminated between genotoxic and non-genotoxic compounds while drawing similarity among chemicals with shared mechanisms. PFAS largely clustered distinctly from classical mutagens. However, dynamic range across PFAS types and durations indicated variable potential for DNA damage. tSNE visualization reinforced phenotypic groupings, with genotoxins clustering separately from non-DNA damaging agents. Discussion: Unsupervised learning approaches applied to TGx-DDI profiles effectively categorizes chemical genotoxicity potential, aiding elucidation of biological response pathways. This transcriptomics-based strategy gives further insight into the role and effect of individual TGx-DDI biomarker genes and complements existing assays by enhancing mechanistic resolution. Overall, TGx-DDI biomarker profiling holds promise for predictive safety screening. [ABSTRACT FROM AUTHOR]

The potential for polycyclic aromatic hydrocarbon (PAH)‐related effects in benthic organisms is commonly estimated from organic carbon‐normalized sediment concentrations based on equilibrium partitioning (EqP). Although this approach is useful for screening purposes, it may overestimate PAH bioavailability by orders of magnitude in some sediments, leading to inflated exposure estimates and potentially unnecessary remediation costs. Recently, passive samplers have been shown to provide an accurate assessment of the freely dissolved concentrations of PAHs, and thus their bioavailability and possible biological effects, in sediment porewater and overlying surface water. We used polyethylene passive sampling devices (PEDs) to measure freely dissolved porewater and water column PAH concentrations at 55 Great Lakes (USA/Canada) tributary locations. The potential for PAH‐related biological effects using PED concentrations were estimated with multiple approaches by applying EqP, water quality guidelines, and pathway‐based biological activity based on in vitro bioassay results from ToxCast. Results based on the PED‐based exposure estimates were compared with EqP‐derived exposure estimates for concurrently collected sediment samples. The results indicate a potential overestimation of bioavailable PAH concentrations by up to 960‐fold using the EqP‐based method compared with measurements using PEDs. Even so, PED‐based exposure estimates indicate a high potential for PAH‐related biological effects at 14 locations. Our findings provide an updated, weight‐of‐evidence–based site prioritization to help guide possible future monitoring and mitigation efforts. Environ Toxicol Chem 2024;43:1509–1523. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. [ABSTRACT FROM AUTHOR]

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). Key Points: 1.Fish exposed to contaminants of emerging concern had histological and macroscopic tissue and organ abnormalities.2.Adverse outcome pathways revealed potential hazardous pathways of individual contaminants of emerging concern to fish.3.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. [ABSTRACT FROM AUTHOR]

A suite of in vitro assays and in silico models were evaluated to identify which best detected the endocrine-disrupting (ED) potential of 10 test chemicals according to their estrogenic, androgenic and steroidogenic (EAS) potential compared to the outcomes from ToxCast. In vitro methods included receptor-binding, CALUX transactivation, H295R steroidogenesis, aromatase activity inhibition and the Yeast oestrogen (YES) and Yeast androgen screen (YAS) assays. The impact of metabolism was also evaluated. The YES/YAS assays exhibited a high sensitivity for ER effects and, despite some challenges in predicting AR effects, is a good initial screening assay. Results from receptor-binding and CALUX assays generally correlated and were in accordance with classifications based on ToxCast assays. ER agonism and AR antagonism of benzyl butyl phthalate were abolished when CALUX assays included liver S9. In silico final calls were mostly in agreement with the in vitro assays, and predicted ER and AR effects well. The efficiency of the in silico models (reflecting applicability domains or inconclusive results) was 43–100%. The percentage of correct calls for ER (50–100%), AR (57–100%) and aromatase (33–100%) effects when compared to the final ToxCast call covered a wide range from highly reliable to less reliable models. In conclusion, Danish (Q)SAR, Opera, ADMET Lab LBD and ProToxII models demonstrated the best overall performance for ER and AR effects. These can be combined with the YES/YAS assays in an initial screen of chemicals in the early tiers of an NGRA to inform on the MoA and the design of mechanistic in vitro assays used later in the assessment. Inhibition of aromatase was best predicted by the Vega, AdmetLab and ProToxII models. Other mechanisms and exposure should be considered when making a conclusion with respect to ED effects. [ABSTRACT FROM AUTHOR]

Posttranscriptional splicing of premessenger RNA (mRNA) is an evolutionarily conserved eukaryotic process for producing mature mRNA that is translated into proteins. Accurate splicing is necessary for normal growth and development, and aberrant splicing is increasingly evident in various human pathologies. To study environmental factors that influence RNA splicing, we employed a fluorescent Caenorhabditis elegans in vivo splicing reporter as a biomarker for splicing fidelity to screen against the US EPA ToxCast chemical library. We identified pararosaniline hydrochloride as a strong modifier of RNA splicing. Through gene expression analysis, we found that pararosaniline activates the oxidative stress response and alters the expression of key RNA splicing regulator genes. Physiological assays show that pararosaniline is deleterious to C. elegans development, reproduction, and aging. Through a targeted RNAi screen, we found that inhibiting protein translation can reverse pararosaniline's effect on the splicing reporter and provide significant protection against long-term pararosaniline toxicity. Together, this study reveals a new chemical modifier of RNA splicing and describes translation inhibition as a genetic mechanism to provide resistance. [ABSTRACT FROM AUTHOR]

Neurotoxic pesticides: Cyfluthrin (CYF), βCYF, endosulfan (ENDO) and chlorpyrifos-oxon (CPFO) were examined using new approach methodologies (NAM) to assess ToxCast in vitro neurotoxic assay activities (50% concentration–AC50s), chemical-target specificity (Z-Scores) and in vitro to in vivo extrapolation with ToxCast assay AC50s as inputs to calculate human Administered Equivalent Dose (AEDHuman mg/kg) through Integrated Chemical Environment (ICE) models. Fold differences (FD) between AEDHuman NAM predictions and adjusted (animal to human interspecies extrapolation) in vivo regulatory neurotoxicity points of departure (POD) were calculated to assess modeled predictions against measured in vivo data. Interspecies (animal to human) in vivo adjusted PODs were 0.147 mg/kg/d (CYF/βCYF), 0.180 mg/kg/d (ENDO) and 0.106 mg/kg/d (CPFO). ToxCast neurotoxicity assay results supported in vivo observations: CYF, βCYF and CPFO showed depressed neuroactivity, while ENDO showed increased neuroactivity. βCYF is known to have twice the toxicity of CYF in vivo but was less toxic in the in vitro assays. Percent assays with Z-Scores greater than 3 for CYF, βCYF, ENDO and CPFO were 100%, 100%, 67% and 71% respectively. Most AEDHuman were less than 10-FD indicating good predictivity of the in vivo adjusted PODs with ICE models. The NAMs were useful for characterizing the main neurotoxic activities and the ICE models were highly predictive of in vivo PODs to support mechanisms in risk assessment. [ABSTRACT FROM AUTHOR]

We developed an integrated, modular approach to predicting chemical toxicity relying on in vitro assay data, linkage of molecular targets to disease categories, and software for ranking chemical activity and examining structural features (chemotypes). We evaluate our approach in a proof-of-concept exercise to identify and prioritize chemicals of potential carcinogenicity concern. We identified 137 cancer pathway-related assays from a subset of U.S. EPA's ToxCast platforms. We mapped these assays to key characteristics of carcinogens and found they collectively assess 5 of 10 characteristics. We ranked all 1061 chemicals screened in Phases I and II of ToxCast by their activity in the selected cancer pathway-related assays using Toxicological Prioritization Index software. More chemicals used as biologically active agents (eg, pharmaceuticals) ranked in the upper 50% versus lower 50%. Twenty-three chemotypes are enriched in the top 5% (n = 54) of chemicals; these features may be important for their activity in cancer pathway-related assays. The biological coverage of the ToxCast assays related to cancer pathways is limited and short-term assays may not capture the biology of some key characteristics. Metabolism is also minimal in the assays. The ability of our approach to identify chemicals with cancer hazard is limited with the current input data, but we expect that our approach can be applied with future iterations of ToxCast and other data for improved chemical prioritization and characterization. The novel approach and proof-of-concept exercise described here for ranking chemicals for potential carcinogenicity concern is modular, adaptable, and amenable to evolving data streams.

Abstract Numerous autism spectrum disorder (ASD) risk genes are associated with Wnt signaling, suggesting that brain development may be especially sensitive to genetic perturbation of this pathway. Additionally, valproic acid, which modulates Wnt signaling, increases risk for ASD when taken during pregnancy. We previously found that an autism‐linked gain‐of‐function UBE3AT485A mutant construct hyperactivated canonical Wnt signaling, providing a genetic means to elevate Wnt signaling above baseline levels. To identify environmental use chemicals that enhance or suppress Wnt signaling, we screened the ToxCast Phase I and II libraries in cells expressing this autism‐linked UBE3AT485A gain‐of‐function mutant construct. Using structural comparisons, we identify classes of chemicals that stimulated Wnt signaling, including ethanolamines, as well as chemicals that inhibited Wnt signaling, such as agricultural pesticides, and synthetic hormone analogs. To prioritize chemicals for follow‐up, we leveraged predicted human exposure data, and identified diethanolamine (DEA) as a chemical that stimulates Wnt signaling in UBE3AT485A–transfected cells, and has a high potential for prenatal exposure in humans. DEA enhanced proliferation in primary human neural progenitor cell lines (phNPC), but did not affect expression of canonical Wnt target genes in NPCs or primary mouse neuron cultures. Instead, we found DEA increased expression of the H3K9 methylation sensitive gene CALB1, consistent with competitive inhibition of the methyl donor enzymatic pathways.

Numerous autism spectrum disorder (ASD) risk genes are associated with Wnt signaling, suggesting that brain development may be especially sensitive to genetic perturbation of this pathway. Additionally, valproic acid, which modulates Wnt signaling, increases risk for ASD when taken during pregnancy. We previously found that an autism‐linked gain‐of‐function UBE3AT485A mutant construct hyperactivated canonical Wnt signaling, providing a genetic means to elevate Wnt signaling above baseline levels. To identify environmental use chemicals that enhance or suppress Wnt signaling, we screened the ToxCast Phase I and II libraries in cells expressing this autism‐linked UBE3AT485A gain‐of‐function mutant construct. Using structural comparisons, we identify classes of chemicals that stimulated Wnt signaling, including ethanolamines, as well as chemicals that inhibited Wnt signaling, such as agricultural pesticides, and synthetic hormone analogs. To prioritize chemicals for follow‐up, we leveraged predicted human exposure data, and identified diethanolamine (DEA) as a chemical that stimulates Wnt signaling in UBE3AT485A–transfected cells, and has a high potential for prenatal exposure in humans. DEA enhanced proliferation in primary human neural progenitor cell lines (phNPC), but did not affect expression of canonical Wnt target genes in NPCs or primary mouse neuron cultures. Instead, we found DEA increased expression of the H3K9 methylation sensitive gene CALB1, consistent with competitive inhibition of the methyl donor enzymatic pathways. [ABSTRACT FROM AUTHOR]

With improved analytical techniques, environmental monitoring studies are increasingly able to report the occurrence of tens or hundreds of chemicals per site, making it difficult to identify the most relevant chemicals from a biological standpoint. For the present study, organic chemical occurrence was examined, individually and as mixtures, in the context of potential biological effects. Sediment was collected at 71 Great Lakes (USA/Canada) tributary sites and analyzed for 87 chemicals. Multiple risk‐based lines of evidence were used to prioritize chemicals and locations, including comparing sediment concentrations and estimated porewater concentrations with established whole‐organism benchmarks (i.e., sediment and water quality criteria and screening values) and with high‐throughput toxicity screening data from the US Environmental Protection Agency's ToxCast database, estimating additive effects of chemical mixtures on common ToxCast endpoints, and estimating toxic equivalencies for mixtures of alkylphenols and polycyclic aromatic hydrocarbons (PAHs). This multiple‐lines‐of‐evidence approach enabled the screening of more chemicals, mitigated the uncertainties of individual approaches, and strengthened common conclusions. Collectively, at least one benchmark/screening value was exceeded for 54 of the 87 chemicals, with exceedances observed at all 71 of the monitoring sites. Chemicals with the greatest potential for biological effects, both individually and as mixture components, were bisphenol A, 4‐nonylphenol, indole, carbazole, and several PAHs. Potential adverse outcomes based on ToxCast gene targets and putative adverse outcome pathways relevant to individual chemicals and chemical mixtures included tumors, skewed sex ratios, reproductive dysfunction, hepatic steatosis, and early mortality, among others. The results provide a screening‐level prioritization of chemicals with the greatest potential for adverse biological effects and an indication of sites where they are most likely to occur. Environ Toxicol Chem 2022;41:1016–1041. 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. [ABSTRACT FROM AUTHOR]

US EPA's Toxicity Forecaster (ToxCastTM) is a tool with potential use in evaluating safer consumer products, conducting chemical alternatives analyses, prioritizing chemicals for exposure monitoring, and ultimately performing screening-level risk assessments. As a case study exploring a potential use of ToxCast, we evaluated ToxCast results for ortho-phthalates focused on the well-established toxicological endpoints of some members of this class. We compared molecular perturbations measured in ToxCast assays with the known apical toxicity endpoints of o-phthalates reported in the open literature to broadly reflect on the predictive capability of the high-throughput screening (HTS) assays. We grouped the ToxCast assays into defined sets to examine o-phthalate activity and potency. This study revealed several links between key molecular events assayed in vitro and chemical-specific hazard traits. In general, parent o-phthalates are more active than their monoester metabolites. The medium-chain length o-phthalate group is also more active than other o-phthalate groups, as supported by Toxicological Priority Index ranking and statistical methods. Some HTS assay results correlated with in vivo findings, but others did not. For example, there was a notable lack of assay activity to explain the known male reproductive toxicity of these compounds. Ultimately, HTS data resources such as ToxCast may inform us of sensitive upstream toxicity endpoints and may assist in the rapid identification of environmental chemical hazards for screening and prioritization. However, this case study shows that the absence of positive results in ToxCast in vitro assays cannot be interpreted as absence of related in vivo toxicity, and limited biological coverage by the assays remains a concern.

Abstract Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.

Toxicology in the 21st Century (Tox21) is a federal collaboration employing a high-throughput robotic screening system to test 10,000 environmental chemicals. One of the primary goals of the program is prioritizing toxicity evaluations through in vitro high-throughput screening (HTS) assays for large numbers of chemicals already in commercial use for which little or no toxicity data is available. Within the Tox21 screening program, disruption in nuclear receptor (NR) signaling represents a particular area of interest. Given the role of NR’s in modulating a wide range of biological processes, alterations of their activity can have profound biological impacts. Farnesoid X receptor (FXR) is a member of the nuclear receptor superfamily that has demonstrated importance in bile acid homeostasis, glucose metabolism, lipid homeostasis and hepatic regeneration. In this study, we re-evaluated 24 FXR agonists and antagonists identified through Tox21 using select orthogonal assays. In transient transactivation assays, 7/8 putative agonists and 4/4 putative inactive compounds were confirmed. Likewise, we confirmed 9/12 antagonists tested. Using a mammalian two hybrid approach we demonstrate that both FXR agonists and antagonists facilitate FXRα-coregulator interactions suggesting that differential coregulator recruitment may mediate activation/repression of FXRα mediated transcription. Additionally, we tested the ability of select FXR agonists and antagonists to facilitate hepatic transcription of FXR gene targets Shp and Bsep in a teleost (Medaka) model. Through application of in vitro cell-based assays, in silico modeling and in vivo gene expressions, we demonstrated the molecular complexity of FXR:ligand interactions and confirmed the ability of diverse ligands to modulate FXRα, facilitate differential coregulator recruitment and activate/repress receptor-mediated transcription. Overall, we suggest a multiplicative approach to assessment of nuclear receptor function may facilitate a greater understanding of the biological and mechanistic complexities of nuclear receptor activities and further our ability to interpret broad HTS outcomes.

The US EPA's Toxicity Forecaster (ToxCast) is a suite of high-throughput in vitro assays to screen environmental toxicants and predict potential toxicity of uncharacterized chemicals. This work examines the relevance of ToxCast assay intended gene targets to putative molecular initiating events (MIEs) of neurotoxicants. This effort is needed as there is growing interest in the regulatory and scientific communities about developing new approach methodologies (NAMs) to screen large numbers of chemicals for neurotoxicity and developmental neurotoxicity. Assay gene function (GeneCards, NCBI-PUBMED) was used to categorize gene target neural relevance (1 = neural, 2 = neural development, 3 = general cellular process, 3 A = cellular process critical during neural development, 4 = unlikely significance). Of 481 unique gene targets, 80 = category 1 (16.6 %); 16 = category 2 (3.3 %); 303 = category 3 (63.0 %); 97 = category 3 A (20.2 %); 82 = category 4 (17.0 %). A representative list of neurotoxicants (548) was researched (ex. PUBMED, PubChem) for neurotoxicity associated MIEs/Key Events (KEs). MIEs were identified for 375 compounds, whereas only KEs for 173. ToxCast gene targets associated with MIEs were primarily neurotransmitter (ex. dopaminergic, GABA)receptors and ion channels (calcium, sodium, potassium). Conversely, numerous MIEs associated with neurotoxicity were absent. Oxidative stress (OS) mechanisms were 79.1 % of KEs. In summary, 40 % of ToxCast assay gene targets are relevant to neurotoxicity mechanisms. Additional receptor and ion channel subtypes and increased OS pathway coverage are identified for potential future assay inclusion to provide more complete coverage of neural and developmental neural targets in assessing neurotoxicity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

Waterborne contaminants were monitored in 69 tributaries of the Laurentian Great Lakes in 2010 and 2014 using semipermeable membrane devices (SPMDs) and polar organic chemical integrative samplers (POCIS). A risk‐based screening approach was used to prioritize chemicals and chemical mixtures, identify sites at greatest risk for biological impacts, and identify potential hazards to monitor at those sites. Analyses included 185 chemicals (143 detected) including polycyclic aromatic hydrocarbons (PAHs), legacy and current‐use pesticides, fire retardants, pharmaceuticals, and fragrances. Hazard quotients were calculated by dividing detected concentrations by biological effect concentrations reported in the ECOTOX Knowledgebase (toxicity quotients) or ToxCast database (exposure–activity ratios [EARs]). Mixture effects were estimated by summation of EAR values for chemicals that influence ToxCast assays with common gene targets. Nineteen chemicals—atrazine, N,N‐diethyltoluamide, di(2‐ethylhexyl)phthalate, dl‐menthol, galaxolide, p‐tert‐octylphenol, 3 organochlorine pesticides, 3 PAHs, 4 pharmaceuticals, and 3 phosphate flame retardants—had toxicity quotients >0.1 or EARs for individual chemicals >10–3 at 10% or more of the sites monitored. An additional 4 chemicals (tributyl phosphate, triethyl citrate, benz[a]anthracene, and benzo[b]fluoranthene) were present in mixtures with EARs >10–3. To evaluate potential apical effects and biological endpoints to monitor in exposed wildlife, in vitro bioactivity data were compared to adverse outcome pathway gene ontology information. Endpoints and effects associated with endocrine disruption, alterations in xenobiotic metabolism, and potentially neuronal development would be relevant to monitor at the priority sites. The EAR threshold exceedance for many chemical classes was correlated with urban land cover and wastewater effluent influence, whereas herbicides and fire retardants were also correlated to agricultural land cover. Environ Toxicol Chem 2021;40:2165–2182. Published 2021. 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. [ABSTRACT FROM AUTHOR]

Background and aim: Endocrine disrupting chemicals (EDCs) constitute a major public health concern because they can induce a large spectrum of adverse effects by interfering with the hormonal system. Rapid identification of potential EDCs using in vitro screenings is therefore critical, particularly for chemicals of emerging concerns such as replacement flame retardants (FRs). The review aimed at identifying (1) data gaps and research needs regarding endocrine disrupting (ED) properties of replacement FRs and (2) potential EDCs among these emerging chemicals. Methods: A systematic search was performed from open literature and ToxCast/Tox21 programs, and results from in vitro tests on the activities of 52 replacement FRs towards five hormone nuclear receptors (NRs) associated with reproductive outcomes (estrogen, androgen, glucocorticoid, progesterone, and aryl hydrocarbon receptors) were compiled and organized into tables. Findings were complemented with information from structure-based in silico model predictions and in vivo information when relevant. Results: For the majority of the 52 replacement FRs, experimental in vitro data on activities towards these five NRs were either incomplete (15 FRs) or not found (24 FRs). Within the replacement FRs for which effect data were found, some appeared as candidate EDCs, such as triphenyl phosphate (TPhP) and tris(1,3-dichloropropyl)phosphate (TDCIPP). The search also revealed shared ED profiles. For example, anti-androgenic activity was reported for 19 FRs and predicted for another 21 FRs. Discussion: This comprehensive review points to critical gaps in knowledge on ED potential for many replacement FRs, including chemicals to which the general population is likely exposed. Although this review does not cover all possible characteristics of ED, it allowed the identification of potential EDCs associated with reproductive outcomes, calling for deeper evaluation and possibly future regulation of these chemicals. By identifying shared ED profiles, this work also raises concerns for mixture effects since the population is co-exposed to several FRs and other chemicals.

Per- and polyfluorinated alkyl substances (PFAS) are ubiquitous, persistent, and toxic chemicals that pose public health risks. Recent carcinogenicity concerns have arisen based on epidemiological studies, animal tumor findings, and mechanistic data. Thousands of PFAS exist; however, current understanding of their toxicity is informed by studies of a select few, namely, perfluorooctanoic acid and perfluorooctanesulfonic acid. Hence, the computational, high-throughput screening tool, the US EPA CompTox Chemical Dashboard's ToxCast, was utilized to explore the carcinogenicity potential of PFAS. Twenty-three major PFAS that had sufficient in vitro ToxCast data and covered a range of structural subclasses were analyzed with the visual analytics software ToxPi, yielding a qualitative and quantitative assessment of PFAS activity in realms closely linked with carcinogenicity. A comprehensive literature search was also conducted to check the consistency of analyses with other mechanistic data streams. The PFAS were found to induce a vast range of biological perturbations, in line with several of the International Agency for Research on Cancer–defined key carcinogen characteristics. Patterns observed varied by length of fluorine-bonded chains and/or functional group within and between each key characteristic, suggesting some structure-based variability in activity. In general, the major conclusions drawn from the analysis, that is, the most notable activities being modulation of receptor-mediated effects and induction of oxidative stress, were supported by literature findings. The study helps enhance understanding of the mechanistic pathways that underlie the potential carcinogenicity of various PFAS and hence could assist in hazard identification and risk assessment for this emerging and relevant class of environmental toxicants. [ABSTRACT FROM AUTHOR]

Cashmeran is a fragrance ingredient. Risk assessments are available but have not focused on its endocrine disruptor potential. The objective was to evaluate Cashmeran as a potential endocrine-disrupting chemical (EDC). The assessment was based on data from US EPA's CompTox Chemicals Dashboard, the Danish (Q)SAR Database, in vitro assays, and in vivo studies. ToxCast assays related to estrogen, androgen, thyroid, and steroidogenesis modalities were Inactive at non-cytotoxic concentrations. In vitro assays demonstrated no estrogenic activity in a human cervical epithelioid carcinoma HeLa cell line and indicated only weak agonist estrogenic activity in Chinese Hamster Ovary (CHO)–K1 cells. In the same test, no agonist or antagonist activity was detected for human androgen receptor (hAR) and thyroid hormone receptor β (hTHRβ) binding. The Danish QSAR database didn't indicate any ED potential. There were no adverse endocrine related effects in either a 90-day repeated gavage dosing study or a reproductive and developmental screening study. Regarding ED potential for environment, the data from two limited environmental ED related studies on Cashmeran did not raise any concern. Data from in vitro and in vivo studies were considered for environmental ED concern. Based on the weight-of-the-evidence, Cashmeran is not expected to cause endocrine effects. • Weight of evidence on endocrine disruption potential of Cashmeran. • Single study showing weak agonist estrogenic activity of Cashmeran was inconclusive. • ED criteria were not met for Cashmeran. • Cashmeran does not warrant inclusion on endocrine disruptors watch lists. [ABSTRACT FROM AUTHOR]

Abstract Background Flame retardants (FRs) are used in most consumer products and textiles to comply with current flammability standards. After the restriction of polybrominated diphenyl ethers, a large number of chemically diverse replacement FRs are increasingly used, but the risk they represent is not yet properly evaluated and their toxicity pathways are still poorly understood. Approach We collected in vivo toxicological information on 62 (including 52 non-regulated) FRs and established five prioritization categories (Cat I to V) based on data availability and toxicological concern. We then considered available in vitro toxicological data from ToxCast, as a complement to in vivo information. By combining these information sources, we then explored relevant toxicity mechanisms for nine selected FRs (Cat I) using the AOP (Adverse Outcome Pathway) framework. Results For 20 FRs (Cat V), toxicological data on mammals were absent. Data available were scarce for another 22 FRs, of which 14 FRs (Cat II) may be of toxicological concern. We found substantial information for only ten replacement FRs, of which nine (Cat I) present some toxicological concern: tris-2-chloroethyl phosphate (TCEP), tris(1,3-dichloropropyl)phosphate (TDCIPP), triphenyl phosphate (TPhP), tricresyl phosphate (TMPP), tetrabromobisphenol A (TBBPA), tri-n-butyl phosphate (TNBP), tri(2-butoxyethyl) phosphate (TBOEP), tris(1-chloro-2-propyl) phosphate (TCIPP), 2-ethylhexyl diphenyl phosphate (EHDPP). ToxCast results confirmed in vivo based categorization for several FRs and identified potential molecular targets. For the nine Cat I FRs, we identified several molecular targets, health outcomes and some potential AOPs. However, the complete toxicity pathways leading from molecular targets to adverse health outcomes are still unknown, with the exception of TBBPA-induced neurotoxicity. Conclusions The approach presented in this study was particularly useful for the categorization of a large group of replacement FRs with relatively low data availability. We highlight priority compounds that critically need more toxicological studies or FRs for which regulatory measures could be envisaged. Our research also suggests that high toxicity indicated by ToxCast is particularly relevant for predicting higher hazard in vivo. Finally, we indicate several gaps and directions for future research, such as molecular targets that could be tested in vitro and health outcomes for cohort studies.

Diesel particulate matter (DPM), a major subset of urban fine particulate matter (PM2.5), raises huge concerns for human health and has therefore been classified as a group 1 carcinogen by the International Agency for Research on Cancer (IARC). However, as DPM is a complex mixture of various chemicals, understanding of DPM’s toxicity mechanism remains limited. As the major exposure route of DPM is through inhalation, we herein investigated its toxicity mechanism based on the Adverse Outcome Pathway (AOP) of pulmonary fibrosis, which we previously submitted to AOPWiki as AOP ID 206 (AOP206). We first screened whether individual chemicals in DPM have the potential to exert their toxicity through AOP206 by using the ToxCast database and deep learning models approach, then confirmed this by examining whether DPM as a mixture alters the expression of the molecular initiating event (MIE) and key events (KEs) of AOP206. For identifying the activeness of the component chemicals of DPM, we used 24 ToxCast assays potentially related to AOP206 and deep learning models based on these assays, which were identified and developed in our previous study. Of the 100 individual chemicals in DPM, 34 were active in PPARγ (MIE)-related assay, of which 17 were active in one or more KEs. To further identify whether individual chemicals in DPM are related to the MIE of AOP206, we performed molecular docking simulation on PPARγ for the chemicals showing activeness. Benzo[e]pyrene, benzo[a]pyrene and other related chemicals were the most likely to bind to PPARγ. In in vitro experiments, PPARγ activity increased with exposure of the DPM mixture, and the protein expression of PPARγ (MIE), and fibronectin (AO) also tended to be increased. Overall, we have demonstrated that AOP206 can be applied to identify the toxicity pathway of DPM. Further, we suggest that applying the AOP approach using ToxCast and deep learning models is useful for identifying potential toxicity pathways of chemical mixtures, such as DPM, by determining the activity of individual chemicals.

Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100–250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations. [ABSTRACT FROM AUTHOR]

Open access new approach methods (NAM) in the US EPA ToxCast program and NTP Integrated Chemical Environment (ICE) were used to investigate activities of four neurotoxic pesticides: endosulfan, fipronil, propyzamide and carbaryl. Concordance of in vivo regulatory points of departure (POD) adjusted for interspecies extrapolation (AdjPOD) to modelled human Administered Equivalent Dose (AED Human ) was assessed using 3-compartment or Adult/Fetal PBTK in vitro to in vivo extrapolation. Model inputs were from Tier 1 (High throughput transcriptomics: HTTr, high throughput phenotypic profiling: HTPP) and Tier 2 (single target: ToxCast) assays. HTTr identified gene expression signatures associated with potential neurotoxicity for endosulfan, propyzamide and carbaryl in non-neuronal MCF-7 and HepaRG cells. The HTPP assay in U-2 OS cells detected potent effects on DNA endpoints for endosulfan and carbaryl, and mitochondria with fipronil (propyzamide was inactive). The most potent ToxCast assays were concordant with specific components of each chemical mode of action (MOA). Predictive adult IVIVE models produced fold differences (FD) < 10 between the AED Human and the measured in vivo AdjPOD. The 3-compartment model was concordant (i.e., smallest FD) for endosulfan, fipronil and carbaryl, and PBTK was concordant for propyzamide. The most potent AED Human predictions for each chemical showed HTTr, HTPP and ToxCast were mainly concordant with in vivo AdjPODs but assays were less concordant with MOAs. This was likely due to the cell types used for testing and/or lack of metabolic capabilities and pathways available in vivo . The Fetal PBTK model had larger FDs than adult models and was less predictive overall., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

New mosquito repellent products (NMRPs) are emerging popular repellents among children. There are increasing reports on children's sensitization reactions caused by NMRPs, while regulations on their productions, sales, or usage are still lacking. One of the reasons could be the missing comprehensive risk assessment. We first conducted a nationwide investigation on children's NMRP usage preferences. Then, we high-throughput screened volatile or semivolatile organic chemicals (VOCs/SVOCs) in five representative NMRPs by the headspace gas chromatography-orbitrap high-resolution mass spectrometry analytical method. After that, toxic compounds were recognized based on the toxicity forecaster (ToxCast) database. A total of 277 VOCs/SVOCs were recognized, and 70 of them were identified as toxic compounds. In a combination of concentrations, toxicities, absorption, distribution, metabolism, and excretion characteristics in the body, 28 chemicals were finally proposed as priority-controlled compounds in NMRPs. Exposure risks of recognized toxic chemicals through NMRPs by inhalation and dermal intake for children across the country were also assessed. Average daily intakes were in the range of 0.20-7.31 mg/kg/day for children in different provinces, and the children in southeastern coastal provinces were found to face higher exposure risks. By controlling the high-priority chemicals, the risks were expected to be reduced by about 46.8% on average. Results of this study are therefore believed to evaluate exposure risks, encourage safe production, and promote reasonable management of NMRPs.

Abstract Background The Toxicological Priority Index (ToxPi) is a method for prioritization and profiling of chemicals that integrates data from diverse sources. However, individual data sources (“assays”), such as in vitro bioassays or in vivo study endpoints, often feature sections of missing data, wherein subsets of chemicals have not been tested in all assays. In order to investigate the effects of missing data and recommend solutions, we designed simulation studies around high-throughput screening data generated by the ToxCast and Tox21 programs on chemicals highlighted by the Agency for Toxic Substances and Disease Registry’s (ATSDR) Substance Priority List (SPL), which helps prioritize environmental research and remediation resources. Results Our simulations explored a wide range of scenarios concerning data (0-80% assay data missing per chemical), modeling (ToxPi models containing from 160-700 different assays), and imputation method (k-Nearest-Neighbor, Max, Mean, Min, Binomial, Local Least Squares, and Singular Value Decomposition). We find that most imputation methods result in significant changes to ToxPi score, except for datasets with a small number of assays. If we consider rank change conditional on these significant changes to ToxPi score, we find that ranks of chemicals in the minimum value imputation, SVD imputation, and kNN imputation sets are more sensitive to the score changes. Conclusions We found that the choice of imputation strategy exerted significant influence over both scores and associated ranks, and the most sensitive scenarios were those involving fewer assays plus higher proportions of missing data. By characterizing the effects of missing data and the relative benefit of imputation approaches across real-world data scenarios, we can augment confidence in the robustness of decisions regarding the health and ecological effects of environmental chemicals

• Neurogenic disrupting chemicals impact key events specific to NVU development. • Combining results from multiple cell-based assays characterizes potential NVU hazard. • Grouping endpoints determines chemicals impacting similar NVU key events. • Literature model hypothesizes additional NVU chemicals for investigation. Development of the neurovascular unit (NVU) is a complex, multistage process that requires orchestrated cell signaling mechanisms across several cell types and ultimately results in formation of the blood-brain barrier. Typical high-throughput screening (HTS) assays investigate single biochemical or single cell responses following chemical insult. As the NVU comprises multiple cell types interacting at various stages of development, a methodology combining high-throughput results across pertinent cell-based assays is needed to investigate potential chemical-induced disruption to the development of this complex cell system. To this end, we implemented a novel method for screening putative NVU disruptors across diverse assay platforms to predict chemical perturbation of the developing NVU. HTS assay results measuring chemical-induced perturbations to cellular key events across angiogenic and neurogenic outcomes in vitro were combined to create a cell-based prioritization of NVU hazard. Chemicals were grouped according to similar modes of action to train a logistic regression literature model on a training set of 38 chemicals. This model utilizes the chemical-specific pairwise mutual information score for PubMed MeSH annotations to represent a quantitative measure of previously published results. Taken together, this study presents a methodology to investigate NVU developmental hazard using cell-based HTS assays and literature evidence to prioritize screening of putative NVU disruptors towards a knowledge-driven characterization of neurovascular developmental toxicity. The results from these screening efforts demonstrate that chemicals representing a range of putative vascular disrupting compound (pVDC) scores can also produce effects on neurogenic outcomes and characterizes possible modes of action for disrupting the developing NVU. [ABSTRACT FROM AUTHOR]

Mitochondrial toxicity drives several adverse health outcomes. Current high-throughput screening assays for chemically induced mitochondrial toxicity typically measure changes to mitochondrial structure and may not detect known mitochondrial toxicants. We adapted a respirometric screening assay (RSA) measuring mitochondrial function to screen ToxCast chemicals in HepG2 cells using a tiered testing strategy. Of 1042 chemicals initially screened at a singlemaximal concentration, 243 actives were identified and rescreened at 7 concentrations. Concentration-response data for 3 respiration phases confirmed activity and indicated a mechanism for 193 mitochondrial toxicants: 149 electron transport chain inhibitors (ETCi), 15 uncouplers and 29 adenosine triphosphate synthase inhibitors. Subsequently, an electron flow assay was used to identify the target complex for 84 of the 149 ETCi. Sixty reference chemicals were used to compare the RSA to existing ToxCast and Tox21 mitochondrial toxicity assays. The RSA was most predictive (accuracy = 90%) of mitochondrial toxicity. The Tox21 mitochondrial membrane potential assay was also highly predictive (accuracy = 87%) of bioactivity but underestimated the potency of well-known ETCi and provided no mechanistic information. The tiered RSA approach accurately identifies and characterizes mitochondrial toxicants acting through diverse mechanisms and at a throughput sufficient to screen large chemical inventories. The electron flow assay provides additional confirmation and detailed mechanistic understanding for ETCi, the most common type of mitochondrial toxicants among ToxCast chemicals. The mitochondrial toxicity screening approach described herein may inform hazard assessment and the in vitro bioactive concentrations used to derive relevant doses for screening level chemical assessment using new approach methodologies. [ABSTRACT FROM AUTHOR]

Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process. [ABSTRACT FROM AUTHOR]

Tox21 and ToxCast are high-throughput in vitro screening programs coordinated by the U.S. National Toxicology Program and the U.S. Environmental Protection Agency, respectively, with the goal of forecasting biological effects in vivo based on bioactivity profiling. The present study investigated whether mechanistic insights in the biological targets of food-relevant chemicals can be obtained from ToxCast results when the chemicals are grouped according to structural similarity. Starting from the 556 direct additives that have been identified in the ToxCast database by Karmaus et al. [Karmaus, A. L., Trautman, T. D., Krishan, M., Filer, D. L., and Fix, L. A. (2017). Curation of food-relevant chemicals in ToxCast. Food Chem. Toxicol.   103 , 174–182.], the results showed that, despite the limited number of assays in which the chemical groups have been tested, sufficient results are available within so-called "DNA binding" and "nuclear receptor" target families to profile the biological activities of the defined chemical groups for these targets. The most obvious activity identified was the estrogen receptor-mediated actions of the chemical group containing parabens and structurally related gallates, as well the chemical group containing genistein and daidzein (the latter 2 being particularly active toward estrogen receptor β as a potential health benefit). These group effects, as well as the biological activities of other chemical groups, were evaluated in a series of case studies. Overall, the results of the present study suggest that high-throughput screening data could add to the evidence considered for regulatory risk assessment of food chemicals and to the evaluation of desirable effects of nutrients and phytonutrients. The data will be particularly useful for providing mechanistic information and to fill data gaps with read-across. [ABSTRACT FROM AUTHOR]

High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis, ~ 765,000 substances). Experimental spectra from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration. [ABSTRACT FROM AUTHOR]

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

Cleft palate has been linked to both genetic and environmental factors that perturb key events during palatal morphogenesis. As a developmental outcome, it presents a challenging, mechanistically complex endpoint for predictive modeling. A data set of 500 chemicals evaluated for their ability to induce cleft palate in animal prenatal developmental studies was compiled from Toxicity Reference Database and the biomedical literature, which included 63 cleft palate active and 437 inactive chemicals. To characterize the potential molecular targets for chemical‐induced cleft palate, we mined the ToxCast high‐throughput screening database for patterns and linkages in bioactivity profiles and chemical structural descriptors. ToxCast assay results were filtered for cytotoxicity and grouped by target gene activity to produce a "gene score." Following unsuccessful attempts to derive a global prediction model using structural and gene score descriptors, hierarchical clustering was applied to the set of 63 cleft palate positives to extract local structure‐bioactivity clusters for follow‐up study. Patterns of enrichment were confirmed on the complete data set, that is, including cleft palate inactives, and putative molecular initiating events identified. The clusters corresponded to ToxCast assays for cytochrome P450s, G‐protein coupled receptors, retinoic acid receptors, the glucocorticoid receptor, and tyrosine kinases/phosphatases. These patterns and linkages were organized into preliminary decision trees and the resulting inferences were mapped to a putative adverse outcome pathway framework for cleft palate supported by literature evidence of current mechanistic understanding. This general data‐driven approach offers a promising avenue for mining chemical‐bioassay drivers of complex developmental endpoints where data are often limited. [ABSTRACT FROM AUTHOR]

Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package httk. These models are designed to be parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human in vitro data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific in vitro data. This package is structured to be augmented with additional chemical data as they become available. Package httk enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening.

Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new strategies are needed to focus time-intensive identification efforts on features with the highest potential to cause adverse effects instead of the most abundant ones. To address this challenge, we developed MLinvitroTox, a machine learning framework that uses molecular fingerprints derived from fragmentation spectra (MS2) for a rapid classification of thousands of unidentified HRMS/MS features as toxic/nontoxic based on nearly 400 target-specific and over 100 cytotoxic endpoints from ToxCast/Tox21. Model development results demonstrated that using customized molecular fingerprints and models, over a quarter of toxic endpoints and the majority of the associated mechanistic targets could be accurately predicted with sensitivities exceeding 0.95. Notably, SIRIUS molecular fingerprints and xboost (Extreme Gradient Boosting) models with SMOTE (Synthetic Minority Oversampling Technique) for handling data imbalance were a universally successful and robust modeling configuration. Validation of MLinvitroTox on MassBank spectra showed that toxicity could be predicted from molecular fingerprints derived from MS2 with an average balanced accuracy of 0.75. By applying MLinvitroTox to environmental HRMS/MS data, we confirmed the experimental results obtained with target analysis and narrowed the analytical focus from tens of thousands of detected signals to 783 features linked to potential toxicity, including 109 spectral matches and 30 compounds with confirmed toxic activity.

The CompTox Chemistry Dashboard (ToxCast) contains one of the largest public databases on Zebrafish (Danio rerio) developmental toxicity. The data consists of 19 toxicological endpoints on unique 1018 compounds measured in relatively low concentration ranges. The endpoints are related to developmental effects occurring in dechorionated zebrafish embryos for 120 hours post fertilization and monitored via gross malformations and mortality. We report the predictive capability of 209 quantitative structure–activity relationship (QSAR) models developed by machine learning methods using penalization techniques and diverse model quality metrics to cope with the imbalanced endpoints. All these QSAR models were generated to test how the imbalanced classification (toxic or non-toxic) endpoints could be predicted regardless which of three algorithms is used: logistic regression, multi-layer perceptron, or random forests. Additionally, QSAR toxicity models are developed starting from sets of classical molecular descriptors, structural fingerprints and their combinations. Only 8 out of 209 models passed the 0.20 Matthew’s correlation coefficient value defined a priori as a threshold for acceptable model quality on the test sets. The best models were obtained for endpoints mortality (MORT), ActivityScore and JAW (deformation). The low predictability of the QSAR model developed from the zebrafish embryotoxicity data in the database is mainly due to a higher sensitivity of 19 measurements of endpoints carried out on dechorionated embryos at low concentrations.

• Organophosphate and N-methyl carbamate pesticides are identified as two common mechanism groups in cumulative risk assessment. • We apply QSAR-, cell-, and small organism-based new approach methodologies (NAMs) to examine their mechanisms of action. • In some cases, mitochondrial toxicity of organophosphate pesticides may augment the disruption of acetylcholine signaling in developmental toxicity. • This study provides a proof of concept for applying NAMs to refine the existing groups based on dissimilar mechanisms of action. • NAMs are also useful to define new common mechanism groups based on structural features. Mitochondrial toxicity has been proposed as a potential cause of developmental defects in humans. We evaluated 51 organophosphate and carbamate pesticides using the U.S. EPA ToxCast and Tox21 databases. Only a small number of them bind directly to cholinesterases in the parent form. The hydrophobicity of organophosphate pesticides is correlated significantly to TSPO binding affinity, mitochondrial membrane potential reduction in HepG2 cells, and developmental toxicity in Caenorhabditis elegans and Danio rerio (p < 0.05). Structural analysis suggests that in some cases the Krebs cycle is a potential target of organophosphate and carbamate exposure at early life stages. The results support the hypothesis that mitochondrial effects of some organophosphate pesticides—particularly those that require enzymatic activation to the oxon form—may augment the documented effects of disruption of acetylcholine signaling. This study provides a proof of concept for applying new approach methodologies to interrogate mechanisms of action for cumulative risk assessment. [ABSTRACT FROM AUTHOR]

Organic chemicals from industrial, agricultural, and residential activities can enter surface waters through regulated and unregulated discharges, combined sewer overflows, stormwater runoff, accidental spills, and leaking septic-conveyance systems on a daily basis. The impact of point and nonpoint contaminant sources can result in adverse biological effects for organisms living in or near surface waters. Assessing the adverse or toxic effects that may result when exposure occurs is complicated by the fact that many commonly used chemicals lack toxicity information or water quality standards. To address these challenges, an exposure-activity ratio (EAR) screening approach was used to prioritize environmental chemistry data in a West Virginia watershed (Wolf Creek). Wolf Creek is a drinking water source and recreation resource with documented water quality impacts from point and nonpoint sources. The EAR screening approach uses high-throughput screening (HTS) data from ToxCast as a method of integrating environmental chemical occurrence and biological effects data. Using water quality schedule 4433, which targets 69 organic waste compounds typically found in domestic and industrial wastewater, chemicals were screened for potential adverse biological affects at multiple sites in the Wolf Creek watershed. Cumulative EAR mixture values were greatest at Sites 2 and 3, where bisphenol A (BPA) and pentachlorophenol exhibited maximum EAR values of 0.05 and 0.002, respectively. Site 2 is downstream of an unconventional oil and gas (UOG) wastewater disposal facility with documented water quality impacts. Low-level organic contaminants were found at all sample sites in Wolf Creek, except Site 10, where Wolf Creek enters the New River. The application of an EAR screening approach allowed our study to extend beyond traditional environmental monitoring methods to identify multiple sites and chemicals that warrant further investigation. Unlabelled Image • Exposure activity ratio approach was used to screen contaminants in source water. • Organic compounds occurred as complex mixtures of 10 or more at multiple sites. • toxEval was used to rapidly analyze and screen environmental monitoring data. • Exposure-activity ratios were observed for chemicals without water quality benchmarks. • Water quality impacts were observed downstream from a wastewater disposal facility. [ABSTRACT FROM AUTHOR]

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

Graphical abstract Highlights • Visualization of ToxCast data availability and density, including water relevant mechanisms of toxicity. • Efficient prioritization strategy for suspect screening data combining occurrence and hazard data. • High throughput approach allows automation. • Little overlap between exposure only and exposure/hazard ratio based prioritization. Abstract Advancements in high-resolution mass spectrometry based methods have enabled a shift from pure target analysis to target, suspect and non-target screening analyses to detect chemicals in water samples. The multitude of suspect chemicals thereby detected needs to be prioritized for further identification, prior to health risk assessment and potential inclusion into monitoring programs. Here, we compare prioritization of chemicals in Dutch water samples based on relative intensities only to prioritization including hazard information based on high-throughput in vitro toxicity data. Over 1000 suspects detected in sewage treatment plant effluent, surface water, groundwater and drinking water samples were ranked based on their relative intensities. Toxicity data availability and density in the ToxCast database were determined and visualized for these suspects, also in regard to water relevant mechanisms of toxicity. More than 500 suspects could be ranked using occurrence/hazard ratios based on more than 1000 different assay endpoints. The comparison showed that different prioritization strategies resulted in significantly different ranking, with only 2 suspects prioritized based on occurrence among the top 20 in the hazard ranking. We therefore propose a novel scheme that integrates both exposure and hazard data, and efficiently prioritizes which features need to be confidently identified first. [ABSTRACT FROM AUTHOR]

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

In August 2015, the US Environmental Protection Agency (EPA) convened a workshop entitled "Advancing non-targeted analyses of xenobiotic chemicals in environmental and biological media." The purpose of the workshop was to bring together the foremost experts in non-targeted analysis (NTA) to discuss the state-of-the-science for generating, interpreting, and exchanging NTA measurement data. During the workshop, participants discussed potential designs for a collaborative project that would use EPA resources, including the ToxCast library of chemical substances, the DSSTox database, and the CompTox Chemicals Dashboard, to evaluate cutting-edge NTA methods. That discussion was the genesis of EPA's Non-Targeted Analysis Collaborative Trial (ENTACT). Nearly 30 laboratories have enrolled in ENTACT and used a variety of chromatography, mass spectrometry, and data processing approaches to characterize ten synthetic chemical mixtures, three standardized media (human serum, house dust, and silicone band) extracts, and thousands of individual substances. Initial results show that nearly all participants have detected and reported more compounds in the mixtures than were intentionally added, with large inter-lab variability in the number of reported compounds. A comparison of gas and liquid chromatography results shows that the majority (45.3%) of correctly identified compounds were detected by only one method and 15.4% of compounds were not identified. Finally, a limited set of true positive identifications indicates substantial differences in observable chemical space when employing disparate separation and ionization techniques as part of NTA workflows. This article describes the genesis of ENTACT, all study methods and materials, and an analysis of results submitted to date.ᅟ [ABSTRACT FROM AUTHOR]

Abstract The ToxCast program has generated in vitro screening data on over a thousand chemicals to assess potential disruption of important biological processes and assist in hazard identification and chemical testing prioritization. Few results have been reported for complex mixtures. To extend these ToxCast efforts to mixtures, we tested extracts from 30 organically grown fruits and vegetables in concentration-response in the BioMAP® assays. BioMAP systems use human primary cells primed with endogenous pathway activators to identify phenotypic perturbations related to proliferation, inflammation, immunomodulation, and tissue remodeling. Clustering of bioactivity profiles revealed separation of these produce extracts and ToxCast chemicals. Produce extracts elicited 87 assay endpoint responses per item compared to 20 per item for ToxCast chemicals. On a molar basis, the produce extracts were 10 to 50-fold less potent and when constrained to the maximum testing concentration of the ToxCast chemicals, the produce extracts did not show activity in as many assay endpoints. Using intake adjusted measures of dose, the bioactivity potential was higher for produce extracts than for agrichemicals, as expected based on the comparatively small amounts of agrichemical residues present on conventionally grown produce. The evaluation of BioMAP readouts and the dose responses for produce extracts showed qualitative and quantitative differences from results with single chemicals, highlighting challenges in the interpretation of bioactivity data and dose-response from complex mixtures. Highlights • Bioactivity of mixtures extracted from 30 organic produce samples was assessed using the BioMAP panel • Produce samples were 10 to 50-fold less potent than ToxCast compounds on an estimated molar basis. • Constraining to maximum testing concentration of ToxCast chemicals, the produce samples displayed less activity. • A relative intake dose metric is proposed to integrate differences in potency. • A framework highlighting approaches for risk-based decision making for mixtures is outlined. [ABSTRACT FROM AUTHOR]

Abstract Several organic contaminants (OCs) have been detected in bald eagle (Haliaeetus leucocephalus) nestling (eaglet) plasma in the upper Midwestern United States. Despite frequent and relatively high concentrations of OCs in eaglets, little is understood about potential biological effects associated with exposure. We screened an existing database of OC concentrations in eaglet plasma collected from the Midwestern United States against bioactivity information from the ToxCast database. ToxCast bioactivity information consists of concentrations expected to elicit responses across a range of biological space (e.g. cellular, developmental, etc.) obtained from a series of high throughput assays. We calculated exposure—activity ratios (EAR) by calculating the ratio of plasma concentrations to concentrations available in ToxCast. Bioactivity data were not available for all detected OCs. Therefore, our analysis provides estimates of potential bioactivity for 19 of the detected OCs in eaglet plasma. Perfluorooctanesulfonic acid (PFOS) EAR values were consistently the highest among all study areas. Maximum EAR values were ≥1 for PFOS, perfluorononanoic acid, and bisphenol A in 99.7, 0.53 and 0.26% of samples, indicating that some plasma concentrations were greater than what may be expected to elicit biological responses. About 125 gene targets, indicative of specific biological pathways, were identified as potentially being affected. Inhibition of several CYP genes, involved in xenobiotic metabolism, were most consistently identified. Other identified biological responses have potential implications for motor coordination, cardiac functions, behavior, and blood circulation. However, it is unclear what these results mean for bald eagles, given that ToxCast data are generated using mammalian-based endpoints. Despite uncertainties and limitations, this method of screening environmental data can be useful for informing future monitoring or research focused on understanding the occurrence and effects of OCs in bald eagles and other similarly-positioned trophic species. Graphical abstract Image 1 Highlights • Organic contaminants in bald eaglet plasma screened for potential sub-lethal effects. • Exposure—activity ratios calculated as plasma concentration/activity concentration. • Maximum exposure—activity ratios >1 for PFOS, PFNA, and bisphenol A. • Consistently high exposure—activity ratios for CYP and BACE1 genes. • Little intra-study area variability, but high inter-study area variability. Comparison of organic contaminant concentrations in bald eaglet plasma to bioactivity data from in-vitro bioassays indicates that PFOS, CYP-related endpoints, and sites near urban areas should be prioritized for future monitoring focused on understanding the effects of organic contaminant exposure in bald eagles. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted the potential of the ToxCast™ database for mechanism-based prioritization of chemicals. To explore the applicability of ToxCast data in the context of regulatory inventory chemicals, we screened 510 priority existing chemicals (PECs) regulated under the Act on the Registration and Evaluation, etc. of Chemical Substances (K-REACH) using ToxCast bioassays. In our analysis, a hit-call data matrix containing 298984 chemical-gene interactions was computed for 949 bioassays with the intended target genes, which enabled the identification of the putative toxicity mechanisms. Based on the reactivity to the chemicals, we analyzed 412 bioassays whose intended target gene families were cytochrome P450, oxidoreductase, transporter, nuclear receptor, steroid hormone, and DNA-binding. We also identified 141 chemicals based on their reactivity in the bioassays. These chemicals are mainly in consumer products including colorants, preservatives, air fresheners, and detergents. Our analysis revealed that in vitro bioactivities were involved in the relevant mechanisms inducing in vivo toxicity; however, this was not sufficient to predict more hazardous chemicals. Overall, the current results point to a potential and limitation in using ToxCast data for chemical prioritization in regulatory context in the absence of suitable in vivo data. • A mechanism-based prioritization approach for identifying potential toxicants was proposed. • Significant ToxCast bioassays and priority existing chemicals were prioritized based the reactiveness. • Genes targeted by selected in vitro bioassays were involved in relevant mechanism inducing apical in vivo toxicity. • A potential and limitation of the application of ToxCast data to regulatory inventory chemicals were presented. [ABSTRACT FROM AUTHOR]

The present study explores the ToxCast/Tox21 database to select candidate bioassays as bioanalytical tools for measuring groups of chemicals in water. To this aim, the ToxCast/Tox21 database was explored for bioassays that detect polycyclic aromatic hydrocarbons (PAHs), aromatic amines (AAs), (chloro)phenols ((C)Ps) and halogenated aliphatic hydrocarbons (HAliHs), which are included in the European and/or Dutch Drinking Water Directives. Based on the analysis of the availability and performance of bioassays included in the database, we concluded that several bioassays are suitable as bioanalytical tools for assessing the presence of PAHs and (C)Ps in drinking water sources. No bioassays were identified for AAs and HAliHs, due to the limited activity of these chemicals and/or the limited amount of data on these chemicals in the database. A series of bioassays was selected that measure molecular or cellular effects that are covered by bioassays currently in use for chemical water quality monitoring. Interestingly, also bioassays were selected that represent molecular or cellular effects that are not covered by bioassays currently applied. The usefulness of these newly identified bioassays as bioanalytical tools should be further evaluated in follow-up studies. Altogether, this study shows how exploration of the ToxCast/Tox21 database provides a series of candidate bioassays as bioanalytical tools for measuring groups of chemicals in water. This assessment can be performed for any group of chemicals of interest (if represented in the database), and may provide candidate bioassays that can be used to complement the currently applied bioassays for chemical water quality assessment. [ABSTRACT FROM AUTHOR]

Rising obesity rates worldwide have socio-economic ramifications. While genetics, diet, and lack of exercise are major contributors to obesity, environmental factors may enhance susceptibility through disruption of hormone homeostasis and metabolic processes. The obesogen hypothesis contends that chemical exposure early in development may enhance adipocyte differentiation, thereby increasing the number of adipocytes and predisposing for obesity and metabolic disease. We previously developed a primary human adipose stem cell (hASC) assay to evaluate the effect of environmental chemicals on PPARG-dependent adipogenesis. Here, the assay was modified to determine the effects of chemicals on the glucocorticoid receptor (GR) pathway. In differentiation cocktail lacking the glucocorticoid agonist dexamethasone (DEX), hASCs do not differentiate into adipocytes. In the presence of GR agonists, adipocyte maturation was observed using phenotypic makers for lipid accumulation, adipokine secretion, and expression of key genes. To evaluate the role of environmental compounds on adipocyte differentiation, progenitor cells were treated with 19 prioritized compounds previously identified by ToxPi as having GR-dependent bioactivity, and multiplexed assays were used to confirm a GR-dependent mode of action. Five chemicals were found to be strong agonists. The assay was also modified to evaluate GR-antagonists, and 8/10 of the hypothesized antagonists inhibited adipogenesis. The in vitro bioactivity data was put into context with extrapolated human steady state concentrations ( Css ) and clinical exposure data (Cmax). These data support using a human adipose-derived stem cell differentiation assay to test the potential of chemicals to alter human GR-dependent adipogenesis. [ABSTRACT FROM AUTHOR]

The development and application of high throughput in vitro assays is an important development for risk assessment in the twenty-first century. However, there are still significant challenges to incorporate in vitro assays into routine toxicity testing practices. In this paper, a robust learning approach was developed to infer the in vivo point of departure (POD) with in vitro assay data from ToxCast and Tox21 projects. Assay data from ToxCast and Tox21 projects were utilized to derive the in vitro PODs for several hundred chemicals. These were combined with in vivo PODs from ToxRefDB regarding the rat and mouse liver to build a high-dimensional robust regression model. This approach separates the chemicals into a majority, well-predicted set; and a minority, outlier set. Salient relationships can then be learned from the data. For both mouse and rat liver PODs, over 93% of chemicals have inferred values from in vitro PODs that are within ± 1 of the in vivo PODs on the log10 scale (the target learning region, or TLR) and R2 of 0.80 (rats) and 0.78 (mice) for these chemicals. This is comparable with extrapolation between related species (mouse and rat), which has 93% chemicals within the TLR and the R2 being 0.78. Chemicals in the outlier set tend to also have more biologically variable characteristics. With the continued accumulation of high throughput data for a wide range of chemicals, predictive modeling can provide a valuable complement for adverse outcome pathway based approach in risk assessment. [ABSTRACT FROM AUTHOR]

The U.S. Environmental Protection Agency's ToxCast program has screened thousands of chemicals for biological activity, primarily using high-throughput in vitro bioassays. Adverse outcome pathways (AOPs) offer a means to link pathway-specific biological activities with potential apical effects relevant to risk assessors. Thus, efforts are underway to develop AOPs relevant to pathway-specific perturbations detected in ToxCast assays. Previous work identified a "cytotoxic burst" (CTB) phenomenon wherein large numbers of the ToxCast assays begin to respond at or near test chemical concentrations that elicit cytotoxicity, and a statistical approach to defining the bounds of the CTB was developed. To focus AOP development on the molecular targets corresponding to ToxCast assays indicating pathway-specific effects, we conducted a meta-analysis to identify which assays most frequently respond at concentrations below the CTB. A preliminary list of potentially important, target-specific assays was determined by ranking assays by the fraction of chemical hits below the CTB compared with the number of chemicals tested. Additional priority assays were identified using a diagnostic-odds-ratio approach which gives greater ranking to assays with high specificity but low responsivity. Combined, the two prioritization methods identified several novel targets (e.g., peripheral benzodiazepine and progesterone receptors) to prioritize for AOP development, and affirmed the importance of a number of existing AOPs aligned with ToxCast targets (e.g., thyroperoxidase, estrogen receptor, aromatase). The prioritization approaches did not appear to be influenced by inter-assay differences in chemical bioavailability. Furthermore, the outcomes were robust based on a variety of different parameters used to define the CTB. [ABSTRACT FROM AUTHOR]

The U.S. Environmental Protection Agency Endocrine Disruptor Screening Program and the Organization for Economic Cooperation and Development (OECD) have used the human adrenocarcinoma (H295R) cell-based assay to predict chemical perturbation of androgen and estrogen production. Recently, a high-throughput H295R (HT-H295R) assay was developed as part of the ToxCast program that includes measurement of 11 hormones, including progestagens, corticosteroids, androgens, and estrogens. To date, 2012 chemicals have been screened at 1 concentration; of these, 656 chemicals have been screened in concentration-response. The objectives of this work were to: (1) develop an integrated analysis of chemical-mediated effects on steroidogenesis in the HT-H295R assay and (2) evaluate whether the HT-H295R assay predicts estrogen and androgen production specifically via comparison with the OECD-validated H295R assay. To support application of HT-H295R assay data to weight-of-evidence and prioritization tasks, a single numeric value based on Mahalanobis distances was computed for 654 chemicals to indicate themagnitude of effects on the synthesis of 11 hormones. The maximum mean Mahalanobis distance (maxmMd) values were high for strong modulators (prochloraz, mifepristone) and lower formoderate modulators (atrazine, molinate). Twenty-five of 28 reference chemicals used for OECD validation were screened in the HT-H295R assay, and produced qualitatively similar results, with accuracies of 0.90/0.75 and 0.81/0.91 for increased/decreased testosterone and estradiol production, respectively. The HT-H295R assay provides robust information regarding estrogen and androgen production, as well as additional hormones. ThemaxmMd fromthis integrated analysis may provide a data-driven approach to prioritizing lists of chemicals for putative effects on steroidogenesis. [ABSTRACT FROM AUTHOR]