Identification of neural-relevant toxcast high-throughput assay intended gene targets: Applicability to neurotoxicity and neurotoxicant putative molecular initiating events.

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.

Potential Hazards of Polycyclic Aromatic Hydrocarbons in Great Lakes Tributaries Using Water Column and Porewater Passive Samplers and Sediment Equilibrium Partitioning.

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.

Assessment of TGx-DDI genes for genotoxicity in a comprehensive panel of chemicals.

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.

Investigating open access new approach methods (NAM) to assess biological points of departure: A case study with 4 neurotoxic pesticides.

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 (AEDHuman) 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 AEDHuman 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 AEDHuman 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.

Screening and Evaluation of Children's Sensitively Toxic Chemicals in New Mosquito Repellent Products Based on a Nationwide Investigation.

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.

Evaluation of new alternative methods for the identification of estrogenic, androgenic and steroidogenic effects: a comparative in vitro/in silico study.

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.

Cashmeran as a potential endocrine disrupting chemical: What does the weight-of-the-evidence indicate?

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 ß (hTHRß) 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.

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).

The ToxCast pipeline: updates to curve-fitting approaches and database structure.

Introduction: The US Environmental Protection Agency Toxicity Forecaster (ToxCast) program makes in vitro medium- and high-throughput screening assay data publicly available for prioritization and hazard characterization of thousands of chemicals. The assays employ a variety of technologies to evaluate the effects of chemical exposure on diverse biological targets, from distinct proteins to more complex cellular processes like mitochondrial toxicity, nuclear receptor signaling, immune responses, and developmental toxicity. The ToxCast data pipeline (tcpl) is an open-source R package that stores, manages, curve-fits, and visualizes ToxCast data and populates the linked MySQL Database, invitrodb. Methods: Herein we describe major updates to tcpl and invitrodb to accommodate a new curve-fitting approach. The original tcpl curve-fitting models (constant, Hill, and gain-loss models) have been expanded to include Polynomial 1 (Linear), Polynomial 2 (Quadratic), Power, Exponential 2, Exponential 3, Exponential 4, and Exponential 5 based on BMDExpress and encoded by the R package dependency, tcplfit2. Inclusion of these models impacted invitrodb (beta version v4.0) and tcpl v3 in several ways: (1) long-format storage of generic modeling parameters to permit additional curve-fitting models; (2) updated logic for winning model selection; (3) continuous hit calling logic; and (4) removal of redundant endpoints as a result of bidirectional fitting. Results and discussion: Overall, the hit call and potency estimates were largely consistent between invitrodb v3.5 and 4.0. Tcpl and invitrodb provide a standard for consistent and reproducible curve-fitting and data management for diverse, targeted in vitro assay data with readily available documentation, thus enabling sharing and use of these data in myriad toxicology applications. The software and database updates described herein promote comparability across multiple tiers of data within the US Environmental Protection Agency CompTox Blueprint.

Identification of pararosaniline as a modifier of RNA splicing in Caenorhabditis elegans.

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.

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

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

Comparison between endocrine activity assessed using ToxCast/Tox21 database and human plasma concentration of sunscreen active ingredients/UV filters.

Sunscreen products are composed of ultraviolet (UV) filters and formulated to reduce exposure to sunlight thereby lessening skin damage. Concerns have been raised regarding the toxicity and potential endocrine disrupting (ED) effects of UV filters. The ToxCast/Tox21 program, that is, CompTox, is a high-throughput in vitro screening database of chemicals that identify adverse outcome pathways, key events, and ED potential of chemicals. Using the ToxCast/Tox21 database, octisalate, homosalate, octocrylene, oxybenzone, octinoxate, and avobenzone, 6 commonly used organic UV filters, were found to have been evaluated. These UV filters showed low potency in these bioassays with most activity detected above the range of the cytotoxic burst. The pathways that were most affected were the cell cycle and the nuclear receptor pathways. Most activity was observed in liver and kidney-based bioassays. These organic filters and their metabolites showed relatively weak ED activity when tested in bioassays measuring estrogen receptor (ER), androgen receptor (AR), thyroid receptor, and steroidogenesis activity. Except for oxybenzone, all activity in the endocrine assays occurred at concentrations greater than the cytotoxic burst. Moreover, except for oxybenzone, plasma concentrations (Cmax) measured in humans were at least 100× lower than bioactive (AC50/ACC) concentrations that produced a response in ToxCast/Tox21 assays. These data are consistent with in vivo animal/human studies showing weak or negligible endocrine activity. In sum, when considered as part of a weight-of-evidence assessment and compared with measured plasma concentrations, the results show these organic UV filters have low intrinsic biological activity and risk of toxicity including endocrine disruption in humans.

Exploring the potential of ToxCast™ data for mechanism-based prioritization of chemicals in regulatory context: Case study with priority existing chemicals (PECs) under K-REACH.

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.

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data.

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.

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.

Screening of organophosphate esters in different indoor environments: Distribution, diffusion, and risk assessment.

ANALYSIS: of air conditioner (AC) filter dust can reveal the level of organophosphate ester (OPE) pollution in indoor environments, but comprehensive research on this topic remains lacking. This study combined non-targeted and targeted analysis to screen and analyze 101 samples of AC filter dust, settled dust, and air obtained in 6 indoor environments. Phosphorus-containing organic compounds account for a large proportion of the organic compounds found in indoor environments, and OPEs might be the main pollutants. Using toxicity data and traditional priority polycyclic aromatic hydrocarbons for toxicity prediction of OPEs, 11 OPEs were prioritized for further quantitative analysis. The concentration of OPEs in AC filter dust was highest, followed in descending order by that in settled dust and that in air. The concentration of OPEs in AC filter dust in the residence was two to seven times greater than that in the other indoor environments. More than 56% of the OPEs in AC filter dust showed significant correlation, while those in settled dust and air were weakly correlated, suggesting that large amounts of OPEs collected over long periods could have a common source. Fugacity results showed that OPEs were transferred easily from dust to air, and that dust was the main source of OPEs. The values of both the carcinogenic risk and the hazard index were lower than the corresponding theoretical risk thresholds, indicating low risk to residents through exposure to OPEs in indoor environments. However, it is necessary to remove AC filter dust in a timely manner to prevent it becoming a pollution sink of OPEs that could be rereleased and endanger human health. This study has important implications for comprehensive understanding of the distribution, toxicity, sources, and risks of OPEs in indoor environments.

Pilot testing and optimization of a larval fathead minnow high throughput transcriptomics assay.

Concentrations at which global gene expression profiles in cells or animals exposed to a test substance start to differ significantly from those of controls have been proposed as an alternative point of departure for use in screening level hazard assessment. The present study describes pilot testing of a high throughput compatible transcriptomics assay with larval fathead minnows. One day post hatch fathead minnows were exposed to eleven different concentrations of three metals, three selective serotonin reuptake inhibitors, and four neonicotinoid-like compounds for 24 h and concentration response modeling was applied to whole body gene expression data. Transcriptomics-based points of departure (tPODs) were consistently lower than effect concentrations reported in apical endpoint studies in fish. However, larval fathead minnow-based tPODs were not always lower than concentrations reported to elicit apical toxicity in other aquatic organisms like crustaceans or insects. Random in silico subsampling of data from the pilot assays was used to evaluate various assay design and acceptance considerations such as transcriptome coverage, number of replicate individuals to sequence per treatment, and minimum number of differentially expressed genes to produce a reliable tPOD estimate. Results showed a strong association between the total number of genes for which a concentration response relationship could be derived and the overall variability in the resulting tPOD estimates. We conclude that, for our current assay design and analysis pipeline, tPODs based on fewer than 15 differentially expressed genes are likely to be unreliable for screening and that interindividual variability in gene expression profiles appears to be a more significant driver of tPOD variability than sample size alone. Results represent initial steps toward developing high throughput transcriptomics assays for use in ecological hazard screening.

Use of computational toxicology models to predict toxicological points of departure: A case study with triazine herbicides.

BACKGROUND: Atrazine simazine and propazine, widely used triazine herbicides on food crops and in residential areas, disrupt the neuroendocrine system raising human health concerns. USEPA developed a PBPK model based on triazine common Mode of Action (MOA)-suppression of luteinizing hormone surge in female rats-to generate human regulatory points of departure (POD: mg/kg/day). We compared triazine Human Administered Equivalent Dose (AEDHuman mg/kg/day) predictions from open access computational tools to the PBPK PODs to assess concordance. METHODS: Computational tools were the following: ToxCast/Tox21 in vitro assays; Toxicogenomic databases to assess concordance with ToxCast/Tox21 targets; integrated chemical environment (ICE) models with ToxCast/Tox21 inputs to predict AEDHuman PODs and population-based age-refined high throughput toxicokinetics (HTTK-Pop) to compare to age-related PBPK PODs. RESULTS: ToxCast/Tox21 assays identified critical targets in the triazine common MOA and gene databases; ICE AEDHuman predictions were mainly concordant with the USEPA PBPK PODs quantitatively. Low fold-differences between PBPK POD and ICE AEDHuman predictions indicated that the ICE models are health-protective. HTTK-Pop age-refinements were within 10-fold of the USEPA PBPK PODs. CONCLUSIONS: CompTox tools were used to identify assay targets in the MOA and identify potential molecular initiating targets in the adverse outcome pathway for potential use in risk assessment.

Confirmation of high-throughput screening data and novel mechanistic insights into FXR-xenobiotic interactions by orthogonal assays.

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.

Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling.

All-trans retinoic acid (ATRA) gradients determine skeletal patterning morphogenesis and can be disrupted by diverse genetic or environmental factors during pregnancy, leading to fetal skeleton defects. Adverse Outcome Pathway (AOP) frameworks for ATRA metabolism, signaling, and homeostasis allow for the development of new approach methods (NAMs) for predictive toxicology with less reliance on animal testing. Here, a data-driven model was constructed to identify chemicals associated with both ATRA pathway bioactivity and prenatal skeletal defects. The phenotype data was culled from ToxRefDB prenatal developmental toxicity studies and produced a list of 363 ToxRefDB chemicals with altered skeletal observations. Defects were classified regionally as cranial, post-cranial axial, appendicular, and other (unspecified) features based on ToxRefDB descriptors. To build a multivariate statistical model, high-throughput screening bioactivity data from >8,070 chemicals in ToxCast/Tox21 across 10 in vitro assays relevant to the retinoid signaling system were evaluated and compared to literature-based candidate reference chemicals in the dataset. There were 48 chemicals identified for effects on both in vivo skeletal defects and in vitro ATRA pathway targets for computational modeling. The list included 28 chemicals with prior evidence of skeletal defects linked to retinoid toxicity and 20 chemicals without prior evidence. The combination of thoracic cage defects and DR5 (direct repeats of 5 nucleotides for RAR/RXR transactivation) disruption was the most frequently occurring phenotypic and target disturbance, respectively. This data model provides valuable AOP elucidation and validates current mechanistic understanding. These findings also shed light on potential avenues for new mechanistic discoveries related to ATRA pathway disruption and associated skeletal dysmorphogenesis due to environmental exposures.