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.

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 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.

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.

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.

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 2023;00:1-18. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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 ToxCast™ database to 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 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.

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.

Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing

Per- and Polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterised for their hazard profiles, the vast majority of PFAS have not been studied. The US Environmental Protection Agency (EPA) undertook a research project to screen ~150 PFAS through an array of different in vitro high throughput toxicity and toxicokinetic tests in order to inform chemical category and read-across approaches. A previous publication described the rationale behind the selection of an initial set of 75 PFAS, whereas herein, we describe how various category approaches were applied and extended to inform the selection of a second set of 75 PFAS from our library of approximately 430 commercially procured PFAS. In particular, we focus on the challenges in grouping PFAS for prospective analysis and how we have sought to develop and apply objective structure-based categories to profile the testing library and other PFAS inventories. We additionally illustrate how these categories can be enriched with other information to facilitate read-across inferences once experimental data become available. The availability of flexible, objective, reproducible and chemically intuitive categories to explore PFAS constitutes an important step forward in prioritising PFAS for further testing and assessment.

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.

ToxCast chemical library Wnt screen identifies diethanolamine as an activator of neural progenitor proliferation

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 UBE3A T485A 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 UBE3A T485A 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 UBE3A T485A -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.

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

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

Risk-Based Prioritization of Organic Chemicals and Locations of Ecological Concern in Sediment From Great Lakes Tributaries

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.

Identifying the link between chemical exposures and breast cancer in African American women via integrated in vitro and exposure biomarker data

Among women, breast cancer is the most prevalent form of cancer worldwide and has the second highest mortality rate of any cancer in the United States. The breast cancer related death rate is 40 % higher in non-Hispanic Black women compared to non-Hispanic White women. The incidence of triple negative breast cancer (TNBC), an aggressive subtype of breast cancer for which there is no targeted therapy, is also approximately three times higher for Black, relative to, White women. The drivers of these differences are poorly understood. Here, we aimed to identify chemical exposures which play a role in breast cancer disparities. Using chemical biomonitoring data from the National Health and Nutrition Examination Survey (NHANES) and biological activity data from the EPA's ToxCast program, we assessed the toxicological profiles of chemicals to which US Black women are disproportionately exposed. We conducted a literature search to identify breast cancer targets in ToxCast to analyze the response of chemicals with exposure disparities in these assays. Forty-three chemical biomarkers are significantly higher in Black women. Investigation of these chemicals in ToxCast resulted in 32,683 assays for analysis, 5172 of which contained nonzero values for the concentration at which the dose-response fitted model reaches the cutoff considered "active". Of these chemicals BPA, PFOS, and thiram are most comprehensively assayed. 2,5-dichlorophenol, 1,4-dichlorobenzene, and methyl and propyl parabens had higher biomarker concentrations in Black women and moderate testing and activity in ToxCast. The distribution of active concentrations for these chemicals in ToxCast assays are comparable to biomarker concentrations in Black women NHANES participants. Through this integrated analysis, we identify that multiple chemicals, including thiram, propylparaben, and p,p' DDE, have disproportionate exposures in Black women and have breast cancer associated biological activity at human exposure relevant doses.

Endocrine disrupting potential of replacement flame retardants - Review of current knowledge for nuclear receptors associated with reproductive outcomes

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.

Identifying Chemicals and Mixtures of Potential Biological Concern Detected in Passive Samplers from Great Lakes Tributaries Using High-Throughput Data and Biological Pathways

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.

Recognition and Prioritization of Chemical Mixtures and Transformation Products in Chinese Estuarine Waters by Suspect Screening Analysis

Chemical mixtures in surface waters could have significant impacts on exposure risks to human beings and pollution stress to aquatic system. By suspect screening analysis of high-resolution mass spectrometry data, occurrence, and compositions of ToxCast chemicals were investigated in grab estuarine water samples from a combination of 20 rivers that represents approximately 70% of the total river flow discharge along the east coast of China. In total, 59 ToxCast chemicals in seven use categories were identified, in which pesticides, intermediates, and pharmaceuticals were the abundant analogues. Significant differences in pollutant composition profiles were noticed, which possibly reflected singular release pattern and geographical-relevant usage preference (especially for herbicides and fungicides in the pesticide category). With the aid of tentative quantitative/semiquantitative measurement, essential contributors to the cumulative pollutant mass discharges and aquatic acute toxicity potentials were focused onto few particular chemicals. Existence of transformation products was further explored, which indicated that the fates of the selected parent ToxCast chemicals could be influenced by dominating transformation reactions (e.g., N-dealkylation and hydroxylation) and possible environmental factors (i.e., microbial activity). The results emphasize the necessity of suspect screening analysis for assessing the influence of terrestrial emissions of pollutants to the surrounding environment.

Exploring Potential Carcinogenic Activity of Per- and Polyfluorinated Alkyl Substances Utilizing High-Throughput Toxicity Screening Data

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.

Investigating Molecular Mechanisms of Immunotoxicity and the Utility of ToxCast for Immunotoxicity Screening of Chemicals Added to Food

The development of high-throughput screening methodologies may decrease the need for laboratory animals for toxicity testing. Here, we investigate the potential of assessing immunotoxicity with high-throughput screening data from the U.S. Environmental Protection Agency ToxCast program. As case studies, we analyzed the most common chemicals added to food as well as per- and polyfluoroalkyl substances (PFAS) shown to migrate to food from packaging materials or processing equipment. The antioxidant preservative tert-butylhydroquinone (TBHQ) showed activity both in ToxCast assays and in classical immunological assays, suggesting that it may affect the immune response in people. From the PFAS group, we identified eight substances that can migrate from food contact materials and have ToxCast data. In epidemiological and toxicological studies, PFAS suppress the immune system and decrease the response to vaccination. However, most PFAS show weak or no activity in immune-related ToxCast assays. This lack of concordance between toxicological and high-throughput data for common PFAS indicates the current limitations of in vitro screening for analyzing immunotoxicity. High-throughput in vitro assays show promise for providing mechanistic data relevant for immune risk assessment. In contrast, the lack of immune-specific activity in the existing high-throughput assays cannot validate the safety of a chemical for the immune system.

Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem

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.

Prioritization of contaminants and biological process targets in the North Sea using toxicity data from ToxCast

The increasing number of chemicals detected in the marine environment underlines the need for appropriate prioritization strategies prior to further testing and potential inclusion into monitoring programs. Here, a prioritization strategy is proposed for chemicals detected in the North Sea over the last decade, through the development of a Concern Index (CI) using exposure and toxicity data obtained from peer-review publications and the ToxCast database, respectively. A total of 158 chemicals were ranked and the most sensitive tested assay endpoints were identified. Additionally, similar analysis was performed for the classes of chemicals and Biological Process Targets (BPTs). By first ranking chemicals currently acknowledged for their high toxicity to the aquatic environment, i.e. naphthalene, salicylic acid and simazine, the obtained results not only reinforce the risk posed by these but also promote a confident extrapolation from mammalian in vitro toxicity data to fish. Furthermore, genes targeted by the most sensitive assays, related to basic cell maintenance processes and immune defense, are highly evolutionarily conserved across species. The identification of these assays further reinforces the importance of a shift from traditional toxicity endpoints to lower levels of biological organization, allowing the detection of adverse effects at lower concentrations.

Identification of toxicity pathway of diesel particulate matter using AOP of PPARγ inactivation leading to pulmonary fibrosis

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.

Toxicity prediction and effect characterization of 90 pharmaceuticals and illicit drugs measured in plasma of fish from a major European river (Sava, Croatia)

Chemical analysis of plasma samples of wild fish from the Sava River (Croatia) revealed the presence of 90 different pharmaceuticals/illicit drugs and their metabolites (PhACs/IDrgs). The concentrations of these PhACs/IDrgs in plasma were 10 to 1000 times higher than their concentrations in river water. Antibiotics, allergy/cold medications and analgesics were categories with the highest plasma concentrations. Fifty PhACs/IDrgs were identified as chemicals of concern based on the fish plasma model (FPM) effect ratios (ER) and their potential to activate evolutionary conserved biological targets. Chemicals of concern were also prioritized by calculating exposure-activity ratios (EARs) where plasma concentrations of chemicals were compared to their bioactivities in comprehensive ToxCast suite of in vitro assays. Overall, the applied prioritization methods indicated stimulants (nicotine, cotinine) and allergy/cold medications (prednisolone, dexamethasone) as having the highest potential biological impact on fish. The FPM model pointed to psychoactive substances (hallucinogens/stimulants and opioids) and psychotropic substances in the cannabinoids category (i.e. CBD and THC). EAR confirmed above and singled out additional chemicals of concern - anticholesteremic simvastatin and antiepileptic haloperidol. Present study demonstrates how the use of a combination of chemical analyses, and bio-effects based risk predictions with multiple criteria can help identify priority contaminants in freshwaters. The results reveal a widespread exposure of fish to complex mixtures of PhACs/IDrgs, which may target common molecular targets. While many of the prioritized chemicals occurred at low concentrations, their adverse effect on aquatic communities, due to continuous chronic exposure and additive effects, should not be neglected.

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

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.

A cross-platform approach to characterize and screen potential neurovascular unit toxicants

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.

Respirometric Screening and Characterization of Mitochondrial Toxicants Within the ToxCast Phase I and II Chemical Libraries

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.

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development

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.

Potential of ToxCast Data in the Safety Assessment of Food Chemicals

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.

High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity

High-throughput and computational tools provide a new opportunity to calculate combined bioactivity of exposure to diverse chemicals acting through a common mechanism. We used high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA's Toxicity and Exposure Forecaster (ToxCast and ExpoCast) to estimate combined estrogen receptor (ER) agonist activity of non-pharmaceutical chemical exposures for the general U.S. population. High-throughput toxicokinetic (HTTK) data provide conversion factors that relate bioactive concentrations measured in vitro (µM), to predicted population geometric mean exposure rates (mg/kg/day). These data were available for 22 chemicals with ER agonist activity and were estimated for other ER bioactive chemicals based on the geometric mean of HTTK values across chemicals. For each chemical, ER bioactivity across ToxCast assays was compared to predicted population geometric mean exposure at different levels of in vitro potency and model certainty. Dose additivity was assumed in calculating a Combined Exposure-Bioactivity Index (CEBI), the sum of exposure/bioactivity ratios. Combined estrogen bioactivity was also calculated in terms of the percent maximum bioactivity of chemical mixtures in human plasma using a concentration-addition model. Estimated CEBIs vary greatly depending on assumptions used for exposure and bioactivity. In general, CEBI values were <1 when using median of the estimated general population chemical intake rates, while CEBI were ≥1 when using the upper 95th confidence bound for those same intake rates for all chemicals. Concentration-addition model predictions of mixture bioactivity yield comparable results. Based on current in vitro bioactivity data, HTTK methods, and exposure models, combined exposure scenarios sufficient to influence estrogen bioactivity in the general population cannot be ruled out. Future improvements in screening methods and computational models could reduce uncertainty and better inform the potential combined effects of estrogenic chemicals.

Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach

Various additives are used in plastic products to improve the properties and the durability of the plastics. Their possible elution from the plastics when plastics are fragmented into micro- and nano-size in the environment is suspected to one of the major contributors to environmental and human toxicity of microplastics. In this context, to better understand the hazardous effect of microplastics, the toxicity of chemical additives was investigated. Fifty most common chemicals presented in plastics were selected as target additives. Their toxicity was systematically identified using apical and molecular toxicity databases, such as ChemIDplus and ToxCast™. Among the vast ToxCast assays, those having intended gene targets were selected for identification of the mechanism of toxicity of plastic additives. Deep learning artificial neural network models were further developed based on the ToxCast assays for the chemicals not tested in the ToxCast program. Using both the ToxCast database and deep learning models, active chemicals on each ToxCast assays were identified. Through correlation analysis between molecular targets from ToxCast and mammalian toxicity results from ChemIDplus, we identified the fifteen most relevant mechanisms of toxicity for the understanding mechanism of toxicity of plastic additives. They are neurotoxicity, inflammation, lipid metabolism, and cancer pathways. Based on these, along with, previously conducted systemic review on the mechanism of toxicity of microplastics, here we have proposed potential adverse outcome pathways (AOPs) relevant to microplastics pollution. This study also suggests in vivo and in vitro toxicity database and deep learning model combined approach is appropriate to provide insight into the toxicity mechanism of the broad range of environmental chemicals, such as plastic additives.

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples

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. Graphical abstract.

Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling

The present study adapted an existing high content imaging-based high-throughput phenotypic profiling (HTPP) assay known as "Cell Painting" for bioactivity screening of environmental chemicals. This assay uses a combination of fluorescent probes to label a variety of organelles and measures a large number of phenotypic features at the single cell level in order to detect chemical-induced changes in cell morphology. First, a small set of candidate phenotypic reference chemicals (n = 14) known to produce changes in the cellular morphology of U-2 OS cells were identified and screened at multiple time points in concentration-response format. Many of these chemicals produced distinct cellular phenotypes that were qualitatively similar to those previously described in the literature. A novel workflow for phenotypic feature extraction, concentration-response modeling and determination of in vitro thresholds for chemical bioactivity was developed. Subsequently, a set of 462 chemicals from the ToxCast library were screened in concentration-response mode. Bioactivity thresholds were calculated and converted to administered equivalent doses (AEDs) using reverse dosimetry. AEDs were then compared to effect values from mammalian toxicity studies. In many instances (68%), the HTPP-derived AEDs were either more conservative than or comparable to the in vivo effect values. Overall, we conclude that the HTPP assay can be used as an efficient, cost-effective and reproducible screening method for characterizing the biological activity and potency of environmental chemicals for potential use in in vitro-based safety assessments.

Concentration-response evaluation of ToxCast compounds for multivariate activity patterns of neural network function

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.

Comparing and contrasting the coverage of publicly available structural alerts for protein binding

The molecular initiating event for many mechanisms of toxicological action comprise the reactive, covalent binding between an exogenous electrophile and an endogenous nucleophile. The target sites for electrophiles are typically peptides, proteins, enzymes or DNA. Of these, the formation of covalent adducts with proteins and DNA are perhaps the most established as they are most closely associated with skin sensitisation and genotoxicity endpoints. As such, being able to identify electrophilic features within a chemical structure provides a starting point to characterise its reactivity profile. There are a number of software tools that have been developed to help identify structural features indicative of electrophilic reactive potential to address various purposes, including: 1) to facilitate category formation for read-across of toxicity effects such as skin sensitisation potential, as well as 2) to profile substances to identify potential confounding factors to rationalise their activity in high-throughput screening (HTS) assays. Here, three such schemes that have been published in the literature as collections of SMARTS patterns and their associated chemical-biological reaction domains have been compared. The goals are 1) to better understand their scope and coverage, and 2) to assess their performance relative to a published skin sensitisation dataset where manual annotations to assign likely mechanistic domains based on expert judgement were already available. The 3 schemes were then applied to the Tox21 library and the consensus outcome was reported to highlight the proportion of chemicals likely to exhibit a reactivity response, specific to a mechanistic reaction domain, but non-specific with respect to target-tissue based activity. ToxPrint fingerprints were computed and activity enrichments computed to compare the structural features identified for the skin sensitisation dataset and Tox21 chemicals for each 'consensus' reaction domain. Enriched ToxPrints were also used to identify ToxCast assays potentially informative for reactivity.

Integration of curated and high-throughput screening data to elucidate environmental influences on disease pathways

Addressing the complex relationship between public health and environmental exposure requires multiple types and sources of data. An important source of chemical data derives from high-throughput screening (HTS) efforts, such as the Tox21/ToxCast program, which aim to identify chemical hazard using primarily in vitro assays to probe toxicity. While most of these assays target specific genes, assessing the disease-relevance of these assays remains challenging. Integration with additional data sets may help to resolve these questions by providing broader context for individual assay results. The Comparative Toxicogenomics Database (CTD), a publicly available database that builds networks of chemical, gene, and disease information from manually curated literature sources, offers a promising solution for contextual integration with HTS data. Here, we tested the value of integrating data across Tox21/ToxCast and CTD by linking elements common to both databases (i.e., assays, genes, and chemicals). Using polymarcine and Parkinson's disease as a case study, we found that their union significantly increased chemical-gene associations and disease-pathway coverage. Integration also enabled new disease associations to be made with HTS assays, expanding coverage of chemical-gene data associated with diseases. We demonstrate how integration enables development of predictive adverse outcome pathways using 4-nonylphenol, branched as an example. Thus, we demonstrate enhancements to each data source through database integration, including scenarios where HTS data can efficiently probe chemical space that may be understudied in the literature, as well as how CTD can add biological context to those results.

Development of a prioritization method for chemical-mediated effects on steroidogenesis using an integrated statistical analysis of high-throughput H295R data

Synthesis of 11 steroid hormones in human adrenocortical carcinoma cells (H295R) was measured in a high-throughput steroidogenesis assay (HT-H295R) for 656 chemicals in concentration-response as part of the US Environmental Protection Agency's ToxCast program. This work extends previous analysis of the HT-H295R dataset and model by examining the utility of a novel prioritization metric based on the Mahalanobis distance that reduced these 11-dimensional data to 1-dimension via calculation of a mean Mahalanobis distance (mMd) at each chemical concentration screened for all hormone measures available. Herein, we evaluated the robustness of mMd values, and demonstrate that covariance and variance of the hormones measured appear independent of the chemicals screened and are inherent to the assay; the Type I error rate of the mMd method is less than 1%; and, absolute fold changes (up or down) of 1.5 to 2-fold have sufficient power for statistical significance. As a case study, we examined hormone responses for aromatase inhibitors in the HT-H295R assay and found high concordance with other ToxCast assays for known aromatase inhibitors. Finally, we used mMd and other ToxCast cytotoxicity data to demonstrate prioritization of the most selective and active chemicals as candidates for further in vitro or in silico screening.

Mitochondria as a target of organophosphate and carbamate pesticides: Revisiting common mechanisms of action with new approach methodologies

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.

Incorporating new approach methodologies in toxicity testing and exposure assessment for tiered risk assessment using the RISK21 approach: Case studies on food contact chemicals

Programs including the ToxCast project have generated large amounts of in vitro high‒throughput screening (HTS) data, and best approaches for the interpretation and use of HTS data, including for chemical safety assessment, remain to be evaluated. To fill this gap, we conducted case studies of two indirect food additive chemicals where ToxCast data were compared with in vivo toxicity data using the RISK21 approach. Two food contact substances, sodium (2-pyridylthio)-N-oxide and dibutyltin dichloride, were selected, and available exposure data, toxicity data, and model predictions were compiled and assessed. Oral equivalent doses for the ToxCast bioactivity data were determined by in-vitro in-vivo extrapolation (IVIVE). For sodium (2-pyridylthio)-N-oxide, bioactive concentrations in ToxCast assays corresponded to low-and no-observed adverse effect levels in animal studies. For dibutyltin dichloride, the ToxCast bioactive concentrations were below the dose range that demonstrated toxicity in animals; however, this was confounded by the lack of toxicokinetic data, necessitating the use of conservative toxicokinetic parameter estimates for IVIVE calculations. This study highlights the potential utility of the RISK21 approach for interpretation of the ToxCast HTS data, as well as the challenges involved in integrating in vitro HTS data into safety assessments.

Characterizing cleft palate toxicants using ToxCast data, chemical structure, and the biomedical literature

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.

Use of high-throughput screening results to prioritize chemicals for potential adverse biological effects within a West Virginia watershed

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.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency

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.

Systems Modeling of Developmental Vascular Toxicity

The more than 80,000 chemicals in commerce present a challenge for hazard assessments that toxicity testing in the 21^st century strives to address through high-throughput screening (HTS) assays. Assessing chemical effects on human development adds an additional layer of complexity to the screening, with a need to capture complex and dynamic events essential for proper embryo-fetal development. HTS data from ToxCast/Tox21 informs systems toxicology models, which incorporate molecular targets and biological pathways into mechanistic models describing the effects of chemicals on human cells, 3D organotypic culture models, and small model organisms. Adverse Outcome Pathways (AOPs) provide a useful framework for integrating the evidence derived from these in silico and in vitro systems to inform chemical hazard characterization. To illustrate this formulation, we have built an AOP for developmental toxicity through a mode of action linked to embryonic vascular disruption (Aop43). Here, we review the model for quantitative prediction of developmental vascular toxicity from ToxCast HTS data and compare the HTS results to functional vascular development assays in complex cell systems, virtual tissues, and small model organisms. ToxCast HTS predictions from several published and unpublished assays covering different aspects of the angiogenic cycle were generated for a test set of 38 chemicals representing a range of putative vascular disrupting compounds (pVDCs). Results boost confidence in the capacity to predict adverse developmental outcomes from HTS in vitro data and model computational dynamics for in silico reconstruction of developmental systems biology. Finally, we demonstrate the integration of the AOP and developmental systems toxicology to investigate the unique modes of action of two angiogenesis inhibitors.

Prioritizing anthropogenic chemicals in drinking water and sources through combined use of mass spectrometry and ToxCast toxicity data

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.

Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard

Generalized Read-Across (GenRA) is a data driven approach which makes read-across predictions on the basis of a similarity weighted activity of source analogues (nearest neighbors). GenRA has been described in more detail in the literature (Shah et al., 2016; Helman et al., 2018). Here we present its implementation within the EPA's CompTox Chemicals Dashboard to provide public access to a GenRA module structured as a read-across workflow. GenRA assists researchers in identifying source analogues, evaluating their validity and making predictions of in vivo toxicity effects for a target substance. Predictions are presented as binary outcomes reflecting presence or absence of toxicity together with quantitative measures of uncertainty. The approach allows users to identify analogues in different ways, quickly assess the availability of relevant in vivo data for those analogues and visualize these in a data matrix to evaluate the consistency and concordance of the available experimental data for those analogues before making a GenRA prediction. Predictions can be exported into a tab-separated value (TSV) or Excel file for additional review and analysis (e.g., doses of analogues associated with production of toxic effects).  GenRA offers a new capability of making reproducible read-across predictions in an easy-to use-interface.

Using prepared mixtures of ToxCast chemicals to evaluate non-targeted analysis (NTA) method performance

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. Graphical abstract ᅟ.

Contaminants in bald eagles of the upper Midwestern U.S.: A framework for prioritizing future research based on in-vitro bioassays

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.

EPA's non-targeted analysis collaborative trial (ENTACT): genesis, design, and initial findings

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. Graphical abstract ᅟ.

A Mechanistic Framework for Integrating Chemical Structure and High-Throughput Screening Results to Improve Toxicity Predictions

Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. Detailed understanding of the MIE provides the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program, coupled with chemical structural information, to generate chemical clusters using three similarity methods pertaining to nine MIEs within an AOP network for hepatic steatosis. Three case studies demonstrate the utility of the mechanistic information held by the MIE for integrating biological and chemical data. Evaluation of the chemical clusters activating the glucocorticoid receptor identified activity differences in chemicals within a cluster. Comparison of the estrogen receptor results with previous work showed that bioactivity data and structural alerts can be combined to improve predictions in a customizable way where bioactivity data are limited. The aryl hydrocarbon receptor (AHR) highlighted that while structural data can be used to offset limited data for new screening efforts, not all ToxCast targets have sufficient data to define robust chemical clusters. In this context, an alternative to additional receptor assays is proposed where assays for proximal key events downstream of AHR activation could be used to enhance confidence in active calls. These case studies illustrate how the AOP framework can support an iterative process whereby in vitro toxicity testing and chemical structure can be combined to improve toxicity predictions. In vitro assays can inform the development of structural alerts linking chemical structure to toxicity. Consequently, structurally related chemical groups can facilitate identification of assays that would be informative for a specific MIE. Together, these activities form a virtuous cycle where the mechanistic basis for the in vitro results and the breadth of the structural alerts continually improve over time to better predict activity of chemicals for which limited toxicity data exist.

Exploration of ToxCast/Tox21 bioassays as candidate bioanalytical tools for measuring groups of chemicals in water

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.