Differentiating Pathway-Specific From Nonspecific Effects in High-Throughput Toxicity Data: A Foundation for Prioritizing Adverse Outcome Pathway Development

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

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

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

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

Linking Mechanistic Effects of Pharmaceuticals and Personal Care Products to Ecologically Relevant Outcomes: A Decade of Progress

There are insufficient toxicity data to assess the ecological risks of many pharmaceuticals and personal care products (PPCPs). While data limitations are not uncommon for contaminants of environmental concern, PPCPs are somewhat unique in that an a priori understanding of their biological activities in conjunction with measurements of molecular, biochemical, or histological responses could provide a foundation for understanding mode(s) of action and predicting potential adverse apical effects. Over the past decade significant progress has been made in the development of new approach methodologies (NAMs) to efficiently quantify these types of endpoints using computational models and pathway-based in vitro and in vivo assays. The availability of open-access knowledgebases to curate biological response (including NAM) data and sophisticated bioinformatics tools to help interpret the information also has significantly increased. Finally, advances in the development and implementation of the adverse outcome pathway framework provide the critical conceptual underpinnings needed to translate NAM data into predictions of the ecologically relevant outcomes required by risk assessors and managers. The evolution and convergence of these various data streams, tools, and concepts provides the basis for a fundamental change in how ecological risks of PPCPs can be pragmatically assessed. Environ Toxicol Chem 2024;43:537-548. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

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

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

Assessing utility of thyroid in vitro screening assays through comparisons to observed impacts in vivo

To better understand endocrine disruption, the U.S. Environmental Protection Agency's (USEPA) Endocrine Disruptor Screening Program (EDSP) utilizes a two-tiered approach to investigate the potential of a chemical to interact with the estrogen, androgen, or thyroid systems. As in vivo testing lacks the throughput to address data gaps on endocrine bioactivity for thousands of chemicals, in vitro high-throughput screening (HTS) methods are being developed to screen larger chemical libraries. The primary objective of this work was to investigate for how many of the 52 chemicals with weight-of-evidence (WoE) determinations from EDSP Tier 1 screening there are available in vitro HTS data supporting a thyroid impact. HTS data from the USEPA ToxCast program and the EDSP WoE were collected for this analysis. Considering the complexity of endocrine disruption and interpreting HTS data, concordance between in vitro activity and in vivo effects ranges from 58 to 78%. Based on this evaluation, we conclude that the current suite of HTS assays is beneficial for prioritizing chemicals for further inquiry; however, without a more detailed analysis, one cannot conclude whether HTS results are the primary mode-of-action. Furthermore, development of in vitro assays for additional thyroid-relevant molecular initiating events is required to effectively predict in vivo thyroid impacts.

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

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

Reactivity of Acrylamides Causes Cytotoxicity and Activates Oxidative Stress Response

Acrylamides are widely used industrial chemicals that cause adverse effects in humans or animals, such as carcinogenicity or neurotoxicity. The excess toxicity of these reactive electrophilic chemicals is especially interesting, as it is mostly triggered by covalent reactions with biological nucleophiles, such as DNA bases, proteins, or peptides. The cytotoxicity and activation of oxidative stress response of 10 (meth)acrylamides measured in three reporter gene cell lines occurred at similar concentrations. Most acrylamides exhibited high excess toxicity, while methacrylamides acted as baseline toxicants. The (meth)acrylamides showed no reactivity toward the hard biological nucleophile 2-deoxyguanosine (2DG) within 24 h, and only acrylamides reacted with the soft nucleophile glutathione (GSH). Second-order degradation rate constants (kGSH) were measured for all acrylamides with N,N'-methylenebis(acrylamide) (NMBA) showing the highest kGSH (134.800 M-1 h-1) and N,N-diethylacrylamide (NDA) the lowest kGSH (2.574 M-1 h-1). Liquid chromatography coupled to high-resolution mass spectrometry was used to confirm the GSH conjugates of the acrylamides with a double conjugate formed for NMBA. The differences in reactivity between acrylamides and methacrylamides could be explained by the charge density of the carbon atoms because the electron-donating inductive effect of the methyl group of the methacrylamides lowered their electrophilicity and thus their reactivity. The differences in reactivity within the group of acrylamides could be explained by the energy of the lowest unoccupied molecular orbital and steric hindrance. Cytotoxicity and activation of oxidative stress response were linearly correlated with the second-order reaction rate constants of the acrylamides with GSH. The reaction of the acrylamides with GSH is hence not only a detoxification mechanism but also leads to disturbances of the redox balance, making the cells more vulnerable to reactive oxygen species. The reactivity of acrylamides explained the oxidative stress response and cytotoxicity in the cells, and the lack of reactivity of the methacrylamides led to baseline toxicity.

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.

Impact of a megacity on the water quality of a tropical estuary assessed by a combination of chemical analysis and in-vitro bioassays

Tropical estuaries are threatened by rapid urbanization, which leads to the spread of thousands of micropollutants and poses an environmental risk to such sensitive aqueous ecosystems. In the present study, a combination of chemical and bioanalytical water characterization was applied to investigate the impact of Ho Chi Minh megacity (HCMC, 9.2 million inhabitants in 2021) on the Saigon River and its estuary and provide a comprehensive water quality assessment. Water samples were collected along a 140-km stretch integrating the river-estuary continuum from upstream HCMC down to the estuary mouth in the East Sea. Additional water samples were collected at the mouth of the four main canals of the city center. Chemical analysis was performed targeting up to 217 micropollutants (pharmaceuticals, plasticizers, PFASs, flame retardants, hormones, pesticides). Bioanalysis was performed using six in-vitro bioassays for hormone receptor-mediated effects, xenobiotic metabolism pathways and oxidative stress response, respectively, all accompanied by cytotoxicity measurement. A total of 120 micropollutants were detected and displayed high variability along the river continuum with total concentration ranging from 0.25 to 78 μg L^-1. Among them, 59 micropollutants were ubiquitous (detection frequency ≥ 80 %). An attenuation was observed in concentration and effect profiles towards the estuary. The urban canals were identified as major sources of micropollutants and bioactivity to the river, and one canal (Bến Nghé) exceeded the effect-based trigger values derived for estrogenicity and xenobiotic metabolism. Iceberg modelling apportioned the contribution of the quantified and the unknown chemicals to the measured effects. Diuron, metolachlor, chlorpyrifos, daidzein, genistein, climbazole, mebendazole and telmisartan were identified as main risk drivers of the oxidative stress response and xenobiotic metabolism pathway activation. Our study reinforced the need for improved wastewater management and deeper evaluations of the occurrence and fate of micropollutants in urbanized tropical estuarine environments.

Using membrane-water partition coefficients in a critical membrane burden approach to aid the identification of neutral and ionizable chemicals that induce acute toxicity below narcosis levels

The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane-water distribution ratio (D_MLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive D_MLW. We use IAM-HPLC based D_MLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMB_narc) of 140 mmol kg^-1 lipid, with a lower limit of 14 mmol kg^-1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum 'toxic ratio' (TR) between CMB_narc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as "unsure amines", the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.

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

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

A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target

In the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals.

Leveraging ToxCast data and protein sequence conservation to complement aquatic life criteria derivation

The USEPA's 1985 guidelines for the derivation of aquatic life criteria (ALC) are robust but data-intensive. For many chemicals, the extensive in vivo data sets required for ALC derivation are not available. Thus, alternative analyses and processes that can provide provisional values to guide states, tribes, and other stakeholders while data accumulate and more rigorous criteria are derived would be beneficial. The overarching purpose of this study was to assess the feasibility of using data from new approach methodologies (NAMs) like ToxCast to derive first-pass, provisional values to guide chemical prioritization and resource management as a complement to traditional ALC derivation. To address this goal, the study objectives were to (1) estimate chemical potency using data from NAMs for nine compounds with available aquatic benchmarks, (2) evaluate the utility of using NAM data to elucidate potential mechanisms of toxicity to guide problem formulation, and (3) determine the species relevance of toxicity pathways for compounds with clearly defined mechanisms of action as a means to evaluate whether minimum data requirements could potentially be waived when deriving a more formal ALC. Points of departure were derived from ToxCast data based on the fifth percentile of the distribution of activity concentration above cutoff values falling below the cytotoxic burst. Mechanistic inferences were made based on active target hits in ToxCast and, where applicable, assessed for taxonomic conservation using SeqAPASS. ToxCast-based point-of-departure aligned relatively closely (six of nine test chemicals within a factor of 10; eight of nine within a factor of 100) with aquatic benchmarks from the USEPA and US Department of Energy (DOE). Moreover, pathways of toxicity gleaned from NAM data were reflective of in vivo-based findings from the literature. These results, while preliminary, and based on a limited number of substances, support the potential application of NAM data to complement traditional ALC derivation approaches and prioritization. Integr Environ Assess Manag 2023;19:224-238. © 2022 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Prioritizing Pharmaceutical Contaminants in Great Lakes Tributaries Using Risk-Based Screening Techniques

In a study of 44 diverse sampling sites across 16 Great Lakes tributaries, 110 pharmaceuticals were detected of 257 monitored. The present study evaluated the ecological relevance of detected chemicals and identified heavily impacted areas to help inform resource managers and guide future investigations. Ten pharmaceuticals (caffeine, nicotine, albuterol, sulfamethoxazole, venlafaxine, acetaminophen, carbamazepine, gemfibrozil, metoprolol, and thiabendazole) were distinguished as having the greatest potential for biological effects based on comparison to screening-level benchmarks derived using information from two biological effects databases, the ECOTOX Knowledgebase and the ToxCast database. Available evidence did not suggest substantial concern for 75% of the monitored pharmaceuticals, including 147 undetected pharmaceuticals and 49 pharmaceuticals with screening-level alternative benchmarks. However, because of a lack of biological effects information, screening values were not available for 51 detected pharmaceuticals. Samples containing the greatest pharmaceutical concentrations and having the highest detection frequencies were from Lake Erie, southern Lake Michigan, and Lake Huron tributaries. Samples collected during low-flow periods had higher pharmaceutical concentrations than those collected during increased-flow periods. The wastewater-treatment plant effluent content in streams correlated positively with pharmaceutical concentrations. However, deviation from this correlation demonstrated that secondary factors, such as multiple pharmaceutical sources, were likely present at some sites. Further research could investigate high-priority pharmaceuticals as well as those for which alternative benchmarks could not be developed. Environ Toxicol Chem 2022;41:2221-2239. 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.

Advances in computational methods along the exposure to toxicological response paradigm

Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

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.

High throughput data-based, toxicity pathway-oriented development of a quantitative adverse outcome pathway network linking AHR activation to lung damages

The quantitative adverse outcome pathway (qAOP) is proposed to inform dose-responses at multiple biological levels for the purpose of toxicity prediction. So far, qAOP models concerning human health are scarce. Previously, we proposed 5 key molecular pathways that led aryl hydrogen receptor (AHR) activation to lung damages. The present study assembled an AOP network based on the gene expression signatures of these toxicity pathways, and validated the network using publicly available high throughput data combined with machine learning models. In addition, the AOP network was quantitatively evaluated with omics approaches and bioassays, using 16HBE-CYP1A1 cells exposed to benzo(a)pyrene (BaP), a prototypical AHR activator. Benchmark dose (BMD) analysis of transcriptomics revealed that AHR gene held the lowest BMD value, whereas AHR pathway held the lowest point of departure (PoD) compared to the other 4 pathways. Targeted bioassays were further performed to quantitatively understand the cellular responses, including ROS generation, DNA damage, interleukin-6 production, and extracellular matrix increase marked by collagen expression. Eventually, response-response relationships were plotted using nonlinear model fitting. The present study developed a highly reliable AOP model concerning human health, and validated as well as quantitatively evaluated it, and such a method is likely to be adoptable for risk assessment.

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.

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.

New framework for a non-animal approach adequately assures the safety of cosmetic ingredients - A case study on caffeine

This case study on the model substance caffeine demonstrates the viability of a 10-step read-across (RAX) framework in practice. New approach methodologies (NAM), including RAX and physiologically-based kinetic (PBK) modelling were used to assess the consumer safety of caffeine. Appropriate animal systemic toxicity data were used from the most relevant RAX analogue while assuming that no suitable animal toxicity data were available for caffeine. Based on structural similarities, three primary metabolites of the target chemical caffeine (theophylline, theobromine and paraxanthine) were selected as its most relevant analogues, to estimate a point of departure in order to support a next generation risk assessment (NGRA). On the basis of the pivotal mode of action (MOA) of caffeine and other methylxanthines, theophylline appeared to be the most potent and suitable analogue. A worst-case aggregate exposure assessment determined consumer exposure to caffeine from different sources, such as cosmetics and food/drinks. Using a PBK model to estimate human blood concentrations following exposure to caffeine, an acceptable Margin of Internal Exposure (MOIE) of 27-fold was derived on the basis of a RAX using theophylline animal data, which suggests that the NGRA approach for caffeine is sufficiently conservative to protect human health.

Assessing the Ecological Risks of Per- and Polyfluoroalkyl Substances: Current State-of-the Science and a Proposed Path Forward

Per- and poly-fluoroalkyl substances (PFAS) encompass a large, heterogenous group of chemicals of potential concern to human health and the environment. Based on information for a few relatively well-understood PFAS such as perfluorooctane sulfonate and perfluorooctanoate, there is ample basis to suspect that at least a subset can be considered persistent, bioaccumulative, and/or toxic. However, data suitable for determining risks in either prospective or retrospective assessments are lacking for the majority of PFAS. In August 2019, the Society of Environmental Toxicology and Chemistry sponsored a workshop that focused on the state-of-the-science supporting risk assessment of PFAS. The present review summarizes discussions concerning the ecotoxicology and ecological risks of PFAS. First, we summarize currently available information relevant to problem formulation/prioritization, exposure, and hazard/effects of PFAS in the context of regulatory and ecological risk assessment activities from around the world. We then describe critical gaps and uncertainties relative to ecological risk assessments for PFAS and propose approaches to address these needs. Recommendations include the development of more comprehensive monitoring programs to support exposure assessment, an emphasis on research to support the formulation of predictive models for bioaccumulation, and the development of in silico, in vitro, and in vivo methods to efficiently assess biological effects for potentially sensitive species/endpoints. Addressing needs associated with assessing the ecological risk of PFAS will require cross-disciplinary approaches that employ both conventional and new methods in an integrated, resource-effective manner. Environ Toxicol Chem 2021;40:564-605. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Refining in vitro and in silico neurotoxicity approaches by accounting for interspecies and interindividual differences in toxicodynamics

INTRODUCTION

The process of chemical risk assessment traditionally relies on animal experiments and associated default uncertainty factors to account for interspecies and interindividual differences. To work toward a more precise and personalized risk assessment, these uncertainty factors should be refined and replaced by chemical-specific adjustment factors (CSAFs).

AREAS COVERED

This concise review discusses alternative (in vitro/in silico) approaches that can be used to assess interspecies and interindividual differences in toxicodynamics, ranging from targeted to more integrated approaches. Although data are available on interspecies differences, the increasing use of human-induced pluripotent stem cell (hiPSC)-derived neurons may provide opportunities to also assess interindividual variability in neurotoxicity. More integrated approaches, like adverse outcome pathways (AOPs) can provide a more quantitative understanding of the toxicodynamics of a chemical.

EXPERT OPINION

To improve chemical risk assessment, refinement of uncertainty factors is crucial. In vitro and in silico models can facilitate the development of CSAFs, but still these models cannot always capture the complexity of the in vivo situation, thereby potentially hampering regulatory acceptance. The combined use of more integrated approaches, like AOPs and physiologically based kinetic models, can aid in structuring data and increasing suitability of alternative approaches for regulatory purposes.

Effect-Based Trigger Values for Mixtures of Chemicals in Surface Water Detected with In Vitro Bioassays

Effect-based trigger (EBT) values for in vitro bioassays are important for surface water quality monitoring because they define the threshold between acceptable and poor water quality. They have been derived for highly specific bioassays, such as hormone-receptor activation in reporter gene bioassays, by reading across from existing chemical guideline values. This read-across method is not easily applicable to bioassays indicative of adaptive stress responses, which are triggered by many different chemicals, and activation of nuclear receptors for xenobiotic metabolism, to which many chemicals bind with rather low specificity. We propose an alternative approach to define the EBT from the distribution of specificity ratios of all active chemicals. The specificity ratio is the ratio between the predicted baseline toxicity of a chemical in a given bioassay and its measured specific endpoint. Unlike many previous read-across methods to derive EBTs, the proposed method accounts for mixture effects and includes all chemicals, not only high-potency chemicals. The EBTs were derived from a cytotoxicity EBT that was defined as equivalent to 1% of cytotoxicity in a native surface water sample. The cytotoxicity EBT was scaled by the median of the log-normal distribution of specificity ratios to derive the EBT for effects specific for each bioassay. We illustrate the new approach using the example of the AREc32 assay, indicative of the oxidative stress response, and 2 nuclear receptor assays targeting the peroxisome proliferator-activated receptor gamma and the arylhydrocarbon receptor. The EBTs were less conservative than previously proposed but were able to differentiate untreated and insufficiently treated wastewater from wastewater treatment plant effluent with secondary or tertiary treatment and surface water. Environ Toxicol Chem 2021;40:487-499. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets

To improve our ability to extrapolate preclinical toxicity to humans, there is a need to understand and quantify the concordance of adverse events (AEs) between animal models and clinical studies. In the present work, we discovered 3011 statistically significant associations between preclinical and clinical AEs caused by drugs reported in the PharmaPendium database of which 2952 were new associations between toxicities encoded by different Medical Dictionary for Regulatory Activities terms across species. To find plausible and testable candidate off-target drug activities for the derived associations, we investigated the genetic overlap between the genes linked to both a preclinical and a clinical AE and the protein targets found to interact with one or more drugs causing both AEs. We discuss three associations from the analysis in more detail for which novel candidate off-target drug activities could be identified, namely, the association of preclinical mutagenicity readouts with clinical teratospermia and ovarian failure, the association of preclinical reflexes abnormal with clinical poor-quality sleep, and the association of preclinical psychomotor hyperactivity with clinical drug withdrawal syndrome. Our analysis successfully identified a total of 77% of known safety targets currently tested in in vitro screening panels plus an additional 431 genes which were proposed for investigation as future safety targets for different clinical toxicities. This work provides new translational toxicity relationships beyond AE term-matching, the results of which can be used for risk profiling of future new chemical entities for clinical studies and for the development of future in vitro safety panels.

2018 Lush Science Prize

The Lush Prize supports animal-free testing by awarding money prizes of up to £350,000 per year to the most effective projects and individuals who have been working towards the goal of replacing animals in product or ingredient safety testing. Since its inception in 2012, the Lush Prize has distributed almost £2 million. Prizes are awarded for developments in five strategic areas: Science; Lobbying; Training; Public Awareness; and Young Researchers. In 2015, the judges also awarded a Black Box prize for the development of the skin sensitisation Adverse Outcome Pathway and its associated in vitro assays. The Science Prize is awarded to researchers whose work the judging panel believe to have made the most significant contribution, in the preceding year, to the replacement of animal testing. This 2018 Science Background paper outlines the research projects that were presented to the Prize judges as potential candidates for the 2018 Lush Science Prize award. To obtain an overview of developments in the field of animal replacement in toxicity research, recent work by the relevant scientific institutions and projects in this area, including the OECD, CAAT, ECVAM, UK NC3Rs, US Tox21 Programme, the ToxCast programme and EU-ToxRisk, was reviewed. Recent developments in toxicity testing research were investigated by searching the relevant literature. Abstracts from conferences focusing on animal replacement in toxicity testing that were held in the preceding 12 months, were also analysed, including those from the 2017 10th World Congress on Alternatives and Animals in the Life Sciences and the 2018 Society of Toxicology annual conference.

Cytotoxicity Burst? Differentiating Specific from Nonspecific Effects in Tox21 in Vitro Reporter Gene Assays

BACKGROUND

High-throughput screening of chemicals with in vitro reporter gene assays in Tox21 has produced a large database on cytotoxicity and specific modes of action. However, the validity of some of the reported activities is questionable due to the "cytotoxicity burst," which refers to the supposition that many stress responses are activated in a nonspecific way at concentrations close to cell death.

OBJECTIVES

We propose a pragmatic method to identify whether reporter gene activation is specific or cytotoxicity-triggered by comparing the measured effects with baseline toxicity.

METHODS

Baseline toxicity, also termed narcosis, is the minimal toxicity any chemical causes. Quantitative structure-activity relationships (QSARs) developed for baseline toxicity in mammalian reporter gene cell lines served as anchors to define the chemical-specific threshold for the cytotoxicity burst and to evaluate the degree of specificity of the reporter gene activation. Measured 10% effect concentrations were related to measured or QSAR-predicted 10% cytotoxicity concentrations yielding specificity ratios (SR). We applied this approach to our own experimental data and to ∼ 8,000 chemicals that were tested in six of the high-throughput Tox21 reporter gene assays.

RESULTS

Confirmed baseline toxicants activated reporter gene activity around cytotoxic concentrations triggered by the cytotoxicity burst. In six Tox21 assays, 37%-87% of the active hits were presumably caused by the cytotoxicity burst (SR < 1 ) and only 2%-14% were specific with SR ≥ 10 against experimental cytotoxicity but 75%-97% were specific against baseline toxicity. This difference was caused by a large fraction of chemicals showing excess cytotoxicity.

CONCLUSIONS

The specificity analysis for measured in vitro effects identified whether a cytotoxicity burst had likely occurred. The SR-analysis not only prevented false positives, but it may also serve as measure for relative effect potency and can be used for quantitative in vitro-in vivo extrapolation and risk assessment of chemicals. https://doi.org/10.1289/EHP6664.

Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library

The interaction of nuclear receptors (NRs) with chemical compounds can cause dysregulation of endocrine signaling pathways, leading to adverse health outcomes due to the disruption of natural hormones. Thus, identifying possible ligands of NRs is a crucial task for understanding the adverse outcome pathway (AOP) for human toxicity as well as the development of novel drugs. However, the experimental assessment of novel ligands remains expensive and time-consuming. Therefore, an in silico approach with a wide range of applications instead of experimental examination is highly desirable. The recently developed novel molecular image-based deep learning (DL) method, DeepSnap-DL, can produce multiple snapshots from three-dimensional (3D) chemical structures and has achieved high performance in the prediction of chemicals for toxicological evaluation. In this study, we used DeepSnap-DL to construct prediction models of 35 agonist and antagonist allosteric modulators of NRs for chemicals derived from the Tox21 10K library. We demonstrate the high performance of DeepSnap-DL in constructing prediction models. These findings may aid in interpreting the key molecular events of toxicity and support the development of new fields of machine learning to identify environmental chemicals with the potential to interact with NR signaling pathways.

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.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

Prioritizing chemicals of ecological concern in Great Lakes tributaries using high-throughput screening data and adverse outcome pathways

Chemical monitoring data were collected in surface waters from 57 Great Lakes tributaries from 2010 to 13 to identify chemicals of potential biological relevance and sites at which these chemicals occur. Traditional water-quality benchmarks for aquatic life based on in vivo toxicity data were available for 34 of 67 evaluated chemicals. To expand evaluation of potential biological effects, measured chemical concentrations were compared to chemical-specific biological activities determined in high-throughput (ToxCast) in vitro assays. Resulting exposure-activity ratios (EARs) were used to prioritize the chemicals of greatest potential concern: 4‑nonylphenol, bisphenol A, metolachlor, atrazine, DEET, caffeine, tris(2‑butoxyethyl) phosphate, tributyl phosphate, triphenyl phosphate, benzo(a)pyrene, fluoranthene, and benzophenone. Water-quality benchmarks were unavailable for five of these chemicals, but for the remaining seven, EAR-based prioritization was consistent with that based on toxicity quotients calculated from benchmarks. Water-quality benchmarks identified three additional PAHs (anthracene, phenanthrene, and pyrene) not prioritized using EARs. Through this analysis, an EAR of 10^-3 was identified as a reasonable threshold above which a chemical might be of potential concern. To better understand apical hazards potentially associated with biological activities captured in ToxCast assays, in vitro bioactivity data were matched with available adverse outcome pathway (AOP) information. The 49 ToxCast assays prioritized via EAR analysis aligned with 23 potentially-relevant AOPs present in the AOP-Wiki. Mixture effects at monitored sites were estimated by summation of EAR values for multiple chemicals by individual assay or individual AOP. Commonly predicted adverse outcomes included impacts on reproduction and mitochondrial function. The EAR approach provided a screening-level assessment for evidence-based prioritization of chemicals and sites with potential for adverse biological effects. The approach aids prioritization of future monitoring activities and provides testable hypotheses to help focus those efforts. This also expands the fraction of detected chemicals for which biologically-based benchmark concentrations are available to help contextualize chemical monitoring results.

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.

Baseline Toxicity and Volatility Cutoff in Reporter Gene Assays Used for High-Throughput Screening

Most studies using high-throughput in vitro cell-based bioassays tested chemicals up to a certain fixed concentration. It would be more appropriate to test up to concentrations predicted to elicit baseline toxicity because this is the minimal toxicity of every chemical. Baseline toxicity is also called narcosis and refers to nonspecific intercalation of chemicals in biological membranes, leading to loss of membrane structure and impaired functioning of membrane-related processes such as mitochondrial respiration. In cells, baseline toxicity manifests as cytotoxicity, which was quantified by a robust live-cell imaging method. Inhibitory concentrations for baseline toxicity varied by orders of magnitude between chemicals and were described by a simple quantitative structure activity relationship (QSAR) with the liposome-water partition constant as a sole descriptor. The QSAR equations were remarkably similar for eight reporter gene cell lines of different cellular origin, six of which were used in Tox21. Mass-balance models indicated constant critical membrane concentrations for all cells and all chemicals with a mean of 69 mmol·kg_lip^-1(95% CI: 49-89), which is in the same range as for bacteria and aquatic organisms and consistent with the theory of critical membrane burden of narcosis. The challenge of developing baseline QSARs for cell lines is that many confirmed baseline toxicants are rather volatile. We deduced from cytotoxicity experiments with semi-volatile chemicals that only chemicals with medium-air partition constants >10,000 L/L can be tested in standard robotic setups without appreciable loss of effect. Chemicals just below that cutoff showed crossover effects in neighboring wells, whereas the effects of chemicals with lower medium-air partition constants were plainly lost. Applying the "volatility cut-off" to >8000 chemicals tested in Tox21 indicated that approximately 20% of Tox21 chemicals could have partially been lost during the experiments. We recommend applying the baseline QSARs together with volatility cut-offs for experimental planning of reporter gene assays, that is, to dose only chemicals with medium-air partition constants >10,000 at concentrations up to the baseline toxicity level.

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.