Prediction of Estrogenic Bioactivity of Environmental Chemical Metabolites

Developing a Screening Strategy to Identify Hepatotoxicity and Drug Interaction Potential of Botanicals

Botanical supplements, herbal remedies, and plant-derived products are used globally. However, botanical dietary supplements are rarely subjected to robust safety testing unless there are adverse reports in post-market surveillance. Botanicals are complex and difficult to assess using current frameworks designed for single constituent substances (e.g. small molecules or discrete chemicals), making safety assessments costly and time-consuming. The liver is a primary organ of concern for potential botanical-induced hepatotoxicity and botanical-drug interactions as it plays a crucial role in xenobiotic metabolism. The NIH-funded Drug Induced Liver Injury Network noted that the number of botanical-induced liver injuries in 2017 nearly tripled from those observed in 2004-2005. New approach methodologies (NAMs) can aid in the rapid and cost-effective assessment of botanical supplements for potential hepatotoxicity. The Hepatotoxicity Working Group within the Botanical Safety Consortium is working to develop a screening strategy that can help reliably identify potential hepatotoxic botanicals and inform mechanisms of toxicity. This manuscript outlines the Hepatotoxicity Working Group's strategy and describes the assays selected and the rationale for the selection of botanicals used in case studies. The selected NAMs evaluated as a part of this effort are intended to be incorporated into a larger battery of assays to evaluate multiple endpoints related to botanical safety. This work will contribute to a botanical safety toolkit, providing researchers with tools to better understand hepatotoxicity associated with botanicals, prioritize and plan future testing as needed, and gain a deeper insight into the botanicals being tested.

Prediction of physicochemical and pharmacokinetic properties of botanical constituents by computational models

Botanicals contain complex mixtures of chemicals most of which lack pharmacokinetic data in humans. Since physicochemical and pharmacokinetic properties dictate the in vivo exposure of botanical constituents, these parameters greatly impact the pharmacological and toxicological effects of botanicals in consumer products. This study sought to use computational (i.e., in silico) models, including quantitative structure-activity relationships (QSAR) and physiologically based pharmacokinetic (PBPK) modeling, to predict properties of botanical constituents. One hundred and three major constituents (e.g., withanolides, mitragynine, and yohimbine) in 13 botanicals (e.g., ashwagandha, kratom, and yohimbe) were investigated. The predicted properties included biopharmaceutical classification system (BCS) classes based on aqueous solubility and permeability, oral absorption, liver microsomal clearance, oral bioavailability, and others. Over half of these constituents fell into BCS classes I and II at dose levels no greater than 100 mg per day, indicating high permeability and absorption (%Fa > 75%) in the gastrointestinal tract. However, some constituents such as glycosides in ashwagandha and Asian ginseng showed low bioavailability after oral administration due to poor absorption (BCS classes III and IV, %Fa < 40%). These in silico results fill data gaps for botanical constituents and could guide future safety studies. For example, the predicted human plasma concentrations may help select concentrations for in vitro toxicity testing. Additionally, the in silico data could be used in tiered or batteries of assays to assess the safety of botanical products. For example, highly absorbed botanical constituents indicate potential high exposure in the body, which could lead to toxic effects.

Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional "QSAR-ready" forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the "QSAR-ready" workflow to generate "MS-ready structures" to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.

Joint Effect of Multiple Metals on Hyperuricemia and Their Interaction with Obesity: A Community-Based Cross-Sectional Study in China

Metal exposures have been inconsistently related to the risk of hyperuricemia, and limited research has investigated the interaction between obesity and metals in hyperuricemia. To explore their associations and interaction effects, 3300 participants were enrolled from 11 districts within 1 province in China, and the blood concentrations of 13 metals were measured to assess internal exposure. Multivariable logistic regression, restricted cubic spline (RCS), Bayesian kernel machine regression (BKMR), and interaction analysis were applied in the single- and multi-metal models. In single-metal models, five metals (V, Cr, Mn, Co, and Zn) were positively associated with hyperuricemia in males, but V was negatively associated with hyperuricemia in females. Following the multi-metal logistic regression, the multivariate-adjusted odds ratios (95% confidence intervals) of hyperuricemia were 1.7 (1.18, 2.45) for Cr and 1.76 (1.26, 2.46) for Co in males, and 0.68 (0.47, 0.99) for V in females. For V and Co, RCS models revealed wavy and inverted V-shaped negative associations with female hyperuricemia risk. The BKMR models showed a significant joint effect of multiple metals on hyperuricemia when the concentrations of five metals were at or above their 55th percentile compared to their median values, and V, Cr, Mn, and Co were major contributors to the combined effect. A potential interaction between Cr and obesity and Zn and obesity in increasing the risk of hyperuricemia was observed. Our results suggest that higher levels of Cr and Co may increase male hyperuricemia risk, while higher levels of V may decrease female hyperuricemia risk. Therefore, the management of metal exposure in the environment and diet should be improved to prevent hyperuricemia.

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements

Regulatory agencies around the world have committed to reducing or eliminating animal testing for establishing chemical safety. Adverse outcome pathways can facilitate replacement by providing a mechanistic framework for identifying the appropriate non-animal methods and connecting them to apical adverse outcomes. This study separated 11,992 chemicals with curated rat oral acute toxicity information into clusters of structurally similar compounds. Each cluster was then assigned one or more ToxCast/Tox21 assays by looking for the minimum number of assays required to record at least one positive hit call below cytotoxicity for all acutely toxic chemicals in the cluster. When structural information is used to select assays for testing, none of the chemicals required more than four assays and 98% required two assays or less. Both the structure-based clusters and activity from the associated assays were significantly associated with the GHS toxicity classification of the chemicals, which suggests that a combination of bioactivity and structural information could be as reproducible as traditional in vivo studies. Predictivity is improved when the in vitro assay directly corresponds to the mechanism of toxicity, but many indirect assays showed promise as well. Given the lower cost of in vitro testing, a small assay battery including both general cytotoxicity assays and two or more orthogonal assays targeting the toxicological mechanism could be used to improve performance further. This approach illustrates the promise of combining existing in silico approaches, such as the Collaborative Acute Toxicity Modeling Suite (CATMoS), with structure-based bioactivity information as part of an efficient tiered testing strategy that can reduce or eliminate animal testing for acute oral toxicity.

Associations of plasma multiple metals with risk of hyperuricemia: A cross-sectional study in a mid-aged and older population of China

BACKGROUND

Metal exposures are suspected to associate with the risk of hyperuricemia (HUA), but the current results are still conflicting.

OBJECTIVE

To investigate the associations between multiple plasma metal exposures and HUA risk.

METHODS

A cross-sectional study was conducted in 1406 Chinese Han adults who underwent routine physical examination in the Eighth Affiliated Hospital of Sun Yat-Sen University in Shenzhen. The plasma levels of 13 metals were measured by the inductively coupled plasma mass spectrometry (ICP-MS). Multivariable logistic, linear regression models, least absolute shrinkage and selection operator (LASSO) penalized regression analysis, and restricted cubic spline (RCS) models were applied to assess the associations.

RESULTS

The median plasma uric acid concentration in HUA group (434 μmol/L) was significantly higher than that in non-HUA group (305 μmol/L). The multivariate-adjusted odds ratios (95% confidence intervals) of HUA were 1.62(1.08-2.43) for magnesium, 1.61(1.05-2.47) for copper, 1.62(1.06-2.49) for zinc, 1.87(1.26-2.81) for arsenic, 1.50(1.01-2.23) for selenium, and 1.70(1.16-2.49) for thallium based on the single-metal logistic regression models, comparing the highest versus the lowest quartile of metal levels. Further multi-metal logistic, linear regression models and the LASSO analysis all indicated positive associations of zinc, arsenic with HUA risk or uric acid levels. RCS model indicated an inverted V-shaped positive association between zinc levels and HUA risk (p for non-linearity = 0.048, p for overall association = 0.022), while arsenic levels showed a positive and linear dose-response relationship with HUA risk (p for non-linearity = 0.892, p for overall association<0.001).

CONCLUSIONS

Higher plasma levels of zinc and arsenic might increase HUA risk and showed positive dose-response relationships. Further cohort studies in larger population are required to testify our findings.

The Alginate Immobilization of Metabolic Enzymes Platform Retrofits an Estrogen Receptor Transactivation Assay With Metabolic Competence

The U.S. EPA Endocrine Disruptor Screening Program utilizes data across the ToxCast/Tox21 high-throughput screening (HTS) programs to evaluate the biological effects of potential endocrine active substances. A potential limitation to the use of in vitro assay data in regulatory decision-making is the lack of coverage for xenobiotic metabolic processes. Both hepatic- and peripheral-tissue metabolism can yield metabolites that exhibit greater activity than the parent compound (bioactivation) or are inactive (bioinactivation) for a given biological target. Interpretation of biological effect data for both putative endocrine active substances, as well as other chemicals, screened in HTS assays may benefit from the addition of xenobiotic metabolic capabilities to decrease the uncertainty in predicting potential hazards to human health. The objective of this study was to develop an approach to retrofit existing HTS assays with hepatic metabolism. The Alginate Immobilization of Metabolic Enzymes (AIME) platform encapsulates hepatic S9 fractions in alginate microspheres attached to 96-well peg lids. Functional characterization across a panel of reference substrates for phase I cytochrome P450 enzymes revealed substrate depletion with expected metabolite accumulation. Performance of the AIME method in the VM7Luc estrogen receptor transactivation assay was evaluated across 15 reference chemicals and 48 test chemicals that yield metabolites previously identified as estrogen receptor active or inactive. The results demonstrate the utility of applying the AIME method for identification of false-positive and false-negative target assay effects, reprioritization of hazard based on metabolism-dependent bioactivity, and enhanced in vivo concordance with the rodent uterotrophic bioassay. Integration of the AIME metabolism method may prove useful for future biochemical and cell-based HTS applications.

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.

Endocrine disrupting chemicals: exposure, effects on human health, mechanism of action, models for testing and strategies for prevention

Endocrine Disrupting Chemicals (EDCs) are a global problem for environmental and human health. They are defined as "an exogenous chemical, or mixture of chemicals, that can interfere with any aspect of hormone action". It is estimated that there are about 1000 chemicals with endocrine-acting properties. EDCs comprise pesticides, fungicides, industrial chemicals, plasticizers, nonylphenols, metals, pharmaceutical agents and phytoestrogens. Human exposure to EDCs mainly occurs by ingestion and to some extent by inhalation and dermal uptake. Most EDCs are lipophilic and bioaccumulate in the adipose tissue, thus they have a very long half-life in the body. It is difficult to assess the full impact of human exposure to EDCs because adverse effects develop latently and manifest at later ages, and in some people do not present. Timing of exposure is of importance. Developing fetus and neonates are the most vulnerable to endocrine disruption. EDCs may interfere with synthesis, action and metabolism of sex steroid hormones that in turn cause developmental and fertility problems, infertility and hormone-sensitive cancers in women and men. Some EDCs exert obesogenic effects that result in disturbance in energy homeostasis. Interference with hypothalamo-pituitary-thyroid and adrenal axes has also been reported. In this review, potential EDCs, their effects and mechanisms of action, epidemiological studies to analyze their effects on human health, bio-detection and chemical identification methods, difficulties in extrapolating experimental findings and studying endocrine disruptors in humans and recommendations for endocrinologists, individuals and policy makers will be discussed in view of the relevant literature.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

The role of fit-for-purpose assays within tiered testing approaches: A case study evaluating prioritized estrogen-active compounds in an in vitro human uterotrophic assay

Chemical risk assessment relies on toxicity tests that require significant numbers of animals, time and costs. For the >30,000 chemicals in commerce, the current scale of animal testing is insufficient to address chemical safety concerns as regulatory and product stewardship considerations evolve to require more comprehensive understanding of potential biological effects, conditions of use, and associated exposures. We demonstrate the use of a multi-level new approach methodology (NAMs) strategy for hazard- and risk-based prioritization to reduce animal testing. A Level 1/2 chemical prioritization based on estrogen receptor (ER) activity and metabolic activation using ToxCast data was used to select 112 chemicals for testing in a Level 3 human uterine cell estrogen response assay (IKA assay). The Level 3 data were coupled with quantitative in vitro to in vivo extrapolation (Q-IVIVE) to support bioactivity determination (as a surrogate for hazard) in a tissue-specific context. Assay AC₅₀s and Q-IVIVE were used to estimate human equivalent doses (HEDs), and HEDs were compared to rodent uterotrophic assay in vivo-derived points of departure (PODs). For substances active both in vitro and in vivo, IKA assay-derived HEDs were lower or equivalent to in vivo PODs for 19/23 compounds (83%). Activity exposure relationships were calculated, and the IKA assay was as or more protective of human health than the rodent uterotrophic assay for all IKA-positive compounds. This study demonstrates the utility of biologically relevant fit-for-purpose assays and supports the use of a multi-level strategy for chemical risk assessment.

Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity

MOTIVATION

Nowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα).

RESULTS

VS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to improve VS performances. In this study, we propose an integrated approach using ligand docking on multiple structural ensembles to reflect receptor flexibility. Then, we investigate the impact of the two different types of features (structure-based and ligand molecular descriptors) on affinity predictions using a random forest algorithm. We find that ligand-based features have lower predictive power (rP = 0.69, R2 = 0.47) than structure-based features (rP = 0.78, R2 = 0.60). Their combination maintains high accuracy (rP = 0.73, R2 = 0.50) on the internal test set, but it shows superior robustness on external datasets. Further improvement and extending the training dataset to include xenobiotics, leads to a novel high-throughput affinity prediction method for ERα ligands (rP = 0.85, R2 = 0.71). The presented prediction tool is provided to the community as a dedicated satellite of the @TOME server in which one can upload a ligand dataset in mol2 format and get ligand docked and affinity predicted.

AVAILABILITY AND IMPLEMENTATION

http://edmon.cbs.cnrs.fr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Consensus on the key characteristics of endocrine-disrupting chemicals as a basis for hazard identification

Endocrine-disrupting chemicals (EDCs) are exogenous chemicals that interfere with hormone action, thereby increasing the risk of adverse health outcomes, including cancer, reproductive impairment, cognitive deficits and obesity. A complex literature of mechanistic studies provides evidence on the hazards of EDC exposure, yet there is no widely accepted systematic method to integrate these data to help identify EDC hazards. Inspired by work to improve hazard identification of carcinogens using key characteristics (KCs), we have developed ten KCs of EDCs based on our knowledge of hormone actions and EDC effects. In this Expert Consensus Statement, we describe the logic by which these KCs are identified and the assays that could be used to assess several of these KCs. We reflect on how these ten KCs can be used to identify, organize and utilize mechanistic data when evaluating chemicals as EDCs, and we use diethylstilbestrol, bisphenol A and perchlorate as examples to illustrate this approach.

In silico prediction of toxicity and its applications for chemicals at work

OBJECTIVE AND METHODS

This study reviewed the concept of in silico prediction of chemical toxicity for prevention of occupational cancer and future prospects in workers' health. In this review, a new approach to determine the credibility of in silico predictions with raw data is explored, and the method of determining the confidence level of evaluation based on the credibility of data is discussed. I searched various papers and books related to the in silico prediction of chemical toxicity and carcinogenicity. The intention was to utilize the most recent reports after 2015 regarding in silico prediction.

RESULTS AND CONCLUSION

The application of in silico methods is increasing with the prediction of toxic risks to human and the environment. The various toxic effects of industrial chemicals have triggered the recognition of the importance of using a combination of in silico models in the risk assessments. In silico occupational exposure models, industrial accidents, and occupational cancers are effectively managed and chemicals evaluated. It is important to identify and manage hazardous substances proactively through the rigorous evaluation of chemicals.

An Approach for Using In Vitro and In Silico Data to Identify Pharmaceuticals with Potential (Anti-)Estrogenic Activity in Aquatic Vertebrates at Environmentally Relevant Concentrations

Endocrine-active pharmaceuticals can cause adverse reproductive and developmental effects in nontarget organisms. Aquatic vertebrates may be susceptible to the effects of such pharmaceuticals given that the structure of hormone receptors and the physiology of the endocrine system are highly conserved across vertebrates. To aid in the regulatory review of the environmental impact of drugs, we demonstrate an approach to screen and support the prioritization of pharmaceuticals based on their ability to interact with estrogen receptors (ERs) at environmentally relevant concentrations. Tox21 in vitro results from ER agonist and antagonist assays were retrieved for 1123 pharmaceuticals. In silico predictions from the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) models were used to estimate ER agonist and antagonist activity for an additional 170 pharmaceuticals not tested in the Tox21 assay platform. The estrogenic effect ratio (EER) and anti-estrogenic effect ratio (AEER) were calculated by comparing the activity concentration at half-maximal response (AC50) for ER agonism and antagonism, respectively, with estimated pharmaceutical concentrations in fish tissue based on estimates of environmental exposures. A total of 73 and 127 pharmaceuticals were identified as ER agonists and antagonists, respectively. As expected, 17β-estradiol and 17α-ethinylestradiol displayed EERs  > 1, and raloxifene and bazedoxifene acetate displayed AEERs > 1, thus indicating that these pharmaceuticals have the potential to reach fish tissue levels that exceed concentrations estimated to interact with ERs. Four pharmaceuticals displayed EERs between 0.1 and 1, and 6 displayed AEERs between 0.1 and 1. This approach may help determine the need for submission of environmental assessment data for new drug applications and support prioritization of pharmaceuticals with the potential to disrupt endocrine signaling in vertebrates. Environ Toxicol Chem 2019;38:2154-2168. © 2019 SETAC.

Simultaneous determination of dissolved phenanthrene and its metabolites by derivative synchronous fluorescence spectrometry with double scans method in aqueous solution

A simple and sensitive derivative synchronous fluorescence spectrometry with double scans (DS-DSFS) method was developed for simultaneous determination of dissolved Phenanthrene (Phe) and its metabolites 1-hydroxy-2-naphthoic acid (1H2NA) and salicylic acid (SA) in aqueous solution. The value of 69 nm was selected as the optimal Δλ conditions for Phe and 1H2NA, and the Δλ value of 55 nm was selected for SA. The overlapping fluorescence emission spectra of Phe, 1H2NA and SA were resolved by DS-DSFS. The signals detected at wavelength of 296 nm for Phe, 352 nm for 1H2NA and 307 nm for SA vary linearly when the concentrations in the range of 4.0-1.0 × 10³ μg L^-1, 4.0-1.2 × 10³ μg L^-1 and 4.0-8.0 × 10² μg L^-1, respectively. The detection limits were 0.08, 0.07 and 0.88 μg L^-1 for Phe, 1H2NA and SA, with the relatively standard deviations less than 5.0%. The established method was successfully applied in the determination of Phe and the metabolites during the biodegradation of dissolved Phe in the lab. It was evidenced that the method has potential for the in situ investigation of PAH biodegradation.

Evaluation of androgen assay results using a curated Hershberger database

A set of 39 reference chemicals with reproducible androgen pathway effects in vivo, identified in the companion manuscript [1], were used to interrogate the performance of the ToxCast/Tox 21 androgen receptor (AR) model based on 11 high throughput assays. Cytotoxicity data and specificity confirmation assays were used to distinguish assay loss-of-function from true antagonistic signaling suppression. Overall agreement was 66% (19/29), with ten additional inconclusive chemicals. Most discrepancies were explained using in vitro to in vivo extrapolation to estimate equivalent administered doses. The AR model had 100% positive predictive value for the in vivo response, i.e. there were no false positives, and chemicals with conclusive AR model results (agonist or antagonist) were consistently positive in vivo. Considering the lack of reproducibility of the in vivo Hershberger assay, the in vitro AR model may better predict specific AR interaction and can rapidly and cost-effectively screen thousands of chemicals without using animals.

Integrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA

Tens-of-thousands of chemicals are registered in the U.S. for use in countless processes and products. Recent evidence suggests that many of these chemicals are measureable in environmental and/or biological systems, indicating the potential for widespread exposures. Traditional public health research tools, including in vivo studies and targeted analytical chemistry methods, have been unable to meet the needs of screening programs designed to evaluate chemical safety. As such, new tools have been developed to enable rapid assessment of potentially harmful chemical exposures and their attendant biological responses. One group of tools, known as "non-targeted analysis" (NTA) methods, allows the rapid characterization of thousands of never-before-studied compounds in a wide variety of environmental, residential, and biological media. This article discusses current applications of NTA methods, challenges to their effective use in chemical screening studies, and ways in which shared resources (e.g., chemical standards, databases, model predictions, and media measurements) can advance their use in risk-based chemical prioritization. A brief review is provided of resources and projects within EPA's Office of Research and Development (ORD) that provide benefit to, and receive benefits from, NTA research endeavors. A summary of EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) is also given, which makes direct use of ORD resources to benefit the global NTA research community. Finally, a research framework is described that shows how NTA methods will bridge chemical prioritization efforts within ORD. This framework exists as a guide for institutions seeking to understand the complexity of chemical exposures, and the impact of these exposures on living systems.

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals

BACKGROUND

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data.

OBJECTIVES

As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models.

METHODS

We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays.

RESULTS

QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q² of 0.25-0.45, mean model errors of 0.70-1.11 log₁₀ units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org.

CONCLUSIONS

An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening

The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of in vitro assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system are unable to be replicated in an in vitro environment. In the current study, a workflow is presented for complementing in vitro testing results with in silico and in vitro techniques to identify inactive parents that may produce active metabolites. A case study applying this workflow involved investigating the influence of metabolism for over 1,400 chemicals considered inactive across18 in vitro assays related to the estrogen receptor (ER) pathway. Over 7,500 first-generation and second-generation metabolites were generated for these in vitro inactive chemicals using an in silico software program. Next, a consensus model comprised of four individual quantitative structure activity relationship (QSAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for ~8-10% of metabolites in each generation, with these metabolites linked to 259 in vitro inactive parent chemicals. Metabolites were enriched in substructures consisting of alcohol, aromatic, and phenol bonds relative to their inactive parent chemicals, suggesting these features are potentially favorable for ER-binding. The workflow presented here can be used to identify parent chemicals that can be potentially bioactive, to aid confidence in high throughput risk screening.

Endocrine Disruptors: Data-based survey of in vivo tests, predictive models and the Adverse Outcome Pathway

The protection from endocrine disruptors is a high regulatory priority. Key issues are the characterization of in vivo assays, and the identification of reference chemicals to validate alternative methods. In this exploration, publicly available databases for in vivo assays for endocrine disruption were collected and compared: Rodent Uterotrophic, Rodent Repeated Dose 28-day Oral Toxicity, 21-Day Fish, and Daphnia magna reproduction assays. Only the Uterotrophic and 21-Day Fish assays results correlated with each other. The in vivo assays data were viewed in relation to the Adverse Outcome Pathway, using as a probe 18 ToxCast in vitro assays for the ER pathway. These are the same data at the basis of the EPA agonist ToxERscore model, whose good predictivity was confirmed. The multivariate comparison of the in vitro/in vivo assays suggests that the interaction with receptors is a major determinant of in vivo results, and is the critical basis for building predictive computational models. In agreement with the above, this work also shows that it is possible to build predictive models for the Uterotrophic and 21-Day Fish assays using a limited selection of Toxcast assays.

Integrated Framework for Identifying Toxic Transformation Products in Complex Environmental Mixtures

Complex environmental mixtures consist of hundreds to thousands of unknown and unregulated organic compounds that may have toxicological relevance, including transformation products (TPs) of anthropogenic organic pollutants. Non-targeted analysis and suspect screening analysis offer analytical approaches for potentially identifying these toxic transformation products. However, additional tools and strategies are needed in order to reduce the number of chemicals of interest and focus analytical efforts on chemicals that may pose risks to humans and the environment. This brief review highlights recent developments in this field and suggests an integrated framework that incorporates complementary instrumental techniques, computational chemistry, and toxicity analysis, for prioritizing and identifying toxic TPs in the environment.