Generalized Concentration Addition Modeling Predicts Mixture Effects of Environmental PPARγ Agonists

Lessons learned from evaluating defined chemical mixtures in a high-throughput estrogen receptor assay system

In this article, we provide a proof of concept evaluating the utility of the U.S. Tox21 high-throughput screening approach to assess the hazard of chemical mixtures using 2 estrogen receptor (ER) assays. A subset of chemicals identified in Phase I of the Tox21 program as active in the ER agonist assay were used to design mixtures for testing in Phase II. Individual chemicals and mixtures were evaluated in 2 cell-based ER alpha (ERα) activation assays: One incorporating a transfected ligand-binding domain in an ERα β-lactamase reporter cell line (ER-bla) and the full-length endogenous receptor in the MCF7 cell line with a luciferase reporter gene (ER-luc). Concentration-response data from individual chemicals were used to predict the joint effect based on mixtures modeling methods and were compared with observed mixtures data to assess model fit. The models tended to overpredict mixture responses in the ER-bla assay, whereas predictions were closer to observed responses in the ER-luc assay, indicating that a full-length endogenous ER is a preferred model for high-throughput mixture analysis. Lessons learned from this research include the importance of analyzing the individual chemicals used for predictions and the mixtures in the same experimental paradigm to minimize variation, developing methods for imputing missing values from incomplete concentration-response curves, and establishing criteria to determine when inactive chemicals should be omitted from mixture predictions.

Reduced bioenergetics and mitochondrial fragmentation in human primary cytotrophoblasts induced by an EGFR-targeting chemical mixture

Exposures to complex environmental chemical mixtures during pregnancy reach and target the feto-placental unit. This study investigates the influence of environmental chemical mixtures on placental bioenergetics. Recognizing the essential role of the epidermal growth factor receptor (EGFR) in placental development and its role in stimulating glycolysis and mitochondrial respiration in trophoblast cells, we explored the effects of chemicals known to disrupt EGFR signaling on cellular energy production. Human primary cytotrophoblasts (hCTBs) and a first-trimester extravillous trophoblast cell line (HTR-8/SVneo) were exposed to a mixture of EGFR-interfering chemicals, including atrazine, bisphenol S, niclosamide, PCB-126, PCB-153, and trans-nonachlor. An RNA sequencing approach revealed that the mixture altered the transcriptional signature of genes involved in cellular energetics. Next, the impact of the mixture on cellular bioenergetics was evaluated using a combination of mitochondrial and glycolytic stress tests, ATP production, glucose consumption, lactate synthesis, and super-resolution imaging. The chemical mixture did not alter basal oxygen consumption but diminished the maximum respiratory capacity in a dose-dependent manner, indicating a disruption of mitochondrial function. The respiratory capacity and ATP production were increased by EGF, while the Chem-Mix reduced both EGF- and non-EGF-mediated oxygen consumption rate in hCTBs. A similar pattern was observed in the glycolytic medium acidification, with EGF increasing the acidification, and the Chem-Mix blocking EGF-induced glycolytic acidification. Furthermore, direct stochastic optical reconstruction microscopy (dSTORM) imaging demonstrated that the Chem-Mix led to a reduction of the mitochondrial network architecture, with findings supported by a decrease in the abundance of OPA1, a mitochondrial membrane GTPase involved in mitochondrial fusion. In conclusion, we demonstrated that a mixture of EGFR-disrupting chemicals alters mitochondrial remodeling, resulting in disturbed cellular bioenergetics, reducing the capacity of human cytotrophoblast cells to generate energy. Future studies should investigate the mechanism by which mitochondrial dynamics are disrupted and the pathological significance of these findings.

Equations for estimating binary mixture toxicity: 3-methyl-2-butanone with a series of electrophiles

Mixture toxicity was determined for 32 binary combinations. One chemical was the non-reactive, non-polar narcotic 3-methyl-2-butanone (always chemical A) and the other was a potentially reactive electrophile (chemical B). Bioluminescence inhibition in Allovibrio fischeri was measured at 15-, 30-, and 45-minutes of exposure for A, B, and the mixture (MX). Concentration-response curves (CRCs) were developed for each chemical and used to develop predicted CRCs for the concentration addition (CA) and independent action (IA) mixture toxicity models. Also, MX CRCs were generated and compared with model predictions using the 45-minute data. Classification of observed mixture toxicity used three specific criteria: 1) predicted IA EC50 vs. CA EC50 values at 45-minutes, 2) consistency of 45-minute MX CRC fit to IA, CA, or otherwise at three effect levels (EC25, EC50 and EC75), and 3) the known/suspected mechanism of toxicity for chemical B. Mixture toxicity was then classified into one of seven groupings. As a result of the predicted IA EC50 being more toxic than the predicted CA EC50, IA represented the greater toxic hazard. For this reason, non-sham MXs having toxicity consistent with CA were classified as being "coincident" with CA rather than mechanistically-consistent with CA. Multiple linear regression analyses were performed to develop equations that can be used to estimate the toxicity of other 3M2B-containing binary mixtures. These equations were developed from the data for both IA and CA, at each exposure duration and effect level. Each equation had a coefficient of determination (r2) above 0.950 and a variance inflation factor <1.2. This approach can potentially reduce the need for mixture testing and is amenable to other model systems and to assays that evaluate toxicity at low effect levels.

Chemical mixture that targets the epidermal growth factor pathway impairs human trophoblast cell functions

Pregnant women are exposed to complex chemical mixtures, many of which reach the placenta. Some of these chemicals interfere with epidermal growth factor receptor (EGFR) activation, a receptor tyrosine kinase that modulates several placenta cell functions. We hypothesized that a mixture of chemicals (Chem-Mix) known to reduce EGFR activation (polychlorinated biphenyl (PCB)-126, PCB-153, atrazine, trans-nonachlor, niclosamide, and bisphenol S) would interfere with EGFR-mediated trophoblast cell functions. To test this, we determined the chemicals' EGFR binding ability, EGFR and downstream effectors activation, and trophoblast functions (proliferation, invasion, and endovascular differentiation) known to be regulated by EGFR in extravillous trophoblasts (EVTs). The Chem-Mix competed with EGF for EGFR binding, however only PCB-153, niclosamide, trans-nonachlor, and BPS competed for binding as single chemicals. The effects of the Chem-Mix on EGFR phosphorylation were tested by exposing the placental EVT cell line, HTR-8/SVneo to control (0.1% DMSO), Chem-Mix (1, 10, or 100 ng/ml), EGF (30 ng/ml), or Chem-Mix + EGF. The Chem-Mix - but not the individual chemicals - reduced EGF-mediated EGFR phosphorylation in a dose dependent manner, while no effect was observed in its downstream effectors (AKT and STAT3). None of the individual chemicals affected EVT cell invasion, but the Chem-Mix reduced EVT cell invasion independent of EGF. In support of previous studies that have explored chemicals targeting a specific pathway (estrogen/androgen receptor), current findings indicate that exposure to a chemical mixture that targets the EGFR pathway can result in a greater impact compared to individual chemicals in the context of placental cell functions.

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.

Occurrences and potential lipid-disrupting effects of phthalate metabolites in humpback dolphins from the South China Sea

Phthalate esters (PAEs) are ubiquitous environmental contaminants, arising growing public concern. Nevertheless, information on the exposure and risks of PAEs in wildlife remains limited. Here, we conducted the first investigation of the occurrences, spatiotemporal trends, and potential risks of twelve metabolites of PAEs (mPAEs) in 74 humpback dolphins from the northern South China Sea during 2005-2020. All twelve mPAEs (∑₁₂mPAEs: 9.6-810.7 ng g^-1 wet weight) were detected in the dolphin liver, and seven major mPAEs showed increasing trends during the study period, indicating high PAE contamination in the coastal environment of South China. Monoethylhexyl phthalate accounted for over half of the ∑₁₂mPAE concentrations. The accumulation of mPAEs in the dolphins was neither age-dependent nor sex-specific. Compared to parent PAEs, mPAEs generally induced higher agonistic effects on the dolphin peroxisome proliferator-activated receptor alpha/gamma (PPARA/G) as master regulators of lipid homeostasis. Although short-term in vitro assays revealed no significant activation of dolphin PPARA/G by tissue-relevant doses of mPAEs, long-term in vivo evidence (i.e., correlations between hepatic mPAEs and blubber fatty acids) suggested that chronic exposure to mPAEs might have impacted lipid metabolism in the dolphin. This study highlighted the potential health risks of PAE exposure on marine mammals.

Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver Cells

There are thousands of chemicals that humans can be exposed to in their everyday environments, the majority of which are currently understudied and lack substantial testing for potential exposure and toxicity. This study aimed to implement in silico methods to characterize the chemicals that co-occur across chemical and product uses in our everyday household environments that also target a common molecular mediator, thus representing understudied mixtures that may exacerbate toxicity in humans. To detail, the Chemical and Products Database (CPDat) was queried to identify which chemicals co-occur across common exposure sources. Chemicals were preselected to include those that target an important mediator of cell health and toxicity, the peroxisome proliferator activated receptor gamma (PPARγ), in liver cells that were identified through query of the ToxCast/Tox21 database. These co-occurring chemicals were thus hypothesized to exert potential joint effects on PPARγ. To test this hypothesis, five commonly co-occurring chemicals (namely, benzyl cinnamate, butyl paraben, decanoic acid, eugenol, and sodium dodecyl sulfate) were tested individually and in combination for changes in the expression of PPARγ and its downstream target, insulin receptor (INSR), in human liver HepG2 cells. Results showed that these likely co-occurring chemicals in household environments increased both PPARγ and INSR expression more significantly when the exposures occurred as mixtures vs. as individual chemicals. Future studies will evaluate such chemical combinations across more doses, allowing for further quantification of the types of joint action while leveraging this method of chemical combination prioritization. This study demonstrates the utility of in silico-based methods to identify chemicals that co-occur in the environment for mixtures toxicity testing and highlights relationships between understudied chemicals and changes in PPARγ-associated signaling.

Predicting the effects of per- and polyfluoroalkyl substance mixtures on peroxisome proliferator-activated receptor alpha activity in vitro

Human exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous, with mixtures of PFAS detected in drinking water, food, household dust, and other exposure sources. Animal toxicity studies and human epidemiology indicate that PFAS may act through shared mechanisms including activation of peroxisome proliferator activated receptor α (PPARα). However, the effect of PFAS mixtures on human relevant molecular initiating events remains an important data gap in the PFAS literature. Here, we tested the ability of modeling approaches to predict the effect of diverse PPARα ligands on receptor activity using Cos7 cells transiently transfected with a full length human PPARα (hPPARα) expression construct and a peroxisome proliferator response element-driven luciferase reporter. Cells were treated for 24 h with two full hPPARα agonists (pemafibrate and GW7647), a full and a partial hPPARα agonist (pemafibrate and mono(2-ethylhexyl) phthalate), or a full hPPARα agonist and a competitive antagonist (pemafibrate and GW6471). Receptor activity was modeled with three additive approaches: effect summation, relative potency factors (RPF), and generalized concentration addition (GCA). While RPF and GCA accurately predicted activity for mixtures of full hPPARα agonists, only GCA predicted activity for full and partial hPPARα agonists and a full agonist and antagonist. We then generated concentration response curves for seven PFAS, which were well-fit with three-parameter Hill functions. The four perfluorinated carboxylic acids (PFCA) tended to act as full hPPARα agonists while the three perfluorinated sulfonic acids (PFSA) tended to act as partial agonists that varied in efficacy between 28-67 % of the full agonist, positive control level. GCA and RPF performed equally well at predicting the effects of mixtures with three PFCAs, but only GCA predicted experimental activity with mixtures of PFSAs and a mixture of PFCAs and PFSAs at ratios found in the general population. We conclude that of the three approaches, GCA most accurately models the effect of PFAS mixtures on hPPARα activity in vitro. Understanding the differences in efficacy with which PFAS activate hPPARα is essential for accurately predicting the effects of PFAS mixtures. As PFAS can activate multiple nuclear receptors, future analyses should examine mixtures effects in intact cells where multiple molecular initiating events contribute to proximate effects and functional changes.

Predicting the effects of per- and polyfluoroalkyl substance mixtures on peroxisome proliferator-activated receptor alpha activity in vitro

Human exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous, with mixtures of PFAS detected in drinking water, food, household dust, and other exposure sources. Animal toxicity studies and human epidemiology indicate that PFAS may act through shared mechanisms including activation of peroxisome proliferator activated receptor α (PPARα). However, the effect of PFAS mixtures on human relevant molecular initiating events remains an important data gap in the PFAS literature. Here, we tested the ability of modeling approaches to predict the effect of diverse PPARα ligands on receptor activity using Cos7 cells transiently transfected with a full length human PPARα (hPPARα) expression construct and a peroxisome proliferator response element-driven luciferase reporter. Cells were treated for 24 h with two full hPPARα agonists (pemafibrate and GW7647), a full and a partial hPPARα agonist (pemafibrate and mono(2-ethylhexyl) phthalate), or a full hPPARα agonist and a competitive antagonist (pemafibrate and GW6471). Receptor activity was modeled with three additive approaches: effect summation, relative potency factors (RPF), and generalized concentration addition (GCA). While RPF and GCA accurately predicted activity for mixtures of full hPPARα agonists, only GCA predicted activity for full and partial hPPARα agonists and a full agonist and antagonist. We then generated concentration response curves for seven PFAS, which were well-fit with three-parameter Hill functions. The four perfluorinated carboxylic acids (PFCA) tended to act as full hPPARα agonists while the three perfluorinated sulfonic acids (PFSA) tended to act as partial agonists that varied in efficacy between 28-67 % of the full agonist, positive control level. GCA and RPF performed equally well at predicting the effects of mixtures with three PFCAs, but only GCA predicted experimental activity with mixtures of PFSAs and a mixture of PFCAs and PFSAs at ratios found in the general population. We conclude that of the three approaches, GCA most accurately models the effect of PFAS mixtures on hPPARα activity in vitro. Understanding the differences in efficacy with which PFAS activate hPPARα is essential for accurately predicting the effects of PFAS mixtures. As PFAS can activate multiple nuclear receptors, future analyses should examine mixtures effects in intact cells where multiple molecular initiating events contribute to proximate effects and functional changes.

Associations of pregnancy phthalate concentrations and their mixture with early adolescent bone mineral content and density: The Health Outcomes and Measures of the Environment (HOME) study

BACKGROUND

The developing fetus may be particularly susceptibility to environmental osteotoxicants, but studies of pregnancy phthalate exposures and childhood bone health are scarce.

OBJECTIVES

To examine relations of pregnancy phthalate exposure biomarkers with early adolescent bone mineral density (BMD) and bone mineral content (BMC) in a prospective birth cohort.

METHODS

We used data from 223 pregnant mothers and their children enrolled in a Cincinnati, OH area cohort from 2003 to 2006. We quantified monoethyl phthalate (MEP), monoisobutyl phthalate, monobutyl phthalate, monobenzyl phthalate, mono-(3-carboxypropyl) phthalate (MCPP), and four metabolites of di-2-ethylhexyl phthalate in maternal urine collected at 16 and 26 weeks gestation, and calculated the average of creatinine-standardized concentrations. Using dual x-ray absorptiometry measures at age 12 years, we calculated BMD and BMC Z-scores for six skeletal sites. In overall and sex-stratified models, we estimated covariate-adjusted associations per 2-fold increase in phthalate biomarker concentrations using linear regression, and estimated joint effects of the phthalate biomarkers mixture using Bayesian kernel machine regression (BKMR) and quantile g-computation.

RESULTS

In single phthalate models, several biomarkers were positively associated with BMC and BMD. For example, each doubling of MEP and MCPP, 1/3rd distal radius BMD Z-score increased by 0.09 (95% CI: 0.01, 0.17) and 0.16 (95% CI: 0.01, 0.31), respectively. For phthalate mixtures, associations were generally U-shaped among males and positive-linear among females, using both statistical methods. Mixture associations were strongest with forearm sites: in BKMR models, increasing all biomarkers from the 50th to 90th percentile was associated with a 0.64 (95% CI: 0.01, 1.28) greater 1/3rd distal radius BMD Z-score in males, and a 0.49 (95% CI: -0.13, 1.10) greater ultradistal radius BMD Z-score in females.

DISCUSSION

In this study, phthalate exposures during gestation were associated with increased BMD Z-scores in early adolescence, though further research is needed to determine implications for long-term skeletal health.

PFAS and Potential Adverse Effects on Bone and Adipose Tissue Through Interactions With PPARγ

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a widely dispersed, broad class of synthetic chemicals with diverse biological effects, including effects on adipose and bone differentiation. PFAS most commonly occur as mixtures and only rarely, if ever, as single environmental contaminants. This poses significant regulatory questions and a pronounced need for chemical risk assessments, analytical methods, and technological solutions to reduce the risk to public and environmental health. The effects of PFAS on biological systems may be complex. Each may have several molecular targets initiating multiple biochemical events leading to a number of different adverse outcomes. An exposure to mixtures or coexposures of PFAS complicates the picture further. This review illustrates how PFAS target peroxisome proliferator-activated receptors. Additionally, we describe how such activation leads to changes in cell differentiation and bone development that contributes to metabolic disorder and bone weakness. This discussion sheds light on the importance of seemingly modest outcomes observed in test animals and highlights why the most sensitive end points identified in some chemical risk assessments are significant from a public health perspective.

Predicting the Activation of the Androgen Receptor by Mixtures of Ligands Using Generalized Concentration Addition

Concentration/dose addition is widely used for compounds that act by similar mechanisms. But it cannot make predictions for mixtures of full and partial agonists for effect levels above that of the least efficacious component. As partial agonists are common, we developed generalized concentration addition, which has been successfully applied to systems in which ligands compete for a single binding site. Here, we applied a pharmacodynamic model for a homodimer receptor system with 2 binding sites, the androgen receptor, that acts according to the classic homodimer activation model: Each cytoplasmic monomer protein binds ligand, undergoes a conformational change that relieves inhibition of dimerization, and binds to DNA response elements as a dimer. We generated individual dose-response data for full (dihydroxytestosterone, BMS564929) and partial (TFM-4AS-1) agonists and a competitive antagonist (MDV3100) using reporter data generated in the MDA-kb2 cell line. We used the Schild method to estimate the binding affinity of MDV3100. Data for individual compounds fit the homodimer pharmacodynamic model well. In the presence of a full agonist, the partial agonist had agonistic effects at low effect levels and antagonistic effects at high levels, as predicted by pharmacological theory. The generalized concentration addition model fits the empirical mixtures data-full/full agonist, full/partial agonist, and full agonist/antagonist-as well or better than relative potency factors or effect summation. The ability of generalized concentration addition to predict the activity of mixtures of different types of androgen receptor ligands is important as a number of environmental compounds act as partial androgen receptor agonists or antagonists.

Concentrations and endocrine disruptive potential of phthalates in marine mammals from the Norwegian Arctic

This study investigated concentrations of phthalates (diesters of phthalic acids) in blubber/adipose tissue of blue whales (Balaenoptera musculus), fin whales (Balaenoptera physalus), bowhead whales (Balaena mysticetus) and polar bears (Ursus maritimus) sampled in the Svalbard Archipelago (extending westward in the case of bowhead whales). Additionally, total concentrations (free and conjugated forms) of eight phthalate monoester metabolites were analysed in plasma of polar bears. Bis(2-ethylhexyl) phthalate (DEHP) was the only phthalate quantified among the 12 phthalates investigated. This compound was present in 6/7 fin whale samples, 4/7 blue whale samples, 2/5 bowhead whale samples and 1/12 polar bear samples. DEHP concentrations ranged from <20-398 ng/g wet weight. Phthalate metabolites, mono-n-butyl phthalate and monoisobutyl phthalate, were found in low concentrations (<1.2 ng/mL) in some of the polar bear samples. In vitro reporter gene assays were used to assess transcriptional activity of fin whale peroxisome proliferator-activated receptor gamma (PPARG), glucocorticoid receptor (GR) and the thyroid hormone receptor beta (THRB) by DEHP and diisononyl phthalate (DiNP). Due to the high degree of similarity of the ligand binding domain in the THRB and PPARG among whales, polar bears and humans, the transactivation results also apply for these species. DEHP showed both agonistic and antagonistic effects towards whale THRB at considerably higher concentrations than measured in the study animals; DiNP was a weak agonist of whale THRB. No significant agonistic or antagonistic effects were detected for DEHP or DiNP for whale PPARG, whereas DEHP and DiNP decreased basal luciferase activity mediated by whale GR at several test concentrations. In conclusion, DEHP was detected in the blubber of marine mammals from the Norwegian Arctic and it appears to have potential to modulate the transcriptional activity of whale THRB, but current DEHP concentrations do not modulate the function of the studied nuclear receptors in adipose tissue of blue whales, fin whales, bowhead whales or polar bears sampled from the Norwegian Arctic.

Physiologically based kinetic modelling based prediction of in vivo rat and human acetylcholinesterase (AChE) inhibition upon exposure to diazinon

The present study predicts in vivo human and rat red blood cell (RBC) acetylcholinesterase (AChE) inhibition upon diazinon (DZN) exposure using physiological based kinetic (PBK) modelling-facilitated reverse dosimetry. Due to the fact that both DZN and its oxon metabolite diazoxon (DZO) can inhibit AChE, a toxic equivalency factor (TEF) was included in the PBK model to combine the effect of DZN and DZO when predicting in vivo AChE inhibition. The PBK models were defined based on kinetic constants derived from in vitro incubations with liver fractions or plasma of rat and human, and were used to translate in vitro concentration-response curves for AChE inhibition obtained in the current study to predicted in vivo dose-response curves. The predicted dose-response curves for rat matched available in vivo data on AChE inhibition, and the benchmark dose lower confidence limits for 10% inhibition (BMDL10 values) were in line with the reported BMDL10 values. Humans were predicted to be 6-fold more sensitive than rats in terms of AChE inhibition, mainly because of inter-species differences in toxicokinetics. It is concluded that the TEF-coded DZN PBK model combined with quantitative in vitro to in vivo extrapolation (QIVIVE) provides an adequate approach to predict RBC AChE inhibition upon acute oral DZN exposure, and can provide an alternative testing strategy for derivation of a point of departure (POD) in risk assessment.

Exploring the Concepts of Concentration Addition and Independent Action Using a Linear Low-Effect Mixture Model

Chemicals emitted into the environment are typically present at low concentrations but may act together in mixtures. Concentration-response curves of in vitro bioassays were often linear for effect levels <30%, and the predictions for concentration addition (CA) of similarly acting chemicals and for independent action (IA) of dissimilarly acting chemicals overlapped. We derived a joint CA/IA mixture model for the low-effect level portion of concentration-response curves. In a first case study, we evaluated the cytotoxicity of over 200 mixtures of up to 17 components that were mixed in concentration ratios as they occurred in river water. The predictions of the full IA model were indistinguishable from the predictions of the full CA model up to 10% effect, confirming the applicability of the joint CA/IA mixture model at low effect levels. In a second case study, we evaluated if environmental concentrations trigger effects at levels low enough for the joint CA/IA mixture model to apply. The detected concentrations were scaled by their toxic potencies to estimate the mixture effect of the detected chemicals in a complex mixture. In 86% of 156 samples the effects fell in the validity range of the joint CA/IA mixture model (<10% effect level), confirming the CA assumption for toxic unit summation. The joint CA/IA mixture model is not suitable for testing specific mixture hypotheses and interactions of chemicals in mixtures, where more refined models are required; but it is helpful for the interpretation of effects of complex (multicomponent) environmental mixtures, especially for water samples with relatively low effect level. Environ Toxicol Chem 2020;39:2552-2559. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Application of generalized concentration addition to predict mixture effects of glucocorticoid receptor ligands

Environmental exposures often occur in complex mixtures and at low concentrations. Generalized concentration addition (GCA) is a method used to estimate the joint effect of receptor ligands that vary in efficacy. GCA models have been successfully applied to mixtures of aryl hydrocarbon receptor (AhR) and peroxisome proliferator-activated receptor gamma (PPARγ) ligands, each of which can be modeled as a receptor with a single binding site. Here, we evaluated whether GCA could be applied to homodimer nuclear receptors, which have two binding sites, to predict the combined effect of full glucocorticoid receptor (GR) agonists with partial agonists. We measured transcriptional activation of GR using a cell-based bioassay. Individual concentration-response curves for dexamethasone (full agonist), prednisolone (full agonist), and medroxyprogesterone 17-acetate (partial agonist) were generated and applied in three additivity models, GCA, effect summation (ES), and relative potency factor (RPF), to generate response surfaces. GCA and RPF yielded adequate predictions of the experimental data for two full agonists. However, GCA fit experimental data significantly better than ES and RPF for all other binary mixtures. This work extends the application of GCA to homodimer nuclear receptors and improves prediction accuracy of mixture effects of GR agonists.

Generalized Concentration Addition Model Predicts Glucocorticoid Activity Bioassay Responses to Environmentally Detected Receptor-Ligand Mixtures

Many glucocorticoid receptor (GR) agonists have been detected in waste and surface waters domestically and around the world, but the way a mixture of these environmental compounds may elicit a total glucocorticoid activity response in water samples remains unknown. Therefore, we characterized 19 GR ligands using a CV1 cell line transcriptional activation assay applicable to water quality monitoring. Cells were treated with individual GR ligands, a fixed ratio mixture of full or partial agonists, or a nonequipotent mixture with full and partial agonists. Efficacy varied (48.09%-102.5%) and potency ranged over several orders of magnitude (1.278 × 10-10 to 3.93 × 10-8 M). Concentration addition (CA) and response addition (RA) mixtures models accurately predicted equipotent mixture responses of full agonists (r2 = 0.992 and 0.987, respectively). However, CA and RA models assume mixture compounds produce full agonist-like responses, and therefore they overestimated observed maximal efficacies for mixtures containing partial agonists. The generalized concentration addition (GCA) model mathematically permits < 100% maximal responses, and fell within the 95% confidence interval bands of mixture responses containing partial agonists. The GCA, but not CA and RA, model predictions of nonequipotent mixtures containing both full and partial agonists fell within the same statistical distribution as the observed values, reinforcing the practicality of the GCA model as the best overall model for predicting GR activation. Elucidating the mechanistic basis of GR activation by mixtures of previously detected environmental GR ligands will benefit the interpretation of environmental sample contents in future water quality monitoring studies.

Generalized concentration addition for ligands that bind to homodimers

Concentration addition/dose addition (CA) has proved to be a powerful tool for estimating the combined effect of mixtures that act by similar mechanisms. We earlier proposed generalized concentration addition (GCA) to deal with the inability of CA to estimate effects of mixtures above the level of the least efficacious component. GCA requires specifying mathematical concentration response functions for each mixture component that must be invertible, yielding real numbers. We construct concentration response functions using pharmacodynamic models of ligand-receptor interaction, an important molecular initiating event for adverse outcome pathways. Here, we extend our earlier work in two novel ways. First, we show how composite functions can be used to extend these predictions to downstream events. Second, we show that GCA can accommodate not only receptors with single binding sites but also receptors that bind ligand at each monomer and then dimerize. The derived concentration response functions for receptors that homodimerize meet the requirements for using GCA.

Assessment of total, ligand-induced peroxisome proliferator activated receptor γ ligand activity in serum

BACKGROUND

Humans are exposed to a complex mixture of environmental chemicals that impact bone and metabolic health, and traditional exposure assessments struggle to capture these exposure scenarios. Peroxisome proliferator activated receptor-gamma (PPARγ) is an essential regulator of metabolic and bone homeostasis, and its inappropriate activation by environmental chemicals can set the stage for adverse health effects. Here, we present the development of the Serum PPARγ Activity Assay (SPAA), a simple and cost-effective method to measure total ligand activity in small volumes of serum.

METHODS

First, we determined essential components of the bioassay. Cos-7 cells were transfected with combinations of expression vectors for human PPARγ and RXRα, the obligate DNA-binding partner of PPARγ, along with PPRE (DR1)-driven luciferase and control eGFP reporter constructs. Transfected cells were treated with rosiglitazone, a synthetic PPARγ ligand and/or LG100268, a synthetic RXR ligand, to characterize the dose response and determine the simplest and most efficacious format. Following optimization of the bioassay, we assessed the cumulative activation of PPARγ by ligands in serum from mice treated with a PPARγ ligand and commercial human serum samples.

RESULTS

Cos-7 cells endogenously express sufficient RXR to support efficacious activation of transfected PPARγ. Co-transfection of an RXR expression vector with the PPARγ expression vector did not increase PPRE transcriptional activity induced by rosiglitazone. Treatment with an RXR ligand marginally increased PPRE transcriptional activity in the presence of transfected PPARγ, and co-treatment with an RXR ligand reduced rosiglitazone-induced PPRE transcriptional activity. Therefore, the final bioassay protocol consists of transfecting Cos-7 cells with a PPARγ expression vector along with the reporter vectors, applying rosiglitazone standards and/or 10 μL of serum, and measuring luminescence and fluorescence after a 24 h incubation. Sera from mice dosed with rosiglitazone induced PPRE transcriptional activity in the SPAA in a dose-dependent and PPARγ-dependent manner. Additionally, human serum from commercial sources induced a range of PPRE transcriptional activities in a PPARγ-dependent manner, demonstrating the ability of the bioassay to detect potentially low levels of ligands.

CONCLUSIONS

The SPAA can reliably measure total PPRE transcriptional activity in small volumes of serum. This system provides a sensitive, straightforward assay that can be reproduced in any cell culture laboratory.

Acute toxicity of copper hydroxide and glyphosate mixture in Clarias gariepinus: interaction and prediction using mixture assessment models

The study aimed to assess the single and joint lethal toxicity, type of interaction and the extent to which simple mathematical model of concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) could predict the joint toxicity of copper hydroxide and glyphosate mixture in Clarias gariepinus. Static bioassay were setup to determine the individual and combined (based on ratio 1:2) lethal concentrations (LCx) of the pesticides. Data from the static bioassays were then fitted into the synergistic ratio (SR), concentration-addition (toxicity unit; TU) and isobologram model to determine the type of interaction between the different classes of pesticides, while the CA, IA and GCA models were used to predicted the observed mixture effects. The estimated 24 h, 48 h, 72 h and 96 h LC50 for copper hydroxide were 198.66 mg/L, 167.51 mg/L, 138.64 mg/L, and 104.82 mg/L; glyphosate were 162.92 mg/L, 103.88 mg/L, 61.95 mg/L, and 52.6l mg/L; while the mixtures were 63.18 mg/L, 59.06 mg/L, 56.42 mg/L, and 50.67 mg/L, respectively. Glyphosate was 2 times more toxic than copper hydroxide to C. gariepinus when acting singly. The SR and RTU was &lt;1 indicate that the interaction between the pesticides was synergistic. Synergism was also corroborated by the isobologram model. The interaction of the mixture of copper hydroxide and glyphosate followed the IA model while the CA and GCA model underestimated the observed mixture effects. The study showed that copper hydroxide was practically non-toxic, while glyphosate and the mixture were slightly toxic to C. gariepinus.

In vitro observations and in silico predictions of xenoestrogen mixture effects in T47D-based receptor transactivation and proliferation assays

Within endocrine disruptor research, evaluation and interpretation of mixture effects and the predictive value for downstream responses still warrant more in-depth investigations. We used an estrogen receptor (ER)-mediated reporter gene assay (ER-CALUX®) and a cell proliferation assay (WST-1 assay), both based on the T47D breast cancer cell line, to test mixtures of heterogeneous xenoestrogens. Observed concentration-response curves were compared to those predicted by the concepts of concentration addition (CA), generalized concentration addition (GCA), and a novel full logistic model (FLM). CA performed better regarding mixture potency (EC50 values), whereas GCA was superior in predicting mixture efficacy (maximal response). In comparison, FLM proved to be highly suitable for in silico mixture effect prediction, combining advantages of both CA and GCA. The inter-assay comparison revealed that ER activation is not necessarily predictive for induction of cell proliferation. The results support the use of models like CA, GCA, or FLM in mixture effect evaluation. However, we conclude that reliable estimations regarding the disruptive potential of mixtures of endocrine active substances require an integrative approach considering more than one assay/endpoint to avoid misinterpretations. The formazan-based WST-1 proliferation assay might be a possible alternative to commonly used proliferation assays in endocrine disrupter research.