Novel full logistic model for estimation of the estrogenic activity of chemical mixtures

Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products

A paradigm of chemical risk assessment is continuously extending from focusing on 'single substances' to more comprehensive approaches that examines the combined toxicity among different components in 'mixtures.' This change aims to account for the cocktail effect arising from the toxicological interactions in mixtures, which can lead to increased risks. More than 1000 potential endocrine-disrupting chemicals (EDCs) have been reported, and they can be included in different industrial and consumer chemical products and released to the environment as pollutants of emerging environmental concern. Although extensive studies involving both experiments and predictions have investigated individual EDCs, predictions of their synergistic effects are still relatively lacking, an area that requires further investigation. In this study, we extensively investigated substances in consumer products, mainly marketed in South Korea, that might exhibit estrogenic activity or reproductive toxicity. A high throughput screening (HTS) assay based on OECD Test Guideline 455 for hERαHeLa-9903 cells was constructed, and 435 substances were screened using the HTS. Thirty-five (potential) estrogenic agonists were selected, and their 1412 binary mixtures that could be prepared in four different ratios were systematically tested, considering the available effective concentrations of substances and the solubility of their resulting mixtures. The best empirical dose-response curves of 35 substances and 917 mixtures were derived in this study. Based on the HTS data, a deep neural network model was developed (area under the curve (AUC): 0.837-0.881) and compared with a random forest model (AUC: 0.656-0.829) to screen for the synergistic estrogenic activity of binary mixtures.

Environmental aspects of wastewater recycling from the point of view of emergent pollutant removal

This study evaluates the removal efficiency of 15 estrogenic endocrine-disrupting compounds in two operational constructed wetlands with different designs: a hybrid system (constructed wetland A) and a horizontal system (constructed wetland B). The assessment involved analyzing composite water samples obtained from passive samplers through liquid chromatography-mass spectrometry coupled with yeast assays. Additionally, grab samples of sludge and sediment were examined to determine the endocrine-disrupting compound's adsorption efficacy. The application of the full logistic model enabled the discernment and ranking of the chemicals contributing to mixture toxicity. The findings revealed constructed wetland A's superior efficacy in the removal of individual endocrine-disrupting compounds (with an average efficiency of 94%) compared to constructed wetland B (60%). Furthermore, constructed wetland A displayed a higher estimated estrogenic activity removal efficiency (83%) relative to constructed wetland B (52%). Estrogenic activity was adequately accounted for (58-120%) in half of the analyzed samples, highlighting estrone as the primary estrogenic agent. The investigation underscores constructed wetlands' effectiveness in purging endocrine-disrupting compounds, suggesting that their integration as secondary or tertiary treatment systems for such pollutants removal merits further exploration.

In Vitro Toxicity Screening of Fifty Complex Mixtures in HepG2 Cells

To develop the risk prediction technology for mixture toxicity, a reliable and extensive dataset of experimental results is required. However, most published literature only provides data on combinations containing two or three substances, resulting in a limited dataset for predicting the toxicity of complex mixtures. Complex mixtures may have different mode of actions (MoAs) due to their varied composition, posing difficulty in the prediction using conventional toxicity prediction models, such as the concentration addition (CA) and independent action (IA) models. The aim of this study was to generate an experimental dataset comprising complex mixtures. To identify the target complex mixtures, we referred to the findings of the HBM4EU project. We identified three groups of seven to ten components that were commonly detected together in human bodies, namely environmental phenols, perfluorinated compounds, and heavy metal compounds, assuming these chemicals to have different MoAs. In addition, a separate mixture was added consisting of seven organophosphate flame retardants (OPFRs), which may have similar chemical structures. All target substances were tested for cytotoxicity using HepG2 cell lines, and subsequently 50 different complex mixtures were randomly generated with equitoxic mixtures of EC10 levels. To determine the interaction effect, we calculated the model deviation ratio (MDR) by comparing the observed EC10 with the predicted EC10 from the CA model, then categorized three types of interactions: antagonism, additivity, and synergism. Dose-response curves and EC values were calculated for all complex mixtures. Out of 50 mixtures, none demonstrated synergism, while six mixtures exhibited an antagonistic effect. The remaining mixtures exhibited additivity with MDRs ranging from 0.50 to 1.34. Our experimental data have been formatted to and constructed for the database. They will be utilized for further research aimed at developing the combined CA/IA approaches to support mixture risk assessment.

Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity

The concerns regarding the potential health threats caused by estrogenic endocrine-disrupting chemicals (EDCs) and their mixtures manufactured by the chemical industry are increasing worldwide. Conventional experimental tests for understanding the estrogenic activity of mixtures are expensive and time-consuming. Although non-testing methods using computational modeling approaches have been developed to reduce the number of traditional tests, they are unsuitable for predicting synergistic effects because current prediction models consider only a single chemical. Thus, the development of predictive models is essential for predicting the mixture toxicity, including chemical interactions. However, selecting suitable computational modeling approaches to develop a high-performance prediction model requires considerable time and effort. In this study, we provide a suitable computational approach to develop a predictive model for the synergistic effects of estrogenic activity. We collected datasets on mixture toxicity based on the synergistic effect of estrogen agonistic activity in binary mixtures. Using the model deviation ratio approach, we classified the labels of the binary mixtures as synergistic or non-synergistic effects. We assessed five molecular descriptors, four machine learning-based algorithms, and a deep learning-based algorithm to provide a suitable computational modeling approach. Compared with other modeling approaches, the prediction model using the deep learning-based algorithm and chemical-protein network descriptors exhibited the best performance in predicting the synergistic effects. In conclusion, we developed a new high-performance binary classification model using a deep neural network and chemical-protein network-based descriptors. The developed model will be helpful for the preliminary screening of the synergistic effects of binary mixtures during the development process of chemical products.

Effects of consumer products chemicals ingredients and their mixtures on the estrogen receptor/androgen receptor transcriptional activation

Unlike the environmental pollutants or industrial chemicals, the chemicals in consumer products may pose higher levels of risks, depending on how the chemicals are used in the products and how humans interact with the products. Recently, endocrine disrupting chemicals in cosmetics, personal care products, cleaners, sunscreens, and vinyl products were analytically quantified and many active chemicals including phthalates, parabens and bisphenols were detected. This indicates a wide range of exposures from common products. In this study, 35 chemicals known to be ingredients of consumer products were selected and screened for the transactivation of estrogen receptors and androgen receptors. From the results of individual chemicals, the activity of binary/ternary mixture prepared from the agonists for the ER transcription activity was measured, and compared to the predicted values obtained by the full logistic model. The measured and the predicted values were found to be very similar. This study may suggest that prediction of mixture activity by proper models would be one of the supportive tools for the risk assessment and sound regulation of chemical mixtures which have potential endocrine disrupting effects in consumer products.

Evaluation of estrogenic and antiestrogenic activity in sludge and explanation of individual compound contributions

Mixture toxicity, including agonistic and antagonistic effects, is an unrevealed environmental problem. Estrogenic endocrine disruptors are known to cause adverse effects for aquatic biota, but causative chemicals and their contributions to the total activity in sewage sludge remain unknown. Therefore, advanced analytical methods, a yeast bioassay and mixture toxicity models were concurrently applied for the characterization of 8 selected sludges with delectable estrogenic activity (and 3 sludges with no activity as blanks) out of 25 samples from wastewater treatment plants (WWTPs). The first applied full logistic model adequately explained total activity by considering the concentrations of the monitored compounds. The results showed that the activity was primarily caused by natural estrogens in municipal WWTP sludge. Nevertheless, activity in a sample originating from a car-wash facility was dominantly caused by partial agonists - nonylphenols - and only a model enabling prediction of all dose-response curve parameters of the final mixture curve explained these results. Antiestrogenic effects were negligible, and effect-directed analysis identified the causative chemicals.

The agonistic bioanalytical equivalent concentration: A novel tool for assessing the endocrine activity of environmental mixtures

Cell-based bioassays are very sensitive and allow integrative effect screening of the whole environmental sample, which is usually composed of a mixture of agonists and antagonists. Measured toxicity is usually expressed as a bioanalytical equivalent concentration. So far, it is not possible to distinguish which part of this value is caused by the agonists and which by the antagonists. In this article, we present a simple method to analyze the dose-response curve of a mixture and to determine an agonistic bioanalytical equivalent concentration: a concentration of a reference chemical that would elicit the same effect as do only agonists in an unknown mixture. The method has been validated using several artificially prepared mixtures of agonists and competitive antagonists measured in a recombinant yeast assay. No difference was observed between the calculated equivalent concentrations and the used concentrations of the agonist in the mixture.

Key parameter optimization using multivariable linear model for the evaluation of the in vitro estrogenic activity assay in T47D cell lines (CXCL-test)

In comparison to analytical tools, bioassays provide higher sensitivity and more complex evaluation of environmental samples and are indispensable tools for monitoring increasing in anthropogenic pollution. Nevertheless, the disadvantage in cellular assays stems from the material variability used within the assays, and an interlaboratory adaptation does not usually lead to satisfactory test sensitivities. The aim of this study was to evaluate the influence of material variability on CXCL12 secretion by T47D cells, the outcome of an estrogenic activity assay, the CXCL-test. For this purpose, the cell line sources, sera suppliers, experimental and seeding media, and the amount of cell/well were tested. The multivariable linear model (MLM), employed as an innovative approach in this field for parameter evaluation, identified that all the tested parameters had significant effects. Knowledge of the contributions of each parameter has permitted step-by-step optimization. The most beneficial approach was seeding 20,000 cells/well directly in treatment medium and using DMEM for the treatment. Great differences in both basal and maximal cytokine secretions among the three tested cell lines and different impacts of each serum were also observed. Altogether, both these biologically based and highly variable inputs were additionally assessed by MLM and a subsequent two-step evaluation, which revealed a lower variability and satisfactory reproducibility of the test. This analysis showed that not only parameter and procedure optimization but also the evaluation methodology must be considered from the perspective of interlaboratory method adaptation. This overall methodology could be applied to all bioanalytical methods for fast multiparameter and accurate analysis.

How to use statistics to claim antagonism and synergism from binary mixture experiments

We review statistical approaches applicable for the analysis of data from binary mixture experiments, which are commonly used in pesticide science for evaluating antagonistic or synergistic effects. Specifically, two different situations are reviewed, one where every pesticide is only available at a single dose level and a mixture simply combines these doses, and one where the pesticides and their mixture are used at increasing doses. The former corresponds to using factorial designs whereas the latter corresponds to fixed-ratio designs. We consider dose addition and independent action as references for lack of antagonistic and synergistic effects. Data from factorial designs should be analyzed using two-way analysis of variance models whereas data from fixed-ratio designs should be analyzed using non-linear dose-response analysis. In most cases, independent action seems the more natural choice for factorial designs. In contrast, dose addition is more appropriate for fixed-ratio designs although dose addition is not equally compatible with all types of dose-response data. Fixed-ratio designs should be preferred as they allow validation of the assumed dose-response relationship and, consequently, provide much stronger claims about antagonistic and synergistic effects than factorial designs. Finally, it should be noted that, in any case, simple ways of summarizing pesticide mixture effects may come at the price of more or less restrictive modeling assumptions. © 2021 Society of Chemical Industry.

An eight-compound mixture but not corresponding concentrations of individual chemicals induces triglyceride accumulation in human liver cells

In real life, organisms are exposed to complex mixtures of chemicals at low concentration levels, whereas research on toxicological effects is mostly focused on single compounds at comparably high doses. Mixture effects deviating from the assumption of additivity, especially synergistic effects, are of concern. In an adverse outcome pathway (AOP)-guided manner, we analyzed the accumulation of triglycerides in human HepaRG liver cells by a mixture of eight steatotic chemicals (amiodarone, benzoic acid, cyproconazole, flusilazole, imazalil, prochloraz, propiconazole and tebuconazole), each present below its individual effect concentration at 1-3 μM. Pronounced and significantly enhanced triglyceride accumulation was observed with the mixture, and similar effects were seen at the level of pregnane-X-receptor activation, a molecular initiating event leading to hepatic steatosis. Transcript pattern analysis indicated subtle pro-steatotic changes at low compound concentrations, which did not exert measurable effects on cellular triglycerides. Mathematical modeling of mixture effects indicated potentially more than additive behavior using a model for compounds with similar modes of action. The present data underline the usefulness of AOP-guided in vitro testing for the identification of mixture effects and highlight the need for further research on chemical mixtures and harmonization of data interpretation of mixture effects.

Combinations of a full and partial agonist: Experimental evidence of curved isoboles

Concentration addition as a classic null model for toxicology and pharmacology is based on Loewe's mathematical formulation and the linearity of the isoboles. Novel mathematical models, however, propose curved isoboles in certain conditions. This article aims to test the hypothesis of the curvature of isoboles in experimental measurements. With the assumption of linear isoboles, a partial agonist acts as an antagonist above its maximal effect level. The isoboles automatically convert to a positive slope. For curved isoboles, a partial agonist acts as an antagonist at higher effect levels than its maximal effect alone. The discrepancies between effect levels were studied with an estrogen receptor binding assay (BMAEREluc/ERα) using a mixture of 17β-estradiol and fulvestrant as a partial agonist. A mixture of 17β-estradiol and fulvestrant acts as a partial agonist and causes the diminishing of the effect level of 17β-estradiol at a significantly higher level than the maximal effect of their partial-agonistic dose-response curve. Measured, elevated effect levels were well predicted by the mathematical model. Nonlinear isoboles may change our understanding and definition of synergism or antagonism and prompt further attention in receptor theory.

Acetylcholine esterase and antioxidant responses in freshwater teleost, Channa punctata exposed to chlorpyrifos and urea

We aimed to understand the toxic effects of two crop protecting agents, organophosphate pesticide, chlorpyrifos (CPF) and fertilizer, urea (U), and their binary mixtures at sublethal concentrations for 28-d in a freshwater fish Channa punctata with a battery of biochemical biomarkers in gill and liver. The study has practical value as such mixtures, so often present together in water in the agro-intensive areas, might be predicted to cause cocktail effects. Both CPF and U inhibited AChE, augmented SOD, CAT, GPx activities, and caused lipid peroxidation and depletion in tissue macromolecules in a concentration and duration-dependent manner. While U alone had less severe effects compared to CPF treatments, complex interactions were observed for three combination doses (1CPF + 1U, 2CPF + 1U, 1CPF + 2U). In their mutual effects, antagonism prevailed over other interactions when CPF and U were in equal proportion in the mixture, while synergism was observed for AchE and key antioxidant enzymes when more U was in the mixture. The present study concluded that urea in water bodies might impart adverse effects in combination with pesticides in non-target aquatic organisms such as fish, and there should be a restriction in its excessive usage.

The mixture effects of bisphenol derivatives on estrogen receptor and androgen receptor

Bisphenol A (BPA) is a well-known for endocrine-disrupting chemical (EDC) and is one of the highest amounts of chemicals produced worldwide. Some countries restrict the use of BPA, which is widely used in the production of a variety products. Considering the toxicity and limitations on use of BPA, efforts are needed to find safer alternatives. Increasingly, bisphenol F (BPF) and bisphenol S (BPS) are alternatives of BPA, which is increasing their exposure levels in various environments. There are many ways to assess whether a chemical is an EDC. Here, we evaluated the endocrine-disrupting risks of the bisphenols by investigating their agonist and antagonist activities with the estrogen (ER), androgen (AR), and aryl hydrocarbon (AhR) receptors. Our results showed that BPA, BPS, and BPF (BPs) have estrogen agonist and androgen antagonist activities and decrease the ERα protein level. Interestingly, a mixture of the BPs had ER and anti-AR activity at lower concentrations than BPs alone. The activation of AhR was not a concentration-dependent effect of BPs, although it was increased significantly. In conclusion, BPs have estrogen agonist and androgen antagonist activities, and the effect of exposure to a BPs mixture differs from that of BPs alone.

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.

Estimation of competitive antagonist affinity by the Schild method and from functional inhibition curves using a novel form of the Gaddum equation

The equilibrium dissociation constant of competitive antagonists represents the affinity of the receptor-ligand interaction, and it is a key characteristic of many therapeutic drugs or toxic compounds. Two commonly used methods by which the affinity of the antagonist can be estimated are Schild analysis and the Cheng-Prusoff method. However, both methods yield different results when applied to systems with slopes not equal to one. The Gaddum equation, which is fundamental for both methods, should be extended to incorporate the slope parameter of the dose-response curves and this extension should diminish the differences between the Schild and Cheng-Prusoff methods. In this study, we derived a novel form of the Gaddum equation with a slope parameter (Hill coefficient) of agonist dose-response curve. We also derived the subsequent equations for Schild and Cheng-Prusoff analysis and we validated the proposed model by the measurement of several known estrogen receptor competitive antagonists. Standardized in vitro yeast reporter gene assay (BMAEREluc/ERα) has been used for the measurements and the range of used antagonist concentrations was 1.37-46.03 μM. By applying our mathematical model, both Schild and Cheng-Prusoff methods provide more similar values of antagonist affinity than the original mathematical approach. The correctness of the model has also been demonstrated by the measurement of a partial agonist with a known receptor affinity. The presented mathematical model significantly reduces the differences in values calculated by the Cheng-Prusoff and Schild methods and yields more accurate estimations of antagonist affinity.

Assessment of agonistic and antagonistic properties of widely used oral care antimicrobial substances toward steroid estrogenic and androgenic receptors

Personal care product consumption has increased in the last decades. A typical representative ingredient, i.e., triclosan, was identified in the scientific literature as an endocrine disruptor, and its use is restricted in several applications. Oral hygiene formulations contain various compounds, including synthetic phenol derivatives, quaternary ammonium compounds (QACs), various amides and amines, or natural essential oils containing terpenes. The aim of this paper was to explore possible endocrine-disrupting effects of these most-used compounds. For this purpose, two different assays based on recombinant yeast (BMAEREluc/ERα; BMAEREluc/AR) and human cell lines (T47D; AIZ-AR) were employed to investigate the agonistic and antagonistic properties of these compounds on human estrogen and androgen receptors. The results showed that none of the compounds were indicated as agonists of the steroid receptors. However, octenidine (OCT, QAC-like) and hexadecylpyridinium (HDP, QAC) were able to completely inhibit both androgenic (IC₅₀ OCT = 0.84 μM; IC₅₀ HDP = 1.66 μM) and estrogenic (IC₅₀ OCT = 0.50 μM; IC₅₀ HDP = 1.64 μM) signaling pathways in a dose-dependent manner. Additionally, chlorhexidine was found to inhibit the 17β-estradiol response, with a similar IC₅₀ (2.9 μM). In contrast, the natural terpenes thymol and menthol were found to be competitive antagonists of the receptors; however, their IC₅₀ values were higher (by orders of magnitude). We tried to estimate the risk associated with the presence of these compounds in environmental matrices by calculating hazard quotients (HQs), and the calculated HQs were found to be close to or greater than 1 only when predicted environmental concentrations were used for surface waters.

BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism

Clinical efficacy regularly requires the combination of drugs. For an early estimation of the clinical value of (potentially many) combinations of pharmacologic compounds during discovery, the observed combination effect is typically compared to that expected under a null model. Mechanistic accuracy of that null model is not aspired to; to the contrary, combinations that deviate favorably from the model (and thereby disprove its accuracy) are prioritized. Arguably the most popular null model is the Loewe Additivity model, which conceptually maps any assay under study to a (virtual) single-step enzymatic reaction. It is easy-to-interpret and requires no other information than the concentration-response curves of the individual compounds. However, the original Loewe model cannot accommodate concentration-response curves with different maximal responses and, by consequence, combinations of an agonist with a partial or inverse agonist. We propose an extension, named Biochemically Intuitive Generalized Loewe (BIGL), that can address different maximal responses, while preserving the biochemical underpinning and interpretability of the original Loewe model. In addition, we formulate statistical tests for detecting synergy and antagonism, which allow for detecting statistically significant greater/lesser observed combined effects than expected from the null model. Finally, we demonstrate the novel method through application to several publicly available datasets.

Mathematical modeling for estrogenic activity prediction of 17β-estradiol and 17α-ethynylestradiol mixtures in wastewater treatment plants effluent

Steroid estrogens such as 17β-Estradiol (E₂) and 17α-Ethynylestradiol (EE₂) are highly potent estrogens that widely detected in environmental samples. Mathematical modelling such as concentration addition (CA) and estradiol equivalent concentration (EEQ) models are usually associated with measuring techniques to assess risk, predict the mixture response and evaluate the estrogenic activity of mixture. Wastewater has played a crucial role because wastewater treatment plant (WWTP) is the major sources of estrogenic activity in aquatic environment. The aims of this is to determine E₂ and EE₂ concentrations in six WWTPs effluent, to predict the estrogenic activity of the WWTPs effluent using CA and EEQ models where lastly the effectiveness of two models is evaluated. Results showed that all the six WWTPs effluent had relative high E₂ concentration (35.1-85.2 ng/L) compared to EE₂ (0.02-1.0 ng/L). The estrogenic activity predicted by CA model was similar among the six WWTPs (105.4 ng/L), due to the similarity of individual dose potency ratio calculated by respective WWTPs. The predicted total EEQ was ranged from 35.1 EEQ-ng/L to 85.3 EEQ-ng/L, explained by high E₂ concentration in WWTPs effluent and E₂ EEF value that standardized to 1.0 μg/L. The CA model is more effective than EEQ model in estrogenic activity prediction because EEQ model used less data and causes disassociation from the predicted behavior. Although both models predicted relative high estrogenic activity in WWTPs effluent, dilution effects in receiving river may lower the estrogenic response to aquatic inhabitants.

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.

Biodegradation of endocrine disruptors in urban wastewater using Pleurotus ostreatus bioreactor

The white rot fungus Pleurotus ostreatus HK 35, which is also an edible industrial mushroom commonly cultivated in farms, was tested in the degradation of typical representatives of endocrine disrupters (EDCs; bisphenol A, estrone, 17β-estradiol, estriol, 17α-ethinylestradiol, triclosan and 4-n-nonylphenol); its degradation efficiency under model laboratory conditions was greater than 90% within 12 days and better than that of another published strain P. ostreatus 3004. A spent mushroom substrate from a local farm was tested for its applicability in various batch and trickle-bed reactors in degrading EDCs in model fortified and real communal wastewater. The reactors were tested under various regimes including a pilot-scale trickle-bed reactor, which was finally tested at a wastewater treatment plant. The result revealed that the spent substrate is an efficient biodegradation agent, where the fungus was usually able to remove about 95% of EDCs together with suppression of the estrogenic activity of the sample. The results showed the fungus was able to operate in the presence of bacterial microflora in wastewater without any substantial negative effects on the degradation abilities. Finally, a pilot-scale trickle-bed reactor was installed in a wastewater treatment plant and successfully operated for 10days, where the bioreactor was able to remove more than 76% of EDCs present in the wastewater.