SAR and QSAR modeling of endocrine disruptors

Transgenerational effects of triazole fungicides on gene expression and egg compounds in non-exposed offspring: A case study using Red-Legged Partridges (Alectoris rufa)

Triazole fungicides are widely used to treat cereal seeds before sowing. Granivorous birds like the Red-legged Partridge (Alectoris rufa) have high exposure risk because they ingest treated seeds that remain on the field surface. As triazole fungicides can act as endocrine disruptors, affecting sterol synthesis and reproduction in birds several months after exposure, we hypothesized that these effects could also impact subsequent generations of exposed birds. To test this hypothesis, we exposed adult partridges (F0) to seeds treated at commercial doses with four different formulations containing triazoles as active ingredients (flutriafol, prothioconazole, tebuconazole, and a mixture of the latter two), simulating field exposure during late autumn sowing. During the subsequent reproductive season, two to four months after exposure, we examined compound allocation of steroid hormones, cholesterol, vitamins, and carotenoids in eggs laid by exposed birds (F1), as well as the expression of genes encoding enzymes involved in sterol biosynthesis in one-day-old chicks of this F1. One year later, F1 animals were paired again to investigate the expression of the same genes in the F2 chicks. We found changes in the expression of some genes for all treatments and both generations. Additionally, we observed an increase in estrone levels in eggs from partridges treated with flutriafol compared to controls, a decrease in tocopherol levels in partridges exposed to the mixture of tebuconazole and prothioconazole, and an increase in retinol levels in partridges exposed to prothioconazole. Despite sample size limitations, this study provides novel insights into the mechanisms of action of the previously observed effects of triazole fungicide-treated seeds on avian reproduction with evidence that the effects can persist beyond the exposure windows, affecting unexposed offspring of partridges fed with treated seeds. The results highlight the importance of considering long-term chronic effects when assessing pesticide risks to wild birds.

Interpretable machine learning for the identification of estrogen receptor agonists, antagonists, and binders

An abnormal hormonal activity or exposure to endocrine-disrupting chemicals (EDCs) can cause endocrine system malfunction. Among the many interactions EDCs can affect is the disruption of estrogen signalling, which can lead to adverse health effects such as cancer, osteoporosis, neurodegenerative diseases, cardiovascular disease, insulin resistance, and obesity. Knowing which chemical can act as an EDC is a significant advantage and a practical necessity. New Approach Methodologies (NAM) computational models offer a quick and cost-effective solution for preliminary hazard assessment of chemicals without animal testing. Therefore, a machine learning approach was used to investigate the relationships between estrogen receptor (ER) activity and chemical structure to identify chemicals that can interact with ER. For this purpose, the consolidated in vitro assay data from ToxCast/Tox21 projects was used for developing Random Forest classification models for ER binding, agonists, and antagonists. The overall classification prediction accuracy reaches up to 82%, depending on whether the model predicted agonists, antagonists, or compounds that bind to the active site. Given the imbalance in endocrine disruption data, the derived models are good candidates for deprioritising chemicals and reducing animal testing. The interpretation of theoretical molecular descriptors of the models was consistent with the molecular interactions known in the ligand binding pocket. The estimated class probabilities enabled the analysis of the applicability domain of the developed models and the assessment of the predictions' reliability, followed by the guidelines for interpreting prediction results. The models are openly accessible and useable at QsarDB.org (http://dx.doi.org/10.15152/QDB.259) according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles.

Development of in silico classification models for binding affinity to the glucocorticoid receptor

The endocrine disrupting properties of chemicals acting through the glucocorticoid receptor (GR) have attracted considerable interest. Since there are few data for most chemicals on their endocrine properties in silico approaches seem to be the most appropriate tool for screening and prioritizing chemicals for planning further experiments. In this work, we developed classification models for binding affinity to the glucocorticoid receptor using the counterpropagation artificial neural network method. We considered two series of 142 and 182 compounds and their binding affinity to the glucocorticoid receptor as agonists and antagonists, respectively. The compounds belong to different chemical classes. The compounds were represented by a set of descriptors calculated with the DRAGON program. The clustering structure of sets was studied with standard principal component method. A weak separation between binders and non-binders was found. Another classification model was developed using the counterpropagation artificial neural network method (CPANN). The final classification models developed were well balanced and showed a high level of accuracy, with 85.7% of GR agonist and 78.9% of GR antagonist correctly assigned in leave-one-out cross-validation.

Computational evaluation of endocrine activity of biocidal active substances

Exposure to endocrine disrupting chemicals is an important public health concern although only a few endocrine disruption chemicals have been identified so far. To speed up their identification, in silico toxicological models appear to be the most appropriate, since the potential endocrine disruption of a large number of compounds can be estimated in a short time. In this study three in silico models (Endocrine disruptome software, VirtualToxLab and COSMOS KNIME) have been used. In silico predictions of the endocrine disruption potential of biocidal active substances have been made and predictions then compared with the available in vitro experimental binding affinities to androgen, estrogen, glucocorticoid and thyroid receptors. The chosen models had similar accuracies (around 60%), while differences were shown between the models in specificity and sensitivity. VirtualToxLab was the most balanced model. Additionally, three combined models were prepared and evaluated. As expected, the majority rule approach model was more accurate and balanced. However, the positive consensus rule model, that improved the specificity of predictions (≥80% for all studied nuclear receptors) was more applicable. This reduction of false positive predictions is especially useful in the search for positive (active) compounds. On the other hand, the novel negative consensus rule model improved the specificity of prediction (≥80% for all studied nuclear receptors), giving good predictions of negative (inactive) compounds that can be excluded from further testing. The results obtained by these combined models have great added value, since they can significantly reduce further experimental testing.

Binary and multi-class classification for androgen receptor agonists, antagonists and binders

Androgens and androgen receptor regulate a variety of biological effects in the human body. The impaired functioning of androgen receptor may have different adverse health effects from cancer to infertility. Therefore, it is important to determine whether new chemicals have any binding activity and act as androgen agonists or antagonists before commercial use. Due to the large number of chemicals that require experimental testing, the computational methods are a viable alternative. Therefore, the aim of the present study was to develop predictive QSAR models for classifying compounds according to their activity at the androgen receptor. A large data set of chemicals from the CoMPARA project was used for this purpose and random forest classification models have been developed for androgen binding, agonistic, and antagonistic activity. In addition, a unique effort has been made for multi-class approach that discriminates between inactive compounds, agonists and antagonists simultaneously. For the evaluation set, the classification models predicted agonists with 80% of accuracy and for the antagonists' and binders' the respective metrics were 72% and 78%. Combining agonists, antagonists and inactive compounds into a multi-class approach added complexity to the modelling task and resulted to 64% prediction accuracy for the evaluation set. Considering the size of the training data sets and their imbalance, the achieved evaluation accuracy is very good. The final classification models are available for exploring and predicting at QsarDB repository (https://doi.org/10.15152/QDB.236).

Toxicity profiling and prioritization of plant-derived antimalarial agents

Human malaria is the most widespread mosquito-borne life-threatening disease worldwide. In the absence of effective vaccines, prevention and treatment of malaria only depend on prophylaxis and drug-based therapy either in monotherapy or in combination. Unfortunately, the number of available antimalarial drugs presenting different mechanisms of action is rather limited. In addition, the appearance of drug-resistance in the parasite strains impacts the efficacy of the treatments. As a result, there is a crucial need to find new drugs to circumvent resistance problems. In the quest to identify new antimalarial agents a huge number of plant-derived compounds (PDCs) have been investigated. Surprisingly in the in silico PDC screening programs, toxicity filters are either never used or so simple that their interest is limited. In this context, the goal of this study was to show how to take advantage of validated toxicity QSAR models for refining the selection of PDCs. From an original data set of 507 PDCs collected from the literature, the use of toxicity filters for endocrine disruption, developmental toxicity, and hepatotoxicity in conjunction with classical pharmacokinetic filters allowed us to obtain a list of 31 compounds of potential interest. The pros and cons of such a strategy have been discussed.

Hershberger Assays for Bisphenol-A and Its Substitute Candidates

Bisphenol-A(BPA) is a member of alkylphenol family, and shows adverse effects including reduced fertility, reproductive tract abnormalities, metabolic disorder, cancer induction, neurotoxicity and immunotoxicity. In the present study, we conducted Hershberger assay to evaluate whether the two candidates to replace BPA have androgenic or antiandrogenic activity. The assay was carried out using immature castrated Sprague-Dawley male rats. After 7 days of the surgery, testosterone propionate (TP, 0.4 mg/kg/day) and test materials (low dose, 40 mg/kg/day; high dose, 400 mg/kg/day) were administered for 10 consecutive days by subcutaneous (s.c.) injection and oral gavage, respectively. Test materials were BPA, isosorbide (ISO) and cyclohexanedimethanol (CHDM). The rats were necropsied, and then the weights of five androgen-dependent tissues [ventral prostate, seminal vesicle, levator ani-bulbocavernosus (LABC) muscle, paired Cowper's glands, and glans penis] and three androgen-insensitive tissues (kidney, spleen and liver) were measured. All test materials including BPA did not exhibit any androgenic activity in the assay. On the contrary, antiandrogen-like activities were found in all test groups, and the order of the intensity was CHDM > BPA > ISO in the five androgen-sensitive tissues. There was no statistical difference between low dose treatment and high dose treatment of BPA group as well as ISO group. In CHDM group, high dose treatment exhibited most severe weight reduction in all measured tissues. There was no statistical difference in androgen-insensitive tissue measurements, except BPA groups. Since the effects of ISO treatment on the accessory sex organs were much less or not present at all when compared to those of BPA, ISO could be a strong candidate to replace BPA. CHDM treatment brought most severe weight reduction in all of androgen-sensitive tissues, so this material should be excluded for further screening of BPA substitute selection.

A Round Trip from Medicinal Chemistry to Predictive Toxicology

Predictive toxicology is a new emerging multifaceted research field aimed at protecting human health and environment from risks posed by chemicals. Such issue is of extreme public relevance and requires a multidisciplinary approach where the experience in medicinal chemistry is of utmost importance. Herein, we will survey some basic recommendations to gather good data and then will review three recent case studies to show how strategies of ligand- and structure-based molecular design, widely applied in medicinal chemistry, can be adapted to meet the more restrictive scientific and regulatory goals of predictive toxicology. In particular, we will report: Docking-based classification models to predict the estrogenic potentials of chemicals. Predicting the bioconcentration factor using biokinetics descriptors. Modeling oral sub-chronic toxicity using a customized k-nearest neighbors (k-NN) approach.

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.

Computational study involving identification of endocrine disrupting potential of herbicides: Its implication in TDS and cancer progression in CRPC patients

Several environmental pollutants, including herbicides, act as endocrine disrupting chemicals (EDCs). They can cause cancer, diabetes, obesity, metabolic diseases and developmental problems. Present study was conducted to screen 608 herbicides for evaluating their endocrine disrupting potential. The screening was carried out with the help of endocrine disruptome docking program, http://endocrinedisruptome.ki.si (Kolsek et al., 2013). This program screens the binding affinity of test ligands to 12 major nuclear receptors. As high as 252 compounds were capable of binding to at least three receptors wherein 10 of them showed affinity with at-least six receptors based on this approach. The latter were ranked as potent EDCs. Majority of the screened herbicides were acting as antagonists of human androgen receptor (hAR). A homology modeling approach was used to construct the three dimensional structure of hAR to understand their binding mechanism. Docking results reveal that the most potent antiandrogenic herbicides would bind to hydrophobic cavity of modeled hAR and may lead to testicular dysgenesis syndrome (TDS) on fetal exposure. However, on binding to T877 mutant AR they seem to act as agonists in castration-resistant prostate cancer (CRPC) patients.

Brucella suis Vaccine Strain 2 Induces Endoplasmic Reticulum Stress that Affects Intracellular Replication in Goat Trophoblast Cells In vitro

Brucella has been reported to impair placental trophoblasts, a cellular target where Brucella efficiently replicates in association with the endoplasmic reticulum (ER), and ultimately trigger abortion in pregnant animals. However, the precise effects of Brucella on trophoblast cells remain unclear. Here, we describe the infection and replication of Brucella suis vaccine strain 2 (B.suis.S2) in goat trophoblast cells (GTCs) and the cellular and molecular responses induced in vitro. Our studies demonstrated that B.suis.S2 was able to infect and proliferate to high titers, hamper the proliferation of GTCs and induce apoptosis due to ER stress. Tunicamycin (Tm), a pharmacological chaperone that strongly mounts ER stress-induced apoptosis, inhibited B.suis.S2 replication in GTCs. In addition, 4 phenyl butyric acid (4-PBA), a pharmacological chaperone that alleviates ER stress-induced apoptosis, significantly enhanced B.suis.S2 replication in GTCs. The Unfolded Protein Response (UPR) chaperone molecule GRP78 also promoted B.suis.S2 proliferation in GTCs by inhibiting ER stress-induced apoptosis. We also discovered that the IRE1 pathway, but not the PERK or ATF6 pathway, was activated in the process. However, decreasing the expression of phosphoIRE1α and IRE1α proteins with Irestatin 9389 (IRE1 antagonist) in GTCs did not affect the proliferation of B.suis.S2. Although GTC implantation was not affected upon B.suis.S2 infection, progesterone secretion was suppressed, and prolactin and estrogen secretion increased; these effects were accompanied by changes in the expression of genes encoding key steroidogenic enzymes. This study systematically explored the mechanisms of abortion in Brucella infection from the viewpoint of pathogen invasion, ER stress and reproductive endocrinology. Our findings may provide new insight for understanding the mechanisms involved in goat abortions caused by Brucella infection.

Prediction of the endocrine disruption profile of pesticides

Numerous manmade chemicals released into the environment can interfere with normal, hormonally regulated biological processes to adversely affect the development and reproductive functions of living species. Various in vivo and in vitro tests have been designed for detecting endocrine disruptors, but the number of chemicals to test is so high that to save time and money, (quantitative) structure-activity relationship ((Q)SAR) models are increasingly used as a surrogate for these laboratory assays. However, most of them focus only on a specific target (e.g. estrogenic or androgenic receptor) while, to be more efficient, endocrine disruption modelling should preferentially consider profiles of activities to better gauge this complex phenomenon. In this context, an attempt was made to evaluate the endocrine disruption profile of 220 structurally diverse pesticides using the Endocrine Disruptome simulation (EDS) tool, which simultaneously predicts the probability of binding of chemicals on 12 nuclear receptors. In a first step, the EDS web-based system was successfully applied to 16 pharmaceutical compounds known to target at least one of the studied receptors. About 13% of the studied pesticides were estimated to be potential disruptors of the endocrine system due to their high predicted affinity for at least one receptor. In contrast, about 55% of them were unlikely to be endocrine disruptors. The simulation results are discussed and some comments on the use of the EDS tool are made.

Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data

BACKGROUND

The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.

RESULTS

The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Moreover, external predictions were made successfully on ten representative known estrogenic chemicals and on a set consisting of >32,000 chemicals.

CONCLUSION

The authors demonstrate that structure-based methods, widely applied to drug discovery programs, can be fairly adapted to exploratory toxicology studies.

Quantification of potential exposure of gray partridge (Perdix perdix) to pesticide active substances in farmlands

Estimating exposure of wild birds to plant protection products is of key importance in the risk assessment process evaluating their harmful potential. In this paper, we propose an ecologically-relevant methodology to estimate potential exposure to active substances (ASs) of a farmland focal bird, the gray partridge Perdix perdix. It is based on bird habitat use of fields at the time of pesticide applications. It accounts for spatio-temporal heterogeneity at population and landscape scales. We identify and quantify the potential exposure to 179 ASs of 140 clutches during pre-laying, laying, and incubation phases, and of 75 coveys. The data come from a large scale field study combining radiotelemetry and a farmer survey. They were collected in 12 different representative sites. The proportion of clutches potentially exposed to a given chemical was ≥5% for 32 ASs; prothioconazole and epoxiconazole ranking first. 71% of clutches were potentially exposed to ≥1 AS and 67% to ≥2 ASs. Mixtures involved 2 to 22 ASs. They emerged from commercial formulations, tank mixtures, bird habitat use, and combinations. ASs were fungicides (53%), herbicides (25%), and insecticides (16%) used on a variety of crops in April-June, when ground-nesting birds are breeding. The European Food Safety Authority conclusions report a long-term first-tier toxicity-to-exposure ratio (TERlt) <5 for 11 out of 19 documented ASs, and higher-tier TERlt <5 for 5 out of 10 ASs. This suggests a potential risk for bird reproduction in farmlands. Globally 13% of coveys were potentially exposed to 18 ASs during the first month (1-4 coveys per AS). The use of our field data in future research and risk assessment is discussed.

Modelling inhibition of avian aromatase by azole pesticides

The potential effects of pesticides and their metabolites on the endocrine system are of major concern to wildlife and human health. In this context, the azole pesticides have earned special attention due to their cytochrome P450 aromatase inhibition potential. Cytochrome P450 aromatase (CYP19) catalyses the conversion of androstenedione and testosterone into oestrone and oestradiol, respectively. Thus, aromatase modulates the oestrogenic balance essential not only for females, but also for male physiology, including gonadal function. Its inhibition affects reproductive organs, fertility and sexual behaviour in humans and wildlife species. Several studies have shown that azole pesticides are able to inhibit human and fish aromatases but the information on birds is lacking. Consequently, it appeared to be of interest to estimate the aromatase inhibition of azoles in three different avian species, namely Gallus gallus, Coturnix coturnix japonica and Taeniopygia guttata. In the absence of the crystal structure of the aromatase enzyme in these bird species, homology models for the individual avian species were constructed using the crystal structure of human aromatase (hAr) (pdb: 3EQM) that showed high sequence similarity for G. gallus (82.0%), T. guttata (81.9%) and C. japonica (81.2%). A homology model with Oncorhynchus mykiss (81.9%) was also designed for comparison purpose. The homology-modelled aromatase for each avian and fish species and crystal structure of human aromatase were selected for docking 46 structurally diverse azoles and related compounds. We showed that the docking behaviour of the chemicals on the different aromatases was broadly the same. We also demonstrated that there was an acceptable level of correlation between the binding score values and the available aromatase inhibition data. This means that the homology models derived on bird and fish species can be used to approximate the potential inhibitory effects of azoles on their aromatase.

In silico models for predicting vector control chemicals targeting Aedes aegypti

Human arboviral diseases have emerged or re-emerged in numerous countries worldwide due to a number of factors including the lack of progress in vaccine development, lack of drugs, insecticide resistance in mosquitoes, climate changes, societal behaviours, and economical constraints. Thus, Aedes aegypti is the main vector of the yellow fever and dengue fever flaviviruses and is also responsible for several recent outbreaks of the chikungunya alphavirus. As for the other mosquito species, the A. aegypti control relies heavily on the use of insecticides. However, because of increasing resistance to the different families of insecticides, reduction of Aedes populations is becoming increasingly difficult. Despite the unquestionable utility of insecticides in fighting mosquito populations, there are very few new insecticides developed and commercialized for vector control. This is because the high cost of the discovery of an insecticide is not counterbalanced by the 'low profitability' of the vector control market. Fortunately, the use of quantitative structure-activity relationship (QSAR) modelling allows the reduction of time and cost in the discovery of new chemical structures potentially active against mosquitoes. In this context, the goal of the present study was to review all the existing QSAR models on A. aegypti. The homology and pharmacophore models were also reviewed. Specific attention was paid to show the variety of targets investigated in Aedes in relation to the physiology and ecology of the mosquito as well as the diversity of the chemical structures which have been proposed, encompassing man-made and natural substances.

Endocrine disruptome--an open source prediction tool for assessing endocrine disruption potential through nuclear receptor binding

Predicting the endocrine disruption potential of compounds is a daunting but essential task. Here we report a new tool for this purpose that we have termed Endocrine Disruptome. It is a free and simple-to-use Web service that runs on an open source platform called Docking interface for Target Systems (DoTS). The molecular docking is handled via AutoDock Vina. Compounds are docked to 18 integrated and well-validated crystal structures of 14 different human nuclear receptors: androgen receptor; estrogen receptors α and β; glucocorticoid receptor; liver X receptors α and β; mineralocorticoid receptor; peroxisome proliferator activated receptors α, β/δ, and γ; progesterone receptor; retinoid X receptor α; and thyroid receptors α and β. Endocrine Disruptome is free of charge and available at http://endocrinedisruptome.ki.si.

On the use of electronic descriptors for QSAR modelling of PCDDs, PCDFs and dioxin-like PCBs

The electronic properties of 29 polychlorinated dibenzo-p-dioxins and dibenzofurans and dioxin-like polychlorinated biphenyls that have been included in the toxic equivalency factor system have been investigated and used to derive quantum mechanical (QM) chemical descriptors for QSAR modelling. Their utility in this context was investigated alongside descriptors based on ultraviolet absorption data and traditional 2D descriptors including log K(ow), polarizability, molecular surface properties, van der Waals volume and selected connectivity indices. The QM descriptors were calculated using the semi-empirical AM1 method and the density functional theory method B3-LYP/6-31G**. Atom-specific and molecular quantum chemical descriptors were calculated to compare the electronic properties of dioxin-like compounds regardless of their chemical class, with particular emphasis on the lateral positions. Multivariate analysis revealed differences between the chemical classes in terms of their electronic properties and also highlighted differences between congeners. The results obtained demonstrated the importance of considering molecular orbital energies, but also indicated that the ratios of the coefficients of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) at the lateral carbons were important. In addition, the digitalized UV spectra contained chemical information that provided crucial insights into dioxin-like activity.

A QSAR study of environmental estrogens based on a novel variable selection method

A large number of descriptors were employed to characterize the molecular structure of 53 natural, synthetic, and environmental chemicals which are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones and may thus pose a serious threat to the health of humans and wildlife. In this work, a robust quantitative structure-activity relationship (QSAR) model with a novel variable selection method has been proposed for the effective estrogens. The variable selection method is based on variable interaction (VSMVI) with leave-multiple-out cross validation (LMOCV) to select the best subset. During variable selection, model construction and assessment, the Organization for Economic Co-operation and Development (OECD) principles for regulation of QSAR acceptability were fully considered, such as using an unambiguous multiple-linear regression (MLR) algorithm to build the model, using several validation methods to assessment the performance of the model, giving the define of applicability domain and analyzing the outliers with the results of molecular docking. The performance of the QSAR model indicates that the VSMVI is an effective, feasible and practical tool for rapid screening of the best subset from large molecular descriptors.

Evaluation of the OECD (Q)SAR Application Toolbox for the profiling of estrogen receptor binding affinities

The determination of binding affinities for the estrogen receptor (ER) is used extensively to assess potential hazards to human health and the environment arising from chemicals that can interfere with natural hormone homeostasis. Given the great number of chemicals to which humans and wildlife are exposed, (quantitative) structure-activity relationship (Q)SAR models for the characterization of ER disruptors represent a fast and cost-efficient alternative to experimental testing. In this toxicological context, the freely available Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox provides a profiler for the categorical profiling of chemicals according to their ER binding propensities. The aim of this study was to evaluate the predictive performances of this profiler. To achieve such a purpose, prediction results with the ER-profiler were compared with experimental binding affinities relative to two large datasets of chemicals (rat and human). The resulting Cooper statistics indicated that the binding affinities of the majority of chemicals included in the retained datasets could be correctly predicted.

An integrated QSAR-PBPK modelling approach for predicting the inhalation toxicokinetics of mixtures of volatile organic chemicals in the rat

The objective of this study was to predict the inhalation toxicokinetics of chemicals in mixtures using an integrated QSAR-PBPK modelling approach. The approach involved: (1) the determination of partition coefficients as well as V(max) and K(m) based solely on chemical structure for 53 volatile organic compounds, according to the group contribution approach; and (2) using the QSAR-driven coefficients as input in interaction-based PBPK models in the rat to predict the pharmacokinetics of chemicals in mixtures of up to 10 components (benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene, and styrene). QSAR-estimated values of V(max) varied compared with experimental results by a factor of three for 43 out of 53 studied volatile organic compounds (VOCs). K(m) values were within a factor of three compared with experimental values for 43 out of 53 VOCs. Cross-validation performed as a ratio of predicted residual sum of squares and sum of squares of the response value indicates a value of 0.108 for V(max) and 0.208 for K(m). The integration of QSARs for partition coefficients, V(max) and K(m), as well as setting the K(m) equal to K(i) (metabolic inhibition constant) within the mixture PBPK model allowed to generate simulations of the inhalation pharmacokinetics of benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene and styrene in various mixtures. Overall, the present study indicates the potential usefulness of the QSAR-PBPK modelling approach to provide first-cut evaluations of the kinetics of chemicals in mixtures of increasing complexity, on the basis of chemical structure.

QSAR models for predicting toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans using quantum chemical descriptors

By partial least square regression, simple quantitative structure-activity relationship (QSAR) models were developed for the toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). Quantum chemical descriptors computed by semi-empirical PM3 method were used as predictor variables. Three optimal QSAR models are developed for 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together, respectively. The cross-validated Q (cum) (2) values for the three QSAR models of 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together are 0.816, 0.629 and 0.603, respectively, indicating good predictive capabilities for the biological toxicity of these PCDD/Fs. The present study suggests that quantum chemical descriptors of POPs indeed govern the binding affinity of these chemicals for aryl hydrocarbon receptors. Moreover, different models contain different molecular descriptors to define respective equation, which suggests that the relationship between molecular structure and the binding affinity of these chemicals for aryl hydrocarbon receptors is complex.

Internet resources for agent-based modelling

The use of agent-based models (ABMs) is steadily increasing in all the disciplines including environmental chemistry and toxicology. This growth is mainly driven by their ability to address problems that conventional modelling techniques cannot, such as the change of scale or the emergence of unanticipated phenomena resulting from interactions between their constitutive goal-directed agents. After a brief introduction on the basic principles of agent-based modelling and the presentation of selected case studies, the main software resources available on the Internet are presented. An attempt is made to estimate the complexity of these tools versus their potentialities and flexibility.

Exploring the binding features of polybrominated diphenyl ethers as estrogen receptor antagonists: docking studies

The polybrominated diphenyl ethers (PBDEs) accumulating in nature are known to be endocrine-disrupting compounds. Of first concern are those interacting with and altering activity of the human estrogen receptor alpha (hERalpha). In this study a docking study was carried out to explore the binding modes of PBDE compounds as hERalpha antagonists. It was found that some of the PBDE compounds with antiestrogenic activity extended into the channel of the estrogen receptor (ER), which is usually occupied by the alkylamine side chain of the ER antagonists raloxifene (RAL) and 4-hydroxytamoxifen (OHT), while most PBDE compounds without antiestrogenic activity adopted binding modes similar to that of ER agonist 17beta-estradiol (E2), located in the binding cavity and which did not protrude into the channel. The present study suggests that pose comparison based on docking is useful for discriminating whether or not PBDE compounds have antiestrogenic activity. Knowing the binding modes of compounds in hERalpha can help to screen out antiestrogenic compounds and further develop descriptive and predictive models in ecotoxicology.

A structural view of nuclear hormone receptor: endocrine disruptor interactions

Endocrine-disrupting chemicals (EDCs) represent a broad class of exogenous substances that cause adverse effects in the endocrine system by interfering with hormone biosynthesis, metabolism, or action. The molecular mechanisms of EDCs involve different pathways including interactions with nuclear hormone receptors (NHRs) which are primary targets of a large variety of environmental contaminants. Here, based on the crystal structures currently available in the Protein Data Bank, we review recent studies showing the many ways in which EDCs interact with NHRs and impact their signaling pathways. Like the estrogenic chemical diethylstilbestrol, some EDCs mimic the natural hormones through conserved protein-ligand contacts, while others, such as organotins, employ radically different binding mechanisms. Such structure-based knowledge, in addition to providing a better understanding of EDC activities, can be used to predict the endocrine-disrupting potential of environmental pollutants and may have applications in drug discovery.

Alkylphenol xenoestrogens with varying carbon chain lengths differentially and potently activate signaling and functional responses in GH3/B6/F10 somatomammotropes

BACKGROUND

Alkylphenols varying in their side-chain lengths [ethyl-, propyl-, octyl-, and nonylphenol (EP, PP, OP, and NP, respectively)] and bisphenol A (BPA) represent a large group of structurally related xenoestrogens that have endocrine-disruptive effects. Their rapid nongenomic effects that depend on structure for cell signaling and resulting functions are unknown.

OBJECTIVES

We compared nongenomic estrogenic activities of alkylphenols with BPA and 17beta-estradiol (E(2)) in membrane estrogen receptor-alpha-enriched GH3/B6/F10 pituitary tumor cells. These actions included calcium (Ca) signaling, prolactin (PRL) release, extracellular-regulated kinase (ERK) phosphorylation, and cell proliferation.

METHODS

We imaged Ca using fura-2, measured PRL release via radioimmunoassay, detected ERK phosphorylation by fixed cell immunoassay, and estimated cell number using the crystal violet assay.

RESULTS

All compounds caused increases in Ca oscillation frequency and intracellular Ca volume at 100 fM to 1 nM concentrations, although long-chain alkylphenols were most effective. All estrogens caused rapid PRL release at concentrations as low as 1 fM to 10 pM; the potency of EP, PP, and NP exceeded that of E(2). All compounds at 1 nM produced similar increases in ERK phosphorylation, causing rapid peaks at 2.5-5 min, followed by inactivation and additional 60-min peaks (except for BPA). Dose-response patterns of ERK activation at 5 min were similar for E2, BPA, and PP, whereas EP caused larger effects. Only E2 and NP increased cell number. Some rapid estrogenic responses showed correlations with the hydrophobicity of estrogenic molecules; the more hydrophobic OP and NP were superior at Ca and cell proliferation responses, whereas the less hydrophobic EP and PP were better at ERK activations.

CONCLUSIONS

Alkylphenols are potent estrogens in evoking these nongenomic responses contributing to complex functions; their hydrophobicity can largely predict these behaviors.

Indirect potable reuse: a sustainable water supply alternative

The growing scarcity of potable water supplies is among the most important issues facing many cities, in particular those using single sources of water that are climate dependent. Consequently, urban centers are looking to alternative sources of water supply that can supplement variable rainfall and meet the demands of population growth. A diversified portfolio of water sources is required to ensure public health, as well as social, economical and environmental sustainability. One of the options considered is the augmentation of drinking water supplies with advanced treated recycled water. This paper aims to provide a state of the art review of water recycling for drinking purposes with emphasis on membrane treatment processes. An overview of significant indirect potable reuse projects is presented followed by a description of the epidemiological and toxicological studies evaluating any potential human health impacts. Finally, a summary of key operational measures to protect human health and the areas that require further research are discussed.

Modelling the depuration rates of polychlorinated biphenyls in Oncorhynchus mykiss with quantum chemical descriptors

Using quantum chemical descriptors and partial least squares regression, a quantitative structure-activity relationship (QSAR) model is developed for the depuration rate constants (log k(d)) of 62 polychlorinated biphenyls (PCBs) in juvenile rainbow trout (Oncorhynchus mykiss). The values of the cross-validated regression coefficient (Qcum(2)) and standard deviation (SD) are 0.655 and 0.05, respectively. The high cross-validated coefficient and low standard deviation indicate that the QSAR model is well predictive. In the QSAR model, the following six descriptors are highly significant: QH(+) (the most positive charge of a hydrogen atom), HOF (standard heat of formation), CCR (core-core repulsion), EE (electronic energy), alpha(2) (squared average molecular polarisability), and S (molecular surface area). The significant descriptors show that the depuration of PCBs in rainbow trout may be mainly attributed to the biota-water partitioning process, and the reactive activity of PCB molecules may play a subordinate role.

Integrated Decision-tree Testing Strategies for Developmental and Reproductive Toxicity with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME conducted a research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for the use of alternative methods (both in vitro and in silico) in developmental and reproductive toxicity testing. It considers many tests based on primary cells and cell lines, and the available expert systems and QSARs for developmental and reproductive toxicity, and also covers tests for endocrine disruption. Ways in which reduction and refinement measures can be used are also discussed, particularly the use of an enhanced one-generation reproductive study, which could potentially replace the two-generation study, and therefore considerably reduce the number of animals required in reproductive toxicity. Decision-tree style integrated testing strategies are also proposed for developmental and reproductive toxicity and for endocrine disruption, followed by a number of recommendations for the future facilitation of developmental and reproductive toxicity testing, with respect to human risk assessment.

Integrated testing and intelligent assessment-new challenges under REACH

BACKGROUND, AIM AND SCOPE

Due to a number of drawbacks associated with the previous regime for the assessment of new and existing chemicals, the European Union established a new regulation concerning the registration, evaluation, authorisation and restriction of chemicals (REACH). All relevant industrial chemicals must now be assessed. Instead of the authorities, industry itself is responsible for the risk assessment. To achieve better and more efficient assessments while reducing animal testing, all information-standard, non-standard and non-testing-has to be used in an integrated manner. To meet these challenges, the current technical guidance documents for risk assessment of new and existing chemicals had to be updated and extended considerably. This was done by experts in a number of REACH Implementation Projects. This paper presents the most relevant results of the expert Endpoint Working Group on Aquatic Toxicity in order to illustrate the change of paradigm in the future assessment of hazards to the aquatic environment by chemical substances.

MAIN FEATURES AND CHALLENGES

REACH sets certain minimum data requirements in order to achieve a high level of protection for human health and the environment. It encourages the assessor to use alternative information instead of or in addition to standard one. This information has to be equivalent to the standard information requirement and adequate to draw overall conclusions with respect to the regulatory endpoints classification and labelling, persistent, bioaccumulative and toxic (PBT) assessment and predicted no-effect concentrations (PNEC) derivation. The main task of the expert working group was to develop guidance on how to evaluate the toxicity of a substance based on integration of information from different sources and of various degrees of uncertainty in a weight of evidence approach.

INTEGRATED TESTING AND INTELLIGENT ASSESSMENT

In order to verify the equivalence and adequacy of different types of information, a flexible sequence of steps was proposed, covering characterisation of the substance, analysis of modes of action, identification of possible analogues, evaluation of existing in vivo and in vitro testing data as well as of QSAR results. Finally, all available data from the different steps have to be integrated to come to an overall conclusion on the toxicity of the substance. This weight of evidence approach is the basis for the development of integrated testing strategies (ITS), in that the available evidence can help to determine subsequent testing steps and is essential for an optimal assessment. Its flexibility helps to meet the different requirements for drawing conclusions on the endpoints classification and labelling, PNEC derivation as well as PBT assessment. The integration of all kinds of additional information in a multi-criteria assessment reduces the uncertainties involved with extrapolation to the ecosystem level. The weight of evidence approach is illustrated by practical examples.

CONCLUSIONS AND PERSPECTIVES

REACH leads to higher challenges in order to make sound decisions with fewer resources, i.e. to move away from extensive standard testing to an intelligent substance-tailored approach. Expert judgement and integrated thinking are key elements of the weight of evidence concept and ITS, potentially leading to better risk assessments. Important sub-lethal effects such as endocrine disruption, which are not covered by the current procedure, can be considered. Conclusions have to be fully substantiated: Risk communication will be an important aspect of future assessments.

Screening of 397 chemicals and development of a quantitative structure--activity relationship model for androgen receptor antagonism

We have screened 397 chemicals for human androgen receptor (AR) antagonism by a sensitive reporter gene assay to generate data for the development of a quantitative structure-activity relationship (QSAR) model. A total of 523 chemicals comprising data on 292 chemicals from our laboratory and data on 231 chemicals from the literature constituted the training set for the model. The chemicals were selected with the purpose of representing a wide range of chemical structures (e.g., organochlorines and polycyclic aromatic hydrocarbons) and various functions (e.g., natural hormones, pesticides, plastizicers, plastic additives, brominated flame retardants, and roast mutagens). In addition, the intention was to obtain an equal number of positive and negative chemicals. Among our own data for the training set, 45.7% exhibited inhibitory activity against the transcriptional activity induced by the synthetic androgen R1881. The MultiCASE expert system was used to construct a QSAR model for AR antagonizing potential. A "5 Times, 2-Fold 50% Cross Validation" of the model showed a sensitivity of 64%, a specificity of 84%, and a concordance of 76%. Data for 102 chemicals were generated for an external validation of the model resulting in a sensitivity of 57%, a specificity of 98%, and a concordance of 92% of the model. The model was run on a set of 176103 chemicals, and 47% were within the domain of the model. Approximately 8% of chemicals was predicted active for AR antagonism. We conclude that the predictability of the global QSAR model for this end point is good. This most comprehensive QSAR model may become a valuable tool for screening large numbers of chemicals for AR antagonism.

Integrated Decision-tree Testing Strategies for Developmental and Reproductive Toxicity with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME conducted a research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for the use of alternative methods (both in vitro and in silico) in developmental and reproductive toxicity testing. It considers many tests based on primary cells and cell lines, and the available expert systems and QSARs for developmental and reproductive toxicity, and also covers tests for endocrine disruption. Ways in which reduction and refinement measures can be used are also discussed, particularly the use of an enhanced one-generation reproductive study, which could potentially replace the two-generation study, and therefore considerably reduce the number of animals required in reproductive toxicity. Decision-tree style integrated testing strategies are also proposed for developmental and reproductive toxicity and for endocrine disruption, followed by a number of recommendations for the future facilitation of developmental and reproductive toxicity testing, with respect to human risk assessment.

Binary classification models for endocrine disrupter effects mediated through the estrogen receptor

Endocrine disrupters (EDs) form an interesting field of application attracting great attention in the recent years. They represent a number of exogenous substances interfering with the function of the endocrine system, including the interfering with developmental processes. In particular EDs are mentioned as substances requiring a more detailed control and specific authorization within REACH, the new European legislation on chemicals, together with other groups of chemicals of particular concern. QSAR represents a challenging method to approach data gap which is foreseen by REACH. The aim of this study was to provide an insight into the use of QSAR models to address ED effects mediated through the estrogen receptor (ER). New predictive models were derived to assess estrogenicity for a very large and heterogeneous dataset of chemical compounds. QSAR binary classifiers were developed based on different data mining techniques such as classification trees, decision forest, fuzzy logic, neural networks and support vector machines. The focus was given to multiple endpoints to better characterize the effects of EDs evaluating both binding (RBA) and transcriptional activity (RA). A possible combination of the models was also explored. A very good accuracy was reached for both RA and RBA models (higher than 80%).

Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools

A number of chemicals released into the environment have the potential to disturb the normal functioning of the endocrine system. These chemicals termed endocrine disruptors (EDs) act by mimicking or antagonizing the normal functions of natural hormones and may pose serious threats to the reproductive capability and development of living species. Batteries of laboratory bioassays exist for detecting these chemicals. However, due to time and cost limitations, they cannot be used for all the chemicals which can be found in the ecosystems. SAR and QSAR models are particularly suited to overcome this problem but they only deal with specific targets/endpoints. The interest to account for profiles of endocrine activities instead of unique endpoints to better gauge the complexity of endocrine disruption is discussed through a SAR study performed on 11,416 chemicals retrieved from the US-NCI database and for which 13 different PASS (Prediction of Activity Spectra for Substances) endocrine activities were available. Various multivariate analyses and graphical displays were used for deriving structure-activity relationships based on specific structural features.