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

Assessment of emerging and persistent contaminants in an anthropogenic-impacted watershed: Application using targeted, non-targeted, and in vitro bioassay techniques

Emerging and persistent contaminants (EPC) pose a significant challenge to water quality monitoring efforts. Effect-based monitoring (EBM) techniques provide an efficient and systematic approach in water quality monitoring, but they tend to be resource intensive. In this study, we investigated the EPC distribution for various land uses using target analysis (TA) and non-target screening (NTS) and in vitro bioassays, both individually and integrated, in the upper Ping River Catchment, northern Thailand. Our findings of NTS showed that urban areas were the most contaminated of all land use types, although agriculture sites had high unexpected pollution levels. We evaluated the reliability of NTS data by comparing it to TA and observed varying inconsistencies likely due to matrix interferences and isobaric compound interferences. Integrating NTS with in vitro bioassays for a thorough analysis posed challenges, primary due to a scarcity of concentration data for key compounds, and potentially additive or non-additive effects of mixture samples that could not be accounted for. While EBM approaches place emphasis on toxic sites, this study demonstrated the importance of considering non-bioactive sites that contain toxic compounds with antagonistic effects that may go undetected by traditional monitoring approaches. The present work emphasizes the importance of improving NTS workflows and ensuring high-quality EBM analyses in future water quality monitoring programs.

Assessment of performance of the profilers provided in the OECD QSAR toolbox for category formation of chemicals

Growing restrictions and bans on animal testing for chemical safety assessment under different regulations have led to an increasing use of alternative methods. Read-across is one of the major approaches used for this purpose, which relies on the identification of toxicological hazards of a data-poor or untested (target) chemical from data on other already-tested (source) similar chemicals. This requires the target substance to be first assigned to a group or category of 'similar' chemicals. The 'similarity' may be in terms of structural features alone, or in combination with certain rules that are based on mechanistic and/or toxicological aspects. In this regard, the OECD QSAR toolbox-a major free-access in silico platform-is widely used to derive toxicity predictions for a range of (eco) toxicological endpoints. The Toolbox allows the user to identify a set of similar chemicals (analogues) by computational 'profilers' that incorporate different structural alerts, or a combination of structural alerts and physicochemical and/or toxicokinetic rules relevant to a specific toxicological endpoint. The overall aim of this study was to assess the performance of the in silico profilers provided in the OECD QSAR Toolbox for reliability for identifying chemical analogues for category formation in a number of high-quality databases on mutagenicity, carcinogenicity, and skin sensitisation. The study also aimed to identify the reasons for any limitations in the performance of the profilers, and propose ways to improve their overall accuracy. The results showed that whilst some structural alerts are fit-for-purpose as such within the acceptable limits, others need refinement or a consideration for their possible exclusion from the profiler. Such refinements are imperative for a reliable use of the profilers in read-across and grouping/categorisation for classification, labelling and risk assessment of chemicals.

Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow

Regulatory frameworks require information on acute fish toxicity to ensure environmental protection. The experimental assessment of this property relies on a substantial number of fish to be tested and it is in conflict with the current drive to replace in vivo testing. For this reason, alternatives to in vivo testing have been proposed during the past years. Among these alternatives, there are Quantitative Structure-Activity Relationships (QSAR) that require the sole knowledge of chemical structure to yield predictions of toxicities. In this context, the OECD QSAR Toolbox is one of the leading QSAR tools for regulatory purposes that enables the prediction of fish toxicities. The aim of this work is to provide evidence about the predictive reliability of the automated workflow for predicting acute toxicity in fish which is embedded within this toolbox. The results herein presented show that the logic underpinning this automated workflow can predict with a reliability that, in the majority of cases, is comparable to inter-laboratory variability and, in a significant number of cases, is also comparable with intra-laboratory variability. Moreover, considerations on the toxic mode of action provided by the OECD tool proved to be helpful in refining predictions and reducing the number of prediction outliers.

Predicting estrogen receptor binding of chemicals using a suite of in silico methods - Complementary approaches of (Q)SAR, molecular docking and molecular dynamics

With the aim of obtaining reliable estimates of Estrogen Receptor (ER) binding for diverse classes of compounds, a weight of evidence approach using estimates from a suite of in silico models was assessed. The predictivity of a simple Majority Consensus of (Q)SAR models was assessed using a test set of compounds with experimental Relative Binding Affinity (RBA) data. Molecular docking was also carried out and the binding energies of these compounds to the ERα receptor were determined. For a few selected compounds, including a known full agonist and antagonist, the intrinsic activity was determined using low-mode molecular dynamics methods. Individual (Q)SAR model predictivity varied, as expected, with some models showing high sensitivity, others higher specificity. However, the Majority Consensus (Q)SAR prediction showed a high accuracy and reasonably balanced sensitivity and specificity. Molecular docking provided quantitative information on strength of binding to the ERα receptor. For the 50 highest binding affinity compounds with positive RBA experimental values, just 5 of them were predicted to be non-binders by the Majority QSAR Consensus. Furthermore, agonist-specific assay experimental values for these 5 compounds were negative, which indicates that they may be ER antagonists. We also showed different scenarios of combining (Q)SAR results with Molecular docking classification of ER binding based on cut-off values of binding energies, providing a rational combined strategy to maximize terms of toxicological interest.

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.

Putative adverse outcome pathways relevant to neurotoxicity

The Adverse Outcome Pathway (AOP) framework provides a template that facilitates understanding of complex biological systems and the pathways of toxicity that result in adverse outcomes (AOs). The AOP starts with an molecular initiating event (MIE) in which a chemical interacts with a biological target(s), followed by a sequential series of KEs, which are cellular, anatomical, and/or functional changes in biological processes, that ultimately result in an AO manifest in individual organisms and populations. It has been developed as a tool for a knowledge-based safety assessment that relies on understanding mechanisms of toxicity, rather than simply observing its adverse outcome. A large number of cellular and molecular processes are known to be crucial to proper development and function of the central (CNS) and peripheral nervous systems (PNS). However, there are relatively few examples of well-documented pathways that include causally linked MIEs and KEs that result in adverse outcomes in the CNS or PNS. As a first step in applying the AOP framework to adverse health outcomes associated with exposure to exogenous neurotoxic substances, the EU Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) organized a workshop (March 2013, Ispra, Italy) to identify potential AOPs relevant to neurotoxic and developmental neurotoxic outcomes. Although the AOPs outlined during the workshop are not fully described, they could serve as a basis for further, more detailed AOP development and evaluation that could be useful to support human health risk assessment in a variety of ways.

Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA)

Chemical regulation is challenged by the large number of chemicals requiring assessment for potential human health and environmental impacts. Current approaches are too resource intensive in terms of time, money and animal use to evaluate all chemicals under development or already on the market. The need for timely and robust decision making demands that regulatory toxicity testing becomes more cost-effective and efficient. One way to realize this goal is by being more strategic in directing testing resources; focusing on chemicals of highest concern, limiting testing to the most probable hazards, or targeting the most vulnerable species. Hypothesis driven Integrated Approaches to Testing and Assessment (IATA) have been proposed as practical solutions to such strategic testing. In parallel, the development of the Adverse Outcome Pathway (AOP) framework, which provides information on the causal links between a molecular initiating event (MIE), intermediate key events (KEs) and an adverse outcome (AO) of regulatory concern, offers the biological context to facilitate development of IATA for regulatory decision making. This manuscript summarizes discussions at the Workshop entitled "Advancing AOPs for Integrated Toxicology and Regulatory Applications" with particular focus on the role AOPs play in informing the development of IATA for different regulatory purposes.

Effect-directed analysis for estrogenic compounds in a fluvial sediment sample using transgenic cyp19a1b-GFP zebrafish embryos

Xenoestrogens may persist in the environment by binding to sediments or suspended particulate matter serving as long-term reservoir and source of exposure, particularly for organisms living in or in contact with sediments. In this study, we present for the first time an effect-directed analysis (EDA) for identifying estrogenic compounds in a sediment sample using embryos of a transgenic reporter fish strain. In the tg(cyp19a1b-GFP) transgenic zebrafish strain, the expression of GFP (green fluorescent protein) in the brain is driven by an oestrogen responsive element in the promoter of the cyp19a1b (aromatase) gene. The selected sediment sample of the Czech river Bilina had already been analysed in a previous EDA using the yeast oestrogen screening assay and had revealed fractions containing estrogenic compounds. When normal phase HPLC (high performance liquid chromatography) fractionation was used for the separation of the sediment sample, the biotest with transgenic fish embryos revealed two estrogenic fractions. Chemical analysis of candidate compounds in these sediment fractions suggested alkylphenols and estrone as candidate compounds responsible for the observed estrogenic effect. Alkylphenol concentrations could partially explain the estrogenicity of the fractions. However, xenoestrogens below the analytical detection limit or non-targeted estrogenic compounds have probably also contributed to the sample's estrogenic potency. The results indicated the suitability of the tg(cyp19a1b-GFP) fish embryo for an integrated chemical-biological analysis of estrogenic effects.

Photolysis of estrone generates estrogenic photoproducts with higher activity than the parent compound

In the present study, we aimed to evaluate the effect of UV-visible irradiation on the estrogenicity of an estrone aqueous solution by using chemical analysis associated with an in vitro bioassay and in silico analysis. An estrone aqueous solution was irradiated with an UV-visible high-pressure mercury lamp. By using the MELN in vitro cellular bioassay, based on the induction of a luciferase reporter gene upon the activation of the estrogen receptor by chemicals, we showed that the estrogenic potency of the solution increased after irradiation. High-performance liquid chromatography fractionation of the photolyzed solution followed by in vitro testing of fractions allowed the quantitation of the estrogenic potency of each fraction. Nine photoproducts were detected and characterized by liquid chromatography-mass spectrometry coupling. The observed estrogenic activity is mediated by mono- and multi-hydroxylated photoproducts; it is influenced by the position of hydroxyl groups on the steroidal skeleton. In addition, a structure-activity analysis of the hydroxylated photoproducts confirmed their ability to act as estrogen receptor ligands.

Assessment of hydroxylated metabolites of polychlorinated biphenyls as potential xenoestrogens: a QSAR comparative analysis∗

Alternative methods, including quantitative structure-activity relationships (QSAR), are being used increasingly when appropriate data for toxicity evaluation of chemicals are not available. Approximately 40 mono-hydroxylated polychlorinated biphenyls (OH-PCBs) have been identified in humans. They represent a health and environmental concern because some of them have been shown to have agonist or antagonist interactions with human hormone receptors. This could lead to modulation of steroid hormone receptor pathways and endocrine system disruption. We performed QSAR analyses using available estrogenic activity (human estrogen receptor ER alpha) data for 71 OH-PCBs. The modelling was performed using multiple molecular descriptors including electronic, molecular, constitutional, topological, and geometrical endpoints. Multiple linear regressions and recursive partitioning were used to best fit descriptors. The results show that the position of the hydroxyl substitution, polarizability, and meta adjacent un-substituted carbon pairs at the phenolic ring contribute towards greater estrogenic activity for these chemicals. These comparative QSAR models may be used for predictive toxicity, and identification of health consequences of PCB metabolites that lack empirical data. Such information will help prioritize such molecules for additional testing, guide future basic laboratory research studies, and help the health/risk assessment community understand the complex nature of chemical mixtures.