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      An ecotoxicological view on neurotoxicity assessment

      review-article
      1 , 2 , , 1 , 3 , 3 , 4 , 5 , 6 , 7 , 7 , 8 , 9 , 10 , 11 , 12 , 12 , 12 , 13 , 14 , 15 , 15 , 15 , 16 , 16 , 17 , 17 , 1 , 18 , 18 , 19 , 20 , 21 , 21 , 21 , 22 , 23 , 24 , 25 , 26 , 26 , 1 ,
      Environmental Sciences Europe
      Springer Berlin Heidelberg
      Eco-neurotoxicity, Neurotoxicity, EDA, REACH, AOP, Behaviour, Computational toxicity, Ecological, Species

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          Abstract

          The numbers of potential neurotoxicants in the environment are raising and pose a great risk for humans and the environment. Currently neurotoxicity assessment is mostly performed to predict and prevent harm to human populations. Despite all the efforts invested in the last years in developing novel in vitro or in silico test systems, in vivo tests with rodents are still the only accepted test for neurotoxicity risk assessment in Europe. Despite an increasing number of reports of species showing altered behaviour, neurotoxicity assessment for species in the environment is not required and therefore mostly not performed. Considering the increasing numbers of environmental contaminants with potential neurotoxic potential, eco-neurotoxicity should be also considered in risk assessment. In order to do so novel test systems are needed that can cope with species differences within ecosystems. In the field, online-biomonitoring systems using behavioural information could be used to detect neurotoxic effects and effect-directed analyses could be applied to identify the neurotoxicants causing the effect. Additionally, toxic pressure calculations in combination with mixture modelling could use environmental chemical monitoring data to predict adverse effects and prioritize pollutants for laboratory testing. Cheminformatics based on computational toxicological data from in vitro and in vivo studies could help to identify potential neurotoxicants. An array of in vitro assays covering different modes of action could be applied to screen compounds for neurotoxicity. The selection of in vitro assays could be guided by AOPs relevant for eco-neurotoxicity. In order to be able to perform risk assessment for eco-neurotoxicity, methods need to focus on the most sensitive species in an ecosystem. A test battery using species from different trophic levels might be the best approach. To implement eco-neurotoxicity assessment into European risk assessment, cheminformatics and in vitro screening tests could be used as first approach to identify eco-neurotoxic pollutants. In a second step, a small species test battery could be applied to assess the risks of ecosystems.

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          Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment.

          Ecological risk assessors face increasing demands to assess more chemicals, with greater speed and accuracy, and to do so using fewer resources and experimental animals. New approaches in biological and computational sciences may be able to generate mechanistic information that could help in meeting these challenges. However, to use mechanistic data to support chemical assessments, there is a need for effective translation of this information into endpoints meaningful to ecological risk-effects on survival, development, and reproduction in individual organisms and, by extension, impacts on populations. Here we discuss a framework designed for this purpose, the adverse outcome pathway (AOP). An AOP is a conceptual construct that portrays existing knowledge concerning the linkage between a direct molecular initiating event and an adverse outcome at a biological level of organization relevant to risk assessment. The practical utility of AOPs for ecological risk assessment of chemicals is illustrated using five case examples. The examples demonstrate how the AOP concept can focus toxicity testing in terms of species and endpoint selection, enhance across-chemical extrapolation, and support prediction of mixture effects. The examples also show how AOPs facilitate use of molecular or biochemical endpoints (sometimes referred to as biomarkers) for forecasting chemical impacts on individuals and populations. In the concluding sections of the paper, we discuss how AOPs can help to guide research that supports chemical risk assessments and advocate for the incorporation of this approach into a broader systems biology framework.
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            Systemic insecticides (neonicotinoids and fipronil): trends, uses, mode of action and metabolites

            Since their discovery in the late 1980s, neonicotinoid pesticides have become the most widely used class of insecticides worldwide, with large-scale applications ranging from plant protection (crops, vegetables, fruits), veterinary products, and biocides to invertebrate pest control in fish farming. In this review, we address the phenyl-pyrazole fipronil together with neonicotinoids because of similarities in their toxicity, physicochemical profiles, and presence in the environment. Neonicotinoids and fipronil currently account for approximately one third of the world insecticide market; the annual world production of the archetype neonicotinoid, imidacloprid, was estimated to be ca. 20,000 tonnes active substance in 2010. There were several reasons for the initial success of neonicotinoids and fipronil: (1) there was no known pesticide resistance in target pests, mainly because of their recent development, (2) their physicochemical properties included many advantages over previous generations of insecticides (i.e., organophosphates, carbamates, pyrethroids, etc.), and (3) they shared an assumed reduced operator and consumer risk. Due to their systemic nature, they are taken up by the roots or leaves and translocated to all parts of the plant, which, in turn, makes them effectively toxic to herbivorous insects. The toxicity persists for a variable period of time—depending on the plant, its growth stage, and the amount of pesticide applied. A wide variety of applications are available, including the most common prophylactic non-Good Agricultural Practices (GAP) application by seed coating. As a result of their extensive use and physicochemical properties, these substances can be found in all environmental compartments including soil, water, and air. Neonicotinoids and fipronil operate by disrupting neural transmission in the central nervous system of invertebrates. Neonicotinoids mimic the action of neurotransmitters, while fipronil inhibits neuronal receptors. In doing so, they continuously stimulate neurons leading ultimately to death of target invertebrates. Like virtually all insecticides, they can also have lethal and sublethal impacts on non-target organisms, including insect predators and vertebrates. Furthermore, a range of synergistic effects with other stressors have been documented. Here, we review extensively their metabolic pathways, showing how they form both compound-specific and common metabolites which can themselves be toxic. These may result in prolonged toxicity. Considering their wide commercial expansion, mode of action, the systemic properties in plants, persistence and environmental fate, coupled with limited information about the toxicity profiles of these compounds and their metabolites, neonicotinoids and fipronil may entail significant risks to the environment. A global evaluation of the potential collateral effects of their use is therefore timely. The present paper and subsequent chapters in this review of the global literature explore these risks and show a growing body of evidence that persistent, low concentrations of these insecticides pose serious risks of undesirable environmental impacts.
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              MetFrag relaunched: incorporating strategies beyond in silico fragmentation

              Background The in silico fragmenter MetFrag, launched in 2010, was one of the first approaches combining compound database searching and fragmentation prediction for small molecule identification from tandem mass spectrometry data. Since then many new approaches have evolved, as has MetFrag itself. This article details the latest developments to MetFrag and its use in small molecule identification since the original publication. Results MetFrag has gone through algorithmic and scoring refinements. New features include the retrieval of reference, data source and patent information via ChemSpider and PubChem web services, as well as InChIKey filtering to reduce candidate redundancy due to stereoisomerism. Candidates can be filtered or scored differently based on criteria like occurence of certain elements and/or substructures prior to fragmentation, or presence in so-called “suspect lists”. Retention time information can now be calculated either within MetFrag with a sufficient amount of user-provided retention times, or incorporated separately as “user-defined scores” to be included in candidate ranking. The changes to MetFrag were evaluated on the original dataset as well as a dataset of 473 merged high resolution tandem mass spectra (HR-MS/MS) and compared with another open source in silico fragmenter, CFM-ID. Using HR-MS/MS information only, MetFrag2.2 and CFM-ID had 30 and 43 Top 1 ranks, respectively, using PubChem as a database. Including reference and retention information in MetFrag2.2 improved this to 420 and 336 Top 1 ranks with ChemSpider and PubChem (89 and 71 %), respectively, and even up to 343 Top 1 ranks (PubChem) when combining with CFM-ID. The optimal parameters and weights were verified using three additional datasets of 824 merged HR-MS/MS spectra in total. Further examples are given to demonstrate flexibility of the enhanced features. Conclusions In many cases additional information is available from the experimental context to add to small molecule identification, which is especially useful where the mass spectrum alone is not sufficient for candidate selection from a large number of candidates. The results achieved with MetFrag2.2 clearly show the benefit of considering this additional information. The new functions greatly enhance the chance of identification success and have been incorporated into a command line interface in a flexible way designed to be integrated into high throughput workflows. Feedback on the command line version of MetFrag2.2 available at http://c-ruttkies.github.io/MetFrag/ is welcome. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0115-9) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                jessica.legradi@bio5.rwth-aachen.de
                Carolina.DiPaolo@bio5.rwth-aachen.de
                M.H.S.Kraak@uva.nl
                h.g.vandergeest@uva.nl
                emma.schymanski@uni.lu
                Williams.Antony@epa.gov
                Milou.Dingemans@kwrwater.nl
                riccardo.massei@ufz.de
                werner.brack@ufz.de
                Xavier.Cousin@ifremer.fr
                Marie.Laure.Begout@ifremer.fr
                ron.van.der.oost@waternet.nl
                alessandra.carion@unamur.be
                victoria.ulloa@gmail.com
                frederic.silvestre@unamur.be
                beate.escher@ufz.de
                Magnus.Engwall@oru.se
                Greta.Nilen@oru.se
                Steffen.Keiter@oru.se
                dieter.pollet@h-da.de
                petra.waldmann@h-da.de
                Cornelia.Kienle@oekotoxzentrum.ch
                Inge.werner@oekotoxzentrum.ch
                ann-cathrin.haigis@rwth-aachen.de
                dries.knapen@uantwerpen.be
                lucia.vergauwen@uantwerpen.be
                M.Spehr@sensorik.rwth-aachen.de
                Schulz.W@lw-online.de
                wibke.busch@ufz.de
                david.leuthold@ufz.de
                Stefan.Scholz@ufz.de
                Colette.vomBerg@eawag.ch
                niladri.basu@mcgill.ca
                camurphy@anr.msu.edu
                alampert@ukaachen.de
                Jochen.Kuckelkorn@uba.de
                tamara.grummt@uba.de
                henner.hollert@bio5.rwth-aachen.de
                Journal
                Environ Sci Eur
                Environ Sci Eur
                Environmental Sciences Europe
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2190-4707
                2190-4715
                14 December 2018
                14 December 2018
                2018
                : 30
                : 1
                : 46
                Affiliations
                [1 ]ISNI 0000 0001 0728 696X, GRID grid.1957.a, Institute for Environmental Research, Department of Ecosystem Analysis, ABBt–Aachen Biology and Biotechnology, , RWTH Aachen University, ; Worringerweg 1, 52074 Aachen, Germany
                [2 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Environment and Health, VU University, ; 1081 HV Amsterdam, The Netherlands
                [3 ]ISNI 0000000084992262, GRID grid.7177.6, FAME-Freshwater and Marine Ecology, Institute for Biodiversity and Ecosystem Dynamics, , University of Amsterdam, ; P.O. Box 94248, 1090 GE Amsterdam, The Netherlands
                [4 ]ISNI 0000 0001 2295 9843, GRID grid.16008.3f, Luxembourg Centre for Systems Biomedicine (LCSB), , University of Luxembourg, ; 6 Avenue du Swing, 4367 Belvaux, Luxembourg
                [5 ]National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA
                [6 ]ISNI 0000 0001 1983 4580, GRID grid.419022.c, KWR Watercycle Research Institute, ; Groningenhaven 7, 3433 PE Nieuwegein, The Netherlands
                [7 ]ISNI 0000 0004 0492 3830, GRID grid.7492.8, Department Effect-Directed Analysis, , Helmholtz Centre for Environmental Research-UFZ, ; Permoserstr. 15, Leipzig, Germany
                [8 ]ISNI 0000 0004 0641 9240, GRID grid.4825.b, Ifremer, UMR MARBEC, Laboratoire Adaptation et Adaptabilités des Animaux et des Systèmes, ; Route de Maguelone, 34250 Palavas-les-Flots, France
                [9 ]GRID grid.417961.c, INRA, UMR GABI, INRA, AgroParisTech, ; Domaine de Vilvert, Batiment 231, 78350 Jouy-en-Josas, France
                [10 ]ISNI 0000 0004 0641 9240, GRID grid.4825.b, Ifremer, Laboratoire Ressources Halieutiques, ; Place Gaby Coll, 17137 L’Houmeau, France
                [11 ]Department of Technology, Research and Engineering, Waternet Institute for the Urban Water Cycle, Amsterdam, The Netherlands
                [12 ]ISNI 0000 0001 2242 8479, GRID grid.6520.1, Laboratory of Evolutionary and Adaptive Physiology, Institute of Life, Earth and Environment, , University of Namur, ; 5000 Namur, Belgium
                [13 ]ISNI 0000 0004 0492 3830, GRID grid.7492.8, Department of Cell Toxicology, , Helmholtz Centre for Environmental Research-UFZ, ; Permoserstr. 15, 04318 Leipzig, Germany
                [14 ]ISNI 0000 0001 2190 1447, GRID grid.10392.39, Eberhard Karls University Tübingen, Environmental Toxicology, Center for Applied Geosciences, ; 72074 Tübingen, Germany
                [15 ]ISNI 0000 0001 0738 8966, GRID grid.15895.30, MTM Research Centre, School of Science and Technology, , Örebro University, ; Fakultetsgatan 1, 70182 Örebro, Sweden
                [16 ]Faculty of Chemical Engineering and Biotechnology, University of Applied Sciences Darmstadt, Stephanstrasse 7, 64295 Darmstadt, Germany
                [17 ]ISNI 0000000121839049, GRID grid.5333.6, Swiss Centre for Applied Ecotoxicology Eawag-EPFL, ; Überlandstrasse 133, 8600 Dübendorf, Switzerland
                [18 ]ISNI 0000 0001 0790 3681, GRID grid.5284.b, Zebrafishlab, Veterinary Physiology and Biochemistry, , University of Antwerp, ; Wilrijk, Belgium
                [19 ]ISNI 0000 0001 0728 696X, GRID grid.1957.a, Institute for Biology II, Department of Chemosensation, , RWTH Aachen University, ; Aachen, Germany
                [20 ]Zweckverband Landeswasserversorgung, Langenau, Germany
                [21 ]ISNI 0000 0004 0492 3830, GRID grid.7492.8, Department of Bioanalytical Ecotoxicology, , UFZ–Helmholtz Centre for Environmental Research, ; Leipzig, Germany
                [22 ]ISNI 0000 0001 1551 0562, GRID grid.418656.8, Department of Environmental Toxicology, , Swiss Federal Institute of Aquatic Science and Technology, ; Eawag, Dübendorf, 8600 Switzerland
                [23 ]ISNI 0000 0004 1936 8649, GRID grid.14709.3b, Faculty of Agricultural and Environmental Sciences, , McGill University, ; Montreal, Canada
                [24 ]ISNI 0000 0001 2150 1785, GRID grid.17088.36, Department of Fisheries and Wildlife, , Michigan State University, ; East Lansing, USA
                [25 ]Institute of Physiology (Neurophysiology), Aachen, Germany
                [26 ]Section Toxicology of Drinking Water and Swimming Pool Water, Federal Environment Agency (UBA), Heinrich-Heine-Str. 12, 08645 Bad Elster, Germany
                Article
                173
                10.1186/s12302-018-0173-x
                6292971
                30595996
                31fe286c-e73c-48f1-bce4-1d14fe96ebe8
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 18 September 2018
                : 31 October 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: 02WRS1419
                Funded by: Joint Research Programme of the Dutch Water companies
                Award ID: BTO2018-2023
                Funded by: norman network
                Categories
                Review
                Custom metadata
                © The Author(s) 2018

                eco-neurotoxicity,neurotoxicity,eda,reach,aop,behaviour,computational toxicity,ecological,species

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