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Adverse Outcome Pathway (AOP) informed modelling of aquatic toxicology - QSARs, read-across and inter-species verification of modes of action Claire Marie Ellison, Przemyslaw Piechota, Judith Clare Madden, Steven Enoch, and Mark Cronin Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b05918 • Publication Date (Web): 18 Feb 2016 Downloaded from http://pubs.acs.org on March 2, 2016
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Environmental Science & Technology
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Adverse Outcome Pathway (AOP) informed modelling of aquatic toxicology - QSARs, read-across
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and inter-species verification of modes of action
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Claire M. Ellison, Przemyslaw Piechota, Judith C. Madden*, Steven J. Enoch, and Mark T.D. Cronin
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School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street,
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Liverpool, L3 3AF, England
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Tel: +44 151 231 2164; Fax: +44 151 2170
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Email:
[email protected] 8
Abstract
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Alternative approaches have been promoted to reduce the number of vertebrate and invertebrate
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animals required for assessment of the potential of compounds to cause harm to the aquatic
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environment. A key philosophy in the development of alternatives is greater understanding of the
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relevant adverse outcome pathway (AOP). One alternative method is the fish embryo toxicity (FET)
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assay. Although the trends in potency have been shown to be equivalent in embryo and adult assays, a
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detailed mechanistic analysis of the toxicity data has yet to be performed; such analysis is vital for a
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full understanding of the AOP. The research presented herein used an updated implementation of the
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Verhaar scheme to categorise compounds into AOP informed categories. These were then used in
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mechanistic (Quantitative) Structure-Activity Relationship ((Q)SAR) analysis
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descriptors governing the distinct mechanisms of acute fish toxicity) are capable of modelling data
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from the FET assay. The results show that compounds do appear to exhibit the same mechanisms of
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toxicity across life stages. Thus this mechanistic analysis supports the argument that the FET assay is
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a suitable alternative testing strategy for the specified mechanisms, and that understanding the AOPs
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is useful for toxicity prediction across test systems.
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1. Introduction
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The acute aquatic toxicity assessment of chemicals has traditionally been performed on species
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representing various trophic levels e.g. algae, invertebrates, and juvenile and adult fish from species
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such as the fathead minnow (Pimephales promelas), rainbow trout (Oncorhynchus mykiss) and
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Japanese medaka (Oryzias latipes) amongst others. However, legislative mandates such as the
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Registration, Evaluation, Authorisation, and restriction of Chemicals (REACH) regulation in the
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European Union have required alternative, non-animal, models to be sought as a replacement for the
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expensive, time-consuming and ethically questionable in vivo assessment methods. One such
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alternative assay is the fish embryo toxicity (FET) test1. The FET has a standardised OECD test
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guideline (number 2362) for the 96hr assay performed with zebrafish (Danio rerio) embryos and,
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although not explicitly stated in the guideline, can be considered as a suitable assessment method, on 1 ACS Paragon Plus Environment
to show that the
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its own or as part of a strategy, for assessing acute aquatic toxicity. For instance, reasonable
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correlations (r>0.85) have been observed between the 50% effect concentrations (EC50) measured in
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the FET and the 50% lethality concentrations (LC50) measured in fish 3-6; although a small number of
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notable mechanisms are poorly predicted in FET (e.g. neurotoxic compounds which require
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behavioural analysis of the embryos for toxicity to be observed 6, 7).
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One of the key philosophies behind the research into alternative methods to animal testing is that of
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understanding mode of action. Recently, the assessment of modes of action has been incorporated into
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adverse outcome pathways (AOPs)8. AOPs define a series of key events (KE), and their relationships
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(key event relationships (KERs)) from an initial exposure, resulting in a molecular initiating event
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(MIE), through to inducing the adverse outcome (AO)9. The MIE may be described in terms of the
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chemically defining features of a molecule that control the interaction with the biological
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macromolecule10, whereas the MIE combined with the required KEs for an AO encompass the
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biological mode of action8. Understanding the AOP can thus aid in the elucidation of the similarities
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and differences in the mode of action between species by identifying key uncertainties, and
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corresponding research gaps, in the biological mechanisms of toxicity11. The rationale for the
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chemical induction of an MIE is a key aspect of understanding the AOP.
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The modes of action for acute aquatic toxicity have been established through studies on fish behaviour
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and physiology
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resultant data14, and more recently on detailed systems biology studies on species from lower taxa
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such as Daphnia magna15. Fish Acute Toxicity Syndromes (FATS) were derived from measurements
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of physiological, biochemical and analytical effects separated into discrete mechanisms, or modes, of
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action12. Building on such knowledge, Verhaar et al16 used fish acute toxicity data to identify clear
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structural rules associated with a variety of modes or mechanisms of action. The Verhaar scheme
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utilises 2D chemical structure to classify potential environmental pollutants into one of four categories
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representing one, or more, mechanisms of action: class 1 (narcosis or baseline toxicity), class 2 (less
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inert compounds), class 3 (unspecific reactivity) and class 4 (compounds and groups of compounds
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acting by a specific mechanism). In addition, Russom et al13 used structural classes to assign
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mechanisms of action to a range of compounds tested on the fathead minnow (Pimephales promelas).
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This grouping of compounds allows for the development of mechanistically based, local Quantitative
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Structure Activity Relationship (QSAR) models and also application of the chemical activity
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principle17.
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Understanding which Verhaar class, or mode/mechanism of action, a compound belongs to is useful
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for hazard characterisation, not only identifying compounds predicted to act by narcosis, but also
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identifying those which may elicit excess toxicity (e.g. compounds in Verhaar classes 3 and 4).
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Within a mechanistic class it is possible to build high quality (Q)SAR models, based on knowledge of
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, as well as mechanistic structure-activity relationship (SAR) analysis on the
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the relevant mechanism/mode of action of toxicity, which are able to estimate relative toxic potency18.
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These mechanistic models have been shown to provide more transparency and greater statistical
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performance than equivalent global models18-21. Understanding the mechanism of action also fits well
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within the AOP framework of toxicity assessment22 and allows inter-species toxicity correlations to be
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applied within a single mechanism23, 24.
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The transparency of the Verhaar classification scheme has assisted in its popularity as a hazard
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characterisation tool and this popularity has in turn led to automated implementations of the scheme
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becoming widely available. One such implementation is available in the Toxtree software. Toxtree
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was developed by Ideaconsult Ltd (Sofia, Bulgaria) under the terms of a contract from the European
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Commission’s Joint Research Centre (JRC). The software encodes several decision trees and
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classification schemes useful for analysing the potential toxicity hazards of compounds25. The
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software is freely available (http://Toxtree.sourceforge.net) and version (2.6) includes two forms of
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the Verhaar decision tree: “Verhaar scheme” and “Verhaar scheme (modified)”. The “Verhaar scheme”
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is the original implementation of the decision tree based directly on the scheme as it is described by
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Verhaar et al16. Enoch et al26 assessed the performance of this implementation and suggested possible
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improvements. These improvements form the basis of “Verhaar scheme (modified)” decision tree.
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Recent work by Ellison et al27 has shown that the implementation of the “Verhaar scheme (modified)”
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tree, and hence the suitability of resultant categories for modelling, can be improved further.
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Specifically, with the use of post-processing filters for polar phenols, reactive aromatic compounds,
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cyclic non-aromatic hydrocarbons and respiratory uncouplers of oxidative phosphorylation, the
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positive predictivity of each of the categories was increased by an average of 5%. These filters are
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available from the authors as a KNIME workflow (www.KNIME.org) for use on the output of
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Toxtree v2.6.
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Whilst the Verhaar scheme is accepted for adult fish, it is not known how applicable it may be to
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other assays such as FET, nor what mechanistic information may be derived from assessing data from
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such assays. Therefore, the aim of this study was to examine if the mechanistic categories, formed by
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using the Verhaar scheme classes implemented through the use of Toxtree v2.6 and the Ellison et al
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KNIME post-processing workflow, are relevant to the AOPs of both juvenile/adult fish and fish
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embryos. To this end AOP relevant mechanistic (Q)SAR analysis was performed on compounds with
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measured toxicity in the FET assay and outliers were highlighted. These outliers were of interest as
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they represent compounds where observed toxicity is in contradiction to that expected according to
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the mechanism of action for that class, and they may provide useful species specific information.
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These compounds may either be acting via different mechanisms in the FET assay, have been
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misclassified by the scheme, or be a misrepresentation of the chemical toxicity due to erroneous data
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or other effects such as volatility and degradation.
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2. Methods
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2.1. Dataset
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Published experimental results from the FET assay performed using zebrafish (Danio rerio) were
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manually curated from two literature sources3, 4 into a single dataset. The names, CAS numbers and
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EC50 values were extracted. The full range of exposure times (24hr – 120hr) were used, which
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included specimens in both the embryonic and eleutheroembryonic stages of development. Data from
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all time points were considered equal as it has been previously established that eleutheroembryo and
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embryo studies generally provided highly similar results4, although some compounds only show
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significant toxicity at the eleutheroembryo stage and hence it was important to include all data points.
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The 24hr testing period could provide a lower indication of toxicity because the test duration was
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insufficient for the compound the reach equilibrium due to the compound’s toxicokinetic properties.
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However, the small number of compounds (n
