An ecotoxicological view on neurotoxicity assessment

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.

Keywords: AOP; Behaviour; Computational toxicity; EDA; Eco-neurotoxicity; Ecological; Neurotoxicity; REACH; Species.

Fig. 1

Key components of eco-neurotoxicity assessment

Fig. 2

Mechanism of action of an AChE inhibitor on the example of the insecticide diazinon (simplified from [117]). After oxidation catalysed by cytochrome p450 monooxygenases, Diazinon is metabolized into Diazoxon which can inhibit acetylcholinesterase. Via further phase 1 and phase 2 metabolization steps the molecule is eliminated

Fig. 3

EDA workflow

Fig. 4

AOP network showing all AOPs relevant to eco-neurotoxicity that are available in the AOP-Wiki (https://aopwiki.org; Accession date: April 30, 2018; AOP numbers 16, 77, 78, 79, 80, 82, 87, 88, 89, 90, 91, 93, 94, 95, 97, 98, 99, 113, 160, 161, 178, 195, 197, 203, 204; Network constructed using Cytoscape 3.6.1). Nodes in the network are key events (KEs), and edges represent key event relationships (KERs). Molecular initiating events are displayed in green, adverse outcomes in red. All other KEs are depicted in blue. Solid edges are adjacent KERs, dashed arrows are KERs that have been defined as non-adjacent (see the OECD’s users’ handbook for developing and assessing AOPs for more information, [333]). Node size represents node degree (the total number of KERs connecting the KE to the network, Villeneuve et al., and edge thickness represents the number of times a given KER is part of constituent AOPs in the network. While some of the AOPs in the network have been published, many are in early stages of development, and none have been reviewed or endorsed by the OECD. This figure and its annotations therefore merely illustrate the current focus areas of eco-neurotoxicity AOP developers in the AOP-Wiki, as well as the interrelatedness of these research topics. The AOP network does not make any inference about the scientific validity of the underlying AOPs, nor can it at this stage be used for in-depth biological interpretation or regulatory application. ACh, acetylcholine; AChE, acetylcholinesterase; 5-HTT, 5-hydroxytryptamine (serotonin) transporter; GABA, gamma-aminobutyric acid; Glu, glutamate; Na, sodium; K, potassium; Cl, chloride

Fig. 5

Assembly of AOP networks showing all AOPs relevant to human (orange) and eco-neurotoxicity (blue) that are available in the AOP-Wiki (https://aopwiki.org; Accession date: April 30, 2018; Eco-neurotoxicity AOP numbers 16, 77, 78, 79, 80, 82, 87, 88, 89, 90, 91, 93, 94, 95, 97, 98, 99, 113, 160, 161, 178, 195, 197, 203, 204; Human neurotoxicity AOP numbers 3, 8, 10, 12, 13, 17, 26, 42, 48, 54, 73, 104, 112, 126, 134, 152, 164, 170, 214, 215, 221, 222, 223, 224, 225, 226, 230, 231, 233, 234, 235, 236; Network constructed using Cytoscape 3.6.1). Nodes in the network are key events (KEs), and edges represent key event relationships (KERs). Molecular initiating events are displayed in green, adverse outcomes in red. All other KEs are depicted in blue. Node size represents node degree (the total number of KERs connecting the KE to the network, [334]). KERs in blue are part of eco-neurotoxicity AOPs and correspond to Fig. 4, KERs in orange are part of human neurotoxicity AOPs. The AOP network does not make any inference about the scientific validity of the underlying AOPs, nor can it at this stage be used for in-depth biological interpretation or regulatory application