8Alternative approaches have been promoted to reduce the number of vertebrate and invertebrate animals 9 required for assessment of the potential of compounds to cause harm to the aquatic environment. A key 10 philosophy in the development of alternatives is greater understanding of the relevant adverse outcome 11 pathway (AOP). One alternative method is the fish embryo toxicity (FET) assay. Although the trends 12 in potency have been shown to be equivalent in embryo and adult assays, a detailed mechanistic analysis 13 of the toxicity data has yet to be performed; such analysis is vital for a full understanding of the AOP. 14 The research presented herein used an updated implementation of the Verhaar scheme to categorise 15 compounds into AOP informed categories. These were then used in mechanistic (Quantitative) 16Structure-Activity Relationship ((Q)SAR) analysis to show that the descriptors governing the distinct 17 mechanisms of acute fish toxicity) are capable of modelling data from the FET assay. The results show 18 that compounds do appear to exhibit the same mechanisms of toxicity across life stages. Thus this 19 mechanistic analysis supports the argument that the FET assay is a suitable alternative testing strategy 20 for the specified mechanisms, and that understanding the AOPs is useful for toxicity prediction across 21 test systems. 22
In this study the performance of a selection of computational models for the prediction of metabolites and/or sites of metabolism was investigated. These included models incorporated in the MetaPrint2D-React, Meteor, and SMARTCyp software. The algorithms were assessed using two data sets: one a homogeneous data set of 28 Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and paracetamol (DS1) and the second a diverse data set of 30 top-selling drugs (DS2). The prediction of metabolites for the diverse data set (DS2) was better than for the more homogeneous DS1 for each model, indicating that some areas of chemical space may be better represented than others in the data used to develop and train the models. The study also identified compounds for which none of the packages could predict metabolites, again indicating areas of chemical space where more information is needed. Pragmatic approaches to using metabolism prediction software have also been proposed based on the results described here. These approaches include using cutoff values instead of restrictive reasoning settings in Meteor to reduce the output with little loss of sensitivity and for directing metabolite prediction by preselection based on likely sites of metabolism.
44The desire to reduce the number of animals used in experiments has highlighted the need to 45 standardise and validate in vitro methods as alternatives to bioaccumulation studies using fish. 46The present work details a process based on five criteria to develop a list of reference 47 compounds to evaluate alternative test methods to standard assays using rainbow trout 48 (Oncorhynchus mykiss). The approach was based on: 1) inclusion of relevant chemical classes 49for bioaccumulation and supported by data on bioconcentration factor (BCF), whole body 50 biotransformation rate (Kmet) and metabolic pathways (criteria 1-2); 2) cover a broad range of 51 bioconcentration potencies, logarithmof octanol-water coefficient (Log Kow), metabolic 52 susceptibility, molecular weight and maximum molecular diameter (criteria 3-4); and 3) 53 identification of chemicals that are unsuitable for in vitro testing according to cut-off values for 54 hydrolysis, volatility in solution and lipophilicity (criterion 5). In silico techniques were 55 employed to predict maximal log BCF, Kmet and the metabolic pathway for those chemicals for 56 which in vivo data for some of these properties were not available. Of the 139 compounds 57 considered as reference compounds, 51 were supported by high quality in vivo BCF, 22 58 compounds were supported by either in vivo Kmet or metabolic biotransformation data and ten 59 chemicals did not pass volatility and lipophilicity cut-off values. The list of reference 60 compounds is anticipated to provide a transparent basis for future experimental assessment of 61 the applicability of alternative methods for bioaccumulation assessment within the larger 62 scientific community. The potential of a compound to bioaccumulate is one of many hazardous properties that needs 67 to be evaluated in risk assessment procedures. Although bioaccumulation refers to the 68 accumulation of a substance in an organism from all routes of exposure (from the environment 69 and diet), the bioaccumulation of chemicals is usually expressed by the bioconcentration factor 70 (BCF) that refers only to its accumulation from the environment in a waterborne exposure. In 71 aquatic risk assessments, BCFs have been measured in fish according to the Organisation for 72Economic Cooperation and Development (OECD) Test Guideline305 [1][2]. 73In vivo test systems for bioaccumulation are demanding in terms of resources and the use of 74 large number of animals per test substance. Coupled with this, compliance with legislation such 75 as the European Union REACH (Registration, Evaluation, Authorisation and restriction of 76 Chemicals) regulation [3] has the potential to increase the demand for animal testing to assess 77 bioaccumulation for a large number of chemicals. Other methods such as in silico (computer-78 based) and in vitro techniques have been proposed as alternatives to in vivo testing since they 79 comply better with the principles of the 3Rs (reduction, refinement and replacement) for animal 80 testing [4]. 81In silic...
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