title: The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach.
abstract: The European Plant Protection Products Regulation 1107/2009 requires that registrants establish whether pesticide metabolites pose a risk to the environment. Fish acute toxicity assessments may be carried out to this end. Considering the total number of pesticide (re-) registrations, the number of metabolites can be considerable, and therefore this testing could use many vertebrates. EFSA's recent "Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters" outlines opportunities to apply non-testing methods, such as Quantitative Structure Activity Relationship (QSAR) models. However, a scientific evidence base is necessary to support the use of QSARs in predicting acute fish toxicity of pesticide metabolites. Widespread application and subsequent regulatory acceptance of such an approach would reduce the numbers of animals used. The work presented here intends to provide this evidence base, by means of retrospective data analysis. Experimental fish LC50 values for 150 metabolites were extracted from the Pesticide Properties Database (http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm). QSAR calculations were performed to predict fish acute toxicity values for these metabolites using the US EPA's ECOSAR software. The most conservative predicted LC50 values generated by ECOSAR were compared with experimental LC50 values. There was a significant correlation between predicted and experimental fish LC50 values (Spearman rs = 0.6304, p < 0.0001). For 62% of metabolites assessed, the QSAR predicted values are equal to or lower than their respective experimental values. Refined analysis, taking into account data quality and experimental variation considerations increases the proportion of sufficiently predictive estimates to 91%. For eight of the nine outliers, there are plausible explanation(s) for the disparity between measured and predicted LC50 values. Following detailed consideration of the robustness of this non-testing approach, it can be concluded there is a strong data driven rationale for the applicability of QSAR models in the metabolite assessment scheme recommended by EFSA. As such there is value in further refining this approach, to improve the method and enable its future incorporation into regulatory guidance and practice.
authors.affiliation: NC3Rs, Gibbs Building, 215 Euston Road, London, NW1 2BE, UK. Electronic address: natalie.burden@nc3rs.org.uk.
authors.forename: Natalie
authors.initials: N
authors.lastname: Burden
authors.affiliation: Syngenta Environmental Safety, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK.
authors.forename: Samuel K
authors.initials: SK
authors.lastname: Maynard
authors.affiliation: BASF SE, Crop Protection - Ecotoxicology, Speyerer Strasse 2, D-67117, Limburgerhof, Germany.
authors.forename: Lennart
authors.initials: L
authors.lastname: Weltje
authors.affiliation: Dow AgroSciences, 3B Park Square, Milton Park, Abingdon, Oxfordshire, OX14 4RN, UK.
authors.forename: James R
authors.initials: JR
authors.lastname: Wheeler
mesh_terms: D000818:Animals; D001711:Biotransformation; D004305:Dose-Response Relationship, Drug; D005399:Fishes; D006801:Humans; D007928:Lethal Dose 50; D008958:Models, Molecular; D015394:Molecular Structure; D010575:Pesticides / Q000737:chemistry / Q000378:metabolism / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012984:Software; D013997:Time Factors; D023382:Toxicity Tests, Acute / Q000379:methods*; D014874:Water Pollutants, Chemical / Q000737:chemistry / Q000378:metabolism / Q000633:toxicity*
chemical_list: D010575:Pesticides; D014874:Water Pollutants, Chemical
keywords: 3Rs; Fish toxicity; Metabolites; Pesticides; QSARs