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RESEARCH PRODUCT

Comparing in vivo data and in silico predictions for acute effects assessment of biocidal active substances and metabolites for aquatic organisms.

Emilio BenfenatiMaria BlazquezIrati RaneroOscar Andreu-sánchezMaría Luisa Fernández-cruz

subject

Quantitative structure–activity relationshipBiocideAquatic OrganismsHealth Toxicology and MutagenesisIn silicoMicroorganismDaphnia magna0211 other engineering and technologiesQuantitative Structure-Activity RelationshipFresh Water02 engineering and technology010501 environmental sciences01 natural sciencesDaphniaModels BiologicalChlorophyceaeMicroalgaeAnimalsComputer Simulation0105 earth and related environmental sciencesEC50021110 strategic defence & security studiesbiologyChemistryPublic Health Environmental and Occupational HealthGeneral Medicinebiology.organism_classificationPollutionAcute toxicityDaphniaEnvironmental chemistryWater Pollutants ChemicalDisinfectants

description

Abstract The purpose of this study was to determine the acute toxicity in aquatic organisms of one biocidal active substance and six metabolites derived from biocidal active substances and to assess the suitability of available QSAR models to predict the obtained values. We have reported the acute toxicity in sewage treatment plant (STP) microorganisms, in the freshwater microalgae Pseudokirchneriella subcapitata and in Daphnia magna following OECD test methods. We have also identified in silico models for acute toxicity of these trophic levels currently available in widely recognized platforms such as VEGA and the OECD QSAR ToolBox. A total of six, four and two models have been selected for Daphnia, algae and microorganisms, respectively. Finally, we have compared the in silico and in vivo data for the seven compounds plus two previously assayed biocidal substances. None of the compounds tested were toxic for Daphnia and STP microorganisms. For microalgae, CGA71019 (1,2,4 triazole) presented an ErC50 value of 38.3 mg/L. The selected in silico models have provided lower EC50 values and are therefore more conservative. Models from the OECD QSAR ToolBox predicted values for 7 out of 9 and for 4 out of 9 chemicals for Daphnia and P. subcapitata, respectively. No predictive models were identified in such platform for STP microorganism's acute effects. In terms of models's specificity, biocide-specific models, developed from curated datasets integrated by biocidal active substances and implemented in VEGA, perform better in the case of microalgae but for Daphnia an alternative, non biocide-specific has revealed a better performance. For STP microorganisms only biocide-specific models have been identified.

10.1016/j.ecoenv.2020.111291https://pubmed.ncbi.nlm.nih.gov/32956865