Development of a hybrid Bayesian network model for predicting acute fish toxicity using multiple lines of evidence. (April 2020)

• Record Type: Journal Article
• Authors: Moe, S. Jannicke; Madsen, Anders L.; Connors, Kristin A.; Rawlings, Jane M.; Belanger, Scott E.; Landis, Wayne G.; Wolf, Raoul; Lillicrap, Adam D.
• Is Part Of: Environmental modelling & software. Volume 126(2020)
• Publication Date: 2020-04
• Subjects: Hybrid bayesian network model -- Acute fish toxicity -- Fish embryo toxicity -- Weight of evidence -- Animal alternatives -- Risk assessment; AFT acute fish toxicity -- BN Bayesian network -- CV coefficient of variation -- EC50 effect concentration for 50% of the test individuals -- ECHA European Chemicals Agency -- FET fish embryo toxicity -- LC50 lethal concentration for 50% of the test individuals -- QSAR quantitative structure-activity relationship -- REACH registration, evaluation, authorisation and restriction of chemicals -- WOE weight of evidence; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104655
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A hybrid Bayesian network (BN) was developed for predicting the acute toxicity of chemicals to fish, using data from fish embryo toxicity (FET) testing in combination with other information. This model can support the use of FET data in a Weight-of-Evidence (WOE) approach for replacing the use of ju-venile fish. The BN predicted correct toxicity intervals for 69%–80% of the tested substances. The model was most sensitive to components quantified by toxicity data, and least sensitive to compo-nents quantified by expert knowledge. The model is publicly available through a web interface. Fur-ther development of this model should include additional lines of evidence, refinement of the discre-tisation, and training with a larger dataset for weighting of the lines of evidence. A refined version of this model can be a useful tool for predicting acute fish toxicity, and a contribution to more quantitative WOE approaches for ecotoxicology and environmental assessment more generally. Graphical abstract: Image 1 Highlights: A Bayesian network (BN) was developed to predict the toxicity of chemicals to fish. The BN uses fish embryo toxicity data in a quantitative weight-of-evidence approach. The BN integrates physical, chemical and toxicological properties of chemicals. Correct toxicity intervals were predicted for 69–80% of test cases. The BN is publicly available for demonstration and testing through a web interface.