Development of a hybrid Bayesian network model for predicting acute fish toxicity using multiple lines of evidence. (April 2020) - Moe, S. Jannicke; Madsen, Anders L.; Connors, Kristin A.; Rawlings, Jane M.; Belanger, Scott E.; Landis, Wayne G.; Wolf, Raoul; Lillicrap, Adam D.
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.