How well can we predict the toxicity of pesticide mixtures to aquatic life?

✍ Scribed by Jason B Belden; Robert J Gilliom; Michael J Lydy

• Publisher: Society of Environmental Toxicology and Chemistry
• Year: 2007
• Tongue: English
• Weight: 520 KB
• Volume: 3
• Category: Article
• ISSN: 1551-3777
• DOI: 10.1002/ieam.5630030307

✦ Synopsis

Abstract

Results of published pesticide mixture toxicity experiments conducted with aquatic organisms were compiled and evaluated to assess the accuracy of predictive mixture models. Three types of models were evaluated: Concentration addition (CA), independent action (IA), and simple interaction (SI). The CA model was the most often tested (207 experiments), followed by SI (59) and IA (37). The reviewed experiments are listed in the Supplemental material to provide a resource for future investigators. The predictive accuracy of each model was quantified for each experiment by the model deviation ratio (MDR), which was calculated by dividing the predicted toxicity by the observed toxicity. Eighty‐eight percent of all experiments that evaluated the CA model had observed effective concentrations within a factor of 2 of predicted values (MDR values from 0.5–2.0). The median MDR was 1, about 5% of MDRs were less than 0.5, and about 5% were greater than 2, indicating unbiased estimates overall. The predictive accuracy of CA and IA models was influenced, however, by the different modes of action (MOA) of the pesticides. For experiments with pesticides with the same MOA, CA more accurately predicted effective concentrations for more experiments compared to IA, which tended to underpredict toxicity. The IA model was somewhat more accurate than the CA model for most mixtures with different MOAs, but in most cases there were relatively small differences between the models. Additionally, 80% of SI experiments had an MDR value below 2.0 despite a bias towards experiments that are likely to have an interaction. Thus, results indicate that the CA model may be used as a slightly conservative, but broadly applicable model with a relatively small likelihood of underestimating effects due to interactions.