Metal Mixtures Modeling Evaluation project: 1. Background. (April 2015)

• Record Type: Journal Article
• Authors: Meyer, Joseph S.; Farley, Kevin J.; Garman, Emily R.
• Is Part Of: Environmental toxicology and chemistry. Volume 34:Number 4(2015:Apr.)
• Page Start: 726
• Page End: 740
• Publication Date: 2015-04
• Subjects: Concentration addition -- Interaction -- Randomization -- Response addition -- Toxic units; Pollution -- Environmental aspects -- Periodicals; Environmental chemistry -- Periodicals; 615.902
• DOI: 10.1002/etc.2792
• Languages: English
• ISSNs: 0730-7268

Abstract

Abstract: Despite more than 5 decades of aquatic toxicity tests conducted with metal mixtures, there is still a need to understand how metals interact in mixtures and to predict their toxicity more accurately than what is currently done. The present study provides a background for understanding the terminology, regulatory framework, qualitative and quantitative concepts, experimental approaches, and visualization and data‐analysis methods for chemical mixtures, with an emphasis on bioavailability and metal–metal interactions in mixtures of waterborne metals. In addition, a Monte Carlo–type randomization statistical approach to test for nonadditive toxicity is presented, and an example with a binary‐metal toxicity data set demonstrates the challenge involved in inferring statistically significant nonadditive toxicity. This background sets the stage for the toxicity results, data analyses, and bioavailability models related to metal mixtures that are described in the remaining articles in this special section from the Metal Mixture Modeling Evaluation project and workshop. It is concluded that although qualitative terminology such as additive and nonadditive toxicity can be useful to convey general concepts, failure to expand beyond that limited perspective could impede progress in understanding and predicting metal mixture toxicity. Instead of focusing on whether a given metal mixture causes additive or nonadditive toxicity, effort should be directed to develop models that can accurately predict the toxicity of metal mixtures. Environ Toxicol Chem 2015;34:726–740. © 2014 SETAC