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Predicting Mixture Toxicity with Models of Additivity
Citation:
Rider, C., G. Dinse, D. Umbach, J. Simmons, AND R. Hertzberg. Predicting Mixture Toxicity with Models of Additivity. Chapter 9, Chemical Mixtures and Combined Chemical and Nonchemical Stressors: Exposure, Toxicity, Analysis and Risk. Springer International Publishing AG, Cham (ZG), Switzerland, , 235-270, (2018). https://doi.org/10.1007/978-3-319-56234-6_9
Impact/Purpose:
The Agency needs better predictive methods and tools to estimate the toxicity of mixtures of chemicals. This chapter focuses on the different concepts of additivity and the varied methods that have been used to predict the toxicity of defined mixtures under an assumption of additivity. The history, assumptions, current applications and applications challenges, and relevance of four general types of additivity (viz., dose addition, independent action, effect summation, and integrated addition) are presented) are presented in a format useful to both toxicologists and risk assessors.
Description:
Researchers in numerous fields (e.g., pharmacology, entomology, toxicology, and epidemiology) have attempted to model the joint action of chemicals using simple formulas based only on knowledge of individual chemical toxicity or pharmacological effect (i.e., dose-response relationships). Collectively, these formulas are referred to as “additivity models”, and they are based on concepts of additivity that include dose addition, independent action, integrated addition, and effect summation. In toxicology, additivity-based predictions are often compared to observed mixture data to assess the presence and magnitude of interactions (greater-than additive or less-than additive) among chemicals. These models can also be used to estimate the toxicity of a defined mixture for comparison to the observed toxicity of a related, but more complex, mixture. Alternatively, additivity models have been used to explore mechanisms of joint action. In general, the steps for investigating joint toxicity using additivity models include: 1) deciding on which additivity model(s) to apply (e.g., dose addition, independent action, or both), 2) collecting dose-response data on individual chemicals, 3) incorporating individual chemical data in an additivity model to generate predictions, and 4) comparing predicted to observed mixture responses. Many of the additivity models have a long and sometimes controversial history. This chapter provides background on several of the common additivity models, illustrates their application with examples, and discusses their advantages and limitations.
