Literature DB >> 16081521

A unifying concept for assessing toxicological interactions: changes in slope.

C Gennings1, W H Carter, R A Carchman, L K Teuschler, J E Simmons, E W Carney.   

Abstract

Robust statistical methods are important to the evaluation of toxicological interactions (i.e., departures from additivity) among chemicals in a mixture. However, different concepts of joint toxic action as applied to the statistical analysis of chemical mixture toxicology data or as used in environmental risk assessment often appear to conflict with one another. A unifying approach for application of statistical methodology in chemical mixture toxicology research is based on consideration of change(s) in slope. If the slope of the dose-response curve of one chemical does not change in the presence of other chemicals, then there is no interaction between the first chemical and the others. Conversely, if the rate of change in the response with respect to dose of the first chemical changes in the presence of the other chemicals, then an interaction is said to exist. This concept of zero interaction is equivalent to the usual approach taken in additivity models in the statistical literature. In these additivity models, the rate of change in the response as a function of the i(th) chemical does not change in the presence of other chemicals in a mixture. It is important to note that Berenbaum's (1985, J. Theor. Biol. 114, 413-431) general and fundamental definition of additivity does not require the chemicals in the mixture to have a common toxic mode of action nor to have similarly shaped dose response curves. We show an algebraic equivalence between these statistical additivity models and the definition of additivity given by Berenbaum.

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Year:  2005        PMID: 16081521     DOI: 10.1093/toxsci/kfi275

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  8 in total

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3.  Additive Dose Response Models: Explicit Formulation and the Loewe Additivity Consistency Condition.

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Authors:  Frederick S Walters; Gerson Graser; Andrea Burns; Alan Raybould
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5.  A Novel Approach to Chemical Mixture Risk Assessment-Linking Data from Population-Based Epidemiology and Experimental Animal Tests.

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6.  Varying impact of neonicotinoid insecticide and acute bee paralysis virus across castes and colonies of black garden ants, Lasius niger (Hymenoptera: Formicidae).

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7.  The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees.

Authors:  Erik Lampa; Lars Lind; P Monica Lind; Anna Bornefalk-Hermansson
Journal:  Environ Health       Date:  2014-07-04       Impact factor: 5.984

8.  Mixture Concentration-Response Modeling Reveals Antagonistic Effects of Estradiol and Genistein in Combination on Brain Aromatase Gene (cyp19a1b) in Zebrafish.

Authors:  Nathalie Hinfray; Cleo Tebby; Benjamin Piccini; Gaelle Bourgine; Sélim Aït-Aïssa; Jean-Marc Porcher; Farzad Pakdel; François Brion
Journal:  Int J Mol Sci       Date:  2018-04-01       Impact factor: 5.923

  8 in total

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