Predicting mixture effects over time with toxicokinetic–toxicodynamic models (GUTS): assumptions, experimental testing, and predictive power

Bart, Sylvain ORCID: https://orcid.org/0000-0002-0290-5546; Jager, Tjalling; Robinson, Alex; Lahive, Elma; Spurgeon, David J. ORCID: https://orcid.org/0000-0003-3264-8760; Ashauer, Roman. 2021 Predicting mixture effects over time with toxicokinetic–toxicodynamic models (GUTS): assumptions, experimental testing, and predictive power. Environmental Science & Technology, 55 (4). 2430-2439. https://doi.org/10.1021/acs.est.0c05282

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Abstract/Summary

Current methods to assess the impact of chemical mixtures on organisms ignore the temporal dimension. The General Unified Threshold model for Survival (GUTS) provides a framework for deriving toxicokinetic–toxicodynamic (TKTD) models, which account for effects of toxicant exposure on survival in time. Starting from the classic assumptions of independent action and concentration addition, we derive equations for the GUTS reduced (GUTS-RED) model corresponding to these mixture toxicity concepts and go on to demonstrate their application. Using experimental binary mixture studies with Enchytraeus crypticus and previously published data for Daphnia magna and Apis mellifera, we assessed the predictive power of the extended GUTS-RED framework for mixture assessment. The extended models accurately predicted the mixture effect. The GUTS parameters on single exposure data, mixture model calibration, and predictive power analyses on mixture exposure data offer novel diagnostic tools to inform on the chemical mode of action, specifically whether a similar or dissimilar form of damage is caused by mixture components. Finally, observed deviations from model predictions can identify interactions, e.g., synergism or antagonism, between chemicals in the mixture, which are not accounted for by the models. TKTD models, such as GUTS-RED, thus offer a framework to implement new mechanistic knowledge in mixture hazard assessments.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1021/acs.est.0c05282
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-)
ISSN:	0013-936X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Biology and Microbiology
Date made live:	19 Feb 2021 12:54 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529687

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