Predicting Mixture Effects over Time with Toxicokinetic − Toxicodynamic Models (GUTS): Assumptions, Experimental Testing, and Predictive Power

Aug 27, 2026

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Current methods assessing how chemical mixtures affect organisms overlook time. The General Unified Threshold model for Survival (GUTS) enables toxicokinetic–toxicodynamic (TKTD) modeling where exposure dynamics drive survival effects over time. Building on independent action and concentration addition, the study derives a GUTS reduced (GUTS-RED) model for mixture toxicity concepts and validates it with binary mixture experiments on Enchytraeus crypticus plus published data for Daphnia magna and Apis mellifera.

LanguageEnglish

CategoryResearch & Report

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Pages10

Reading Time25 min

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Abstract

Introduction

Model Extension and Application

Downloaded via CTR FOR ECOLOGY & HYDROLOGY on February 19, 2021 at 12 : 3 1:46 (UTC) . This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. [pubs.acs.org/est](pubs.acs.org/est) Article Predicting Mixture Eﬀects over Time with Toxicokinetic − Toxicodynamic Models (GUTS): Assumptions, Experimental Testing, and Predictive Power Sylvain Bart,* Tjalling Jager, Alex Robinson, Elma Lahive, David J. Spurgeon, and Roman Ashauer Cite This: Environ. Sci. Technol. 2021, 55, 2430−2439 Read Online See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles. ACCESS Metrics & More Article Recommendations *sı Supporting Information ABSTRACT: 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. ■ INTRODUCTION Human activities release a plethora of chemicals into the environment1 that can lead to eﬀects on nontarget organisms. The environmental risk assessment (ERA) for individual chemicals is established with experimental designs and data robust analysis methods, including methods in place.2 While risk assessment may take a chemical-by-chemical approach, in practice, ecosystems are subject to many inputs from agricultural, industrial, and domestic sources. These sources result in a wide range of mixture exposure scenarios that can aﬀect nontarget organisms.3−5 The dominant approaches to predict mixture eﬀects ignore the time dimension.5 As toxicity is a process in time, so is the action of mixtures.6 Therefore, mixture eﬀect assessment needs diagnostic tools that account for these temporal aspects. To explain and predict the eﬀects of mixtures, toxicokinetic − toxicodynamic models (TKTD models), which simulate the time course of processes leading to toxicity, oﬀer a promising approach.7−9 Previous studies have presented TKTD models to analyze eﬀects of mixtures on survival that have been successfully applied to mixture datasets. 10−17 In the past decade, the development of the General Uniﬁed Threshold model for Survival (GUTS) framework ﬁrmly established the concept of damage dynamics, which takes place between the internal concentration and the eﬀect.18, 19 This concept is central to the GUTS framework as it provides an explanation for the time course of mortality, including cases where internal concentration kinetics fail.20,21 Recently, as the broad relevance of damage dynamics became clearer, this conc

FAQs

Why do existing mixture toxicity methods need to account for time?

Because toxicity and the action of mixture components unfold as time-dependent processes. Ignoring temporal dynamics limits diagnostic and predictive capability.

What framework does the document use to model mixture effects over time?

It uses toxicokinetic–toxicodynamic (TKTD) models built within the General Unified Threshold model for Survival (GUTS), specifically extending to GUTS-RED for mixture concepts.

How is the predictive power of the extended GUTS-RED framework evaluated?

Through experimental binary mixture studies with Enchytraeus crypticus and by applying the framework to previously published datasets for Daphnia magna and Apis mellifera, then comparing predicted and observed mixture effects.

Aug 27, 2026

Upload by Angel