Cargando…
Predicting Mixture Effects over Time with Toxicokinetic–Toxicodynamic Models (GUTS): Assumptions, Experimental Testing, and Predictive Power
[Image: see text] 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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7893709/ https://www.ncbi.nlm.nih.gov/pubmed/33499591 http://dx.doi.org/10.1021/acs.est.0c05282 |
_version_ | 1783653100996788224 |
---|---|
author | Bart, Sylvain Jager, Tjalling Robinson, Alex Lahive, Elma Spurgeon, David J. Ashauer, Roman |
author_facet | Bart, Sylvain Jager, Tjalling Robinson, Alex Lahive, Elma Spurgeon, David J. Ashauer, Roman |
author_sort | Bart, Sylvain |
collection | PubMed |
description | [Image: see text] 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. |
format | Online Article Text |
id | pubmed-7893709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-78937092021-02-22 Predicting Mixture Effects over Time with Toxicokinetic–Toxicodynamic Models (GUTS): Assumptions, Experimental Testing, and Predictive Power Bart, Sylvain Jager, Tjalling Robinson, Alex Lahive, Elma Spurgeon, David J. Ashauer, Roman Environ Sci Technol [Image: see text] 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. American Chemical Society 2021-01-26 2021-02-16 /pmc/articles/PMC7893709/ /pubmed/33499591 http://dx.doi.org/10.1021/acs.est.0c05282 Text en © 2021 The Authors. Published by American Chemical Society This is an open access article published under a Creative Commons Attribution (CC-BY) License (http://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html) , which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. |
spellingShingle | Bart, Sylvain Jager, Tjalling Robinson, Alex Lahive, Elma Spurgeon, David J. Ashauer, Roman Predicting Mixture Effects over Time with Toxicokinetic–Toxicodynamic Models (GUTS): Assumptions, Experimental Testing, and Predictive Power |
title | Predicting
Mixture Effects over Time with
Toxicokinetic–Toxicodynamic Models
(GUTS): Assumptions, Experimental Testing, and Predictive Power |
title_full | Predicting
Mixture Effects over Time with
Toxicokinetic–Toxicodynamic Models
(GUTS): Assumptions, Experimental Testing, and Predictive Power |
title_fullStr | Predicting
Mixture Effects over Time with
Toxicokinetic–Toxicodynamic Models
(GUTS): Assumptions, Experimental Testing, and Predictive Power |
title_full_unstemmed | Predicting
Mixture Effects over Time with
Toxicokinetic–Toxicodynamic Models
(GUTS): Assumptions, Experimental Testing, and Predictive Power |
title_short | Predicting
Mixture Effects over Time with
Toxicokinetic–Toxicodynamic Models
(GUTS): Assumptions, Experimental Testing, and Predictive Power |
title_sort | predicting
mixture effects over time with
toxicokinetic–toxicodynamic models
(guts): assumptions, experimental testing, and predictive power |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7893709/ https://www.ncbi.nlm.nih.gov/pubmed/33499591 http://dx.doi.org/10.1021/acs.est.0c05282 |
work_keys_str_mv | AT bartsylvain predictingmixtureeffectsovertimewithtoxicokinetictoxicodynamicmodelsgutsassumptionsexperimentaltestingandpredictivepower AT jagertjalling predictingmixtureeffectsovertimewithtoxicokinetictoxicodynamicmodelsgutsassumptionsexperimentaltestingandpredictivepower AT robinsonalex predictingmixtureeffectsovertimewithtoxicokinetictoxicodynamicmodelsgutsassumptionsexperimentaltestingandpredictivepower AT lahiveelma predictingmixtureeffectsovertimewithtoxicokinetictoxicodynamicmodelsgutsassumptionsexperimentaltestingandpredictivepower AT spurgeondavidj predictingmixtureeffectsovertimewithtoxicokinetictoxicodynamicmodelsgutsassumptionsexperimentaltestingandpredictivepower AT ashauerroman predictingmixtureeffectsovertimewithtoxicokinetictoxicodynamicmodelsgutsassumptionsexperimentaltestingandpredictivepower |