How to analyse and account for interactions in mixture toxicity with toxicokinetic-toxicodynamic models.
Acute toxicity
Effect modelling
Mixture hazard
Survival assay
TKTD modelling
Journal
The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500
Informations de publication
Date de publication:
15 Oct 2022
15 Oct 2022
Historique:
received:
08
04
2022
revised:
20
06
2022
accepted:
25
06
2022
pubmed:
3
7
2022
medline:
24
8
2022
entrez:
2
7
2022
Statut:
ppublish
Résumé
The assessment of chemical mixture toxicity is one of the major challenges in ecotoxicology. Chemicals can interact, leading to more or less effects than expected, commonly named synergism and antagonism respectively. The classic ad hoc approach for the assessment of mixture effects is based on dose-response curves at a single time point, and is limited to identifying a mixture interaction but cannot provide predictions for untested exposure durations, nor for scenarios where exposure varies in time. We here propose a new approach using toxicokinetic-toxicodynamic modelling: The General Unified Threshold model of Survival (GUTS) framework, recently extended for mixture toxicity assessment. We designed a dedicated mechanistic interaction module coupled with the GUTS mixture model to i) identify interactions, ii) test hypotheses to identify which chemical is likely responsible for the interaction, and finally iii) simulate and predict the effect of synergistic and antagonistic mixtures. We tested the modelling approach experimentally with two species (Enchytraeus crypticus and Mamestra brassicae) exposed to different potentially synergistic mixtures (composed of: prochloraz, imidacloprid, cypermethrin, azoxystrobin, chlorothalonil, and chlorpyrifos). Furthermore, we also tested the model with previously published experimental data on two other species (Bombus terrestris and Daphnia magna) exposed to pesticide mixtures (clothianidin, propiconazole, dimethoate, imidacloprid and thiacloprid) found to be synergistic or antagonistic with the classic approach. The results showed an accurate simulation of synergistic and antagonistic effects for the different tested species and mixtures. This modelling approach can identify interactions accounting for the entire time of exposure, and not only at one time point as in the classic approach, and provides predictions of the mixture effect for untested mixture exposure scenarios, including those with time-variable mixture composition.
Identifiants
pubmed: 35779734
pii: S0048-9697(22)04145-6
doi: 10.1016/j.scitotenv.2022.157048
pii:
doi:
Substances chimiques
Insecticides
0
Chlorpyrifos
JCS58I644W
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
157048Informations de copyright
Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Roman Ashauer reports a relationship with Syngenta Crop Protection AG that includes: employment.