Concentration addition and response addition to analyze mixture toxicity: Is it worth testing?

Iwasaki, Yoshiaki; Gauthier, P.

doi:10.1002/etc.3263

Cited by 17 publications

(11 citation statements)

References 12 publications

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“…Based on the mixture toxicity studies conducted here, it is evident that existing mixture hazard prediction models (CA and IA) can provide a first pass approach to assessment based on mode of action. Further, the ability to analyse mixture effects in pollinators using toxicokinetic and toxicodynamic DEBtox modelling approaches is demonstrated as a more comprehensive and mechanistically based approach that incorporates key physiological and resource allocation based traits into mixture effect assessment [40, 41]. …”

Section: Discussionmentioning

confidence: 99%

Comparing bee species responses to chemical mixtures: Common response patterns?

et al. 2017

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Pollinators in agricultural landscapes can be exposed to mixtures of pesticides and environmental pollutants. Existing mixture toxicity modelling approaches, such as the models of concentration addition and independent action and the mechanistic DEBtox framework have been previously shown as valuable tools for understanding and ultimately predicting joint toxicity. Here we apply these mixture models to investigate the potential to interpret the effects of semi-chronic binary mixture exposure for three bee species: Apis mellifera, Bombus terrestris and Osmia bicornis within potentiation and mixture toxicity experiments. In the potentiation studies, the effect of the insecticide dimethoate with added propiconazole fungicide and neonicotinoid insecticide clothianidin with added tau-fluvalinate pyrethroid acaricide showed no difference in toxicity compared to the single chemical alone. Clothianidin toxicity showed a small scale, but temporally conserved increase in exposure conducted in the presence of propiconazole, particularly for B. terrestris and O. bicornis, the latter showing a near three-fold increase in clothianidin toxicity in the presence of propiconazole. In the mixture toxicity studies, the dominant response patterns were of additivity, however, binary mixtures of clothianidin and dimethoate in A. mellifera, B. terrestris and male O. bicornis there was evidence of a predominant antagonistic interaction. Given the ubiquitous nature of exposures to multiple chemicals, there is an urgent need to consider mixture effects in pollinator risk assessments. Our analyses suggest that current models, particularly those that utilise time-series data, such as DEBtox, can be used to identify additivity as the dominant response pattern and also those examples of interactions, even when small-scale, that may need to be taken into account during risk assessment.

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Section: Discussionmentioning

confidence: 99%

Comparing bee species responses to chemical mixtures: Common response patterns?

et al. 2017

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“…The results predicted by CA are not always in agreement with the experimental results, and when the CA prediction curve is higher or lower than the condence interval, there is interaction, synergism or antagonism, within a mixture. [17][18][19][20][21][22] The experimental observations of mixture rays with obvious synergism at some exposure time points and their 95% condence interval, tting curves and CA prediction results are shown in Fig. 3 (of the rest of CRCs exhibiting additive action are given in Fig.…”

Section: The Time-dependent Synergism Of Ve-component Mixturesmentioning

confidence: 99%

“…Usually, analysis of toxicity interaction within mixtures is relative to a standard reference model, so it is very important to select a suitable reference model when evaluating the toxicity (CA) is considered to be a relatively conservative model for predicting mixture toxicity 14 and widely used by many scholars. [15][16][17][18] However, CA model cannot quantitatively evaluate the degree of synergism or antagonism. Deviation from CA model (dCA) can visualize the difference between experimental observation and model prediction.…”

Section: Introductionmentioning

confidence: 99%

“…[22][23][24] The inhibition data of single antibiotic and its vecomponent mixtures to V. qinghaiensis at different exposure time (0.25, 2, 4, 8, and 12 h) were determined by timedependent microplate toxicity analysis method. [25][26][27] The concentration-effect data were tted by nonlinear least square method, the toxicity interaction of mixture systems was analysed by concentration addition model (CA) with 95% condence interval, [17][18][19][20][21][22]28 and the degree of toxicity interaction was characterized by deviation from CA model (dCA). Besides, the luminescence inhibition mechanism of ve antibiotics and their ve-component mixtures to V. qinghaiensis was preliminarily determined by observing the cell morphology.…”

Section: Introductionmentioning

confidence: 99%

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Time-dependent synergism of five-component mixture systems of aminoglycoside antibiotics toVibrio qinghaiensissp.-Q67 induced by a key component

et al. 2020

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“…[15][16][17][18][19] The binary mixture system of GTPs and two pesticides was constructed by the direct equipartition ray design (EquRay) method. 20 In addition, the effect residual ratio (ERR) 19 method based on the concentration addition model (CA) [21][22][23][24][25][26] was used to evaluate the interaction between pesticides and GTPs. The bacteriostatic mechanism of the three substances and their mixtures to Q67 was preliminarily determined by observing the cellular morphology.…”

Section: Introductionmentioning

confidence: 99%