Jan Baas

A biology-based approach for mixture toxicity of multiple endpoints over the life cycle.

Typical approaches for analyzing mixture ecotoxicity data only provide a description of the data; they cannot explain observed interactions, nor explain why mixture effects can change in time and differ between endpoints. To improve our understanding of mixture toxicity we need to explore biology-based models. In this paper, we present an integrated approach to deal with the toxic effects of mixtures on growth, reproduction and survival, over the life cycle. Toxicokinetics is addressed with a one-compartment model, accounting for effects of growth. Each component of the mixture has its own toxicokinetics model, but all compounds share the effect of body size on uptake kinetics. The toxicodynamic component of the method is formed by an implementation of dynamic energy budget theory; a set of simple rules for metabolic organization that ensures conservation of mass and energy. Toxicant effects are treated as a disruption of regular metabolic processes such as an increase in maintenance costs. The various metabolic processes interact, which means that mixtures of compounds with certain mechanisms of action have to produce a response surface that deviates from standard models (such as ‘concentration addition’). Only by separating these physiological interactions from the chemical interactions between mixture components can we hope to achieve generality and a better understanding of mixture effects. For example, a biology-based approach allows for educated extrapolations to other mixtures, other species, and other exposure situations. We illustrate our method with the interpretation of partial life-cycle data for two polycyclic aromatic hydrocarbons in Daphnia magna.

Understanding toxicity as processes in time.

Studies in ecotoxicology usually focus on a single end point (typically mortality, growth, or reproduction) at a standardized exposure time. The exposure time is chosen irrespective of the properties of the chemical under scrutiny, but should depend on the organism of choice in combination with the compound(s) of interest. This paper discusses the typical patterns for toxic effects in time that can be observed for the most encountered endpoints growth reproduction and survival. Ignoring the fact that toxicity is a process in time can lead to severe bias in environmental risk assessment. We show that especially EC(x) values for sublethal endpoints can show very distinct patterns in time. We recommend that the test duration for survival as an endpoint should be extended till the incipient LC(50) is observed. Given the fact that toxicity data for single compounds show clear patterns in time, it is to be expected that effects of mixtures will also be strongly dependent on time. The few examples that have been published support this statement.

Modeling the effects of binary mixtures on survival in time

In general, effects of mixtures are difficult to describe, and most of the models in use are descriptive in nature and lack a strong mechanistic basis. The aim of this experiment was to develop a process-based model for the interpretation of mixture toxicity measurements, with effects of binary mixtures on survival as a starting point. The survival of Folsomia candida was monitored daily for 21 d during the exposure to six binary mixtures of cadmium, copper, lead, and zinc in a loamy sand soil. The measurements were used to develop a model to describe survival in time. The model consists of two parts: A one-compartment model that describes uptake and elimination of the compounds, and a hazard model describing survival. The model was very successful in describing the data and at finding possible interactions. The mixture of copper and lead showed a slight antagonistic effect, the other mixtures showed no interaction. The model is straightforward in its biological assumptions and does not require a mode-of-action a priori choice of the mixture that might influence the modeled interaction of the components in the mixture. The model requires measurements at intermediate time points, but runs with relatively few parameters and is robust in finding interactions. When mixture effects are considered at only one time point, care should be taken with the assignment of interactions because these may be different for different points during the time course of the experiments.

A model to analyze effects of complex mixtures on survival.

In ecotoxicology there is a growing interest in effects of mixtures. The aim of this research was to develop a biology-based model that describes effects of mixtures on survival in time. The model works from the individual compounds in the mixture. Such an approach requires parameters for each individual compound in the mixture. For narcotic compounds we underpinned theoretical relations between the toxic parameters and the logK(ow) with experimental data by analyzing almost 300 datasets from the open literature, allowing a vast reduction in effort in the assessment of effects of mixtures. To illustrate the use of the model we simulated the effect of a mixture of 14 PAHs on the survival of Pimephales promelas. The simulation showed that due to the combined effect of the compounds in the mixture effects can be seen at very low concentrations.

Ecotoxicological Applications of Dynamic Energy Budget Theory

The dynamic energy budget (DEB) theory for metabolic organisation specifies quantitatively the processes of uptake of substrate by organisms and its use for the purpose of maintenance, growth, maturation and reproduction. It applies to all organisms. Animals are special because they typically feed on other organisms. This couples the uptake of the different required substrates, and their energetics can, therefore, be captured realistically with a single reserve and a single structure compartment in biomass. Effects of chemical compounds (e.g. toxicants) are included by linking parameter values to internal concentrations. This involves a toxico-kinetic module that is linked to the DEB, in terms of uptake, elimination and (metabolic) transformation of the compounds. The core of the kinetic module is the simple one-compartment model, but extensions and modifications are required to link it to DEBs. We discuss how these extensions relate to each other and how they can be organised in a coherent framework that deals with effects of compounds with varying concentrations and with mixtures of chemicals. For the one-compartment model and its extensions, as well as for the standard DEB model for individual organisms, theory is available for the co-variation of parameter values among different applications, which facilitates model applications and extrapolations.

A review of DEB-theory in assessing toxic effects of mixtures.

In this manuscript we review the use of mechanistic models to interpret effects of mixtures of compounds within the framework of the Dynamic Energy Budget (DEB) theory. Within this approach the effect of a mixture is built up from the effects of the individual components making up the mixture. Understanding effects of mixtures is essential as it is impossible to assess effects of all possible mixtures experimentally. In contrast to the more classical way of interpreting effects of mixtures with concentration addition or effect addition models, DEB theory offers a single consistent framework to understand effects of mixtures on growth, reproduction and survival in an integrated, way. It systematically incorporates exposure time and biology of the organisms, including the natural links between the processes of feeding, maintenance, growth, development and reproduction. We also give directions for an experimental setup to interpret the results within the DEB framework. The DEB framework was successfully applied to assess effects of complex mixtures on survival and binary mixtures on sub-lethal endpoints. It gives the possibility to explain observed interactions by the underlying biological mechanisms or pinpoint interactions. We expect this approach to help in identifying key mechanisms and enable to focus further research in cooperation with modelers and experimentalists to improve our understanding of the mechanisms underlying mixture toxicity.

Time is of the essence

An organism is a dynamic system, and its life history results from underlying processes in time. The effects of biological and chemical stressors on this system therefore also change temporally. In the present short communication, we emphasize the importance of including time as a factor in stress ecology and ecotoxicology and argue that current standard test protocols may benefit considerably from this, improving data interpretation and thus also risk assessment and risk management.

Scaling relationships based on partition coefficients and body sizes have similarities and interactions

The LC50 of compounds with a similar biological effect, at a given exposure period, is frequently plotted log–log against the octanol–water partition coefficient and a straight line is fitted for interpolation purposes. This is also frequently done for physiological properties, such as the weight-specific respiration rate, as function of the body weight of individuals. This paper focuses on the remarkable observation that theoretical explanations for these relationships also have strong similarities. Both can be understood as result of the covariation of the values of parameters of models of a particular type for the underlying processes, while this covariation follows logically from the model structure. The one-compartment model for the uptake and elimination of compounds by organisms is basic to the BioConcentration Factor (BCF), or the partition coefficient; the standard Dynamic Energy Budget model is basic to the (ultimate) body size. The BCF is the ratio of the uptake and the elimination rates; the maximum body length is the ratio of the assimilation (i.e. uptake of resources) and the maintenance (i.e. use of resources) rates. This paper discusses some shortcomings of descriptive approaches and conceptual aspects of theoretical explanations. The strength of the theory is in the combination of why metabolic transformation depends both on the BCF and the body size. We illustrate the application of the theory with several data sets from the literature. †Presented at the 12th International Workshop on Quantitative Structure–Activity Relationships in Environment Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.

Model-based experimental design for assessing effects of mixtures of chemicals

We exposed flour beetles (Tribolium castaneum) to a mixture of four poly aromatic hydrocarbons (PAHs). The experimental setup was chosen such that the emphasis was on assessing partial effects. We interpreted the effects of the mixture by a process-based model, with a threshold concentration for effects on survival. The behavior of the threshold concentration was one of the key features of this research. We showed that the threshold concentration is shared by toxicants with the same mode of action, which gives a mechanistic explanation for the observation that toxic effects in mixtures may occur in concentration ranges where the individual components do not show effects. Our approach gives reliable predictions of partial effects on survival and allows for a reduction of experimental effort in assessing effects of mixtures, extrapolations to other mixtures, other points in time, or in a wider perspective to other organisms.

Evaluating the Combined Toxicity of Cu and ZnO Nanoparticles: Utility of the Concept of Additivity and a Nested Experimental Design

Little is understood regarding the effects of mixtures of different metal-based nanoparticles (NPs). Using concentration-addition (CA) and independent-action (IA) models, we evaluated the combined toxicity of Cu and ZnO NPs based on five nested combinations, i.e., Cu(NO3)2-CuNPs, Zn(NO3)2-ZnONPs, Cu(NO3)2-ZnONPs, Zn(NO3)2-CuNPs, and CuNPs-ZnONPs on root elongation of Lactuca sativa L. The CA and IA models performed equally well in estimating the toxicity of mixtures of Cu(NO3)2-CuNPs, Zn(NO3)2-ZnONPs, and Zn(NO3)2-CuNPs, whereas the IA model was significantly better for fitting the data of Cu(NO3)2-ZnONPs and CuNPs-ZnONPs mixtures. Dissolved Cu proved to be the most toxic metal species to lettuce roots in the tests, followed by Cu NPs, dissolved Zn, and ZnO NPs, respectively. An antagonistic effect was observed for ZnO NPs on the toxicity of Cu NPs. This antagonistic effect is expected to be the result of interactions between dissolved Cu and dissolved Zn, particulate Zn and dissolved Zn, particulate Cu and dissolved Zn, and between particulate Zn and dissolved Cu. In general terms, assuming additivity gives a first indication of the combined toxicity with soluble and insoluble metal particles, both being important in driving the toxicity of metal-based NPs to higher plants.