Mixture toxicity workshop

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Topics

Mixture toxicity concepts and Risk assessment (Thomas Backhaus)

Background

The typical situation found in ecosystems is characterised by a multitude of different toxicants that act simultaneously on exposed organisms. However, traditional ecotoxicological studies as well as risk assessment approaches typically simplify this situation by using a compound-by-compound approach that assumes that the momentarily investigated chemical is the only toxicant present in the environment. Obviously, this is a gross simplification of the actual situation. What makes it absolutely vital to finally consider the joint action of all chemicals that make up a certain pollution situation, is that the toxicity of a mixture is usually higher than the toxicity of each compound alone. In addition, to determine the effect of a known mixture on a species, concepts have been developed to be able to predict the mixture toxicity from the toxicity of the individual contaminants. The two most commonly used models to do these predictions are Concentration Addition (CA) and Independent Action (IA). CA is based on the theory that non-interacting chemicals differ only in potency (similar mode of action) and can as such be regarded as dilutions of one another. The concept of IA is valid for mixtures where each compound acts on a different system/receptor (dissimilar mode of action), so together they contribute to a common response.

About the course:

This part of the course is designed to provide an overview of the principal concepts (mainly CA and IA) in mixture toxicology and ecotoxicology, on present approaches in mixture experimentation and on how the toxicity of chemical mixtures can be predicted and assessed. Special care will be taken to provide methods and tools for appropriately designing mixture experiments. The course will furthermore provide an overview of how current scientific approaches in mixture are taken up (or ignored) in environmental regulations such as REACH, the Water Frame Directive or pesticide and biocide regulations.

Calculus (Nathalie Vanhoudt and Nele Horemans)

To acquire some hands on experience on predicting toxicity effects a calculus session is foreseen. In this session we will start with an experimental dataset on which we can calculate single dose-response curves and prediction according to the two concepts of concentration addition (CA) and Independent Action (IA).

Deviations from CA and IA (Claus Svendsen)

Background

As introduced in the session on mixture toxicity concepts, both con centration addition (CA) and independent action (IA) are based on the assumption that the chemicals in the mixture do not affect the biological activity of the other chemicals in the mixture. However, the measured response of the mixture can deviate markedly from the expected response based on the reference models. When the measured response is greater than expected, the response is said to be synergistic. The term antagonistic is used when the measured response is lower than expected. However, more complex response patters occur depending on the dose level or dose ratio of the different toxicants.

About the course

In this part of the course various means of identifying and analysing deviations from the reference models will be covered. This will include methods to look at fixed ration mixtures as well as more elaborate designs and methods for full response surface analysis. The latter will illustrate how a stepwise statistically based data analysis procedure can provide ecotoxicologically meaningful conclusions about combined dose responses and the patterns of deviations observed. At the end developments that may allow prediction of the possible range of deviations from the reference models based on information from other mixture information will be explored.

Biology-based approaches for mixture ecotoxicology(Tjalling Jager)

Background

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 approaches. Such approaches attempt to explain the observed response on all endpoints over the entire life cycle of the organism. Biology-based approaches follow the principle of Toxicokinetic Toxicodynamic (TKTD) modelling; explicitly and quantitatively addressing toxicokinetics (the uptake and transport and excretion from the body) and toxicodynamics (the quantitative relationship between internal concentrations and the development of effects on endpoints over time). For the toxicodynamic component in ecotoxicology, we need an approach that dynamically links the effects on different endpoints such as growth, development and reproduction, without requiring measurements at the sub-individual level. At this moment, the theory of Dynamic Energy Budgets (DEB) is the only candidate for such a method. DEB-theory is a general theory for all organisms that consists of a set of simple rules for metabolic organization, ensuring conservation of mass and energy. Toxicant effects are treated as a disruption of regular metabolic processes such as an increase in maintenance costs. Mixture toxicity (or other forms of multiple stresses) can be included in a natural way by assuming that compounds (after being taken up) affect either the same or different metabolic processes. Subsequently, DEB-theory ensures that the effects on these processes are translated to life-history effects in a consistent manner. Inevitably, 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.

About the course

This part of the course will start with lectures on the background of biology-based modelling, a conceptual description of DEB-theory, and the effects of toxicants within this theory. Several examples will demonstrate how treatment of mixture effects follows naturally within this framework. After that, the participants will be able to get some hands on experience with a simplified biology-based model in Excel. Finally, we will conclude this session with an evaluation and discussion among the participants about the various views on mixture toxicity that have been presented in this course.

Linear and generalized linear models in R(Stefan Van Dongen)

Background

Predicting toxicity of chemicals especially in the low concentration range is necessary to estimate safe concentrations of exposure used e.g. in risk assessments. In addition to predict No-observed-effect-concentrations (NOEC) model-based approaches have been proposed that provide statistically estimated low-effect concentrations (e.g., EC10, EC5, EC1) and their 5% lower confidence limits. These approaches are based on fitting a mathematical model to toxicity data, whereby the concentration-effect relationship is usually determined by non-linear regression. Together with non-linear regression, linear and generalised linear models are the most widespread used statistical models in biology, including toxicology. Such models allow hypothesis testing and prediction of new values. Applying these models requires a minimal theoretical insight in these models and experience in interpreting the output of statistical packages.

About the course

This part of the course will start with lectures on the background of linear and generalised linear modelling . Evaluation of model fit will be briefly tackled. Several examples will demonstrate how hypotheses can be tested and parameter estimates should be interpreted. Hands-on experience with worked examples will be provided in R, a freeware statistical package.

Not to forget

To be able to participate in the calculus session you will need a laptop with Excel installed. The statistical methods will be shown using the statistical program "R" which is as freeware available on the internet.

If you can't bring your own laptop please inform us on your registration form.