Effect modelling, TKTD, population modelling and more
Modelling has increasingly become an important toolset in ecotoxicological and ecological risk assessments. This has also been acknowledged by EFSA (2014) by releasing a scientific opinion on good modelling practice. In this opinion it is described how models need to be described, parameterised and validated and how modelling scenarios need to be developed. Various modelling methods are used to address open questions, which are not easy to answer with experimental studies, or address uncertainties by testing the impact of an uncertainty on the risk. Depending on the regulatory question various different types of models are used. A snap shot of some of these is given in the following section. These represent only examples of models that have been used and developed by our modellers in the last decades. Our experience with ecological modelling extends to about 20 years and we have been involved as hearing experts at EFSA on modelling related (and other) issues.
TKTD/body burden modelling (BBM) and feeding behaviour
Effect data, used in ecological risk assessments is usually related to exposure dynamics from laboratory studies with a very different exposure profile compared to field conditions. TKTD and BBM can help to bridge this gap. For vertebrates this can be done based on feeding behaviour. Regarding aquatic organisms, EFSA (2018) released a scientific opinion on the state of the art of TKTD models. In this opinion available TKTD models are reviewed and evaluated regarding their readiness to use in the regulatory risk assessment. Specifically, GUTS models (see below) are mentioned as ready to used in risk assessments.
What is toxicokinetic modelling?
A simple example: When animals feed on food items, food residues take a while until they reach the blood or an organ where effects might manifest. At the same time residues are also eliminated, e.g. via urine. Toxicokinetic modelling (TK or body burden modelling), combined with information on feeding behaviour, can therefore be used to obtain a realistic estimate of concentrations in animal bodies. These internal concentrations may then be used to simulate subsequent effects by toxicodynamic modelling (TD modelling). These methods have originally been developed for pharmaceutical studies, where they have reached a much more complex level compared to ecotoxicology.
Depending on the available data, differently complex TKTD modes can be developed, parameterised and validated. We help to find the perfect model to get the most out of your available data and show which additional data could further increase the robustness of model applications. Using our software KModeller, we can develop, parameterize and validate even very complex, tailor-made TKTD models to improve your risk assessment.
The ‘General Unified Threshold model of Survival’ (GUTS) framework describes a collection of models intended to be used for the prediction of survival. GUTS models are considered as ready to be used for regulatory purposes (EFSA, 2018). The approach consists of two different assumptions (i.e. special cases of the GUTS model) that may lead to different survival predictions:
- All individuals in a population have the same sensitivity level to a toxicant, but the death of each single individual is based on stochastics (i.e. on coincidence). This assumption is called ‘stochastic death’ (SD).
- Each individual dies immediately when reaching its internal threshold concentration for a toxicant, but due to individual sensitivities in the population individuals may die at different times. This assumption is called ‘individual tolerance’ (IT).
We help to calibrate and validate GUTS models that fits to your data, e.g. using software such as openGUTS (https://openguts.info/, developed by WSC Scientific GmbH) and proprietary software solutions. Or if you haven’t generated the required data when can help to plan studies. Subsequently we support you predicting animal survival for your prolonged constant or time-varying exposure profiles.
Population modelling is an approach for the evaluation of population level effects and recovery. It helps to translate individual effects to the protection goals defined by EFSA (2009, 2010). When a risk assessment indicates that an effect on a particular species is possible, population models can help to evaluate if this effect on individuals may impact the overall population development. Furthermore, population models help to further characterise and better understand risk. Another application of models it to ask “what if” questions, e.g. what if application of substance would take place in autumn instead of spring or what if the focal species would be 2x more sensitive than the tested one.
We have developed a set of validated models for the most important focal species, such as Common vole, Field vole, Wood mouse, European rabbit, Brown hare, Common shrew and others, and also for several bird species. For many terrestrial species, such as small mammals, landscape structure plays an important role regarding the population risk and the recovery potential (see e.g. Wang & Grimm, 2010). While theoretical risk assessments don’t take the landscape structure into account, population models can easily include the landscape structure. Also field studies can be recreated in order to extrapolate effect to beyond the end of a study. However, for other species, the landscape structure is less relevant, e.g. because individuals can easily travel in short time over large distances (e.g. wood pigeon). For these species, our population models are “spatially implicit”.
Apart from conducting risk assessments with our own models, we also assist and coordinate population-level risk assessments with other public models. I.e. when you would like to use a specific, existing population model and need an experienced expert for the preparation of the subsequent population-level risk assessment or for presentation at authorities. Based on our experience and participation in workshops and workgroups at EFSA and US EPA, e.g. as hearing experts, we understand what authorities expect from a population-level risk assessment.
- European Food Safety Authority (EFSA) 2010. Guidance Document on Risk Assessment for Birds & Mammals on request from EFSA. EFSA J. 7: 1438.
- European Food Safety Authority (EFSA) 2010. Scientific Opinion on the development of specific protection goal options for environmental risk assessment of pesticides, in particular in relation to the revision of the Guidance Documents on Aquatic and Terrestrial Ecotoxicology (SANCO/3268/2001 and SANCO/10329/2002). EFSA J. 8: 1821.
- European Food Safety Authority (EFSA) 2014. Scientific Opinion on good modelling practice in the context of mechanistic effect models for risk assessment of plant protection products. EFSA J. 12: 3589.
- European Food Safety Authority (EFSA) 2018. Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms. EFSA J. 16: 5377.
- Kleinmann, J. U. and Wang, M. 2017. Modeling individual movement decisions of brown hare (Lepus europaeus) as a key concept for realistic spatial behavior and exposure: A population model for landscape-level risk assessment. Environ. Toxicol. Chem. 36:2299-2307.
- Wang M. 2012. Population level risk assessments – Science or fiction? Integrated Environmental Assessment and Management, Integr. Environ. Assess. Manag. 8: 383-385.
- Wang M. and Luttik R. 2012. Population level risk assessment: practical considerations for evaluation of population models from a risk assessor’s perspective. Environ. Sci. Europe 24: 3.
- Kramer V.J., Etterson M.A., Hecker M., Murphy C.A., Roesijadi G., Spade D.J., Spromberg J., Wang M. and Ankley G.T., 2011. Adverse Outcome Pathways and Ecological Risk Assessment: Bridging to Population Level Effects. Environ. Toxicol. Chem. 30: 64–76.
- Wang M. and Grimm V. 2010. Population models in pesticide risk assessment: Lessons for assessing population-level effects, recovery, and alternative exposure scenarios from modeling a small mammal. Environ. Toxicol. Chem. 29: 1292-1300.