TKTD models as well as other mechanistic effect models can be applied for a variety of questions in environmental risk assessment of chemicals. For example there is a lot of interest in the prediction of effects from fluctuating or pulsed exposure to pollutants (also referred to as intermittent, episodic or time-variable exposure).
Integrated presentation of ecological risk from multiple stressors
Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic. (open access, Nature Scientific Reports)
Highly time-variable exposure to chemicals - toward an assessment strategy
Ashauer & Brown (2013). Highly time-variable exposure to chemicals - toward an assessment strategy. Integrated Environmental Assessment and Management (link to paper at IEAM).
Abstract: Organisms in the environment experience fluctuating, pulsed, or intermittent exposure to pollutants. Accounting for effects of such exposures is an important challenge for environmental risk assessment, particularly given the simplified design of standard ecotoxicity tests. Dynamic simulation using toxicokinetic-toxicodynamic (TK-TD) models describes the processes that link exposure with effects in an organism and provides a basis for extrapolation to a range of exposure scenarios. In so doing, TK-TD modeling makes the risk assessment more robust and aids use and interpretation of experimental data. Toxicokinetic-toxicodynamic models are well-developed for predicting survival of individual organisms and are increasingly applied to sublethal endpoints. In the latter case particularly, linkage to individual-based models (IBMs) allows extrapolation to population level as well as accounting for differences in effects of toxicant exposure at different stages in the life cycle. Extrapolation between species remains an important constraint because there is currently no systematic understanding of species traits that cause differences in the relevant processes. Toxicokinetic-toxicodynamic models allow interrogation of exposure profiles to determine intrinsic toxicity potential rather than using absolute maximum concentrations or time-weighted averages as surrogates. A decision scheme is proposed to guide selection of risk assessment approaches using dose extrapolation based on Haber's Law, TK-TD models, and/or IBMs depending on the nature of toxic effect and timing in relation to life history.
Using toxicokinetic-toxicodynamic modeling as an acute risk assessment refinement approach in vertebrate ecological risk assessment.
Virginie Ducrot, Roman Ashauer, Agnieszka J Bednarska, Silvia Hinarejos, Pernille Thorbek and Gabriel Weyman (2016): Using toxicokinetic-toxicodynamic modeling as an acute risk assessment refinement approach in vertebrate ecological risk assessment.
Integr Environ Assess Manag 2016;12:32–45. (open access link)
Recent guidance identified toxicokinetic-toxicodynamic (TK-TD) modeling as a relevant approach for risk assessment refinement. Yet, its added value compared to other refinement options is not detailed, and how to conduct the modeling appropriately is not explained. This case study addresses these issues through 2 examples of individual-level risk assessment for 2 hypothetical plant protection products: 1) evaluating the risk for small granivorous birds and small omnivorous mammals of a single application, as a seed treatment in winter cereals, and 2) evaluating the risk for fish after a pulsed treatment in the edge-of-field zone. Using acute test data, we conducted the first tier risk assessment as defined in the European Food Safety Authority (EFSA) guidance. When first tier risk assessment highlighted a concern, refinement options were discussed. Cases where the use of models should be preferred over other existing refinement approaches were highlighted. We then practically conducted the risk assessment refinement by using 2 different models as examples. In example 1, a TK model accounting for toxicokinetics and relevant feeding patterns in the skylark and in the wood mouse was used to predict internal doses of the hypothetical active ingredient in individuals, based on relevant feeding patterns in an in-crop situation, and identify the residue levels leading to mortality. In example 2, a TK-TD model accounting for toxicokinetics, toxicodynamics, and relevant exposure patterns in the fathead minnow was used to predict the time-course of fish survival for relevant FOCUS SW exposure scenarios and identify which scenarios might lead to mortality. Models were calibrated using available standard data and implemented to simulate the time-course of internal dose of active ingredient or survival for different exposure scenarios. Simulation results were discussed and used to derive the risk assessment refinement endpoints used for decision. Finally, we compared the “classical” risk assessment approach with the model-based approach. These comparisons showed that TK and TK-TD models can bring more realism to the risk assessment through the possibility to study realistic exposure scenarios and to simulate relevant mechanisms of effects (including delayed toxicity and recovery). Noticeably, using TK-TD models is currently the most relevant way to directly connect realistic exposure patterns to effects. We conclude with recommendations on how to properly use TK and TK-TD model in acute risk assessment for vertebrates.
Pesticide risk assessment: modelling fish survival under fluctuating exposure
In the study "A method to predict and understand fish survival under dynamic chemical stress using standard ecotoxicity data" we apply TKTD modelling to a problem in pesticide risk assessment. The abstract is provided below and the study is available at the journal's website (open access).
Abstract: The authors present a method to predict fish survival under exposure to fluctuating concentrations and repeated pulses of a chemical stressor. The method is based on toxicokinetic-toxicodynamic modeling using the general unified threshold model of survival (GUTS) and calibrated using raw data from standard fish acute toxicity tests. The model was validated by predicting fry survival in a fish early life stage test. Application of the model was demonstrated by using Forum for Co-ordination of Pesticide Fate Models and Their Use surface water (FOCUS-SW) exposure patterns as model input and predicting the survival of fish over 485 d. Exposure patterns were also multiplied by factors of five and 10 to achieve higher exposure concentrations for fish survival predictions. Furthermore, the authors quantified how far the exposure profiles were below the onset of mortality by finding the corresponding exposure multiplication factor for each scenario. The authors calculated organism recovery times as additional characteristic of toxicity as well as number of peaks, interval length between peaks, and mean duration as additional characteristics of the exposure pattern. The authors also calculated which of the exposure patterns had the smallest and largest inherent potential toxicity. Sensitivity of the model to parameter changes depends on the exposure pattern and differs between GUTS individual tolerance and GUTS stochastic death. Possible uses of the additional information gained from modeling to inform risk assessment are discussed. (link at journal, open access)
Risk assessment of fluctuating concentrations in small catchment
Ashauer R, Wittmer I, Stamm C and Escher BI (2011): Environmental Risk Assessment of Fluctuating Diazinon Concentrations in an Urban and Agricultural Catchment
Using Toxicokinetic–Toxicodynamic Modeling. Environmental Science & Technology, 45 (22), 9783-9792. (link at
journal here).
Temporally resolved environmental risk assessment of fluctuating concentrations of micropollutants is presented. We separated the prediction of toxicity over time from the extrapolation from one to many species and from acute to sublethal effects. A toxicokinetic–toxicodynamic (TKTD) model predicted toxicity caused by fluctuating concentrations of diazinon, measured by time-resolved sampling over 108 days from three locations in a stream network, representing urban, agricultural and mixed land use. We calculated extrapolation factors to quantify variation in toxicity among species and effect types based on available toxicity data, while correcting for different test durations with the TKTD model. Sampling from the distribution of extrapolation factors and prediction of time-resolved toxicity with the TKTD model facilitated subsequent calculation of the risk of undesired toxic events. Approximately one-fifth of aquatic organisms were at risk and fluctuating concentrations were more toxic than their averages. Contribution of urban and agricultural sources of diazinon to the overall risk varied. Thus using fixed concentrations as water quality criteria appears overly simplistic because it ignores the temporal dimension of toxicity. However, the improved prediction of toxicity for fluctuating concentrations may be small compared to uncertainty due to limited diversity of toxicity data to base the extrapolation factors on.