Survival is a relevant endpoint for many questions related to the effects of chemicals in the environment. Making sense of mortality, as a process over time, requires mechanism-based models, known as toxicokinetic-toxicodynamic (TKTD) models. All published TKTD models for survival can now be viewed as members of an over-arching framework: GUTS. Click on the book cover or here to view the e-book.
Most recent papers can be downloaded below or at the links because they are open access. The remaining papers can be downloaded here, but that requires a password. Email me for the password or specific papers and I'll send them to you.
72. Bart S, Short S, Jager T, Eagles EJ, Robinson A, Badder C, et al. (2022): How to analyse and account for interactions in mixture toxicity with toxicokinetic-toxicodynamic models. Sci. Total. Environ. 843:157048. (link)
The assessment of chemical mixture toxicity is one of the major challenges in ecotoxicology. Chemicals can interact, leading to more or less effects than expected, commonly named synergism and antagonism respectively. The classic ad hoc approach for the assessment of mixture effects is based on dose-response curves at a single time point, and is limited to identifying a mixture interaction but cannot provide predictions for untested exposure durations, nor for scenarios where exposure varies in time. We here propose a new approach using toxicokinetic-toxicodynamic modelling: The General Unified Threshold model of Survival (GUTS) framework, recently extended for mixture toxicity assessment. We designed a dedicated mechanistic interaction module coupled with the GUTS mixture model to i) identify interactions, ii) test hypotheses to identify which chemical is likely responsible for the interaction, and finally iii) simulate and predict the effect of synergistic and antagonistic mixtures. We tested the modelling approach experimentally with two species (Enchytraeus crypticus and Mamestra brassicae) exposed to different potentially synergistic mixtures (composed of: prochloraz, imidacloprid, cypermethrin, azoxystrobin, chlorothalonil, and chlorpyrifos). Furthermore, we also tested the model with previously published experimental data on two other species (Bombus terrestris and Daphnia magna) exposed to pesticide mixtures (clothianidin, propiconazole, dimethoate, imidacloprid and thiacloprid) found to be synergistic or antagonistic with the classic approach. The results showed an accurate simulation of synergistic and antagonistic effects for the different tested species and mixtures. This modelling approach can identify interactions accounting for the entire time of exposure, and not only at one time point as in the classic approach, and provides predictions of the mixture effect for untested mixture exposure scenarios, including those with time-variable mixture composition.
71. Nickisch Born Gericke D, Rall BC, Singer A, Ashauer R. (2022): Fish Species Sensitivity Ranking Depends on Pesticide Exposure Profiles. Environ. Toxicol. Chem. 41(7):1732-41. (link)
In the regulatory environmental risk assessment of plant protection products, the exposure tested in standard toxicity tests assumes simple exposure dynamics, such as constant exposure at the first stage of testing. However, environmental exposure can be highly dynamic. A species response to exposure is governed by toxicokinetics (TK) and toxicodynamics (TD). Therefore, it can be expected that the sensitivity of a species to a substance is dependent on the interplay of TKTD processes with the dynamics of the exposure. We investigated whether exposure dynamics affects species sensitivity of five fish species and if their sensitivity rankings differ among exposure profiles. We analyzed individual survival under projected surface water exposure to benzovindiflupyr. For this purpose, we calibrated compound- and species-specific reduced general unified threshold models of survival (GUTS-RED) models from standard laboratory toxicity data with the assumptions of stochastic death and individual tolerance. Using the calibrated models, we generated species sensitivity distributions based on median lethal profile multiplication factors for three characteristic exposure profiles. The analysis was performed using different GUTS-RED implementations: openGUTS (MATLAB® and Windows® versions) and the R package morse. The sensitivity rankings of the fish species changed as a function of exposure profile. For a multiple-peak scenario, rainbow trout was the most sensitive species. For a single peak followed by a slow concentration decline the most sensitive species was the fathead minnow (GUTS-RED-stochastic death) or the common carp (GUTS-RED-individual tolerance). Our results suggest that a single most sensitive species cannot be defined for all situations, all exposure profiles, and both GUTS-RED variants.
70. Sherborne N, Jager T, Goussen B, Trijau M, Ashauer R. (2022): The application and limitations of exposure multiplication factors in sublethal effect modelling. Scientific Reports. 12(1):6031. (link)
Thanks to growing interest and research in the field, toxicokinetic–toxicodynamic (TKTD) models are close to realising their potential in environmental risk assessment (ERA) of chemicals such as plant protection products. A fundamental application is to find a multiplicative scale factor which—when applied to an exposure profile—results in some specified effect relative to a control. The approach is similar to applying assessment factors to experimental results, common in regulatory frameworks. It also relies on the same core assumption: that increasing the scaling always produces more extreme effects. Unlike experimental approaches, TKTD models offer an opportunity to interrogate this assumption in a mathematically rigorous manner. For four well-known TKTD models we seek to prove that the approach guarantees a unique scale factor for any percentage effect. Somewhat surprisingly, certain model configurations may have multiple scale factors which result in the same percentage effect. These cases require a more cautious regulatory approach and generate open biological and mathematical questions. We provide examples of the violations and suggest how to deal with them. Mathematical proofs provide the strongest possible backing for TKTD modelling approaches in ERA, since the applicability of the models can be determined exactly.
69. Turschwell MP, Connolly SR, Schäfer RB, De Laender F, Campbell MD, Mantyka-Pringle C, et al. (2022): Interactive effects of multiple stressors vary with consumer interactions, stressor dynamics and magnitude. Ecol. Lett. 25(6):1483-96. (link)
Predicting the impacts of multiple stressors is important for informing ecosystem management but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively or antagonistically. Here, we use process-based models to study how interactions generalise across three levels of biological organisation (physiological, population and consumer-resource) for a two-stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics and consumer presence. Our results help explain why meta-analyses of multiple stressor experimental results have struggled to identify predictors of consistently non-additive interactions in the natural environment. Experiments run over extended temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management.
68. Martin T, Hodson ME, Ashauer R. (2022): Modelling the effects of variability in feeding rate on growth – a vital step for DEB-TKTD modelling. Ecotox. Environ. Safe. 232:113231. (link)
A major limitation of dietary toxicity studies on rodents is that food consumption often differs between treatments. The control treatment serves as a reference of how animals would have grown if not for the toxicant in their diet, but this comparison unavoidably conflates the effects of toxicity and feeding rate on body weight over time. A key advantage of toxicity models based on dynamic energy budget theory (DEB) is that chemical stress and food consumption are separate model inputs, so their effects on growth rate can be separated. To reduce data requirements, DEB convention is to derive a simplified feeding input, f, from food availability; its value ranges from zero (starvation) to one (food available ad libitum). Observed food consumption in dietary toxicity studies shows that, even in the control treatment, rats limit their food consumption, contradicting DEB assumptions regarding feeding rate. Relatively little work has focused on addressing this mismatch, but accurately modelling the effects of food intake on growth rate is essential for the effects of toxicity to be isolated. This can provide greater insight into the results of chronic toxicity studies and allows accurate extrapolation of toxic effects from laboratory data. Here we trial a new method for calculating f, based on the observed relationships between food consumption and body size in laboratory rats. We compare model results with those of the conventional DEB method and a previous effort to calculate f using observed food consumption data. Our results showed that the new method improved model accuracy while modelled reserve dynamics closely followed observed body fat percentage over time. The new method assumes that digestive efficiency increases with body size. Verifying this relationship through data collection would strengthen the basis of DEB theory and support the case for its use in ecological risk assessment.
67. Jager T, Trijau M, Sherborne N, Goussen B, Ashauer R. (2022): Considerations for using reproduction data in toxicokinetic-toxicodynamic modeling. Integrated Environmental Assessment and Management. 18(2): 479-87. (link)
Toxicokinetic–toxicodynamic (TKTD) modeling is essential to make sense of the time dependence of toxic effects, and to interpret and predict consequences of time-varying exposure. These advantages have been recognized in the regulatory arena, especially for environmental risk assessment of pesticides, where time-varying exposure is the norm. We critically evaluate the link between the modeled variables in TKTD models and the observations from laboratory ecotoxicity tests. For the endpoint reproduction, this link is far from trivial. The relevant TKTD models for sublethal effects are based on dynamic energy budget (DEB) theory, which specifies a continuous investment flux into reproduction. In contrast, experimental tests score egg or offspring release by the mother. The link between model and data is particularly troublesome when a species reproduces in discrete clutches and, even more so, when eggs are incubated in the mother's brood pouch (and release of neonates is scored in the test). This situation is quite common among aquatic invertebrates (e.g., cladocerans, amphipods, mysids), including many popular test species. In this discussion paper, we treat these and other issues with reproduction data, reflect on their potential impact on DEB-TKTD analysis, and provide preliminary recommendations to correct them. Both modelers and users of model results need to be aware of these complications, as ignoring them could easily lead to unnecessary failure of DEB-TKTD models during calibration, or when validating them against independent data for other exposure scenarios.
66. Forbes VE, Agatz A, Ashauer R, Butt KR, Capowiez Y, Duquesne S, Ernst G, Focks A, Gergs A, Hodson ME et al. 2021. Mechanistic effect modeling of earthworms in the context of pesticide risk assessment: Synthesis of the foresee workshop. Integrated Environmental Assessment and Management. 17(2):352-363. (link)
Earthworms are important ecosystem engineers, and assessment of the risk of plant protection products toward them is part of the European environmental risk assessment (ERA). In the current ERA scheme, exposure and effects are represented simplistically and are not well integrated, resulting in uncertainty when the results are applied to ecosystems. Modeling offers a powerful tool to integrate the effects observed in lower tier laboratory studies with the environmental conditions under which exposure is expected in the field. This paper provides a summary of the (In)Field Organism Risk modEling by coupling Soil Exposure and Effect (FORESEE) Workshop held 28–30 January 2020 in Düsseldorf, Germany. This workshop focused on toxicokinetic–toxicodynamic (TKTD) and population modeling of earthworms in the context of ERA. The goal was to bring together scientists from different stakeholder groups to discuss the current state of soil invertebrate modeling and to explore how earthworm modeling could be applied to risk assessments, in particular how the different model outputs can be used in the tiered ERA approach. In support of these goals, the workshop aimed at addressing the requirements and concerns of the different stakeholder groups to support further model development. The modeling approach included 4 submodules to cover the most relevant processes for earthworm risk assessment: environment, behavior (feeding, vertical movement), TKTD, and population. Four workgroups examined different aspects of the model with relevance for risk assessment, earthworm ecology, uptake routes, and cross‐species extrapolation and model testing. Here, we present the perspectives of each workgroup and highlight how the collaborative effort of participants from multidisciplinary backgrounds helped to establish common ground. In addition, we provide a list of recommendations for how earthworm TKTD modeling could address some of the uncertainties in current risk assessments for plant protection products.
65. Bart S, Jager T, Robinson A, Lahive E, Spurgeon DJ, Ashauer R (2021): Predicting mixture effects over time with toxicokinetic–toxicodynamic models (guts): Assumptions, experimental testing, and predictive power. Environ Sci Technol. 55(4):2430-2439. (open access link)
Current methods to assess the impact of chemical mixtures on organisms ignore the temporal dimension. The General Unified Threshold model for Survival (GUTS) provides a framework for deriving toxicokinetic–toxicodynamic (TKTD) models, which account for effects of toxicant exposure on survival in time. Starting from the classic assumptions of independent action and concentration addition, we derive equations for the GUTS reduced (GUTS-RED) model corresponding to these mixture toxicity concepts and go on to demonstrate their application. Using experimental binary mixture studies with Enchytraeus crypticus and previously published data for Daphnia magna and Apis mellifera, we assessed the predictive power of the extended GUTS-RED framework for mixture assessment. The extended models accurately predicted the mixture effect. The GUTS parameters on single exposure data, mixture model calibration, and predictive power analyses on mixture exposure data offer novel diagnostic tools to inform on the chemical mode of action, specifically whether a similar or dissimilar form of damage is caused by mixture components. Finally, observed deviations from model predictions can identify interactions, e.g., synergism or antagonism, between chemicals in the mixture, which are not accounted for by the models. TKTD models, such as GUTS-RED, thus offer a framework to implement new mechanistic knowledge in mixture hazard assessments.
64. Sherborne N, Galic N, Ashauer R (2020): Sublethal effect modelling for environmental risk assessment of chemicals: Problem definition, model variants, application and challenges. Sci Total Environ. 745:141027. (open access link)
Bioenergetic models, and specifically dynamic energy budget (DEB) theory, are gathering a great deal of interest as a tool to predict the effects of realistically variable exposure to toxicants over time on an individual animal. Here we use aquatic ecological risk assessment (ERA) as the context for a review of the different model variants within DEB and the closely related DEBkiss theory (incl. reserves, ageing, size & maturity, starvation). We propose a coherent and unifying naming scheme for all current major DEB variants, explore the implications of each model's underlying assumptions in terms of its capability and complexity and analyse differences between the models (endpoints, mathematical differences, physiological modes of action). The results imply a hierarchy of model complexity which could be used to guide the implementation of simplified model variants. We provide a decision tree to support matching the simplest suitable model to a given research or regulatory question. We detail which new insights can be gained by using DEB in toxicokinetic-toxicodynamic modelling, both generally and for the specific example of ERA, and highlight open questions. Specifically, we outline a moving time window approach to assess time-variable exposure concentrations and discuss how to account for cross-generational exposure. Where possible, we suggest valuable topics for experimental and theoretical research.
63. Goussen B, Rendal C, Sheffield D, Butler E, Price OR, Ashauer R (2020): Bioenergetics modelling to analyse and predict the joint effects of multiple stressors: Meta-analysis and model corroboration. Sci Total Environ. 749:141509. (open access link)
Understanding the consequences of the combined effects of multiple stressors—including stress from man-made chemicals—is important for conservation management, the ecological risk assessment of chemicals, and many other ecological applications. Our current ability to predict and analyse the joint effects of multiple stressors is insufficient to make the prospective risk assessment of chemicals more ecologically relevant because we lack a full understanding of how organisms respond to stress factors alone and in combination. Here, we describe a Dynamic Energy Budget (DEB) based bioenergetics model that predicts the potential effects of single or multiple natural and chemical stressors on life history traits. We demonstrate the plausibility of the model using a meta-analysis of 128 existing studies on freshwater invertebrates. We then validate our model by comparing its predictions for a combination of three stressors (i.e. chemical, temperature, and food availability) with new, independent experimental data on life history traits in the daphnid Ceriodaphnia dubia. We found that the model predictions are in agreement with observed growth curves and reproductive traits. To the best of our knowledge, this is the first time that the combined effects of three stress factors on life history traits observed in laboratory studies have been predicted successfully in invertebrates. We suggest that a re-analysis of existing studies on multiple stressors within the modelling framework outlined here will provide a robust null model for identifying stressor interactions, and expect that a better understanding of the underlying mechanisms will arise from these new analyses. Bioenergetics modelling could be applied more broadly to support environmental management decision making.
62. Ashauer R, Kuhl R, Zimmer E, Junghans M (2020): Effect modeling quantifies the difference between the toxicity of average pesticide concentrations and time-variable exposures from water quality monitoring. Environ Toxicol Chem. 39(11):2158-2168. (open access link)
Synthetic chemicals are frequently detected in water bodies, and their concentrations vary over time. Water monitoring programs typically employ either a sequence of grab samples or continuous sampling, followed by chemical analysis. Continuous time‐proportional sampling yields the time‐weighted average concentration, which is taken as proxy for the real, time‐variable exposure. However, we do not know how much the toxicity of the average concentration differs from the toxicity of the corresponding fluctuating exposure profile. We used toxicokinetic–toxicodynamic models (invertebrates, fish) and population growth models (algae, duckweed) to calculate the margin of safety in moving time windows across measured aquatic concentration time series (7 pesticides) in 5 streams. A longer sampling period (14 d) for time‐proportional sampling leads to more deviations from the real chemical stress than shorter sampling durations (3 d). The associated error is a factor of 4 or less in the margin of safety value toward underestimating and an error of factor 9 toward overestimating chemical stress in the most toxic time windows. Under‐ and overestimations occur with approximate equal frequency and are very small compared with the overall variation, which ranged from 0.027 to 2.4 × 10 (margin of safety values). We conclude that continuous, time‐proportional sampling for a period of 3 and 14 d for acute and chronic assessment, respectively, yields sufficiently accurate average concentrations to assess ecotoxicological effects.
61. Agatz A, Ashauer R, Sweeney P, Brown CD (2020): A knowledge-based approach to designing control strategies for agricultural pests. Agric Syst. 183:102865. (open access link)
Chemical control of insect pests remains vital to agricultural productivity, but limited mechanistic understanding of the interactions between crop, pest and chemical control agent have restricted our capacity to respond to challenges such as the emergence of resistance and demands for tighter environmental regulation. Formulating effective control strategies that integrate chemical and non-chemical management for soil-dwelling pests is particularly problematic owing to the complexity of the soil-root-pest system and the variability that occurs between sites and between seasons. Here, we present a new concept, termed COMPASS, that integrates ecological knowledge on pest development and behaviour together with crop physiology and mechanistic understanding of chemical distribution and toxic action within the rhizosphere. The concept is tested using a two-dimensional systems model (COMPASS-Rootworm) that simulates root damage in maize from the corn rootworm Diabrotica spp. We evaluate COMPASS-Rootworm using 119 field trials that investigated the efficacy of insecticidal products and placement strategies at four sites in the USA over a period of ten years. Simulated root damage is consistent with measurements for 109 field trials. Moreover, we disentangle factors influencing root damage and pest control, including pest pressure, weather, insecticide distribution, and temporality between the emergence of crop roots and pests. The model can inform integrated pest management, optimize pest control strategies to reduce environmental burdens from pesticides, and improve the efficiency of insecticide development.
60. Martin, T., Thompson, H., Thorbek, P. & Ashauer, R. (2019): Toxicokinetic–Toxicodynamic Modeling of the Effects of Pesticides on Growth of Rattus norvegicus. Chem. Res. Toxicol. 32, 2281-2294. (link)
Ecological risk assessment is carried out for chemicals such as pesticides before they are released into the environment. Such risk assessment currently relies on summary statistics gathered in standardized laboratory studies. However, these statistics extract only limited information and depend on duration of exposure. Their extrapolation to realistic ecological scenarios is inherently limited. Mechanistic effect models simulate the processes underlying toxicity and so have the potential to overcome these issues. Toxicokinetic–toxicodynamic (TK–TD) models operate at the individual level, predicting the internal concentration of a chemical over time and the stress it places on an organism. TK–TD models are particularly suited to addressing the difference in exposure patterns between laboratory (constant) and field (variable) scenarios. So far, few studies have sought to predict sublethal effects of pesticide exposure to wild mammals in the field, even though such effects are of particular interest with respect to longer term exposure. We developed a TK–TD model based on the dynamic energy budget (DEB) theory, which can be parametrized and tested solely using standard regulatory studies. We demonstrate that this approach can be used effectively to predict toxic effects on the body weight of rats over time. Model predictions separate the impacts of feeding avoidance and toxic action, highlighting which was the primary driver of effects on growth. Such information is relevant to the ecological risk posed by a compound because in the environment alternative food sources may or may not be available to focal species. While this study focused on a single end point, growth, this approach could be expanded to include reproductive output. The framework developed is simple to use and could be of great utility for ecological and toxicological research as well as to risk assessors in industry and regulatory agencies.
59. Perkins, E. J. et al. (2019): Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment. Environ. Toxicol. Chem. 38, 1850-1865 (link).
An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway‐based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual‐based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications.
58. Martin, T., P. Thorbek and R. Ashauer (2019): Common ground between growth models of rival theories: a useful illustration for beginners. Ecological Modelling, 407: 108712. (link)
Dynamic energy budget theory (DEB) and the metabolic theory of ecology (MTE) both seek to quantify the processes of resource acquisition and use but differ in their underlying mechanisms and assumptions. Some in-depth comparisons of the theories have been conducted in the literature but require a level of knowledge that is likely to be beyond most newcomers to the topic. We reduce the theories to their simplest forms, their models for growth under optimal conditions, and present a side by side comparison of the model equations and key assumptions. This shows considerable overlap in how both theories characterise growth rate while also highlighting fundamental differences, such as MTE’s use of taxon specific parameters. Comparing DEB and MTE in this way provides an accessible platform to help beginners gain a better understanding from the existing literature. (link to paper)
57. Riedl, V., Agatz, A., Benstead, R., & Ashauer, R. (2019): Factors Affecting the Growth of Pseudokirchneriella subcapitata in Single-Species Tests: Lessons for the Experimental Design and the Reproducibility of a Multitrophic Laboratory Microcosm. Environmental Toxicology and Chemistry, 38(5), 1120-1131. (link)
The need for an integrated risk assessment at an ecologically relevant scale (e.g., at the population/community levels) has been acknowledged. Multispecies systems with increased ecological complexity, however, are difficult if not impossible to reproduce. The laboratory‐scale microcosm TriCosm (Pseudokirchneriella subcapitata, Ceriodaphnia dubia, Hydra viridissima) of intermediate complexity was developed for the reproducible assessment of chemical effects at the population/community levels. The system dynamics were repeatable in the short term, but interexperimental variation of algal dynamics in the long term triggered knock‐on effects on grazer and predator populations. We present 20 experiments to assess the effects of 12 factors (test medium, vessel type/condition, shaking speed, light intensity/regime, inoculation density, medium preparation components, metal concentration/composition, buffering salt type/concentration) on algal growth in the TriCosm enclosure. Growth rates varied between ≤ 0 and 1.40 (± 0.21) and generally were greatest with increased shaking speed, light exposure, medium buffer, or aeration time. Treatments conducted in dishes with aseptically prepared, lightly buffered, and/or hardly aerated medium resulted in low growth rates. We found that inter‐experimental variation of algal dynamics in the TriCosm was caused by a modification of medium preparation (omission of medium aeration) with the aim of reducing microbial contamination. Our findings highlight the facts that consistency in experimental procedures and in‐depth understanding of system components are indispensable to achieve repeatability. (link to paper)
56. Duckworth J., Jager T., Ashauer R. (2019): Automated, high-throughput measurement of size and growth curves of small organisms in well plates. Scientific Reports 9. (open access link)
Organism size and growth curves are important biological characteristics. Current methods to measure organism size, and in particular growth curves, are often resource intensive because they involve many manual steps. Here we demonstrate a method for automated, high-throughput measurements of size and growth in individual aquatic invertebrates kept in microtiter well-plates. We use a spheroid counter (Cell3iMager, cc-5000) to automatically measure size of seven different freshwater invertebrate species. Further, we generated calibration curves (linear regressions, all p < 0.0001, r2 >=0.9 for Ceriodaphnoa dubia, Asellus aquaticus, Daphnia magna, Daphnia pulex; r2 >=0.8 for Hyalella azteca, Chironomus spec. larvae and Culex spec. larvae) to convert size measured on the spheroid counter to traditional, microscope based, length measurements, which follow the longest orientation of the body. Finally, we demonstrate semi-automated measurement of growth curves of individual daphnids (C. dubia and D. magna) over time and find that the quality of individual growth curves varies, partly due to methodological reasons. Nevertheless, this novel method could be adopted to other species and represents a step change in experimental throughput for measuring organisms’ shape, size and growth curves. It is also a significant qualitative improvement by enabling high-throughput assessment of inter-individual variation of growth.
55. Witton, J. T. et al. (2018) Quantifying pesticide deposits and spray patterns at micro-scales on apple (Malus domesticus) leaves with a view to arthropod exposure. Pest Manag. Sci. (link, open access)
Pesticides used in commercial crop systems can adversely affect non‐target arthropod populations. The spatial distribution of pesticide residues is rarely studied at scales relevant to these populations. Here, we combine two methods for assessing pesticide spray deposits at spatial scales relevant to non‐target arthropods found in apple orchards. Pesticide residues were determined on individual apple leaves through conventional residue analysis; water‐sensitive paper was used to investigate spatial distributions in deposits at the micro‐scale. We also evaluated how accurately a digital image analysis program estimated pesticide residues.
We found that mean pesticide spray coverage on water‐sensitive paper varied by up to 6.1% (95% CI 9.4%, 2.7%) within an apple orchard, and leaf residues varied by up to 0.95 (95% CI 0.54, 1.36) mg kg−1 within a tree. Leaf residues based on analytical chemistry were six times lower than pesticide deposition estimated through image analysis of water‐sensitive paper, although these correlated strongly. This correlation allowed estimation of actual residues by application of a correction factor.
Our method demonstrates accurate estimation of pesticide deposits at the individual leaf scale through digital analysis of water‐sensitive paper and is a low‐cost, rapid alternative to conventional residue analysis techniques.
54. Riedl V, Agatz A, Benstead R & Ashauer R (2018): A standardized tritrophic small-scale system (TriCosm) for the assessment of stressor-induced effects on aquatic community dynamics. Environ. Toxicol. Chem. (link)
Chemical impacts on the environment are routinely assessed in single‐species tests. They are employed to measure direct effects on nontarget organisms, but indirect effects on ecological interactions can only be detected in multispecies tests. Micro‐ and mesocosms are more complex and environmentally realistic, yet they are less frequently used for environmental risk assessment because resource demand is high, whereas repeatability and statistical power are often low. Test systems fulfilling regulatory needs (i.e., standardization, repeatability, and replication) and the assessment of impacts on species interactions and indirect effects are lacking. In the present study we describe the development of the TriCosm, a repeatable aquatic multispecies test with 3 trophic levels and increased statistical power. High repeatability of community dynamics of 3 interacting aquatic populations (algae, Ceriodaphnia, and Hydra) was found with an average coefficient of variation of 19.5% and the ability to determine small effect sizes. The TriCosm combines benefits of both single‐species tests (fulfillment of regulatory requirements) and complex multispecies tests (ecological relevance) and can be used, for instance, at an intermediate tier in environmental risk assessment. Furthermore, comparatively quickly generated population and community toxicity data can be useful for the development and testing of mechanistic effect models.
53. Jager T & Ashauer R (2018): How to evaluate the quality of toxicokinetic—Toxicodynamic models in the context of environmental risk assessment. Integrated Environmental Assessment and Management. (link)
Environmental risk assessment (ERA) of chemicals relies on the combination of exposure and effects assessment. Exposure concentrations are commonly estimated using mechanistic fate models, but the effects side is restricted to descriptive statistical treatment of toxicity data. Mechanistic effect models are gaining interest in a regulatory context, which has also sparked discussions on model quality and good modeling practice. Proposals for good modeling practice of effect models currently focus very much on population and community models, whereas effects models also exist at the individual level, falling into the category of toxicokinetic–toxicodynamic (TKTD) models. In contrast to the higher‐level models, TKTD models are usually completely parameterized by fitting them to experimental data. In fact, one of their explicit aims is to replace descriptive methods for data analysis. Furthermore, the construction of these models does not fit into an orderly modeling cycle, given that most TKTD models have been under continuous development for decades and are being applied by many different research groups, for many different purposes. These aspects have considerable consequences for the application of frameworks for model evaluation. For example, classical sensitivity analysis becomes rather meaningless when all model parameters are fitted to a data set. We illustrate these issues with the General Unified Threshold model for Survival (GUTS), relate them to the quality issues for currently used models in ERA, and provide recommendations for the evaluation of TKTD models and their analyses.
52. Ashauer R & Jager T (2018): Physiological Modes of Action across Species and Toxicants: The Key to Predictive Ecotoxicology. Environmental Sciences: Processes & Impacts. (link to paper, open access)
As ecotoxicologists we strive for a better understanding of how chemicals affect our environment. Humanity needs tools to identify those combinations of man-made chemicals and organisms most likely to cause problems. In other words: which of the millions of species are at risk from pollution? And which of the tens of thousands of chemicals contribute most to the risk? We identified our poor knowledge on physiological modes of action (how a chemical affects the energy allocation in an organism), and how they vary across species and toxicants, as a major knowledge gap. We also find that the key to predictive ecotoxicology is the systematic, rigorous characterization of physiological modes of action because that will enable more powerful in vitro to in vivo toxicity extrapolation and in silico ecotoxicology. In the near future, we expect a step change in our ability to study physiological modes of action by improved, and partially automated, experimental methods. Once we have populated the matrix of species and toxicants with sufficient physiological mode of action data we can look for patterns, and from those patterns infer general rules, theory and models.
51. Ashauer R, O'Connor I, Escher BI (2017): Toxic Mixtures in Time - The Sequence Makes the Poison. Environmental Science & Technology, 51(5). (link to paper, open access)
“The dose makes the poison”. This principle assumes that once a chemical is cleared out of the organism (toxicokinetic recovery), it no longer has any effect. However, it overlooks the other process of re-establishing homeostasis, toxicodynamic recovery, which can be fast or slow depending on the chemical. Therefore, when organisms are exposed to two toxicants in sequence, the toxicity can differ if their order is reversed. We test this hypothesis with the freshwater crustacean Gammarus pulex and four toxicants that act on different targets (diazinon, propiconazole, 4,6-dinitro-o-cresol, 4-nitrobenzyl chloride). We found clearly different toxicity when the exposure order of two toxicants was reversed, while maintaining the same dose. Slow toxicodynamic recovery caused carry-over toxicity in subsequent exposures, thereby resulting in a sequence effect–but only when toxicodynamic recovery was slow relative to the interval between exposures. This suggests that carry-over toxicity is a useful proxy for organism fitness and that risk assessment methods should be revised as they currently could underestimate risk. We provide the first evidence that carry-over toxicity occurs among chemicals acting on different targets and when exposure is several days apart. It is therefore not only the dose that makes the poison but also the exposure sequence.
50. Goussen BRM, Price OR, Rendal C, Ashauer R. Integrated presentation of ecological risk from multiple stressors. Scientific Reports (2016). (link to paper)
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.
49. De Laender F, Rohr JR, Ashauer R, Baird DJ, Berger U, Eisenhauer N et al. Reintroducing Environmental Change Drivers in Biodiversity-Ecosystem Functioning Research. Trends in Ecology & Evolution (2016). (link to paper)
For the past 20 years, research on biodiversity and ecosystem functioning (B-EF) has only implicitly considered the underlying role of environmental change. We illustrate that explicitly reintroducing environmental change drivers in B-EF research is needed to predict the functioning of ecosystems facing changes in biodiversity. Next we show how this reintroduction improves experimental control over community composition and structure, which helps to provide mechanistic insight on how multiple aspects of biodiversity relate to function and how biodiversity and function relate in food webs. We also highlight challenges for the proposed reintroduction and suggest analyses and experiments to better understand how random biodiversity changes, as studied by classic approaches in B-EF research, contribute to the shifts in function that follow environmental change.
48. Franco A, Price OR, Marshall S, Jolliet O, Van den Brink PJ, Rico A et al. Toward refined environmental scenarios for ecological risk assessment of down-the-drain chemicals in freshwater environments. Integrated Environmental Assessment and Management (2016). (link to paper)
Current regulatory practice for chemical risk assessment suffers from the lack of realism in conventional frameworks. Despite significant advances in exposure and ecological effect modeling, the implementation of novel approaches as high-tier options for prospective regulatory risk assessment remains limited, particularly among general chemicals such as down-the-drain ingredients. While reviewing the current state of the art in environmental exposure and ecological effect modeling, we propose a scenario-based framework that enables a better integration of exposure and effect assessments in a tiered approach. Global- to catchment-scale spatially explicit exposure models can be used to identify areas of higher exposure and to generate ecologically relevant exposure information for input into effect models. Numerous examples of mechanistic ecological effect models demonstrate that it is technically feasible to extrapolate from individual-level effects to effects at higher levels of biological organization and from laboratory to environmental conditions. However, the data required to parameterize effect models that can embrace the complexity of ecosystems are large and require a targeted approach. Experimental efforts should, therefore, focus on vulnerable species and/or traits and ecological conditions of relevance. We outline key research needs to address the challenges that currently hinder the practical application of advanced model-based approaches to risk assessment of down-the-drain chemicals.
47. Agatz A, Ashauer R, Sweeney P & Brown CD: Prediction of pest pressure on corn root nodes: the POPP-Corn model. Journal of Pest Science (2016). (link to paper)
A model for the corn rootworm Diabrotica spp. combined with a temporally explicit model for development of corn roots across the soil profile was developed to link pest ecology, root damage and yield loss. Development of the model focused on simulating root damage from rootworm feeding in accordance with observations in the field to allow the virtual testing of efficacy from management interventions in the future. We present the model and demonstrate its applicability for simulating root damage by comparison between observed and simulated pest development and root damage (assessed according to the node injury scale from 0 to 3) for field studies from the literature conducted in Urbana, Illinois (US), between 1991 and 2014. The model simulated the first appearance of larvae and adults to within a week of that observed in 88 and 71 % of all years, respectively, and in all cases to within 2 weeks of the first sightings recorded for central Illinois. Furthermore, in 73 % of all years simulated root damage differed by <0.5 node injury scale points compared to the observations made in the field between 2005 and 2014 even though accurate information for initial pest pressure (i.e. number of eggs in the soil) was not measured at the sites or available from nearby locations. This is, to our knowledge, the first time that pest ecology, root damage and yield loss have been successfully interlinked to produce a virtual field. There are potential applications in investigating efficacy of different pest control measures and strategies.
46. Ashauer, R. et al.: Modelling survival: exposure pattern, species sensitivity and uncertainty. Scientific Reports 6, Article number: 29178 (2016). (open access link)
The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.
45. Albert C, Vogel S, Ashauer R (2016): Computationally Efficient
Implementation of a Novel Algorithm for the General Unified Threshold Model of Survival (GUTS). PLOS Computational Biology 12(6): e1004978. doi:10.1371/journal.pcbi.1004978. (open access
The General Unified Threshold model of Survival (GUTS) provides a consistent mathematical framework for survival analysis. However, the calibration of GUTS models is computationally challenging. We present a novel algorithm and its fast implementation in our R package, GUTS, that help to overcome these challenges. We show a step-by-step application example consisting of model calibration and uncertainty estimation as well as making probabilistic predictions and validating the model with new data. Using self-defined wrapper functions, we show how to produce informative text printouts and plots without effort, for the inexperienced as well as the advanced user. The complete ready-to-run script is available as supplemental material. We expect that our software facilitates novel re-analysis of existing survival data as well as asking new research questions in a wide range of sciences. In particular the ability to quickly quantify stressor thresholds in conjunction with dynamic compensating processes, and their uncertainty, is an improvement that complements current survival analysis methods.
44. Ashauer R (2016): Post-ozonation in a municipal wastewater treatment plant improves water quality in the receiving stream. Environmental Sciences Europe 2016, 28:1. (open access link)
Removal of organic micropollutants from wastewater by post-ozonation has been investigated in a municipal wastewater treatment plant (WWTP) temporarily upgraded with full-scale ozonation, followed by sand filtration, as an additional treatment step of the secondary effluent. Here, the SPEAR (species at risk) indicator was used to analyse macroinvertebrate abundance data that were collected in the receiving stream before, during and after ozonation to investigate whether ozonation improved the water quality. The SPEAR values indicate a better water quality downstream the WWTP during ozonation. With ozonation the relative abundance of vulnerable macroinvertebrates in the stream receiving the treated wastewater increases from 18 % (CI 15–21 %) to 30 % (CI 28–32 %). This increase of 12 % (CI 8–16 %) indicates improved ecological quality of the stream and shifts classification according to the Water Framework Directive from poor to moderate.
The SPEAR concept, originally developed to indicate pesticide stress, also appears to indicate toxic stress by a mixture of various micropollutants including pharmaceuticals, personal care products and pesticides. The responsiveness of the SPEAR indicator means that those macroinvertebrates that are vulnerable to pesticide pollution are also vulnerable to micropollutants from WWTPs. The change in the macroinvertebrate community downstream the WWTP indicates that toxicity by pollutants decreased by more than one order of magnitude during ozonation. Ozonation followed by sand filtration has favourable impacts on the composition of the macroinvertebrate community and can improve the water quality in the receiving stream.
43. 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.
42. Stadnicka-Michalak J, Schirmer K, Ashauer R (2015): Toxicology across scales: Cell population growth in
vitro predicts reduced fish growth. Sci. Adv. (link, open access)
Environmental risk assessment of chemicals is essential but often relies on ethically controversial and expensive methods. We show that tests using cell cultures, combined with modeling of
toxicological effects, can replace tests with juvenile fish. Hundreds of thousands of fish at this developmental stage are annually used to assess the influence of chemicals on growth. Juveniles
are more sensitive than adult fish, and their growth can affect their chances to survive and reproduce. Thus, to reduce the number of fish used for such tests, we propose a method that can
quantitatively predict chemical impact on fish growth based on in vitro data. Our model predicts reduced fish growth in two fish species in excellent agreement with measured in vivo data of two
pesticides. This promising step toward alternatives to fish toxicity testing is simple, inexpensive, and fast and only requires in vitro data for model calibration.
41. Ashauer R, O’Connor I, Hintermeister A, Escher BI (2015): Death Dilemma and Organism Recovery in
Ecotoxicology. Environ. Sci. Technol. (link, open access)
Why do some individuals survive after exposure to chemicals while others die? Either, the tolerance threshold is distributed among the individuals in a population, and its exceedance leads to certain death, or all individuals share the same threshold above which death occurs stochastically. The previously published General Unified Threshold model of Survival (GUTS) established a mathematical relationship between the two assumptions. According to this model stochastic death would result in systematically faster compensation and damage repair mechanisms than individual tolerance. Thus, we face a circular conclusion dilemma because inference about the death mechanism is inherently linked to the speed of damage recovery. We provide empirical evidence that the stochastic death model consistently infers much faster toxicodynamic recovery than the individual tolerance model. Survival data can be explained by either, slower damage recovery and a wider individual tolerance distribution, or faster damage recovery paired with a narrow tolerance distribution. The toxicodynamic model parameters exhibited meaningful patterns in chemical space, which is why we suggest toxicodynamic model parameters as novel phenotypic anchors for in vitro to in vivo toxicity extrapolation. GUTS appears to be a promising refinement of traditional survival curve analysis and dose response models.
40. Carter LJ, Ashauer R, Ryan JJ & Boxall ABA (2014): Minimised Bioconcentration Tests: A Useful Tool for
Assessing Chemical Uptake into Terrestrial and Aquatic Invertebrates? Environ. Sci. Technol. 48, 13497-13503 (link).
Current guidelines for determining bioconcentration factors (BCF) and uptake and depuration rate constants require labor intensive studies with large numbers of organisms. A minimized approach has recently been proposed for fish BCF studies but its applicability to other taxonomic groups is unknown. In this study, we therefore evaluate the use of the minimized approach for estimating BCF and uptake and depuration rate constants for chemicals in aquatic and terrestrial invertebrates. Data from a range of previous BCF studies were resampled to calculate BCFs and rate constants using the minimized method. The resulting values were then compared to values obtained using full study designs. Results demonstrated a good correlation for uptake rate constants, a poor correlation for depuration rate constants and a very good correlation between the BCFs obtained using the traditional and minimized approach for a variety of organic compounds. The minimized approach therefore has merit in deriving bioconcentration factors and uptake rate constants but may not be appropriate for deriving depuration rate constants for use in, for example, toxico-kinetic toxico-dynamic modeling. The approach uses up to 70% fewer organisms, requires less labor and has lower analytical costs. The minimized design therefore could be a valuable approach for running large multifactorial studies to assess bioconcentration of the plethora of chemicals that occur in the environment into the many taxonomic groups that occur in the environment. The approach should therefore help in accelerating the development of our understanding of factors and processes affecting uptake of chemicals into organisms in the environment.
39. Galic N, et al. (2014): Modeling the contribution of toxicokinetic and toxicodynamic processes to the
recovery of Gammarus pulex populations after exposure to pesticides. Environ. Toxicol. Chem. 33, 1476-1488 (link).
Because aquatic macroinvertebrates may be exposed regularly to pesticides in edge-of-the-field water bodies, an accurate assessment of potential adverse effects and subsequent population recovery is essential. Standard effect risk assessment tools are not able to fully address the complexities arising from multiple exposure patterns, nor can they properly address the population recovery process. In the present study, we developed an individual-based model of the freshwater amphipod Gammarus pulex to evaluate the consequences of exposure to 4 compounds with different modes of action on individual survival and population recovery. Effects on survival were calculated using concentration–effect relationships and the threshold damage model (TDM), which accounts for detailed processes of toxicokinetics and toxicodynamics. Delayed effects as calculated by the TDM had a significant impact on individual survival and population recovery. We also evaluated the standard assessment of effects after short-term exposures using the 96-h concentration–effect model and the TDM, which was conservative for very short-term exposure. An integration of a TKTD submodel with a population model can be used to explore the ecological relevance of ecotoxicity endpoints in different exposure environments.
38. Kookana, R. S. et al. (2014): Nanopesticides: Guiding Principles for Regulatory Evaluation of
Environmental Risks. Journal of Agricultural and Food Chemistry 62, 4227-4240 (link).
Nanopesticides or nano plant protection products represent an emerging technological development that, in relation to pesticide use, could offer a range of benefits including increased efficacy, durability, and a reduction in the amounts of active ingredients that need to be used. A number of formulation types have been suggested including emulsions (e.g., nanoemulsions), nanocapsules (e.g., with polymers), and products containing pristine engineered nanoparticles, such as metals, metal oxides, and nanoclays. The increasing interest in the use of nanopesticides raises questions as to how to assess the environmental risk of these materials for regulatory purposes. Here, the current approaches for environmental risk assessment of pesticides are reviewed and the question of whether these approaches are fit for purpose for use on nanopesticides is addressed. Potential adaptations to existing environmental risk assessment tests and procedures for use with nanopesticides are discussed, addressing aspects such as analysis and characterization, environmental fate and exposure assessment, uptake by biota, ecotoxicity, and risk assessment of nanopesticides in aquatic and terrestrial ecosystems. Throughout, the main focus is on assessing whether the presence of the nanoformulation introduces potential differences relative to the conventional active ingredients. The proposed changes in the test methodology, research priorities, and recommendations would facilitate the development of regulatory approaches and a regulatory framework for nanopesticides.
37. Nyman A-M, Schirmer K, Ashauer R (2014): Importance of Toxicokinetics for Interspecies Variation in
Sensitivity to Chemicals. Environ. Sci. Technol. 48, 5946-5954 (link).
Interspecies variation in sensitivity to synthetic chemicals can be orders of magnitude large. Species traits causing the variation can be related to toxicokinetics (uptake, distribution, biotransformation, elimination) or toxicodynamics (interaction with biological target sites). We present an approach to systematically measure and model the contribution of uptake, biotransformation, internal distribution, and elimination kinetics toward species sensitivity differences. The aim is to express sensitivity as target tissue specific, internal lethal concentrations. A case study with the pesticides diazinon, imidacloprid, and propiconazole and the aquatic invertebrates Gammarus pulex, Gammarus fossarum, and Lymnaea stagnalis illustrates the approach. L. stagnalis accumulates more pesticides than Gammaridae when measured in whole organisms but less in target tissues such as the nervous system. Toxicokinetics, i.e. biotransformation and distribution, explain the higher tolerance of L. stagnalis to the insecticide diazinon when compared to Gammaridae. L. stagnalis was again more tolerant to the other neurotoxicant imidacloprid; however, the difference in sensitivity could not be explained by toxicokinetics alone, indicating the importance of toxicodynamic differences. Sensitivity to propiconazole was comparable among all species and, when expressed as internal lethal concentrations, falls in the range of baseline toxicity.
36. Agatz A, Ashauer R, Brown CD (2014): Imidacloprid perturbs feeding of Gammarus pulex at
environmentally relevant concentrations. Environ. Toxicol. Chem. 33, 648-653 (link).
Changes in food uptake by detritivorous macroinvertebrates could disrupt the ecosystem service of leaf litter breakdown, necessitating the study of shredding under anthropogenic influences. The impact of the neonicotinoid insecticide imidacloprid on the feeding rate of individual Gammarus pulex was measured at a daily resolution both during and after a 4-d exposure period. The authors found that imidacloprid inhibits feeding of G. pulex during exposure at concentrations ≥30 µg/L and that there was no recovery in feeding on transfer into clean media for 3 d. Exposure to imidacloprid at concentrations ≥0.81 µg/L and ≤9.0 µg/L resulted in increased feeding after exposure even though there was no significant effect on feeding during the exposure itself. Comparison with the literature shows that concentrations found to influence feeding lie within the range of estimated and measured environmental concentrations. Additionally, effects on feeding rate were observed at concentrations 2 orders of magnitude lower than those causing mortality. The lethal concentration for 50% of test organisms after 4 d of exposure (270 µg/L, literature data) and the effect concentration for a reduction in feeding by 50% (5.34 µg/L) were used for this comparison. The present study discusses the potential that effects on feeding may evoke effects at the population level or disturb leaf litter breakdown in the environment.
35. Stadnicka-Michalak J, Tanneberger K, Schirmer K, Ashauer R (2014): Measured and Modeled Toxicokinetics in Cultured Fish Cells and
Application to In Vitro - In Vivo Toxicity Extrapolation. PLoS ONE 9, (link, open
Effect concentrations in the toxicity assessment of chemicals with fish and fish cells are generally based on external exposure concentrations. External concentrations as dose metrics, may, however, hamper interpretation and extrapolation of toxicological effects because it is the internal concentration that gives rise to the biological effective dose. Thus, we need to understand the relationship between the external and internal concentrations of chemicals. The objectives of this study were to: (i) elucidate the time-course of the concentration of chemicals with a wide range of physicochemical properties in the compartments of an in vitro test system, (ii) derive a predictive model for toxicokinetics in the in vitro test system, (iii) test the hypothesis that internal effect concentrations in fish (in vivo) and fish cell lines (in vitro) correlate, and (iv) develop a quantitative in vitro to in vivo toxicity extrapolation method for fish acute toxicity. To achieve these goals, time-dependent amounts of organic chemicals were measured in medium, cells (RTgill-W1) and the plastic of exposure wells. Then, the relation between uptake, elimination rate constants, and log KOW was investigated for cells in order to develop a toxicokinetic model. This model was used to predict internal effect concentrations in cells, which were compared with internal effect concentrations in fish gills predicted by a Physiologically Based Toxicokinetic model. Our model could predict concentrations of non-volatile organic chemicals with log KOW between 0.5 and 7 in cells. The correlation of the log ratio of internal effect concentrations in fish gills and the fish gill cell line with the log KOW was significant (r>0.85, p = 0.0008, F-test). This ratio can be predicted from the log KOW of the chemical (77% of variance explained), comprising a promising model to predict lethal effects on fish based on in vitro data.
34. Chiaia-Hernandez AC, Ashauer R, Moest M, Hollingshaus T, Jeon J, Spaak P, Hollender J (2013). Bioconcentration of Organic Contaminants in Daphnia Resting Eggs. Environmental Science & Technology 47, (18), 10667–10675 (link to paper in ES&T journal).
Organic contaminants detected in sediments from Lake Greifensee and other compounds falling in the log Dow range from 1 to 7 were selected to study the bioconcentration of organic contaminants in sediments in Daphnia resting eggs (ephippia). Our results show that octocrylene, tonalide, triclocarban, and other personal care products, along with pesticides and biocides can accumulate in ephippia with log BCF values up to 3. Data on the uptake and depuration kinetics show a better fit toward a two compartment organism model over a single compartment model due to the differences in ephippial egg content in the environment. The obtained BCFs correlate with hydrophobicity for neutral compounds. Independence between BCF and hydrophobicity was observed for partially ionized compounds with log Dow values around 1. Internal concentrations in ephippia in the environment were predicted based on sediment concentrations using the equilibrium partitioning model and calculated BCFs. Estimated internal concentration values ranged between 1 and 68,000 μg/kglip with triclocarban having the highest internal concentrations followed by tonalide and triclosan. The outcomes indicate that contaminants can be taken up by ephippia from the water column or the pore water in the sediment and might influence fitness and sexual reproduction in the aquatic key species of the genus Daphnia.
33. Jeon, Kurth, Ashauer & Hollender (2013). Comparative Toxicokinetics of Organic Micropollutants in Freshwater Crustaceans. Environmental Science & Technology (link to paper at ES&T)
Abstract: Exposure and depuration experiments for Gammarus pulex and Daphnia magna were conducted to quantitatively analyze biotransformation products (BTPs) of organic micropollutants (tramadol, irgarol, and terbutryn). Quantification for BTPs without available standards was performed using an estimation method based on physicochemical properties. Time-series of internal concentrations of micropollutants and BTPs were used to estimate the toxicokinetic rates describing uptake, elimination, and biotransformation processes. Bioaccumulation factors (BAF) for the parents and retention potential factors (RPF), representing the ratio of the internal amount of BTPs to the parent at steady state, were calculated. Nonlinear correlation of excretion rates with hydrophobicity indicates that BTPs with lower hydrophobicity are not always excreted faster than the parent compound. For irgarol, G.pulex showed comparable elimination, but greater uptake and BAF/RPF values than D.magna. Further, G. pulex had a whole set of secondary transformations that D. magna lacked. Tramadol was transformed more and faster than irgarol and there were large differences in toxicokinetic rates for the structurally similar compounds irgarol and terbutryn. Thus, predictability of toxicokinetics across species and compounds needs to consider biotransformation and may be more challenging than previously thought because we found large differences in closely related species and similar chemical structures.
32. 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.
31. Nyman, Hintermeister, Schirmer & Ashauer (2013). The Insecticide Imidacloprid Causes Mortality of the Freshwater Amphipod Gammarus pulex by Interfering with Feeding Behavior. PLOS ONE (link)
Abstract: If an organism does not feed, it dies of starvation. Even though some insecticides which are used to control pests in agriculture can interfere with feeding behavior of insects and other invertebrates, the link from chemical exposure via affected feeding activity to impaired life history traits, such as survival, has not received much attention in ecotoxicology. One of these insecticides is the neonicotinoid imidacloprid, a neurotoxic substance acting specifically on the insect nervous system. We show that imidacloprid has the potential to indirectly cause lethality in aquatic invertebrate populations at low, sublethal concentrations by impairing movements and thus feeding. We investigated feeding activity, lipid content, immobility, and survival of the aquatic arthropod Gammarus pulex under exposure to imidacloprid. We performed experiments with 14 and 21 days duration, both including two treatments with two high, one day pulses of imidacloprid and one treatment with a low, constant concentration. Feeding of G. pulex as well as lipid content were significantly reduced under exposure to the low, constant imidacloprid concentration (15 µg/L). Organisms were not able to move and feed – and this caused high mortality after 14 days of constant exposure. In contrast, feeding and lipid content were not affected by repeated imidacloprid pulses. In these treatments, animals were mostly immobilized during the chemical pulses but did recover relatively fast after transfer to clean water. We also performed a starvation experiment without exposure to imidacloprid which showed that starvation alone does not explain the mortality in the constant imidacloprid exposure. Using a multiple stressor toxicokinetic-toxicodynamic modeling approach, we showed that both starvation and other toxic effects of imidacloprid play a role for determining mortality in constant exposure to the insecticide. (Link to paper in PLOS ONE journal, open access)
30. Ashauer, Thorbek, Warinton, Wheeler & Maund (2013). A method to predict and understand fish survival under dynamic chemical stress using standard ecotoxicity data. Environmental Toxicology & Chemistry (link at ET&C, 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 ET&C journal, open access)
29. Boxall, Fogg, Ashauer, Bowles, Sinclair, Colyer & Brain (2013: Effects of repeated pulsed herbicide exposures on the growth of aquatic
macrophytes. Environmental Toxicology and Chemistry. (link at ET&C)
Many contaminants are released into aquatic systems intermittently in a series of pulses. Pulse timing and magnitude can vary according to usage, compound-specific physicochemical properties, and use area characteristics. Standard laboratory ecotoxicity tests typically employ continuous exposure concentrations over defined durations and thus may not accurately and realistically reflect the effects of certain compounds on aquatic organisms, resulting in potential over- or underestimation. Consequently, the relative effects of pulsed (2 and 4 d) and continuous exposures of the duckweed Lemna minor to isoproturon, metsulfuron-methyl, and pentachlorophenol over a period of 42 d were explored in the present study. At the highest test concentrations, exposure of L. minor to pulses of metsulfuron-methyl resulted in effects on growth similar to those of an equivalent continuous exposure. For isoproturon, pulsed exposures had a lower impact than a corresponding continuous exposure, whereas the effect of pentachlorophenol delivered in pulses was greater. These differences may be explained by compound-specific uptake and degradation or dissipation rates in plants and the recovery potential that occurs following pulses for different pesticides. Given these results, use of a simple time-weighted average approach to estimate effects of intermittent exposures from short-term standard toxicity studies may not provide an accurate prediction that reflects realistic exposure scenarios. Development of mechanistic modeling approaches may facilitate better estimates of effects from intermittent exposures.
28. Kretschmann A., Ashauer R., Hollender J., Escher B.I. (2012). Toxicokinetic and toxicodynamic model for diazinon toxicity—mechanistic explanation of differences in the sensitivity of Daphnia magna and Gammarus pulex. Environmental Toxicology and Chemistry. in press, (available online).
A mechanistic toxicokinetic and toxicodynamic model for acute toxic effects (immobilization, mortality) of the organothiophosphate insecticide diazinon in Daphnia magna is presented. The model was parameterized using measured external and internal (whole-body) concentrations of diazinon, its toxic metabolite diazoxon, and the inactive metabolite 2-isopropyl-6-methyl-4-pyrimidinol, plus acetylcholinesterase (AChE) activity measured during exposure to diazinon in vivo. The toxicokinetic and toxicodynamic model provides a coherent picture from exposure to the resulting toxic effect on an organism level through internally formed metabolites and the effect on a molecular scale. A very fast reaction of diazoxon with AChE (pseudo first-order inhibition rate constant ki = 3.3 h−1) compared with a slow formation of diazoxon (activation rate constant kact = 0.014 h−1) was responsible for the high sensitivity of D. magna toward diazinon. Recovery of AChE activity from inhibition was slow and rate-determining (99% recovery within 16 d), compared with a fast elimination of diazinon (99% elimination within 17 h). The obtained model parameters were compared with toxicokinetic and toxicodynamic parameters of Gammarus pulex exposed to diazinon from previous work. This comparison revealed that G. pulex is less sensitive because of a six times faster detoxification of diazinon and diazoxon and an approximately 400 times lower rate for damage accrual. These differences overcompensate the two times faster activation of diazinon to diazoxon in G. pulex compared to D. magna. The present study substantiates theoretical considerations that mechanistically based effect models are helpful to explain sensitivity differences among different aquatic invertebrates.
27. Nyman, A.-M.; Schirmer, K.; Ashauer, R. (2012). Toxicokinetic-toxicodynamic modelling of survival of Gammarus pulex in multiple pulse exposures to propiconazole: model assumptions, calibration data requirements and predictive power. Ecotoxicology, 21(7), p. 1828-1840. (link to paper, free access)
Toxicokinetic-toxicodynamic (TKTD) models quantify the time-course of internal concentration, which is defined by uptake, elimination and biotransformation (TK), and the processes which lead to the toxic effects (TD). TKTD models show potential in predicting pesticide effects in fluctuating concentrations, but the data requirements and validity of underlying model assumptions are not known. We calibrated TKTD models to predict survival of Gammarus pulex in propiconazole exposure and investigated the data requirements. In order to assess the need of TK in survival models, we included or excluded simulated internal concentrations based on pre-calibrated TK. Adding TK did not improve goodness of fits. Moreover, different types of calibration data could be used to model survival, which might affect model parameterization. We used two types of data for calibration: acute toxicity (standard LC50, 4 d) or pulsed toxicity data (total length 10 d). The calibration data set influenced how well the survival in the other exposure scenario was predicted (acute to pulsed scenario or vice versa). We also tested two contrasting assumptions in ecotoxicology: stochastic death and individual tolerance distribution. Neither assumption fitted to data better than the other. We observed in 10-d toxicity experiments that pulsed treatments killed more organisms than treatments with constant concentration. All treatments received the same dose, i.e. the time-weighted average concentration was equal. We studied mode of toxic action of propiconazole and it likely acts as a baseline toxicant in G. pulex during 10-days of exposure for the endpoint survival.
26. Stadnicka, J; Schirmer, K; Ashauer, R (2012). Predicting Concentrations of Organic Chemicals in Fish by Using Toxicokinetic Models. Environ. Sci. Technol. 46 (6), 3273-3280. (link to journal) or download here (free access)
Quantification of chemical toxicity continues to be generally based on measured external concentrations. Yet, internal chemical concentrations have been suggested to be a more suitable parameter. To better understand the relationship between the external and internal concentrations of chemicals in fish, and to quantify internal concentrations, we compared three toxicokinetic (TK) models with each other and with literature data of measured concentrations of 39 chemicals. Two one-compartment models, together with the physiologically based toxicokinetic (PBTK) model, in which we improved the treatment of lipids, were used to predict concentrations of organic chemicals in two fish species: rainbow trout (Oncorhynchus mykiss) and fathead minnow (Pimephales promelas). All models predicted the measured internal concentrations in fish within 1 order of magnitude for at least 68% of the chemicals. Furthermore, the PBTK model outperformed the one-compartment models with respect to simulating chemical concentrations in the whole body (at least 88% of internal concentrations were predicted within 1 order of magnitude using the PBTK model). All the models can be used to predict concentrations in different fish species without additional experiments. However, further development of TK models is required for polar, ionizable, and easily biotransformed compounds.
25. Ashauer, R; Hintermeister, A; O'Connor, I; Elumelu, M; Hollender, J; Escher, BI (2012). Significance of Xenobiotic Metabolism for Bioaccumulation Kinetics of Organic Chemicals in Gammarus pulex. Environ. Sci. Technol. 46 (6), 3498-3508. (link to journal) or download here (free access)
Bioaccumulation and biotransformation are key toxicokinetic processes that modify toxicity of chemicals and sensitivity of organisms. Bioaccumulation kinetics vary greatly among organisms and chemicals; thus, we investigated the influence of biotransformation kinetics on bioaccumulation in a model aquatic invertebrate using fifteen 14C-labeled organic xenobiotics from diverse chemical classes and physicochemical properties (1,2,3-trichlorobenzene, imidacloprid, 4,6-dinitro-o-cresol, ethylacrylate, malathion, chlorpyrifos, aldicarb, carbofuran, carbaryl, 2,4-dichlorophenol, 2,4,5-trichlorophenol, pentachlorophenol, 4-nitrobenzyl-chloride, 2,4-dichloroaniline, and sea-nine (4,5-dichloro-2-octyl-3-isothiazolone)). We detected and identified metabolites using HPLC with UV and radio-detection as well as high resolution mass spectrometry (LTQ-Orbitrap). Kinetics of uptake, biotransformation, and elimination of parent compounds and metabolites were modeled with a first-order one-compartment model. Bioaccumulation factors were calculated for parent compounds and metabolite enrichment factors for metabolites. Out of 19 detected metabolites, we identified seven by standards or accurate mass measurements and two via pathway analysis and analogies to other compounds. 1,2,3-Trichlorobenzene, imidacloprid, and 4,6-dinitro-o-cresol were not biotransformed. Dietary uptake contributed little to overall uptake. Differentiation between parent and metabolites increased accuracy of bioaccumulation parameters compared to total 14C measurements. Biotransformation dominated toxicokinetics and strongly affected internal concentrations of parent compounds and metabolites. Many metabolites reached higher internal concentrations than their parents, characterized by large metabolite enrichment factors.
24. Meredith‐Williams M, Carter LJ, Fussell R, Raffaelli D, Ashauer R, Boxall ABA (2012): Uptake and depuration of pharmaceuticals in aquatic invertebrates. Environmental Pollution, 165, 250-258. (link at journal)
The uptake and depuration of a range of pharmaceuticals in the freshwater shrimp (Gammarus pulex) and the water boatman (Notonecta glauca) was studied. For one compound, studies were also done using the freshwater snail Planobarius corneus. In G. pulex, bioconcentration factors (BCFs) ranged from 4.6 to 185,900 and increased in the order moclobemide < 5-fluoruracil < carbamazepine < diazepam < carvedilol < fluoxetine. In N. glauca BCFs ranged from 0.1 to 1.6 and increased in the order 5-fluorouracil < carbamazepine < moclobemide < diazepam < fluoxetine < carvedilol. For P. corneus, the BCF for carvedilol was 57.3. The differences in degree of uptake across the three organisms may be due to differences in mode of respiration, behaviour and the pH of the test system. BCFs of the pharmaceuticals for each organism were correlated to the pH-corrected liposome–water partition coefficient of the pharmaceuticals.
23. 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) or (download below).
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.
22. Ashauer R, Agatz A, Albert C, Ducrot V, Galic N, Hendriks AJ, Jager T, Kretschmann A, O’Connor I, Rubach MN, Nyman A-M, Schmitt W, Stadnicka J, van den Brink PJ, Preuss TG (2011): Toxicokinetic-toxicodynamic modelling of quantal and graded sub-lethal endpoints - a brief discussion of concepts. Environmental Toxicology and Chemistry, 30, (11), 2519-2524. (link at journal)
We report on the advantages and problems of using toxicokinetic-toxicodynamic (TKTD) models for the analysis, understanding, and simulation of sublethal effects. Only a few toxicodynamic approaches for sublethal effects are available. These differ in their effect mechanism and emphasis on linkages between endpoints. We discuss how the distinction between quantal and graded endpoints and the type of linkage between endpoints can guide model design and selection. Strengths and limitations of two main approaches and possible ways forward are outlined.
21. Albert C, Ashauer R, Künsch HR, Reichert P (2012): Bayesian Experimental Design for a Toxicokinetic-Toxicodynamic Model.
Journal of Statistical Planning and Inference, 142, 263-275. (link at journal)
The aim of this study is to apply the Bayesian method of identifying optimal experimental designs to a toxicokinetic–toxicodynamic model that describes the response of aquatic organisms to time dependent concentrations of toxicants. As for experimental designs, we restrict ourselves to pulses and constant concentrations. A design of an experiment is called optimal within this set of designs if it maximizes the expected gain of knowledge about the parameters. Focus is on parameters that are associated with the auxiliary damage variable of the model that can only be inferred indirectly from survival time series data. Gain of knowledge through an experiment is quantified both with the ratio of posterior to prior variances of individual parameters and with the entropy of the posterior distribution relative to the prior on the whole parameter space. The numerical methods developed to calculate expected gain of knowledge are expected to be useful beyond this case study, in particular for multinomially distributed data such as survival time series data.
20. Kretschmann A, Ashauer R, Hitzfeld K, Spaak P, Hollender J, Escher BI (2011): Mechanistic Toxicodynamic Model for Receptor-Mediated Toxicity of Diazoxon, the Active Metabolite of Diazinon, in Daphnia magna. Environmental Science and Technology, 45, (11), 4980-4987. (link at journal)
The organothiophosphate diazinon inhibits the target site acetylcholinesterase only after activation to its metabolite diazoxon. Commonly, the toxicity of xenobiotics toward aquatic organisms is expressed as a function of the external concentration and the resulting effect on the individual level after fixed exposure times. This approach does not account for the time dependency of internal processes such as uptake, metabolism, and interaction of the toxicant with the target site. Here, we develop a mechanistic toxicodynamic model for Daphnia magna and diazoxon, which accounts for the inhibition of the internal target site acetylcholinesterase and its link to the observable effect, immobilization, and mortality. The model was parametrized by experiments performed in vitro with the active metabolite diazoxon on enzyme extracts and in vivo with the parent compound diazinon. The mechanism of acetylcholinesterase inhibition was shown to occur irreversibly in two steps via formation of a reversible enzyme-inhibitor complex. The corresponding kinetic parameters revealed a very high sensitivity of acetylcholinesterase from D. magna toward diazoxon, which corresponds well with the high toxicity of diazinon toward this species. Recovery of enzyme activity but no recovery from immobilization was observed after in vivo exposure to diazinon. The toxicodynamic model combining all in vitro and in vivo parameters was successfully applied to describe the time course of immobilization in dependence of acetylcholinesterase activity during exposure to diazinon. The threshold value for enzyme activity below which immobilization set in amounted to 40% of the control activity. Furthermore, the model enabled the prediction of the time-dependent diazoxon concentration directly present at the target site.
19. Kretschmann A, Ashauer R, Preuss TG, Spaak P, Escher BI, Hollender J (2011): Toxicokinetic Model Describing Bioconcentration and Biotransformation of Diazinon in Daphnia magna. Environmental Science and Technology, 45, (11), 4995-5002. (link at journal)
A toxicokinetic model for Daphnia magna, which simulates the internal concentration of the insecticide diazinon, its detoxification product 2-isopropyl-6-methyl-4-pyrimidinol, and its active metabolite diazoxon, is presented. During in vivo exposure to diazinon with and without inhibition of cytochrome P450 by piperonyl butoxide, the parent compound as well as its metabolites were quantified with high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) in extracts of D. magna. Rate constants of all relevant toxicokinetic steps were obtained by modeling the time course of the internal concentrations with a multicomponent first-order kinetics model. When cytochrome P450 was inhibited, the kinetic bioconcentration factor (BCF) of diazinon increased from 17.8 to 51.0 mL·gww-1. This clearly indicates that diazinon is biotransformed to a high degree by cytochrome P450 in D. magna. The dominant elimination step of diazinon was shown to be its oxidative dearylation to pyrimidinol (62% of total elimination) with a corresponding rate constant of 0.16 h-1. In contrast, oxidative activation to diazoxon with a rate constant of 0.02 h-1 amounted to only 8% of the total elimination. During exposure to diazinon, the active metabolite diazoxon could be detected only in very low concentrations (approximately 0.5% of the parent compound), presumably due to a very fast reaction with the target site acetylcholinesterase. During the exposure experiments (no feeding of daphnids), an exponential decline of the lipid content in D. magna with a first-order rate constant of 0.013 h-1 was observed. For short exposure times (≥24 h), this had only a minor influence on the determined TK parameters. Such a TK model containing detailed biotransformation processes is an important tool for estimation of the toxic potential of chemicals, particularly, when active metabolites are formed inside an organism.
18. Ashauer R, Hintermeister A, Potthoff E, Escher BI (2011): Acute toxicity of organic chemicals to Gammarus pulex correlates with sensitivity of Daphnia magna across most modes of action. Aquatic Toxicology, 103:38-45. (link at journal)
We investigated the sensitivity of the freshwater crustacean amphipod Gammarus pulex towards organic xenobiotic compounds in comparison to the sensitivity of the crustacean cladoceran Daphnia magna. In addition we studied the influence of the chemical's mode of action on the relationship between the sensitivity of G. pulex and that of D. magna. We tested the acute toxicity of twelve compounds (Malathion, Aldicarb, Carbofuran, 2,4-dichloroaniline, 2,4-dichlorophenol, 1,2,3-trichlorobenzene, 4,6-dinitro-o-cresol, 2,4,5-trichlorophenol, Ethylacrylate, 4-nitrobenzyl-chloride, Sea-nine, Imidacloprid) with different modes of action and physicochemical properties towards the freshwater amphipod G. pulex in laboratory experiments. Additional toxicity data was collected from the peer-reviewed literature and databases (data pairs for 44 chemicals in total). The chemicals were assigned to seven mode of action groups. The relationship between the sensitivity of G. pulex (48. h-LC50s and 96. h-LC50s) and that of D. magna (48. h-EC50s) was investigated using regression analysis and correlation plots. G. pulex is two to three orders of magnitude more sensitive towards neonicotinoids than D. magna (P= 0.0046, n= 3). For organophosphates we found that D. magna is more sensitive than G. pulex by approximately a factor of six (P= 0.0256, n= 6). There was no significant difference between the sensitivity of D. magna and that of G. pulex in any of the other mode of action groups; however chemicals with the same mode of action grouped together in the same area of the correlation plot. Without the neonicotinoids 75% of all G. pulex toxicity data were within one order of magnitude of the D. magna data and 100% within two orders of magnitude. The regressions with all data and with all data minus neonicotinoids were both significant linear relationships with slopes around one and intercept around zero. Thus, G. pulex is generally equally sensitive towards organic xenobiotics as D. magna.
17. Rubach MN, Ashauer R, Buchwalter DB, de Lange HJ, Hamer M, Preuss TG, Töpke K, Maund SJ. (2011). Framework for traits-based assessment in ecotoxicology. Integrated environmental assessment and management, 7(2):172-186. (link at journal)
A key challenge in ecotoxicology is to assess the potential risks of chemicals to the wide range of species in the environment on the basis of laboratory toxicity data derived from a limited number of species. These species are then assumed to be suitable surrogates for a wider class of related taxa. For example, Daphnia spp. are used as the indicator species for freshwater aquatic invertebrates. Extrapolation from these datasets to natural communities poses a challenge because the extent to which test species are representative of their various taxonomic groups is often largely unknown, and different taxonomic groups and chemicals are variously represented in the available datasets. Moreover, it has been recognized that physiological and ecological factors can each be powerful determinants of vulnerability to chemical stress, thus differentially influencing toxicant effects at the population and community level. Recently it was proposed that detailed study of species traits might eventually permit better understanding, and thus prediction, of the potential for adverse effects of chemicals to a wider range of organisms than those amenable for study in the laboratory. This line of inquiry stems in part from the ecology literature, in which species traits are being used for improved understanding of how communities are constructed, as well as how communities might respond to, and recover from, disturbance (see other articles in this issue). In the present work, we develop a framework for the application of traits-based assessment. The framework is based on the population vulnerability conceptual model of Van Straalen in which vulnerability is determined by traits that can be grouped into 3 major categories, i.e., external exposure, intrinsic sensitivity, and population sustainability. Within each of these major categories, we evaluate specific traits as well as how they could contribute to the assessment of the potential effects of a toxicant on an organism. We then develop an example considering bioavailability to explore how traits could be used mechanistically to estimate vulnerability. A preliminary inventory of traits for use in ecotoxicology is included; this also identifies the availability of data to quantify those traits, in addition to an indication of the strength of linkage between the trait and the affected process. Finally, we propose a way forward for the further development of traits-based approaches in ecotoxicology.
16. Jager T, Albert C, Preuss TG, Ashauer R (2011): General unified threshold model of survival - a toxicokinetic-toxicodynamic framework for ecotoxicology. Environmental Science and Technology, 45(7), 2529-2540. (link at journal)
Toxicokinetic-toxicodynamic models (TKTD models) simulate the time-course of processes leading to toxic effects on organisms. Even for an apparently simple endpoint as survival, a large number of very different TKTD approaches exist. These differ in their underlying hypotheses and assumptions, although often the assumptions are not explicitly stated. Thus, our first objective was to illuminate the underlying assumptions (individual tolerance or stochastic death, speed of toxicodynamic damage recovery, threshold distribution) of various existing modeling approaches for survival and show how they relate to each other (e.g., critical body residue, critical target occupation, damage assessment, DEBtox survival, threshold damage). Our second objective was to develop a general unified threshold model for survival (GUTS), from which a large range of existing models can be derived as special cases. Specific assumptions to arrive at these special cases are made and explained. Finally, we illustrate how special cases of GUTS can be fitted to survival data. We envision that GUTS will help increase the application of TKTD models in ecotoxicological research as well as environmental risk assessment of chemicals. It unifies a wide range of previously unrelated approaches, clarifies their underlying assumptions, and facilitates further improvement in the modeling of survival under chemical stress.
--> Also Tjalling has produced a Matlab version (link)
15. Ashauer R, Escher BI (2010): Advantages of toxicokinetic and toxicodynamic modelling in aquatic ecotoxicology and risk assessment. Journal of Environmental Monitoring, 12:2056 - 2061. (link at journal)
Toxicokinetic-toxicodynamic (TK-TD) models simulate the processes that lead to toxicity at the level of organisms over time. These dynamic simulation models quantify toxicity, but more importantly they also provide a conceptual framework to better understand the causes for variability in different species' sensitivity to the same compound as well as causes for different toxicity of different compounds to the same species. Thus TK-TD models bring advantages for very diverse ecotoxicological questions as they can address two major challenges: the large number of species that are potentially affected and the large number of chemicals of concern. The first important benefit of TK-TD models is the role that they can play to formalize established knowledge about toxicity of compounds, sensitivity of organisms, organism recovery times and carry-over toxicity. The second important aspect of TK-TD models is their ability to simulate temporal aspects of toxicity which makes them excellent extrapolation tools for risk assessment of fluctuating or pulsed exposures to pollutants. We provide a general introduction to the concept of TK-TD modelling for environmental scientists and discuss opportunities as well as current limitations.
14. Escher BI, Ashauer R, Dyer S, Hermens JLM, Lee J-H, Leslie HA, Mayer P, Meador JP, Warne MSJ (2010): Crucial role of mechanisms and modes of toxic action for understanding tissue residue toxicity and internal effect concentrations of organic chemicals. Integrated Environmental Assessment and Management, 7:28-49. (link at journal)
This article reviews the mechanistic basis of the tissue residue approach for toxicity assessment (TRA). The tissue residue approach implies that whole-body or organ concentrations (residues) are a better dose metric for describing toxicity to aquatic organisms than is the aqueous concentration typically used in the external medium. Although the benefit of internal concentrations as dose metrics in ecotoxicology has long been recognized, the application of the tissue residue approach remains limited. The main factor responsible for this is the difficulty of measuring internal concentrations. We propose that environmental toxicology can advance if mechanistic considerations are implemented and toxicokinetics and toxicodynamics are explicitly addressed. The variability in ecotoxicological outcomes and species sensitivity is due in part to differences in toxicokinetics, which consist of several processes, including absorption, distribution, metabolism, and excretion (ADME), that influence internal concentrations. Using internal concentrations or tissue residues as the dose metric substantially reduces the variability in toxicity metrics among species and individuals exposed under varying conditions. Total internal concentrations are useful as dose metrics only if they represent a surrogate of the biologically effective dose, the concentration or dose at the target site. If there is no direct proportionality, we advise the implementation of comprehensive toxicokinetic models that include deriving the target dose. Depending on the mechanism of toxicity, the concentration at the target site may or may not be a sufficient descriptor of toxicity. The steady-state concentration of a baseline toxicant associated with the biological membrane is a good descriptor of the toxicodynamics of baseline toxicity. When assessing specific-acting and reactive mechanisms, additional parameters (e.g., reaction rate with the target site and regeneration of the target site) are needed for characterization. For specifically acting compounds, intrinsic potency depends on 1) affinity for, and 2) type of interaction with, a receptor or a target enzyme. These 2 parameters determine the selectivity for the toxic mechanism and the sensitivity, respectively. Implementation of mechanistic information in toxicokinetic-toxicodynamic (TK-TD) models may help explain timedelayed effects, toxicity after pulsed or fluctuating exposure, carryover toxicity after sequential pulses, and mixture toxicity.We believe that this mechanistic understanding of tissue residue toxicity will lead to improved environmental risk assessment.
13. Rubach MR, Ashauer R, Maund SJ, Baird DJ, van den Brink PJ (2010): Toxicokinetic variation in 15 freshwater arthropod species exposed to the insecticide chlorpyrifos. Environmental Toxicology and Chemistry, 29(10):2225-2234. (link at journal)
Recent advances in modeling the processes of the toxicity of chemicals-toxicokinetics (TK) and toxicodynamics (TD)-are improving environmental risk assessment (ERA) through prediction of effects from time-varying exposure. This has been achieved by linking chemical fate and toxicological effects mechanistically, based on internal concentrations, through the tissue residue approach. However, certain questions remain: for example, how do TK and TD differ among species and how does this relate to differences in species sensitivity? In a series of experiments, we studied the TK of [14C]chlorpyrifos in 15 freshwater arthropod species, two of which were studied in juvenile and adult life stages. Uptake (kin) and elimination (kout) rate constants were fitted using a one-compartment single first-order kinetic model. The application of two complementary parameter estimation methods facilitated the calculation of bioconcentration factors (BCF) with prediction intervals and 95% depuration times (t95) for all tested species. Extremely slow elimination was observed in some species as well as high overall variation in kin, kout, BCF, and t95 across the tested aquatic arthropod species. This variation has implications for the development of TKTD approaches in ERA, including assessing fluctuating exposure concentrations and the interpretation of observed toxicity responses in the laboratory and in the field.
12. Ashauer R, Caravatti I, Hintermeister A & Escher BI (2010): Bioaccumulation kinetics of organic xenobiotic pollutants in the freshwater invertebrate Gammarus pulex modelled with prediction intervals. Environmental Toxicology and Chemistry, 29(7):1625-1636. (link at journal)
Uptake and elimination rate constants, bioaccumulation factors, and elimination times in the freshwater arthropod Gammarus pulex were measured for 14 organic micropollutants covering a wide range of hydrophobicity (imidacloprid, aldicarb, ethylacrylate, 4,6-dinitro-o-cresol, carbofuran, malathion, 4-nitrobenzyl-chloride, 2,4-dichloroaniline, Sea-Nine, 2,4-dichlorophenol, diazinon, 2,4,5-trichlorophenol, 1,2,3-trichlorobenzene, and hexachlorobenzene; all 14C-labeled). The toxicokinetic parameters were determined by least-square fitting of a one-compartment first-order toxicokinetic model, followed by Markov Chain Monte Carlo parameter estimation. The parameter estimation methods used here account for decreasing aqueous concentrations during the exposure phase or increasing aqueous concentrations during the elimination phase of bioaccumulation experiments. It is not necessary to keep exposure concentrations constant or zero during uptake and elimination, respectively. Neither is it required to achieve steady state during the exposure phase; hence, tests can be shorter. Prediction intervals, which take the between-parameter correlation into account, were calculated for bioaccumulation factors and simulations of internal concentrations under variable exposure. The lipid content of Gammarus pulex was 1.3% of wet weight, consisting of 25% phospholipids and 75% triglycerides. Size-dependent bioaccumulation was observed for eight compounds, although the magnitudes of the relationships were too small to be of practical relevance. Elimination times ranged from 0.45 to 20 d, and bioaccumulation factors ranged from 1.7 to 4,449 L/kg. The identified compounds with unexpectedly long elimination times should be given priority in future studies investigating the biotransformation of these compounds.
11. Ashauer R, Hintermeister A, Caravatti I, Kretschmann A, Escher BI (2010): Toxicokinetic-toxicodynamic modeling explains carry-over toxicity from exposure to diazinon by slow organism recovery. Environmental Science and Technology, 44(10):3963-3971. (link at journal)
Carry-over toxicity occurs when organisms exposed to an environmental toxicant survive but carry some damage resulting in reduced fitness. Upon subsequently encountering another exposure event stronger effects are possible if the organisms have not yet fully recovered. Carry-over toxicity was observed after exposure of the freshwater amphipod Gammarus pulex to repeated pulses of diazinon with varying intervals. Uptake, biotransformation and depuration kinetics were determined. Metabolites were identified and quantified (diazoxon, 2-isopropyl-6-methyl-4-pyrimidinol, one nonidentified metabolite). Parameters of a process-based toxicokinetic-toxicodynamic model were determined by least-squares fitting followed by Markov Chain Monte Carlo parameter estimation. Model parametrization was based on the time-course of measured internal concentrations of diazinon and its metabolite diazoxon in combination with the pulsed toxicity experiment. Prediction intervals, which take the covariation between parameters into account, were calculated for bioaccumulation factors, organism recovery time and simulations of internal concentrations as well as the time-course of survival under variable exposure. Organism recovery time was 28 days (95% prediction interval 25−31 days), indicating the possibility for carry-over toxicity from exposure events several weeks apart. The slow organism recovery and carry-over toxicity was caused by slow toxicodynamic recovery; toxicokinetic processes alone would have resulted in a recovery time of only 1−2 days.
10. Ashauer R (2010): Toxicokinetic-toxicodynamic modelling in an individual based context - consequences of parameter variability. Ecological Modelling, 221(9): 1325-1328. (link at journal)
Toxicokinetic–toxicodynamic (TKTD) models simulate the time-course of toxicant concentration in the organism and toxicity at the level of the organism. A link between TKTD models that simulate survival and individual based models for populations (IBMs) is proposed which allows TKTD parameters to vary between individuals. The TKTD-IBM predicts different survival in response to toxicants when TKTD parameters vary amongst individuals compared to the survival predicted with fixed TKTD parameters. The model with fixed parameters represents the concept of stochastic death whereas the model with variable parameters behaves, at least partly, according to the individual tolerance distribution concept. The whole set of TKTD parameters of an individual can be interpreted as constituting “individual tolerance”.
9. Grimm G, Ashauer R, Forbes V, Hommen U, Preuss TG, Schmidt A, van den Brink PJ, Wogram J, Thorbek P (2009): CREAM: a European project on mechanistic effect models for ecological risk assessment of chemicals. Environmental Science and Pollution Research 16(6): 614-617. (link at journal)
8. Preuss TG, Hommen U, Alix A, Ashauer R, van den Brink P, Chapman P, Ducrot V, Forbes V, Grimm V, Schäfer D, Streissl, Thorbeck P (2009): Mechanistic effect models for ecological risk assessment of chemicals (MEMoRisk) - a new SETAC-Europe Advisory Group. Environmental Science and Pollution Research 16(3): 250-252. (link at journal)
7. Blackwell PA, Kay P, Ashauer R, Boxall ABA (2009): Effects of agricultural conditions on the leaching behaviour of veterinary antibiotics in soil. Chemosphere 75: 13-19. (link at journal)
Antibiotics may be released to soils during the application of manure as fertiliser to land. The compounds may subsequently be transported to and contaminate groundwater and surface waters. This paper describes a series of lysimeter-based studies to explore the leaching behaviour of three veterinary antibiotics (sulfachloropyridazine, oxytetracycline and tylosin) under different conditions that could occur in the agricultural environment. The specific objectives were to: (1) explore the influence of slurry amendment and incorporation on leaching; (2) assess the effects of climate on leaching behaviour; and (3) evaluate the predictive capability of a leaching model used in the regulatory assessment of veterinary medicines. Sulfachloropyridazine was detected sporadically in leachate at concentrations up to 0.66 μg L−1 under typical irrigation conditions and more frequently at concentrations up to 8.5 μg L−1 under extreme irrigation conditions. Incorporation and timing of rainfall had no effect on leaching behaviour. Oxytetracycline and tylosin were not detected in any leachate samples. These differences in behaviour were explained by the sorption and persistence characteristics of the compounds. Comparison of the experimental measurements with simulations from the leaching model indicated that the model greatly underestimates the transport of antibiotics to groundwater which raises questions over the application of these models in the regulatory risk assessment process.
6. Ashauer R, Brown CD (2008): Toxicodynamic assumptions in ecotoxicological hazard models. Environmental Toxicology and Chemistry 27(8): 1817-1821. (link at journal)
Existing toxicokinetic and toxicodynamic models and dynamic formulations of popular ecotoxicological concepts (e.g., the critical body residue concept) are examined. Their underlying assumptions about speed of recovery and thresholds are clarified, and a rigorous mathematical treatment shows that they can all be placed within a unifying framework. Such analysis aids in the selection of appropriate ecotoxicological models.
5. Ashauer R, Boxall ABA, Brown CD (2007c): Modelling combined effects of pulsed exposure to carbaryl and chlorpyrifos on Gammarus pulex. Environmental Science and Technology 41(15): 5535-5541. (link at journal)
Aquatic risk assessment can be improved if we are able to quantitatively predict the effects resulting from sequential pulsed exposure to multiple compounds. We evaluate two modeling approaches, both extended to suit multiple compounds, the semi-mechanistic threshold damage model (TDM), and a model based on time-weighted averages (TWA). The TDM predicts that recovery of damage to Gammarus pulex from exposure to chlorpyrifos takes longer than that from exposure to carbaryl and consequently that the sequence of exposure matters. We measured survival of the freshwater invertebrate Gammarus pulex after sequential pulsed exposure to carbaryl and chlorpyrifos. Two groups of organisms were exposed to a first pulse of either carbaryl or chlorpyrifos for 1 day and then, after a recovery period of two weeks, to a second pulse with the other compound. The comparison of mortalities caused by each pulse, as well as combined mortalities in both treatments, show that the sequence of exposure to pulses of contaminants does indeed matter. Previous exposure to chlorpyrifos leads to significantly increased mortality from subsequent pulses of carbaryl, but not the other way round.The TDM facilitates a process-based ecotoxicological explanation by simulating the recovery dynamics and outperforms the TWA model.
4. Ashauer R, Boxall ABA, Brown CD (2007b): Simulating toxicity of carbaryl to Gammarus pulex after sequential pulsed exposure. Environmental Science and Technology 41(15): 5528-5534. (link at journal)
Aquatic nontarget organisms are typically exposed to sequential pulses of contaminants with fluctuating concentrations. We use the semimechanistic threshold damage model (TDM) to simulate survival of the aquatic invertebrate Gammarus pulex after sequential pulsed exposure to carbaryl and compare it to a simpler model based on time-weighted averages (TWA). The TDM is a process-based model and we demonstrate how to parametrize it with data from an uptake and elimination experiment together with data from a survival experiment with sequential pulses. The performance of the two models is compared by the fit to the first survival experiment and the simulation of another, independent survival experiment with different exposure patterns. Measured internal concentrations in the first survival experiment are used to evaluate the toxicokinetic submodel of the TDM. The TDM outperforms the TWA model, facilitates understanding of the underlying ecotoxicological processes, permits calculation of recovery times (3, 15, and 25 days for pentachlorophenol, carbaryl and chlorpyrifos respectively) and enables us to predict the effects of long-term exposure patterns with sequential pulses or fluctuating concentrations. We compare the parameters of the TDM for carbaryl, pentachlorophenol and chlorpyrifos and discuss implications for ecotoxicology and risk assessment.
3. Ashauer R, Boxall ABA, Brown CD (2007a): New ecotoxicological model to simulate survival of aquatic invertebrates after exposure to fluctuating and sequential pulses of pesticides. Environmental Science and Technology 41(4): 1480-1486. (link at journal)
Aquatic nontarget organisms are exposed to fluctuating concentrations or sequential pulses of contaminants, so we need to predict effects resulting from such patterns of exposure. We present a process-based model, the Threshold Damage Model (TDM), that links exposure with effects and demonstrate how to simulate the survival of the aquatic invertebrate Gammarus pulex, Based on survival experiments of up to 28 days duration with three patterns of repeated exposure pulses and fluctuating concentrations of two pesticides with contrasting modes of action (pentachlorophenol and chlorpyrifos) we evaluate the new model and compare it to two approaches based on time-weighted averages. Two models, the Threshold Damage Model and the time-weighted averages fitted to pulses, are able to simulate the observed survival (mean errors 15% or less, r2 between 0.77 and 0.96). The models are discussed with respect to their theoretical base, data needs, and potential for extrapolation to different scenarios. The Threshold Damage Model is particularly useful because its parameters can be used to calculate recovery times, toxicokinetics are separated from toxicodynamics, and parameter values reflect the mode of action.
2. Ashauer R, Boxall ABA, Brown CD (2006): Uptake and elimination of chlorpyrifos and pentachlorophenol into the freshwater amphipod Gammarus pulex. Archives of Environmental Contamination and Toxicology. 51: 542-548. (link at journal)
Uptake and elimination rates were determined for chlorpyrifos (CPF) and pentachlorophenol (PCP) in the freshwater amphipod Gammarus pulex. Internal concentrations of the two pesticides were measured over a three-day exposure phase and a subsequent three-day elimination phase. Rate constants were obtained by fitting measured internal concentrations to a one-compartment single first-order model. The uptake rate constants were 747 ± 61 [L kg−1 day−1] for CPF and 89 ± 7 [L kg−1 day−1] for PCP. The elimination rate constants were 0.45 ± 0.05 [day−1] for CPF and 1.76 ± 0.14 [day−1] for PCP. The resulting bioconcentration factors at steady state were 1660 and 51 for CPF and PCP, respectively. The parameter estimation method and possible variability due to varying lipid content are briefly discussed.
1. Ashauer R, Boxall ABA, Brown CD (2006): Predicting effects on aquatic organisms from fluctuating or pulsed exposure to pesticides. Environmental Toxicology and Chemistry 25(7): 1899-1912. (link at journal)
Exposure of aquatic nontarget organisms to pesticides almost always occurs as pulses or fluctuating concentrations. Extrapolation from laboratory to field thus depends on an understanding and ability to simulate effects resulting from these types of exposure. This paper reviews models that may be used to predict effects on aquatic organisms resulting from time-varying exposure to pesticides. We evaluate and compare the theoretical basis of these models and their applicability to the simulation of effects from fluctuating exposures. The many different models rest on only a few basic concepts with differing degrees of mechanistic character. Building on this critical review, we select the most appropriate models and propose modifications. Two process-based models, the threshold hazard model and the modified damage assessment model, represent the optimum descriptions that are available at present. They could facilitate a better understanding of the ecotoxicity of different compound and species combinations and even mixtures of noninteracting compounds. The possibility to model lethal and sublethal effects allows applications in risk assessment, standard setting, and ecological modeling.