Evaluation of models to estimate the bioaccumulation of organic chemicals in earthworms.

Modelling approaches to estimate the bioaccumulation of organic chemicals by earthworms are important for improving the realism in risk assessment of chemicals. However, the applicability of existing models is uncertain, partly due to the lack of independent datasets to test them. This study therefore conducted a comprehensive literature review on existing empirical and kinetic models that estimate the bioaccumulation of organic chemicals in earthworms and gathered two independent datasets from published literature to evaluate the predictive performance of these models. The Belfroid et al. (1995a) model is the best-performing empirical model, with 91.2% of earthworm body residue simulations within an order of magnitude of observation. However, this model is limited to the more hydrophobic pesticides and to the earthworm species Eisenia fetida or Eisenia andrei. The kinetic model proposed by Jager et al. (2003b) which out-performs that of Armitage and Gobas (2007), predicted uptake of PCB 153 in the earthworm E. andrei to within a factor of 10. However, the applicability of Jager et al.'s model to other organic compounds and other earthworm species is unknown due to the limited evaluation dataset. The model needs to be parameterised for different chemical, soil, and species types prior to use, which restricts its applicability to risk assessment on a broad scale. Both the empirical and kinetic models leave room for improvement in their ability to reliably predict bioaccumulation in earthworms. Whether they are fit for purpose in environmental risk assessment needs careful consideration on a case by case basis.

Three perspectives on the prediction of chemical effects in ecosystems.

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism- and organism-level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro- and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco-evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real-world eco-evolutionary view.

Modelling the effects of the pyrethroid insecticide cypermethrin on the life cycle of the soil dwelling annelid Enchytraeus crypticus, an original experimental design to calibrate a DEB-TKTD model.

The Dynamic Energy Budget theory (DEB) enables ecotoxicologists to model the effects of chemical stressors on organism life cycles through the coupling of toxicokinetic-toxicodynamic (TK-TD) models. While good progress has been made in the application of DEB-TKTD models for aquatic organisms, applications for soil fauna are scarce, due to the lack of dedicated experimental designs suitable for collecting the required time series effect data. Enchytraeids (Annelida: Clitellata) are model organisms in soil ecology and ecotoxicology. They are recognised as indicators of biological activity in soil, and chemical stress in terrestrial ecosystems. Despite this, the application of DEB-TKTD models to investigate the impact of chemicals has not yet been tested on this family. Here we assessed the impact of the pyrethroid insecticide cypermethrin on the life cycle of Enchytraeus crypticus. We developed an original experimental design to collect the data required for the calibration of a DEB-TKTD model for this species. E. crypticus presented a slow initial growth phase that has been successfully simulated with the addition of a size-dependent food limitation for juveniles in the DEB model. The DEB-TKTD model simulations successfully agreed with the data for all endpoints and treatments over time. The highlighted physiological mode of action (pMoA) for cypermethrin was an increase of the growth energy cost. The threshold for effects on survival was estimated at 73.14 mg kg- 1, and the threshold for effects on energy budget (i.e., sublethal effects) at 19.21 mg kg- 1. This study demonstrates that DEB-TKTD models can be successfully applied to E. crypticus as a representative soil species, and may improve the ecological risk assessment for terrestrial ecosystems, and our mechanistic understanding of chemical effects on non-target species.

Characterization of patterns and variability in the dynamics of outdoor aquatic mesocosms: exploring the capabilities and challenges in data supporting aquatic system models.

Aquatic mesocosms are complex test systems used within regulatory risk assessment of plant protection products. These model ecosystems allow researchers to capture interactions of multiple species under realistic environmental conditions. They enable assessment of direct and indirect effects of stressors at all trophic levels (i.e., from primary producers to secondary consumers) and impacts on ecosystem functions. Due to the limited ability to test the multitude of potential exposure scenarios, cross-linking aquatic mesocosm studies with virtual mesocosms, i.e., aquatic system models (ASMs), can serve to meet the demand for more environmental realism and ecological relevance in risk assessment. In this study, full control data sets from seven aquatic mesocosm studies conducted at a single test facility under GLP were analysed graphically and using descriptive statistics. Thereby, not only a comprehensive data base but also an insight into the species present, their dynamics over time, and variability in unchallenged mesocosms was observed. While consistency in dynamics could be discerned for physical and chemical parameters, variability was evident for several biological endpoints. This variability points to amplification of small differences over time as well as to stochastic processes. The outline of existing gaps and uncertainties in data leads to the estimation of what can be expected to be captured and predicted by ASMs.

Interactive effects of multiple stressors vary with consumer interactions, stressor dynamics and magnitude.

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.

Modelling the effects of variability in feeding rate on growth - a vital step for DEB-TKTD modelling.

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.

Predicting Mixture Effects over Time with Toxicokinetic-Toxicodynamic Models (GUTS): Assumptions, Experimental Testing, and Predictive Power.

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.

A knowledge-based approach to designing control strategies for agricultural pests.

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.

Toxicokinetic-Toxicodynamic Modeling of the Effects of Pesticides on Growth of Rattus norvegicus.

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.

Computationally Efficient Implementation of a Novel Algorithm for the General Unified Threshold Model of Survival (GUTS).

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.

Toxic Mixtures in Time-The Sequence Makes the Poison.

"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.

Death Dilemma and Organism Recovery in Ecotoxicology.

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.

Importance of toxicokinetics for interspecies variation in sensitivity to chemicals.

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.

Minimised bioconcentration tests: a useful tool for assessing chemical uptake into terrestrial and aquatic invertebrates?

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.

Earthworm lipid content and size help account for differences in pesticide bioconcentration between species.

The uptake and elimination kinetics of pesticides from soil to earthworms are important in characterising the risk of pesticides to soil organisms and the risk from secondary poisoning. However, the understanding of the relative importance of chemical, soil, and species differences in determining pesticide bioconcentration into earthworms is limited. Furthermore, there is insufficient independent data in the literature to fully evaluate existing predictive bioconcentration models. We conducted kinetic uptake and elimination experiments for three contrasting earthworm species (Lumbricus terrestris, Aporrectodea caliginosa, Eisenia fetida) in five soils using a mixture of five pesticides (log Kow 1.69 - 6.63). Bioconcentration increased with pesticide hydrophobicity and decreased with soil organic matter. Bioconcentration factors were comparable between earthworm species for hydrophilic pesticides due to the similar water content of earthworm species. Inter-species variations in bioconcentration of hydrophobic pesticides were primarily accounted for by earthworm lipid content and specific surface area (SSA). Existing bioconcentration models either failed to perform well across earthworm species and for more hydrophilic compounds (log Kow < 2) or were not parameterised for a wide range of compounds and earthworm species. Refined models should incorporate earthworm properties (lipid content and SSA) to account for inter-species differences in pesticide uptake from soil.

Fate of synthetic chemicals in the agronomic insect pest Spodoptera littoralis: experimental feeding-contact assay and toxicokinetic model.

Insecticides prevent or reduce insect crop damage, maintaining crop quality and quantity. Physiological traits, such as an insect's feeding behavior, influence the way insecticides are absorbed and processed in the body (toxicokinetics), which can be exploited to improve species selectivity. To fully understand the uptake of insecticides, it is essential to study their total uptake and toxicokinetics independent of their toxic effects on insects. We studied the toxicokinetics (TK) of insecticidally inactive test compounds incorporating agro-like structural motifs in larvae of the Egyptian cotton leafworm (Spodoptera littoralis, Lepidoptera), and their distribution across all biological matrices, using laboratory experiments and modeling. We measured Spodoptera larval behavior and temporal changes of whole-body concentrations of test compounds during feeding on treated soybean leaf disks and throughout a subsequent depuration period. Differences in the distribution of the total quantities of compounds were found between the biological matrices leaf, larva, and feces. Rate constants for uptake and elimination of test compounds were derived by calibrating a toxicokinetic model to the whole-body concentrations. Uptake and elimination rate constants depended on the physicochemical properties of the test compounds. Increasing hydrophobicity increased the bioaccumulation potential of test compounds. Incomplete quantities in larval matrices indicated that some compounds may undergo biotransformation. As fecal excretion was a major elimination pathway, the variable time of release and number of feces pellets led to a high variability in the body burden. We provide quantitative models to predict the toxicokinetics and bioaccumulation potential of inactive insecticide analogs (parent compounds) in Spodoptera.

Comparative toxicokinetics of organic micropollutants in freshwater crustaceans.

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.

Bioconcentration of organic contaminants in Daphnia resting eggs.

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.

A Framework for Algae Modeling in Regulatory Risk Assessment.

The use of toxicokinetic-toxicodynamic (TKTD) modeling in regulatory risk assessment of plant protection products is increasingly popular, especially since the 2018 European Food Safety Authority (EFSA) opinion on TKTD modeling announced that several established models are ready for use in risk assessment. With careful adherence to the guidelines laid out by EFSA, we present a stepwise approach to validation and use of the Simple Algae Model Extended (SAM-X) for regulatory submission in Tier 2C. We demonstrate how the use of moving time windows across time-variable exposure profiles can generate thousands of virtual laboratory mimic simulations that seamlessly predict the effects of time-variable exposures across a full exposure profile while maintaining the laboratory conditions of the standard Organisation for Economic Co-operation and Development (OECD) growth inhibition test. Thus, every virtual laboratory test has a duration of 72 h, with OECD medium and constant light and temperature conditions. The only deviation from the standard test setup is the replacement of constant exposure conditions for time-variable concentrations. The present study demonstrates that for simulation of 72-h toxicity tests, the nutrient dynamics in the SAM-X model are not required, and we propose the alternative use of a simplified model version. For risk assessment, in accordance with the EFSA guidelines we use a median exposure profile of 10 as a threshold, meaning that if a time window within the exposure profile causes 50% growth inhibition when magnified by a factor of 10, the threshold will have been exceeded. We present a simplified example for chlorotoluron and isoproturon. The present case study brings to life our proposed framework for TKTD modeling of algae to establish whether a given exposure can be considered to be of low risk. Environ Toxicol Chem 2023;42:1823-1838. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.