A Toxicokinetic-Toxicodynamic Modeling Workflow Assessing the Quality of Input Mortality Data.

Toxicokinetic-toxicodynamic (TKTD) models simulate organismal uptake and elimination of a substance (TK) and its effects on the organism (TD). The Reduced General Unified Threshold model of Survival (GUTS-RED) is a TKTD modeling framework that is well established for aquatic risk assessment to simulate effects on survival. The TKTD models are applied in three steps: parameterization based on experimental data (calibration), comparing predictions with independent data (validation), and prediction of endpoints under environmental scenarios. Despite a clear understanding of the sensitivity of GUTS-RED predictions to the model parameters, the influence of the input data on the quality of GUTS-RED calibration and validation has not been systematically explored. We analyzed the performance of GUTS-RED calibration and validation based on a unique, comprehensive data set, covering different types of substances, exposure patterns, and aquatic animal species taxa that are regularly used for risk assessment of plant protection products. We developed a software code to automatically calibrate and validate GUTS-RED against survival measurements from 59 toxicity tests and to calculate selected model evaluation metrics. To assess whether specific survival data sets were better suited for calibration or validation, we applied a design in which all possible combinations of studies for the same species-substance combination are used for calibration and validation. We found that uncertainty of calibrated parameters was lower when the full range of effects (i.e., from high survival to high mortality) was covered by input data. Increasing the number of toxicity studies used for calibration further decreased parameter uncertainty. Including data from both acute and chronic studies as well as studies under pulsed and constant exposure in model calibrations improved model predictions on different types of validation data. Using our results, we derived a workflow, including recommendations for the sequence of modeling steps from the selection of input data to a final judgment on the suitability of GUTS-RED for the data set. Environ Toxicol Chem 2024;43:197-210. © 2023 Bayer AG and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Acute toxicity of pesticide mixtures to honey bees is generally additive, and well predicted by Concentration Addition.

Understanding the frequency of non-additive effects of pesticides (synergism and antagonism) is important in the context of risk assessment. The goal of this study was to investigate the prevalence of non-additive effects of pesticides to honey bees (Apis mellifera). We investigated a large set of mixtures including insecticides and fungicides of different chemical modes of action and classes. The mixtures included represent a relevant sample of pesticides that are currently used globally. We investigated whether the experimental toxicity of the mixtures could be predicted based on the Concentration Addition (CA) model for acute contact and oral adult bee toxicity tests. We measured the degree of deviation from the additivity predictions of the experimental toxicity based on the well-known Mixture Deviation Ratio (MDR). Further, we investigated the appropriate MDR thresholds that should be used for the identification of non-additive effects based on acceptable rates for false positive (alpha) and true positive (beta) findings. We found that a deviation factor of MDR = 5 is a sound reference for labeling potential non-additive effects in acute adult bee experimental designs when assuming a typical Coefficient of Variation (CV%) = 100 in the determination of the LD50 of a pesticide (a factor of 2× deviation in the LD 50 resulting from inter-experimental variability). We found that only 2.4 % and 9 % of the mixtures evaluated had an MDR > 5 and MDR < 0.2, respectively. The frequency and magnitude of deviation from additivity found for bees in this study are consistent with those of other terrestrial and aquatic taxa. Our findings suggest that additivity is a good baseline for predicting the toxicity of pesticide mixtures to bees, and that the rare cases of synergy of pesticide mixtures to bees are not random but have a mechanistic basis.

Risk from unintentional environmental mixtures in EU surface waters is dominated by a limited number of substances.

Unintentional environmental mixtures happen when multiple chemicals co-occur in the environment. A generic mixture assessment factor (MAF), has been proposed to account for this. The MAF is a number by which safe exposure levels for single chemicals are divided to ensure protection against combined exposures to multiple chemicals. Two key elements to judge the appropriateness of a generic MAF are (1) defining the scope of mixtures that need to be addressed by a MAF (i.e.: simple mixtures vs complex mixtures), and (2) the existence of common risk drivers across large spatial scales. Simple mixtures with one to three risk drivers can easily be addressed by chemical-by-chemical regulatory action. Our work provides evidence on the prevalence and complexity of cumulative risk in EU freshwaters based on chemical monitoring data from one of the largest databases in the EU. With 334 chemicals being monitored, low complexity mixtures (one to 3 three risk drivers) dominated. After excluding metals, only 15 out of 307 chemicals (5 %) were most frequent chemical risk drivers. When these 15 chemicals were excluded from the analysis, 95 % of all monitoring site - year combinations did not pose a concern for cumulative risk. Most of these 15 chemicals are already banned or listed in various priority lists, showing that current regulatory frameworks were effective in identifying drivers of single chemical and cumulative risk. Although the monitoring data do not represent the entirety of environmental mixtures in the EU, the observed patterns of (1) limited prevalence of truly complex mixtures, and (2) limited number of overall risk drivers, argue against the need for implementing a generic MAF as a regulatory tool to address risk from unintentional mixtures in EU freshwaters.

Selecting appropriate focal species for assessing the risk to birds from newly drilled pesticide-treated winter cereal fields in France.

Identifying focal bird species appropriate to the situation in which a plant protection product is used is important for refined risk assessment (EFSA ). We analyzed the results of extensive field observations of newly drilled cereal fields in France in autumn over 2 seasons to determine real bird focal species. In 2011, birds were observed before and after drilling on wheat and barley fields drilled with imidacloprid-treated seeds (i.e., "treatment" fields) or seeds treated with compounds other than imidacloprid (i.e., "alternative treatment" fields). Bird abundance, species richness, and diversity were significantly higher in wheat fields than barley fields; these findings led us to monitor only wheat fields in 2012. Statistical analyses did not show a significant effect of the drilling itself or between the treatment fields and the alternative treatment fields on the number and type of bird species. These results led to the pooling of 2011 data on all fields for focal species determination. Similarly, all bird monitoring data generated in 2012 before and after drilling were pooled and analyzed. Rules for determination of candidate focal species detailed in the EFSA () guidance were followed. Carrion crow, wood pigeon, gray partridge, skylark, common starling, and pied wagtail were the bird species most frequently observed on wheat fields. This list of candidate species was processed to determine the most relevant focal species according to the method of Dietzen et al. (); this process resulted in the selection of skylark, gray partridge, wood pigeon, and pied wagtail as focal species to assess risks to birds for pesticides applied during drilling of winter cereals in France (September through November). Integr Environ Assess Manag 2019;00:000-000. © 2018 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Evaluation of a novel test design to determine uptake of chemicals by plant roots.

A new hydroponic study design to determine uptake of chemicals by plant roots was tested by (i) investigating uptake of [14C]-1,2,4-triazole by wheat plants in a ring test with ten laboratory organizations and (ii) studying uptake of ten other radiolabelled chemicals by potato, tomato or wheat plants in two laboratories. Replicate data from the ring test were used to calculate plant uptake factor (PUF) values (uptake into roots and shoots) and transpiration stream concentration factor (TSCF) values (uptake into shoots). Average PUF for 1,2,4-triazole was 0.73 (n=39, 95% confidence interval (CI): 0.64, 0.82) and the corresponding TSCF value was 1.03 (n=49, 95% CI: 0.76, 1.3). Boxplots and subsequent classification tree analysis of PUF and TSCF values showed that potential outlier values were >1.38 and were observed for PUF replicates with low biomass increase (ratio of final to initial biomass ≤1.739) and small initial biomass (≤1.55g) and for TSCF replicates with an increase in biomass of <0.67g over a period of eight days. Considering only valid replicate data, average values of PUF and TSCF were 0.65 (n=33, 95% CI: 0.57, 0.73) and 0.64 (n=39, 95% CI: 0.58, 0.70). The additional experiments with ten chemicals and three plant species showed that uptake was low for polar substances of high molecular weight (≥394g/mol) and that TSCF values increased with log Kow values of the tested chemicals ranging from -1.54 to 1.88 (polynomial equation with R2=0.64). A cluster analysis for three of the compounds that were tested on wheat and tomato indicated that the plant uptake was mainly determined by the substance. Overall, the findings show that the hydroponic study design allows for reliable quantification of plant uptake over a range of compound/crop combinations.

Large-scale monitoring of effects of clothianidin-dressed oilseed rape seeds on pollinating insects in Northern Germany: effects on red mason bees (Osmia bicornis).

The aim of this study was to investigate the effects of Elado® (10 g clothianidin & 2 g beta-cyfluthrin/kg seed)-dressed oilseed rape on the development and reproduction of mason bees (Osmia bicornis) as part of a large-scale monitoring field study in Northern Germany, where oilseed rape is usually cultivated at 25-33 % of the arable land. Both reference and test sites comprised 65 km2 in which no other crops attractive to pollinating insects were present. Six study locations were selected per site and three nesting shelters were placed at each location. Of these locations, three locations were directly adjacent to oilseed rape fields, while the other three locations were situated 100 m distant from the nearest oilseed rape field. At each location, 1500 cocoons of O. bicornis were placed into the central nesting shelter. During the exposure phase, nest building activities and foraging behaviour were assessed repeatedly. Cocoons were harvested in autumn to assess parasitization and reproduction including larval development. The following spring, the emergence of the next generation of adults from cocoons was monitored. High reproductive output and low parasitization rates indicated that Elado®-dressed oilseed rape did not cause any detrimental effects on the development or reproduction of mason bees.

Large-scale monitoring of effects of clothianidin-dressed OSR seeds on pollinating insects in Northern Germany: effects on large earth bumble bees (Bombus terrestris).

The aim of this study was to investigate the effects of Elado®-dressed winter oilseed rape (OSR, 10 g clothianidin & 2 g beta-cyfluthrin/kg seed) on the development, reproduction and behaviour of large earth bumble bees (Bombus terrestris) as part of a large-scale monitoring field study in Northern Germany, where OSR is usually cultivated at 25-33 % of the arable land. Both reference and test sites comprised 65 km2 in which no other crops attractive to pollinating insects were present. Six study locations were selected per site and 10 bumble bee hives were placed at each location. At each site, three locations were directly adjacent to OSR fields and three locations were situated 400 m distant from the nearest OSR field. The development of colonies was monitored from the beginning of OSR flowering in April until June 2014. Pollen from returning foragers was analysed for its composition. An average of 44 % of OSR pollen was found in pollen loads of bumble bees indicating that OSR was a major resource for the colonies. At the end of OSR flowering, hives were transferred to a nature reserve until the end of the study. Colony development in terms of hive weight and the number of workers showed a typical course with no statistically significant differences between the sites. Reproductive output was comparatively high and not negatively affected by the exposure to treated OSR. In summary, Elado®-dressed OSR did not cause any detrimental effects on the development or reproduction of bumble bee colonies.

Large-scale monitoring of effects of clothianidin-dressed oilseed rape seeds on pollinating insects in Northern Germany: effects on honey bees (Apis mellifera).

Possible effects of clothianidin seed-treated oilseed rape on honey bee colonies were investigated in a large-scale monitoring project in Northern Germany, where oilseed rape usually comprises 25-33 % of the arable land. For both reference and test sites, six study locations were selected and eight honey bee hives were placed at each location. At each site, three locations were directly adjacent to oilseed rape fields and three locations were situated 400 m away from the nearest oilseed rape field. Thus, 96 hives were exposed to fully flowering oilseed rape crops. Colony sizes and weights, the amount of honey harvested, and infection with parasites and diseases were monitored between April and September 2014. The percentage of oilseed rape pollen was determined in pollen and honey samples. After oilseed rape flowering, the hives were transferred to an extensive isolated area for post-exposure monitoring. Total numbers of adult bees and brood cells showed seasonal fluctuations, and there were no significant differences between the sites. The honey, which was extracted at the end of the exposure phase, contained 62.0-83.5 % oilseed rape pollen. Varroa destructor infestation was low during most of the course of the study but increased at the end of the study due to flumethrin resistance in the mite populations. In summary, honey bee colonies foraging in clothianidin seed-treated oilseed rape did not show any detrimental symptoms as compared to colonies foraging in clothianidin-free oilseed rape. Development of colony strength, brood success as well as honey yield and pathogen infection were not significantly affected by clothianidin seed-treatment during this study.

Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares.

Kinetic parameters of environmental fate processes are usually inferred by fitting appropriate kinetic models to the data using standard nonlinear least squares (NLS) approaches. Although NLS is appropriate to estimate the optimum parameter values, it implies restrictive assumptions on data variances when the confidence limits of the parameters must also be determined. Particularly in the case of degradation and metabolite formation, the assumption of equal error variance is often not realistic because the parent data usually show higher variances than those of the metabolites. Conventionally, such problems would be tackled by weighted NLS regression, which requires prior knowledge about the data errors. Instead of implicitly assuming equal error variances or giving arbitrary weights decided by the researcher, we use an iteratively reweighted least squares (IRLS) algorithm to obtain the maximum likelihood estimates of the model parameters and the error variances specific for the different species in a model. A study with simulated data shows that IRLS gives reliable results in the case of both unequal and equal error variances. We also compared results obtained by NLS and IRLS, with probability distributions of the parameters inferred with a Markov-Chain Monte-Carlo (MCMC) approach for data from aerobic transformation of different chemicals in soil. Confidence intervals obtained by IRLS and MCMC are consistent, whereas NLS leads to very different results when the error variances are distinctly different between different species. Because the MCMC results can be assumed to reflect the real parameter distribution imposed by the observed data, we conclude that IRLS generally yields more realistic estimates of confidence intervals for model parameters than NLS.

Statistical analysis of chemical transformation kinetics using Markov-Chain Monte Carlo methods.

For the risk assessment of chemicals intentionally released into the environment, as, e.g., pesticides, it is indispensable to investigate their environmental fate. Main characteristics in this context are transformation rates and partitioning behavior. In most cases the relevant parameters are not directly measurable but are determined indirectly from experimentally determined concentrations in various environmental compartments. Usually this is done by fitting mathematical models, which are usually nonlinear, to the observed data and such deriving estimates of the parameter values. Statistical analysis is then used to judge the uncertainty of the estimates. Of particular interest in this context is the question whether degradation rates are significantly different from zero. Standard procedure is to use nonlinear least-squares methods to fit the models and to estimate the standard errors of the estimated parameters from Fisher's Information matrix and estimated level of measurement noise. This, however, frequently leads to counterintuitive results as the estimated probability distributions of the parameters based on local linearization of the optimized models are often too wide or at least differ significantly in shape from the real distribution. In this paper we identify the shortcoming of this procedure and propose a statistically valid approach based on Markov-Chain Monte Carlo sampling that is appropriate to determine the real probability distribution of model parameters. The effectiveness of this method is demonstrated on three data sets. Although it is generally applicable to different problems where model parameters are to be inferred, in the present case for simplicity we restrict the discussion to the evaluation of metabolic degradation of chemicals in soil. It is shown that the method is successfully applicable to problems of different complexity. We applied it to kinetic data from compounds with one and five metabolites. Additionally, using simulated data, it is shown that the MCMC method estimates the real probability distributions of parameters well and much better than the standard optimization approach.