How to analyse and account for interactions in mixture toxicity with toxicokinetic-toxicodynamic models

Integrating Time-Resolved nrf2 Gene-Expression Data into a Full GUTS Model as a Proxy for Toxicodynamic Damage in Zebrafish Embryo

The immense production of the chemical industry requires an improved predictive risk assessment that can handle constantly evolving challenges while reducing the dependency of risk assessment on animal testing. Integrating omics data into mechanistic models offers a promising solution by linking cellular processes triggered after chemical exposure with observed effects in the organism. With the emerging availability of time-resolved RNA data, the goal of integrating gene expression data into mechanistic models can be approached. We propose a biologically anchored TKTD model, which describes key processes that link the gene expression level of the stress regulator nrf2 to detoxification and lethality by associating toxicodynamic damage with nrf2 expression. Fitting such a model to complex data sets consisting of multiple endpoints required the combination of methods from molecular biology, mechanistic dynamic systems modeling, and Bayesian inference. In this study, we successfully integrate time-resolved gene expression data into TKTD models and thus provide a method for assessing the influence of molecular markers on survival. This novel method was used to test whether nrf2 can be applied to predict lethality in zebrafish embryos. With the presented approach, we outline a method to successfully approach the goal of a predictive risk assessment based on molecular data.

Fostering the waste management with environmental-resource interacting attribute

Effective waste management is essential for achieving sustainability, yet challenges persist in resource recovery and mitigating environmental impacts. The environmental-resource interacting attribute framework quantifies these difficulties in waste processes, revealing attribute bias and guiding treatment pathway selection. Here we analyze twelve waste categories and reveal significant variability in recyclability and environmental impact. For instance, copper slag demonstrates a range of 25.25-285.46 bit-1 (the unit is the inverse of information entropy), indicating greater recyclability, while downcycling pathways exhibit the highest values (up to 285.46 bit-1), emphasizing resource recovery (up to 77.45° while degree indicates bias). It proposes a classification of waste based on environmental-resource interacting attribute values, prioritizing materials with high recovery potential to prevent irreversible losses. The framework offers insights into waste recyclability and environmental burdens, serving as an alternative tool for advancing waste management towards a circular economy and reducing carbon emissions.

Reviewing the use of zebrafish for the detection of neurotoxicity induced by chemical mixtures through the analysis of behaviour

The knowledge and assessment of mixtures of chemical pollutants in the aquatic environment is a complex issue that is often challenging to address. In this review, we focused on the use of zebrafish (Danio rerio), a vertebrate widely used in biomedical research, as a model for detecting the effects of chemical mixtures with a focus on behaviour. Our aim was to summarize the current status of the ecotoxicological research in this sector. Specifically, we limited our research to the period between January 2012 and September 2023, including only those works aimed at detecting neurotoxicity through behavioural endpoints, utilizing zebrafish at one or more developmental stages, from egg to adult. Additionally, we gathered the findings for every group of chemicals involved and summarised data from all the works we included. At the end of the screening process 101 papers were considered eligible for inclusion. Results show a growing interest in zebrafish at all life stages for this kind of research in the last decade. Also, a wide variety of different assays, involving different senses, was used in the works we surveyed, with exposures ranging from acute to chronic. In conclusion, the results of this study show the versatility of zebrafish as a model for the detection of mixture toxicity although, for what concerns behavioural analysis, the lack of standardisation of methods and endpoints might still be limiting.

Joint toxic mechanism of clothianidin and prochloraz in the earthworm (Eisenia fetida)

Pesticides are typically present as combinations within soil ecosystems and have detrimental effects on untamed surroundings. However, the collective impacts and fundamental mechanisms of pesticides on soil living beings are currently inadequately assessed. In our current work, we evaluated the interactive consequences of clothianidin (CLO) and prochloraz (PRO) on earthworms (Eisenia fetida) using several toxicological tests, such as acute adverse effects, biocatalytic activity, and alterations in transcriptional activity. The findings revealed that CLO (with a 14-day LC50 value of 6.08 mg kg-1) exhibited greater toxicity compared to PRO (with a 14-day LC50 value of 79.41 mg kg-1). Moreover, the combinations of CLO and PRO had synergistic acute effects on E. fetida. Additionally, the activities of POD, CAT, and GST were significantly varied in most instances of single and mixed treatments when compared to the control. Surprisingly, the transcriptional levels of four genes (gst, sod, crt, and ann), related to oxidative load, metabolic detoxification systems, endoplasmic reticulum, and oxytocin neuropeptide, respectively, were also altered in response to single and mixture exposures, as compared to the control. Alterations in enzyme activity and gene transcriptional level could serve as early indicators for detecting co-exposure to pesticides. The findings of this research offered valuable holistic understanding regarding the toxicity of pesticide combinations on earthworms. Further research should be conducted to investigate the persistent effects of pesticide mixtures on terrestrial invertebrates in order to draw definitive conclusions about the associated risks.

Co-exposure to deltamethrin and cyazofamid: variations in enzyme activity and gene transcription in the earthworm (Eisenia fetida)

Pesticide formulations are typically applied as mixtures, and their synergistic effects can increase toxicity to the organisms in the environment. Despite pesticide mixtures being the leading cause of pesticide exposure incidents, little attention has been given to assessing their combined toxicity and interactions. This survey purposed to reveal the cumulative toxic effects of deltamethrin (DEL) and cyazofamid (CYA) on earthworms (Eisenia fetida) by examining multiple endpoints. Our findings revealed that the LC50 values of DEL for E. fetida, following 7- and 14-day exposures, ranged from 887.7 (728-1095) to 1552 (1226-2298) mg kg-1, while those of CYA ranged from 316.8 (246.2-489.4) to 483.2 (326.1-1202) mg kg-1. The combinations of DEL and CYA induced synergistic influences on the organisms. The contents of Cu/Zn-SOD and CarE showed significant variations when exposed to DEL, CYA, and their combinations compared to the untreated group. Furthermore, the mixture administration resulted in more pronounced alterations in the expression of five genes (hsp70, tctp, gst, mt, and crt) associated with cellular stress, carcinogenesis, detoxification, and endoplasmic reticulum compared to single exposures. In conclusion, our comprehensive findings provided detailed insights into the cumulative toxic effects of chemical mixtures across miscellaneous endpoints and concentration ranges. These results underscored the importance of considering mixture administration during ecological risk evaluations of chemicals.

Toxicity of representative organophosphate, organochlorine, phenylurea, dinitroaniline, carbamate, and viologen pesticides to the growth and survival of H. vulgaris, L. minor, and C. elegans

Pesticides are commonly found in the environment and pose a risk to target and non-target species; therefore, employing a set of bioassays to rapidly assess the toxicity of these chemicals to diverse species is crucial. The toxicity of nine individual pesticides from organophosphate, organochlorine, phenylurea, dinitroaniline, carbamate, and viologen chemical classes and a mixture of all the compounds were tested in three bioassays (Hydra vulgaris, Lemna minor, and Caenorhabditis elegans) that represent plant, aquatic, and soil-dwelling species, respectively. Multiple endpoints related to growth and survival were measured for each model, and EC10 and EC50 values were derived for each endpoint to identify sensitivity patterns according to chemical classes and target organisms. L. minor had the lowest EC10 and EC50 values for seven and five of the individual pesticides, respectively. L. minor was also one to two orders of magnitude more sensitive to the mixture compared to H. vulgaris and C. elegans, where EC50 values were calculated to be 0.00042, 0.0014, and 0.038 mM, respectively. H. vulgaris was the most sensitive species to the remaining individual pesticides, and C. elegans consistently ranked the least sensitive to all tested compounds. When comparing the EC50 values across all pesticides, the endpoints of L. minor were correlated with each other while the endpoints measured in H. vulgaris and C. elegans were clustered together. While there was no apparent relationship between the chemical class of pesticide and toxicity, the compounds were more closely clustered based on target organisms (herbicide vs insecticide). The results of this study demonstrate that the combination of these plant, soil, and aquatic specie can serve as representative indicators of pesticide pollution in environmental samples.

Predicting the Metal Mixture Toxicity with a Toxicokinetic-Toxicodynamic Model Considering the Time-Dependent Adverse Outcome Pathways

Chemicals mainly exist in ecosystems as mixtures, and understanding and predicting their effects are major challenges in ecotoxicology. While the adverse outcome pathway (AOP) and toxicokinetic-toxicodynamic (TK-TD) models show promise as mechanistic approaches in chemical risk assessment, there is still a lack of methodology to incorporate the AOP into a TK-TD model. Here, we describe a novel approach that integrates the AOP and TK-TD models to predict mixture toxicity using metal mixtures (specifically Cd-Cu) as a case study. We preliminarily constructed an AOP of the metal mixture through temporal transcriptome analysis together with confirmatory bioassays. The AOP revealed that prolonged exposure time activated more key events and adverse outcomes, indicating different modes of action over time. We selected a potential key event as a proxy for damage and used it as a measurable parameter to replace the theoretical parameter (scaled damage) in the TK-TD model. This refined model, which connects molecular responses to organism outcomes, effectively predicts Cd-Cu mixture toxicity over time and can be extended to other metal mixtures and even multicomponent mixtures. Overall, our results contribute to a better understanding of metal mixture toxicity and provide insights for integrating the AOP and TK-TD models to improve risk assessment for chemical mixtures.

Predicting the occurrence of antagonism within ternary guanidine mixture pollutants based on the concentration ratio of components

The widespread prevalence and coexistence of diverse guanidine compounds pose substantial risks of potential toxicity interactions, synergism or antagonism, to environmental organisms. This complexity presents a formidable challenge in assessing the risks associated with various pollutants. Hence, a method that is both accurate and universally applicable for predicting toxicity interactions within mixtures is crucial, given the unimaginable diversity of potential combinations. A toxicity interaction prediction method (TIPM) developed in our past research was employed to predict the toxicity interaction, within guanidine compound mixtures. Here, antagonism were found in the mixtures of three guanidine compounds including chlorhexidine (CHL), metformin (MET), and chlorhexidine digluconate (CDE) by selecting Escherichia coli (E. coli) as the test organism. The antagonism in the mixture was probably due to the competitive binding of all three guanidine compounds to the anionic phosphates of E. coli cell membranes, which eventually lead to cell membrane rupture. Then, a good correlation between toxicity interactions (antagonisms) and components' concentration ratios (pis) within binary mixtures (CHL-MET, CHL-CDE, MET-CDE) was established. Based on the correlation, the TIPM was constructed and accurately predicted the antagonism in the CHL-MET-CDE ternary mixture, which once again proved the accuracy and applicability of the TIPM method. Therefore, TIPM can be suggested to identify or screen rapidly the toxicity interaction within ternary mixtures exerting potentially adverse effects on the environment.

Mortality Pattern of Poecilus cupreus Beetles after Repeated Topical Exposure to Insecticide─Stochastic Death or Individual Tolerance?

The mortality of organisms exposed to toxicants has been attributed to either stochastic processes or individual tolerance (IT), leading to the stochastic death (SD) and IT models. While the IT model follows the principles of natural selection, the relevance of the SD model has been debated. To clarify why the idea of stochastic mortality has found its way into ecotoxicology, we investigated the mortality of Poecilus cupreus (Linnaeus, 1758) beetles from pesticide-treated oilseed rape (OSR) fields and unsprayed meadows, subjected to repeated insecticide treatments. We analyzed the mortality with the Kaplan-Meier estimator and general unified threshold model for survival (GUTS), which integrates SD and IT assumptions. The beetles were exposed three times, ca. monthly, to the same dose of Proteus 110 OD insecticide containing thiacloprid and deltamethrin, commonly used in the OSR fields. Kaplan-Meier analysis showed that the mortality of beetles from meadows was much higher after the first treatment than after the next two, indicating the IT model. Beetles from the OSR displayed approximately constant mortality after the first and second treatments, consistent with the SD model. GUTS analysis did not conclusively identify the better model, with the IT being marginally better for beetles from meadows and the SD better for beetles from OSR fields.

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.

A novel method for predicting the emergence of toxicity interaction in ternary mixtures

The environment is teeming with a wide variety of pollutants, but the complexity and diversity of their combinations make it difficult to fully assess their toxicity interaction. A novel toxicity interaction prediction method (TIPM) based on the three-dimensional (3D) surface form of the concentration addition (CA) deviation model (dCA) was proposed to predict the emergence of toxicity interaction in ternary mixtures. Doxycycline hyclate (DH), bromoacetic acid (BAA) and iodoacetic acid (IAA) were used as target pollutants. The toxicity of binary and ternary mixtures designed by the direct equipartition ray design method (EquRay) and the uniform design ray method (UD-Ray) against Escherichia coli (E. coli) was determined by using a time-dependent microplate toxicity analysis (t-MTA) method. The toxicity interaction within mixtures was characterized qualitatively and quantitatively using dCA 3D surface modeling and the emergence of DH-MAA-IAA toxicity interaction was predicted by TIPM. The results showed that the dCA 3D surface model could well characterize the toxicity interactions of the mixtures, and toxicity interaction was closely related to the components' concentration ratio (pi). TIPM could predict the emergence of DH-MAA-IAA toxicity interactions well based on the relationship. Due the model is only related to the toxicity interactions and pi value of a mixture, so it can be suggested to predict toxicity interaction within the more complex multicomponent mixtures, which provides a novel approach for the environmental risk assessment and prediction of hazardous substances.

Toxicokinetic-Toxicodynamic Model to Assess Thermal Stress

Temperature is a crucial environmental factor affecting the distribution and performance of ectothermic organisms. This study introduces a new temperature damage model to interpret their thermal stress. Inspired by the ecotoxicological damage model in the General Unified Threshold model for Survival (GUTS) framework, the temperature damage model assumes that damage depends on the balance between temperature-dependent accumulation and constant repair. Mortality due to temperature stress is driven by the damage level exceeding a threshold. Model calibration showed a good agreement with the measured survival of Gammarus pulex exposed to different constant temperatures. Further, model simulations, including constant temperatures, daily temperature fluctuations, and heatwaves, demonstrated the model's ability to predict temperature effects for various environmental scenarios. With this, the present study contributes to the mechanistic understanding of temperature as a single stressor while facilitating the incorporation of temperature as an additional stressor alongside chemicals in mechanistic multistressor effect models.

Recommendations to reduce the streetlight effect and gray areas limiting the knowledge of the effects of plant protection products on biodiversity

Preserving biodiversity against the adverse effects of plant protection products (PPPs) is a major environmental and societal issue. However, despite intensive investigation into the ecotoxicological effects of PPPs, the knowledge produced remains fragmented given the sheer diversity of PPPs. This is due, at least in part, to a strong streetlight effect in the field of ecotoxicology. Indeed, while some PPPs have been investigated in numerous ecotoxicological studies, there are many for which the scientific literature still has little or no information on their ecotoxicological risks and effects. The PPPs under the streetlight include a large variety of legacy substances and a more limited number of more recent or currently-in-use substances, such as the herbicide glyphosate and the neonicotinoid insecticides. Furthermore, many of the most recent PPPs (including those used in biocontrol) and PPP transformation products (TPs) resulting from abiotic and/or biotic degradation are rarely addressed in the international literature in the field of ecotoxicology. Here, based on a recent collective scientific assessment of the effects of PPPs on biodiversity and ecosystem services in the French and European contexts, this article sets out to illustrate the limitations and biases caused by the streetlight effect and numbers of gray areas, and issue recommendations on how to overcome them.

Chemical Mixtures and Multiple Stressors: Same but Different?

Ecosystems are strongly influenced by multiple anthropogenic stressors, including a wide range of chemicals and their mixtures. Studies on the effects of multiple stressors have largely focussed on nonchemical stressors, whereas studies on chemical mixtures have largely ignored other stressors. However, both research areas face similar challenges and require similar tools and methods to predict the joint effects of chemicals or nonchemical stressors, and frameworks to integrate multiple chemical and nonchemical stressors are missing. We provide an overview of the research paradigms, tools, and methods commonly used in multiple stressor and chemical mixture research and discuss potential domains of cross-fertilization and joint challenges. First, we compare the general paradigms of ecotoxicology and (applied) ecology to explain the historical divide. Subsequently, we compare methods and approaches for the identification of interactions, stressor characterization, and designing experiments. We suggest that both multiple stressor and chemical mixture research are too focused on interactions and would benefit from integration regarding null model selection. Stressor characterization is typically more costly for chemical mixtures. While for chemical mixtures comprehensive classification systems at suborganismal level have been developed, recent classification systems for multiple stressors account for environmental context. Both research areas suffer from rather simplified experimental designs that focus on only a limited number of stressors, chemicals, and treatments. We discuss concepts that can guide more realistic designs capturing spatiotemporal stressor dynamics. We suggest that process-based and data-driven models are particularly promising to tackle the challenge of prediction of effects of chemical mixtures and nonchemical stressors on (meta-)communities and (meta-)food webs. We propose a framework to integrate the assessment of effects for multiple stressors and chemical mixtures. Environ Toxicol Chem 2023;42:1915-1936. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Quantifying synergistic interactions: a meta-analysis of joint effects of chemical and parasitic stressors

The global biodiversity crisis emphasizes our need to understand how different stressors (climatic, chemical, parasitic, etc.) interact and affect biological communities. We provide a comprehensive meta-analysis investigating joint effects of chemical and parasitic stressors for 1064 chemical-parasitic combinations using the Multiplicative model on mortality of arthropods. We tested both features of the experimental setup (control mortality, stressor effect level) and the chemical mode of action, host and parasite phylogeny, and parasite-host interaction traits as explanatory factors for deviations from the reference model. Synergistic interactions, defined as higher mortality than predicted, were significantly more frequent than no interactions or antagony. Experimental setup significantly affected the results, with studies reporting high (> 10%) control mortality or using low stressor effects (< 20%) being more synergistic. Chemical mode of action played a significant role for synergy, but there was no effects of host and parasite phylogeny, or parasite-host interaction traits. The finding that experimental design played a greater role in finding synergy than biological factors, emphasize the need to standardize the design of mixed stressor studies across scientific disciplines. In addition, combinations testing more biological traits e.g. avoidance, coping, and repair processes are needed to test biology-based hypotheses for synergistic interactions.

Acute, chronic, and genotoxic studies on the protopine total alkaloids of the Macleaya cordata (willd.) R. Br. in rodents

The protopine alkaloids are widely distributed within the opium poppy family and have a wide range of pharmacological effects. MPTA is a product of the protopine total alkaloids extracted from the Macleaya cordata (Willd.) R. Br. Previously, we reported good anti-inflammatory activity of MPTA as well as oral acute and sub-chronic toxicity studies in rats. In order to perform a systematic toxicological safety assessment of MPTA, oral acute toxicity, genotoxicity (bone marrow cell chromosome aberration test, sperm abnormality test, bone marrow cell micronucleus test, and rat teratogenicity test), and chronic toxicity in mice were performed in this study. In the oral acute toxicity test, the LD₅₀ in ICR mice was 481.99 mg/kg, with 95% confidence limits ranging from 404.27 to 574.70 mg/kg. All three mutagenicity tests tested negative in the range of 60.25–241.00 mg/kg. The results of the teratogenicity test in rats showed no reproductive or embryonic developmental toxicity at only 7.53 mg/kg, which can be considered as a no observed effect level (NOEL) for the teratogenicity test. Therefore, MPTA is safe for use at the doses tested, but attention should be paid to the potential risk to pregnant animals and the safety evaluation and toxicity mechanisms in target animals should be further investigated.

Cumulative Risk Meets Inter-Individual Variability: Probabilistic Concentration Addition of Complex Mixture Exposures in a Population-Based Human In Vitro Model

Although humans are continuously exposed to complex chemical mixtures in the environment, it has been extremely challenging to investigate the resulting cumulative risks and impacts. Recent studies proposed the use of “new approach methods,” in particular in vitro assays, for hazard and dose−response evaluation of mixtures. We previously found, using five human cell-based assays, that concentration addition (CA), the usual default approach to calculate cumulative risk, is mostly accurate to within an order of magnitude. Here, we extend these findings to further investigate how cell-based data can be used to quantify inter-individual variability in CA. Utilizing data from testing 42 Superfund priority chemicals separately and in 8 defined mixtures in a human cell-based population-wide in vitro model, we applied CA to predict effective concentrations for cytotoxicity for each individual, for “typical” (median) and “sensitive” (first percentile) members of the population, and for the median-to-sensitive individual ratio (defined as the toxicodynamic variability factor, TDVF). We quantified the accuracy of CA with the Loewe Additivity Index (LAI). We found that LAI varies more between different mixtures than between different individuals, and that predictions of the population median are generally more accurate than predictions for the “sensitive” individual or the TDVF. Moreover, LAI values were generally <1, indicating that the mixtures were more potent than predicted by CA. Together with our previous studies, we posit that new approach methods data from human cell-based in vitro assays, including multiple phenotypes in diverse cell types and studies in a population-wide model, can fill critical data gaps in cumulative risk assessment, but more sophisticated models of in vitro mixture additivity and bioavailability may be needed. In the meantime, because simple CA models may underestimate potency by an order of magnitude or more, either whole-mixture testing in vitro or, alternatively, more stringent benchmarks of cumulative risk indices (e.g., lower hazard index) may be needed to ensure public health protection.