Assessing the effects of field-relevant pesticide mixtures for their compliance with the concentration addition model - An experimental approach with Daphnia magna

Predicting the Combined Effects of Multiple Stressors and Stress Adaptation in Gammarus pulex

Global change confronts organisms with multiple stressors causing nonadditive effects. Persistent stress, however, leads to adaptation and related trade-offs. The question arises: How can the resulting effects of these contradictory processes be predicted? Here we show that Gammarus pulex from agricultural streams were more tolerant to clothianidin (mean EC50 148 μg/L) than populations from reference streams (mean EC50 67 μg/L). We assume that this increased tolerance results from a combination of physiological acclimation, epigenetic effects, and genetic evolution, termed as adaptation. Further, joint exposure to pesticide mixture and temperature stress led to synergistic interactions of all three stressors. However, these combined effects were significantly stronger in adapted populations as shown by the model deviation ratio (MDR) of 4, compared to reference populations (MDR = 2.7). The pesticide adaptation reduced the General-Stress capacity of adapted individuals, and the related trade-off process increased vulnerability to combined stress. Overall, synergistic interactions were stronger with increasing total stress and could be well predicted by the stress addition model (SAM). In contrast, traditional models such as concentration addition (CA) and effect addition (EA) substantially underestimated the combined effects. We conclude that several, even very disparate stress factors, including population adaptations to stress, can act synergistically. The strong synergistic potential underscores the critical importance of correctly predicting multiple stresses for risk assessment.

A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations

Environmental chemicals, such as PFAS, exist as mixtures and are frequently encountered at varying concentrations, which can lead to serious health effects, such as cancer. Therefore, understanding the dose-dependent toxicity of chemical mixtures is essential for health risk assessment. However, comprehensive methods to assess toxicity and identify the mechanisms of these harmful mixtures are currently absent. In this study, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine-learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM that is targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies using nearly a thousand experimental mixtures. This empirical dataset was expanded with assumption-based virtual mixtures, compensating for the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed machine-learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The AI-HNN achieved overall accuracies of over 80%, with the AUC exceeding 90%. In the final phase, we demonstrated the superior performance and predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, through rigorous literature and statistical validations, along with experimental dose-response zebrafish-embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone AI models, showing extensive enhancements in identifying toxic chemicals and mixtures and their mechanisms. This study is the first to develop a hybrid NAM that integrates AI with a pathophysiology method to comprehensively predict chemical-mixture toxicity, carcinogenicity, and mechanisms.

Dose-dependent joint resistance action of antibacterial mixtures in their hormetic effects on bacterial resistance based on concentration addition model

The judgment of joint resistance action is significant for evaluating the resistance risk of antibacterial mixture. Using bacterial mutation frequency (MF) and conjugative transfer frequency (CTF) to respectively characterize the bacterial endogenous and exogenous resistance, mutation unit and conjugative transfer unit have been proposed to judge the joint resistance action of antibacterial mixture at a certain dose. However, these methods could not evaluate the antibacterial mixture's joint resistance action at a larger concentration-range. In this study, the concentration addition for bacterial resistance (CA-BR) approach was used to judge the joint resistance actions between kanamycin sulfate (KAN) and some other typical antibacterial agents, including sulfonamides (SAs), sulfonamide potentiators (SAPs), and silver antibacterial compounds (SACs). Through comparing the hormetic dose-response curves of the binary mixtures on the MF (or CTF) in Escherichia coli (E. coli) and the corresponding CA-BR curves calculated from the hormetic dose-responses of the single agents, the joint resistance actions between KAN and other agents were judged to exhibit dose-dependent feature: with the increase of mixture concentration, the joint mutation actions between KAN and SAs (or SAPs) were fixed at synergism, and the joint mutation actions between KAN and SACs varied from antagonism to synergism; the joint conjugative transfer actions between KAN and other agents changed from antagonism to synergism. Mechanistic explanation suggested that the heterogeneous pattern of joint resistance action had a close relationship with the interplays among the agents' modes of action, and meanwhile was significantly influenced by their joint survival pressure on E. coli. This study reveals the dose-dependent feature for the joint resistance action of antibacterial mixture and highlights the importance of exposure concentration, which will benefit clarifying the resistance risk of antibacterial mixture in the environment.

Proportional contributions to organic chemical mixture effects in groundwater and surface water

Semi-quantitative GC-MS and LC-MS measurements of organic chemicals in groundwater and surface waters were used to assess the overall magnitude and contribution of the most important substances to calculated mixture hazard. Here we use GC-MS and LC-MS measurements taken from two separate national monitoring programs for groundwater and surface water in England, in combination with chronic species sensitivity distribution (SSD) HC50 values published by Posthuma et al. (2019, Environ. Toxicol. Chem, 38, 905-917) to calculate individual substance hazard quotients and mixture effects using a concentration addition approach. The mixture analysis indicated that, as anticipated, there was an increased hazard from the presence of a cocktail of substances at sites compared to the hazard for any single chemical. The magnitude of the difference between the hazard attributed to the most important chemical and the overall mixture effect, however, was not large. Thus, the most toxic chemical contributed ≥ 20% of the calculated mixture effect in >99% of all measured groundwater and surface water samples. On the basis of this analysis, a 5 fold assessment factor placed on the risk identified for any single chemical would offer a high degree of in cases where implementation of a full mixture analysis was not possible. This finding is consistent with previous work that has assessed chemical mixture effects within field monitoring programs and as such provides essential underpinning for future policy and management decisions on how to effectively and proportionately manage mixture risks.

A critical review of effect modeling for ecological risk assessment of plant protection products

A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).

Combined toxic impacts of thiamethoxam and four pesticides on the rare minnow (Gobiocypris rarus)

To examine pesticide mixture toxicity to aqueous organisms, we assessed the single and combined toxicities of thiamethoxam and other four pesticides (chlorpyrifos, beta-cypermethrin, tetraconazole, and azoxystrobin) to the rare minnow (Gobiocypris rarus). Data from 96-h semi-static toxicity assays of various developmental phases (embryonic, larval, juvenile, and adult phases) showed that beta-cypermethrin, chlorpyrifos, and azoxystrobin had the highest toxicities to G. rarus, and their LC₅₀ values ranged from 0.0031 to 0.86 mg a.i. L^-1, from 0.016 to 6.38 mg a.i. L^-1, and from 0.39 to 1.08 mg a.i. L^-1, respectively. Tetraconazole displayed a comparatively high toxicity, and its LC₅₀ values ranged from 3.48 to 16.73 mg a.i. L^-1. By contrast, thiamethoxam exhibited the lowest toxic effect with LC₅₀ values ranging from 37.85 to 351.9 mg a.i. L^-1. Rare minnow larvae were more sensitive than embryos to all the pesticides tested. Our data showed that a pesticide mixture of thiamethoxam-tetraconazole elicited synergetic toxicity to G. rarus. Moreover, pesticide mixtures containing beta-cypermethrin in combination with chlorpyrifos or tetraconazole also had synergetic toxicities to fish. The majority of pesticides are presumed to have additive toxicity, while our data emphasized that the concurrent existence of some chemicals in the aqueous circumstance could cause synergetic toxic effect, leading to severe loss to the aqueous environments in comparison with their single toxicities. Thence, the synergetic impacts of chemical mixtures should be considered when assessing the ecological risk of chemicals.

Long-term exposure of Daphnia magna to carbendazim: how it affects toxicity to another chemical or mixture

Aquatic organisms might be exposed episodically or continuously to chemicals for long-term periods throughout their life span. Pesticides are one example of widely used chemicals and thus represent a potential hazard to aquatic organisms. In addition, these chemicals may be present simultaneously in the environment or as pulses, being difficult to predict accurately how their joint effects will take place. Therefore, the aim of the present study was to investigate how Daphnia magna (clone k6) exposed throughout generations to a model pesticide (the fungicide carbendazim) would react upon an exposure to another chemical compound (triclosan) and to a mixture of both chemicals (carbendazim and triclosan). Responses of daphnids continuously exposed to carbendazim and kept in clean medium will be compared using immobilization tests and the comet assay (DNA integrity). The results showed that triclosan presented similar toxicity to daphnids exposed for 12 generations (F12) to carbendazim (similar 48-h-LC₅₀ values for immobilization data), when compared with daphnids kept in clean medium. However, at subcellular level, daphnids previously exposed to carbendazim for 12 generations (F12) showed different responses than those from clean medium, presenting a higher toxicity; a general higher percentage of DNA damage was observed, after exposure to a range of concentrations of triclosan and to the binary combination of triclosan + carbendazim. The patterns of toxicity observed for the binary mixture triclosan + carbendazim were generally similar for daphnids in clean medium and daphnids exposed to carbendazim, with a dose level deviation with antagonism observed at low doses of the chemical mixture for the immobilization data and a dose ratio deviation with synergism mainly caused by triclosan for DNA damage. With this study, we contributed to the knowledge on long-term induced effects of carbendazim exposure, while looking at the organismal sensitivity to another chemical (triclosan) and to a mixture of carbendazim and triclosan using lethality as an endpoint at the individual level and DNA damage as a subcellular endpoint.

Antioxidant defence system is responsible for the toxicological interactions of mixtures: A case study on PFOS and PFOA in Daphnia magna

Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) are two types of perfluorinated compounds (PFCs) frequently studied in recent years due to their potential for bioaccumulation and toxicity to humans. Usually, PFCs can co-exist in various environment. Therefore, over- or under-estimated risk assessments would result if antagonism or synergism occurred in mixture toxicity. In the present study, the acute and chronic toxicities of single and mixtures of PFOA and PFOS to Daphnia magna were investigated. PFOS was more toxic than PFOA, both in 48-h acute toxicity and 21-d chronic toxicity. In acute toxicity tests, mixture toxicities showed strong synergistic effects on mortality. The experimental EC₅₀ of the mixture is 4.44 × 10^-5 mol/L, whereas the predicted EC₅₀ is 8.19 × 10^-5 mol/L by Concentration Addition Model and 9.73 × 10^-5 mol/L by Independent Action Model. In chronic toxicity tests, synergistic effects were also found in the aspects of offspring. The offspring rate is reduced significantly to 39.8% at the 9.61 × 10^-7 mol/L of mixture, while, PFOS and PFOA do not have effects when they are tested individually at corresponding concentrations. To explore the potential mechanism of the synergistic effect, the interactions between PFCs and proteins, including acetylcholinesterase, superoxide dismutase, catalase, ecdysone receptor and glutathione-S-transferase, were investigated by the Molecular Docking. The docking results revealed that the driving forces for the binding of PFCs with proteins were predominantly hydrophobic and hydrogen-bonding interactions. Based on the binding models, we deduced that the potential mechanism of synergism is that PFOS and PFOA have similar binding modes with catalase and have different binding modes with superoxide dismutase. Overall, these data provide experimental evidence that there is strong synergism in acute and chronic toxicity of mixtures to D. magna and demonstrate that molecular structure of some components of the antioxidant defence system contributes to the synergistic interaction.