Global decline of amphibian populations has been correlated with a range of endogenous and exogenous variables including their unique physiology and ecology, exposure to chemicals, habitat reduction, climate change, as well as biological hazards such as emerging infectious diseases. The African clawed frog (Xenopus laevis) is an OECD test species used in toxicity testing as a specific proxy for humans and environmentally relevant species, for which acute toxicity data for a range of chemicals have been generated historically by industry, a number of public health agencies and academia. Of particular relevance are mechanistic effects of endocrine-active substances on metamorphosis and the thyroid axis, resulting in developmental toxicity. From such toxicity data, no open-source quantitative structure-activity relationships (QSARs) have been developed as in silico tools to predict such toxicity for data-poor chemicals in X. laevis. Such QSAR models can provide a quantitative starting point for the hazard assessment of chemicals in other anuran amphibians. This manuscript provides a description of the data collection and curation from the largest historical databases including the US EPA ECOTOX knowledgebase and the Ortiz-Santaliestra databases available for Xenopus embryos as acute median lethal concentrations (LC50-12 h) for a total of 349 unique structures and 1978 individual entries. After data curation, the database contained 359 individual entries for a total of 175 compounds, and were computed using the negative logarithm of molar concentrations expressed as 12 h log 1/LC50 mmol/L. Subsequently, the database was then split into training set, test set and prediction set with 120, 40 and 13 compounds, respectively. These datasets were then used for the development and validation of two different QSAR models: 1. A k-Nearest Neighbours (k-NN) models using istKNN (in silico tools - KNN). 2. A multiple linear regression model (MLR) using the QSARINS (QSAR-INSUBRIA) software version 2.2.4. Overall, the QSAR models performed well for predicting acute toxicity of chemicals in Xenopus embryos and the MLR model performed slightly better than the k-NN model with correlation coefficients of 0.76 and 0.75 and root mean square errors of 0.63 and 0.67, respectively. However, underestimation of predictions for highly toxic compounds were observed and these limitations are discussed for both the k-NN and multiple linear regression model in the light of mechanistic interpretation and expert knowledge. Variability in the experimental datasets as well as under-representation of the most toxic compounds in the database are highlighted as major drivers influencing such underpredictions. Future directions from the present work include the modelling of other endpoints and developmental stages as well as other amphibian species using the available, although limited, data. Overall, it can be foreseen in the near future that such databases and models will be important to develop more performant in silico models, and ultimately to develop NAMs for ecotoxicity assessment of chemicals in anuran amphibians while reducing animal testing.

The decline of amphibian populations is a major concern in environmental protection, and likely involves multiple stressors, such as loss of habitat, temperature changes, chemical exposure and pathogens. Mechanistic models can be helpful to understand and predict amphibian response to multiple stressors, but amphibian physiology and life history are unique, requiring adaptions to generic models.Thus, the goal of this study was to implement and test a whole life-cycle Dynamic Energy Budget (DEB) model for amphibians and test its ability to reproduce and predict metamorphosis traits.The model includes a dedicated reserve compartment for the biomass that is used to fuel metabolic processes during metamorphic climax, and a decline in feeding rates for metamorphizing individuals. While the model formulation was partially guided by a minimization of the number of parameters, a global sensitivity analysis indicated nevertheless that a wide range of metamorphosis traits could be produced.The model reproduced larval growth and metamorphosis traits in Discoglossus galganoi with account for individual variability in the time to reach metamorphosis with high accuracy.Based on the D. galganoi parameters as a reference, we fitted the model to a subset of the same life-history data for Pelophylax perezi larvae, attempting to predict the duration of metamorphosis and wet mass at the end of metamorphosis. For P. perezi, the duration of metamorphosis was slightly under-predicted, and the wet mass at the end of metamorphosis was under-predicted by a factor of 0.6. Given that D. galganoi and P. perepzi are rather distinct in their developmental strategies and stem from different families of anurans, this result does show potential for possible model applications to anuran species with poorer data coverage, in particular if data from a more closely related reference species is available. Open questions remain with respect to implementing the fasting period during metamorphic climax in models based on DEBkiss, while maintaining the fit to metamorphic mass change data.

The transition from animal-based chemical risk assessment to Next-Generation Risk Assessment requires the integration of human-relevant New Approach Methodologies and mechanistic kinetic data to support physiologically based kinetic (PBK) modelling for quantitative in vitro-to-in vivo extrapolation. This study investigated isoform-specific and extrahepatic glutathione (GSH) conjugation of Microcystin-LR (MC-LR), a widespread cyanobacterial toxin of emerging concern in food and feed safety. MC-LR detoxification occurs via spontaneous GSH conjugation or catalysed by glutathione-S-transferases (GSTs). A panel of recombinant human GSTs and pooled human and rat intestinal and kidney cytosols was incubated with 1-60 µM MC-LR to characterise kinetic parameters and intrinsic clearance (Cli). The tested GST isoforms catalysed MC-LR conjugation with efficiencies spanning a 15-fold range, with P1 and T1 showing the highest activity. Isoforms exhibited distinct kinetic behaviours, including positive cooperativity, influencing their relative contribution across substrate concentrations. Human intestinal and renal cytosols showed GST-mediated metabolic efficiencies comparable to hepatic data, attributable to the high expression of GSTP1 and T1. However, when scaling to whole-organ Cli, the hepatic enzymatic conjugation exceeded extrahepatic one. In rats, intestine and kidney detoxification capacity was low with respect to the hepatic one, the latter resulting 3.3-fold more efficient than the human GST-mediated reaction. Although the spontaneous conjugation in vitro accounted for 70-80% of total conjugate formation, when the reaction rate scaled to the total hepatic cytosolic volume, its relative contribution was lower than the GST-mediated one. In addition the enzymatic reaction became dominant even in vitro under GSH depletion, at low MC-LR concentrations, typical of long term exposure. These findings underscore the relevance of GST isoform distribution, polymorphism, and GSH availability in driving inter-organ, interspecies, and inter-individual variability in MC-LR detoxification. Overall, this study fills critical data gaps on MC-LR kinetics and provides quantitative parameters to inform PBK model development.

The use of plant protection products (PPPs) remains a major concern for biodiversity, ecosystem integrity, and human health, even under robust regulatory oversight. Current regulatory assessments are often fragmented: they typically examine single substances and single crops in isolation at local or simplified scales. As a result, they struggle to capture the cumulative and combined effects of multiple PPPs and the way exposure propagates and interacts across space and time (fields, crops, and seasons). Our previously proposed landscape-based Environmental Risk Assessment (ERA) framework offered an integrative solution. By jointly representing agricultural practices, environmental characteristics, species movement among habitats, and the combined impacts of multiple PPPs, the current framework delivers predictions that are more adapted to the field reality. These insights are valuable both for regulatory decision-making and for understanding how PPP risks contribute to the overall environmental stress. In this manuscript, we explore the needs, challenges, opportunities, and modelling tools for implementing a landscape-based ERA in both prospective (ex-ante) and retrospective (ex-post) contexts. Drawing on expert discussions and collaborative initiatives, we propose a conceptual framework with four pillars: (1) flexibility to meet diverse user and stakeholder needs, different decision contexts, and varying data availability; (2) ecological realism, the capacity to represent multiple stressors, cumulative effects, exposure pathways driven by species movement, and recovery dynamics; (3) data integration and transparency, combining monitoring and regulatory datasets for calibration, validation, uncertainty analysis, and reproducibility; and (4) regulatory uptake and interoperability, ensuring compatibility with existing ERA methodologies and producing outputs that can be interpreted and used at the landscape level across jurisdictions and tools. Beyond regulatory compliance, landscape-based ERA is a dynamic and adaptative system that provides a robust scientific basis for setting protection goals, designing targeted risk mitigation measures, shaping sustainable agricultural strategies, and communicating realistic, multi-stressor risk trade-offs to stakeholders and the public.

IntroductionIn natural environments, amphibians are exposed to individual chemical substances, and regularly also to mixtures of chemicals. At the same time, prospective risk assessment methods that account for exposure to mixtures across substances, species and environmental conditions are not implemented, partly because the experimental assessment of mixtures is extremely resource-intensive. Due to their specific life cycle, as well as susceptibility to anthropogenic and biological stressors, amphibians are of particular relevance for the environmental risk assessment of chemicals. Therefore, we set out to develop a model that can account for toxic effects of chemical mixtures in amphibians, specifically anurans, that integrates established toxicokinetic-toxicodynamic modelling principles with an amphibian-specific dynamic energy budget model (AmphiDEB-TKTD).Methods and ResultsWe calibrated the AmphiDEB-TKTD model to single-substance toxicity data for Flupyradifurone and 2,4-D in \textit{Discoglossus galganoi} larvae, and found that the model can reproduce observed effects on larval growth and metamorphosis traits. Also, we could use the data to identify the underlying physiological modes of action (PMoA), since growth and timing of metamorphosis are differentially affected through different PMoAs. A cross-validation with data on effects at Gosner stage 46 further clarified the physiological mode of action, and demonstrated that effects on Gosner stage 46 can be predicted from effects on larvae up to Gosner stage 42, if an appropriate PMoA has been identified. Concerning binary mixture toxicity, the model correctly predicted a dominance effect of 2,4-D in a mixture with Flupyradifurone. This can be explained through the different PMoAs of Flupyradifurone (decrease in growth efficiency) and 2,4-D (increase in maintenance costs), which qualitatively differ in the severity of their effects, regarding the propagation to different apical endpoints.Discussion and ConclusionsWe conclude that the AmphiDEB-TKTD model appears as a practical tool for the risk assessment of chemical mixtures for anurans, while also being useful for the investigation of mechanisms of toxicity. Since the model has only been validated on a substance combination of different PMoAs, further investigations should include the validation with substance combinations of identical PMoAs before more general claims about predictive capacity can be made. Furthermore, validation studies with additional species, especially ones exhibiting contrasting life histories, are desirable.

Domestic cats (Felis catus) are among the most popular pets in the world, with the global domestic cat population generally estimated to exceed 600 million and potentially approach 1 billion when feral populations are included. As hypercarnivores, cats exhibit unique metabolic deficiencies, particularly in phase II conjugation enzymes (e.g., glucuronidation, glycine conjugation), which impair elimination of phenolic xenobiotics including pharmaceuticals, feed additives, and contaminants. Consequently, the European Food Safety Authority (EFSA) Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) recommends an additional default uncertainty factor (UF) of 5 for such compounds. Physiologically based kinetic (PBK) modelling offers a mechanistic approach to refine these default factors using chemical-specific kinetic data and such models for the domestic cat are not currently available to the scientific and risk assessment community. Hence, this manuscript focuses on the development and validation of a generic PBK model for the species Felis catus according to the six-step process from the template of the Organisation for Economic Cooperation and Development (OECD) guidance document on characterisation, validation and reporting of PBK models for regulatory purposes. The model integrates meta-analysed physiological parameters from the peer-reviewed literature and 11 perfusion limited compartments. The model has been validated using chemical-specific inputs for 15 pharmaceuticals using in vitro and in vivo clearances to compare in vivo to in vivo and in vitro to in vivo predictions with the available experimental data for plasma maximum concentration (Cmax) and area-under-the-curve (AUC) values in blood after oral and intravenous exposure. Impact of bioavailability on model performance has also been assessed using conservative default values and reported or estimated values. In addition, global sensitivity analysis using the Sobol method identified the muscle:blood partition coefficient as the dominant parameter influencing model output variance. Overall, the generic PBK cat model performed well and most predictions accounting for bioavailability using in vitro derived clearance yielded 86% of Cmax predictions and 64% of AUC predictions were within 2-3-fold of the experimental data as recommended by the OECD. Future applications and refinements of the model with regard to NGRA of food and feed chemicals are highlighted.

Phomopsins are mycotoxins mainly contaminating lupin-derived food matrices posing safety concerns, with phomopsin A considered the most potent congener. Their toxicity is linked to disruption of microtubule dynamics, yet the shortage of toxicokinetic and toxicodynamic data prevents adequate human risk assessment, representing a critical gap for food safety. To bridge this data gap through New Approach Methodologies (NAMs), a computational 3D modelling pipeline, combining molecular dynamics simulations and binding free energy calculations, was applied to investigate the interactions of phomopsin A and selected congeners with the human α/β-tubulin assembly. The whole set of phomopsins showed a comparable mode of binding, with some exhibiting geometrically and energetically distinct, yet comparably stable, interaction profiles. Dechlorination emerged as a critical destabilising factor. By enabling a mechanism-based read-across across the congeneric series, these findings provide a supported hazard prioritisation of phomopsin analogues, identifying phomopsin A, iso-phomopsin A, and phomopsinamine A as high-priority compounds for toxicological investigation. Overall, this NAMs-driven approach sets the groundwork for a more informed hazard characterisation and mechanistic interpretation of the phomopsin family. By helping prioritise which congeners warrant further investigation, it also provides a useful basis for future human exposure assessment, once complemented by toxicokinetic and occurrence data.

Geometric deep learning is an emerging technique in Artificial Intelligence (AI) driven cheminformatics, however the unique implications of different Graph Neural Network (GNN) architectures are poorly explored, for this space. This study compared performances of Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs) and Graph Isomorphism Networks (GINs), applied to 7 different toxicological assay datasets of varying data abundance and endpoint, to perform binary classification of assay activation. Following pre-processing of molecular graphs, enforcement of class-balance and stratification of all datasets across 5 folds, Bayesian optimisations were carried out, for each GNN applied to each assay dataset (resulting in 21 unique Bayesian optimisations). Optimised GNNs performed at Area Under the Curve (AUC) scores ranging from 0.728-0.849 (averaged across all folds), naturally varying between specific assays and GNNs. GINs were found to consistently outperform GCNs and GATs, for the top 5 of 7 most data-abundant toxicological assays. GATs however significantly outperformed over the remaining 2 most data-scarce assays. This indicates that GINs are a more optimal architecture for data-abundant environments, whereas GATs are a more optimal architecture for data-scarce environments. Subsequent analysis of the explored higher-dimensional hyperparameter spaces, as well as optimised hyperparameter states, found that GCNs and GATs reached measurably closer optimised states with each other, compared to GINs, further indicating the unique nature of GINs as a GNN algorithm.

Chemical risk assessment is evolving from traditional deterministic approaches to embrace proba­bilistic methodologies, where risk of hazard manifestation is understood as a more or less probable event depending on exposure, individual factors, and stochastic processes. This is driven by advancements in human stem cells, complex tissue engineering, high-performance computing, and cheminformatics, and is more recently facilitated by large-scale artificial intelligence models. These innovations enable a more nuanced understanding of chemical hazards, capturing the complexity of biological responses and variability within populations. However, each technology comes with its own uncertainties impacting on the estimation of hazard probabilities. This shift addresses the limitations of point estimates and thresholds that oversimplify hazard assessment, allowing for the integration of kinetic variability and uncertainty metrics into risk models. By leveraging modern technologies and expansive toxicological data, probabilistic approaches offer a comprehensive evaluation of chemical safety. This paper summarizes a workshop held in 2023 and discusses the technological and data-driven enablers, and the challenges faced in their implementation, with particular focus on perturbation of biology as the basis of hazard estimates. The future of toxico­logical risk assessment lies in the successful integration of these probabilistic models, promising more accurate and holistic hazard evaluations.

Monensin is an ionophore antibiotic widely used in veterinary medicine, known for its marked species-specific toxicity with Equus caballus being the most sensitive species, Sus scrofa partially tolerant, while Gallus gallus is the most tolerant. It has been reported that such variability may depends on differences in CYP3A-mediated metabolism, particularly related to the O-demethylation reaction, which plays a key role in monensin detoxification. In this study, we applied a 3D modelling-based pipeline, including molecular docking, dynamics simulations and machine learning driven post-processing analysis, to investigate the interaction of monensin with CYP3A isoforms from chickens, pigs, and horses. The data collected unveiled interspecies and isoform-specific differences in both monensin binding and stability within CYP3A isoforms, providing a molecular basis for the species-related toxicity profile and O-demethylation efficiency previously described. These results were also corroborated by differences in terms of amino acid residues shaping the tested CYP3A catalytic sites. These findings provide mechanistic insights into interspecies variability regarding monensin detoxification and highlight the impact of in silico approaches on mechanistic toxicology studies related to risk assessment.

Toxicokinetic (TK) modeling provides critical information linking chemical exposures to tissue concentrations, predicting persistence in the body and determining the route(s) of elimination. Unfortunately, TK data are not available for most chemicals in commerce and the environment. To better understand and address these important information gaps, researchers and regulatory scientists from the international consortium of Accelerating the Pace of Chemical Risk Assessment herein present a flexible framework for characterizing the suitability of TK new approach methods (NAMs) to address chemical risk questions. High throughput toxicokinetics (HTTK) combines chemical-specific in vitro measures of TK with reproducible transparent and open-source TK models. HTTK supports the interpretation of data from in vitro bioactivity NAMs in a public health risk context and enhances the interpretation of biomonitoring data. A tiered framework has been developed focusing on two key aspects: (1) the regulatory decision context and (2) chemical properties and data. Differing levels of certainty are needed for relative risk prioritization, prospective risk assessment, and for protecting susceptible populations. Here HTTK is described with respect to measurement and modeling applications, relevant decision contexts, applicable chemistry, value of information, and certainty of predictions. In some cases, quantitative structure-property relationship (QSPR) models exist as alternatives to measurement and are discussed when they are appropriate. A series of examples applying the decision trees in specific public health scenarios are provided to illustrate that writing short responses, prompted by the decision trees and supported by the discussion and references collected here, may provide defensible written justification for or against the use of HTTK. The framework is intended to serve as a guide to chemical regulators and risk assessors who are interested to know when and where HTTK might be used for public health safety or risk decision making and when further expert guidance is needed.

Abstract This editorial provides an update on research & innovation (R&I) needs that can support EFSA's regulatory science in the coming years. The paper presents research needs for EFSA's work in a number of domains: omics technologies; gut microbiome; new approach methodologies (NAMs); allergenicity risk assessment; aggregate exposure assessment and environmental risk assessment (ERA). In briefly describing R&I needs, the document also addresses emerging challenges and opportunities. The authors acknowledge that this overview is not exhaustive and refer to earlier publications for additional R&I needs, as well as to the roadmaps for a more in‐depth presentation. Finally, the document calls for transdisciplinary research, reflecting on the interdependencies between human, animal, plant and environmental health. This editorial will be valuable to stakeholders, research agenda setters and funders, both public and private, in formulating calls for research and project funding related to food safety.

The 2018 LUCAS (Land Use and Coverage Area frame Survey) Soil Pesticides survey provides a European Union (EU)-scale assessment of 118 pesticide residues in more than 3473 soil sites. This study responds to the policy need to develop risk-based indicators for pesticides in the environment. Two mixture risk indicators are presented for soil based, respectively, on the lowest and the median of available No Observed Effect Concentration (NOECsoil,min and NOECsoil,50) from publicly available toxicity datasets. Two further indicators were developed based on the corresponding equilibrium concentration in the aqueous phase and aquatic toxicity data, which are available as species sensitivity distributions. Pesticides were quantified in 74.5% of the sites. The mixture risk indicator based on the NOECsoil,min exceeds 1 in 14% of the sites and 0.1 in 23%. The insecticides imidacloprid and chlorpyrifos and the fungicide epoxiconazole are the largest contributors to the overall risk. At each site, one or a few substances drive mixture risk. Modes of actions most likely associated with mixture effects include modulation of acetylcholine metabolism (neonicotinoids and organophosphate substances) and sterol biosynthesis inhibition (triazole fungicides). Several pesticides driving the risk have been phased out since 2018. Following LUCAS surveys will determine the effectiveness of substance-specific risk management and the overall progress toward risk reduction targets established by EU and UN policies. Newly generated data and knowledge will stimulate needed future research on pesticides, soil health, and biodiversity protection. Integr Environ Assess Manag 2024;20:1639-1653. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Scorpions are the oldest known arachnids and include some of the earliest invertebrates to have become fully terrestrial, with fossil records dating back to the Silurian period approximately 444–419 million years ago. Scorpion anatomy features two main segments: a frontal prosoma, to which eight legs are attached, and an opisthosoma, divided in mesosoma as the main body and metasoma as the tail. Taxonomically, scorpions belong to the class Arachnida which includes spiders (order: Araneae), ticks and mites (order: Acarina) among other orders, and form the order Scorpiones. Over 2200 species have been described and these are distributed all over the earth with the exception of Antarctica. Relatively few scorpion genera are considered of public health importance with regards to clinical consequences of their stings and these are mostly found in Africa, Middle East and Central-Southern America. Such genera of clinical relevance include Androctonus, Buthus, Centruroides, Hemiscorpius, Hottentotta, Leiurus, Odontobuthus, Parabuthus and Tityus and around 30 species. Scorpion venoms encode toxins, primarily acting on sodium and potassium ion channels which are responsible for clinical effects resulting in the depolarization of excitable nerve and muscle cells. In addition, scorpion venoms also contain non-toxic metalloproteinases, calcium channel and chloride channel toxins, bradykinin-potentiating peptides, serine protease inhibitors, phospholipase A2 enzymes as well as defensins and antimicrobial peptides. Scorpion envenomation and associated symptoms are usually classified as Class I-mild, Class II-moderate and Class III-severe. The clinical management of scorpion stings is described here within a protocol.

Tissue affinities are conventionally determined from in vivo steady-state tissue and plasma or plasma-water chemical concentration data. In silico approaches were initially developed for preclinical species but standardly applied and tested in human physiologically-based kinetic (PBK) models. Recently, generic PBK models for farm animals have been made available and require partition coefficients as input parameters. In the current investigation, data for species-specific tissue compositions have been collected, and prediction of chemical distribution in various tissues of livestock species for cattle, chicken, sheep and swine have been performed. Overall, tissue composition was very similar across the four farm animal species. However, small differences were observed in moisture, fat and protein content in the various organs within each species. Such differences could be attributed to factors such as variations in age, breed, and weight of the animals and general conditions of the animal itself. With regards to the predictions of tissue:plasma partition coefficients, 80 %, 71 %, 77 % of the model predictions were within a factor 10 using the methods of Berezhkovskiy (2004), Rodgers and Rowland (2006) and Schmitt (2008). The method of Berezhkovskiy (2004) was often providing the most reliable predictions except for swine, where the method of Schmitt (2008) performed best. In addition, investigation of the impact of chemical classes on prediction performance, all methods had very similar reliability. Notwithstanding, no clear pattern regarding specific chemicals or tissues could be detected for the values predicted outside a 10fold change in certain chemicals or specific tissues. This manuscript concludes with the need for future research, particularly focusing on lipophilicity and species differences in protein binding.