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.

Abstract The European Commission asked EFSA to update its 2012 risk assessment on Emerging and Novel brominated flame retardants (BFRs) in food. Since the previous Opinion, further information was collected for a total of 27 BFRs. The CONTAM Panel concluded that TDBPP, DBPNG and TBNPA are considered genotoxic and carcinogenic. Based on positive in vitro mutagenicity in mammalian cells, DBE‐DBCH is considered genotoxic. EBTEBPI, DBDPE, PBB‐Acr and TTBP‐TAZ are not genotoxic. Insufficient or absent data precluded the Panel to conclude on the genotoxic potential of the remaining BFRs. Reproductive/developmental toxicity and/or neurotoxicity/neurodevelopmental effects were reported for DBNPG, BEH‐TEBP, EH‐TBB, DBDPE and TDBP‐TAZTO. Reference Points were identified for TDBPP, DBNPG, BEH‐TEBP, EH‐TBB, DBDPE and TDBP‐TAZTO. No health‐based guidance values could be derived for any of the BFRs. Occurrence data were submitted to EFSA for eight BFRs (BEH‐TEBP, EH‐TBB, BTBPE, DBDPE, HBB, PBEB, PBT, TBX). Dietary exposure could be estimated only for BTBPE and HBB, due to the lack of quantitative data for the other BFRs. However, no risk characterisation could be performed for these BFRs due to the lack of a Reference Point. Using the limited available information on dietary exposure estimates reported in the scientific literature, risk characterisation could be performed for BEH‐TEBP, EH‐TBB and DBDPE, and the CONTAM Panel concluded that dietary exposure does not raise a health concern. The Panel considered that the level of certainty was 90% or higher for most of the reported conclusions. Very limited or no information on hazard or exposure were available for HBCYD, TBCO, DBHCTD, HCTBPH, BDBP‐TAZTO, DBP‐TAZTO, OBTMPI, DBS, HEEHP‐TEBP, 4’‐PeBPO‐BDE208 or TTBNPP. Recommendations were formulated to address data gaps, including the need for access to original data, and for the collection of biomonitoring data, occurrence data in food, genotoxicity information and toxicological studies to support the identification of a Reference Point.

Abstract Among its objectives, the European Food Safety Authority working group on effect models in environmental risk assessment has worked towards the development and maintenance of a framework to facilitate the assessment of effect models within the scope of the European Food Safety Authority's activities. To fulfil this objective, the working group self‐tasked to lay the groundwork for interpreting the FAIR (i.e. Findability, Accessibility, Interoperability, Reproducibility) guiding principles – originally developed for scientific data management and stewardship – for mechanistic effect models in the regulatory environmental risk assessment of pesticides. The working group identified three main areas in which the FAIR guiding principles may be applied, namely: (I) the data underlying a particular model (or model use), (II) the computer model and (III) the model assessment. The document explores existing resources that may support the implementation of the FAIR guiding principles and provides a specific interpretation of the principles for each of the areas relevant to mechanistic effect models. Important challenges and potential blockers are identified, but the working group argues that working towards more ‘FAIRness’ would ultimately lead to a more efficient review process and better integration of mechanistic effect models in the regulatory environmental risk assessment of pesticides, with benefits for all stakeholders. While the aim of the present exercise is to stimulate discussion within the modelling community and avoid being overly prescriptive, recommendations for future action are given to address some of the challenges.

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).

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product Scansmoke SEF7525 (SF-004), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003. This opinion refers to the assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. Scansmoke SEF7525 is obtained from a tar produced from a mixture of red oak, white oak, maple, beech and hickory. Based on the compositional data, the Panel noted that the identified and quantified proportion of the solvent-free fraction amounts to 32.6 weight (wt)%, thus the applied method does not meet the legal quality criterion that at least 50% of the solvent-free fraction shall be identified and quantified. At the maximum proposed use levels, dietary exposure estimates calculated with Food Additive Intake Model (FAIM) ranged from 0.6 to 3.8 mg/kg body weight (bw) per day at the mean and from 1.1 to 10.1 mg/kg bw per day at the 95th percentile. Based on the available information on genotoxicity on 44 identified components, the Panel concluded that two substances in the Primary Product, styrene and benzofuran, raise a potential concern for genotoxicity. In addition, a potential concern for genotoxicity was identified for the unidentified part of the mixture. Considering that the exposure estimates for styrene and benzofuran are above the threshold of toxicological concern (TTC) value of 0.0025 kg/kg bw per day for DNA-reactive mutagens and/or carcinogens and since further data are needed to clarify their potential genotoxicity, the Panel concluded that the potential safety concern for genotoxicity of the Primary Product cannot be ruled out.

New Approach Methodologies (NAMs) provide tools for supporting both human and environmental risk assessment (HRA and ERA). This short review provides recent insights regarding the use of NAMs in ERA of food and feed chemicals. We highlight the usefulness of tiered methods supporting weight-of-evidence approaches in relation to problem formulation (i.e., data availability, time, and resource availability). In silico models, including quantitative structure activity relationship models, support filling data gaps when no chemical property or ecotoxicological data are available, and biologically-based models (e.g., toxicokinetic-toxicodynamic models, dynamic energy models, physiologically-based models and species sensitivity distributions) are applicable in more data rich situations, including landscape-based modelling approaches. Particular attention is given to provide practical examples to apply the approaches described in real-world settings. We conclude with future perspectives, with regards to the need for addressing complex challenges such as chemical mixtures and multiple stressors in a wide range of organisms and ecosystems.

This publication is linked to the following EFSA Supporting Publications articles: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2023.EN-8441/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2023.EN-8440/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2023.EN-8437/full.

We have reported here a quantitative read-across structure-activity relationship (q-RASAR) model for the prediction of binary mixture toxicity (acute contact toxicity) in honey bees. Both the quantitative structure-activity relationship (QSAR) and the similarity-based read-across algorithms are used simultaneously for enhancing the predictability of the model. Several similarity and error-based parameters, obtained from the read-across prediction tool, have been put together with the structural and physicochemical descriptors to develop the final qRASAR model. The calculated statistical and validation metrics indicate the goodness-of-fit, robustness, and good predictability of the partial least squares (PLS) regression model. Machine learning algorithms like ridge regression, linear support vector machine (SVM), and non-linear SVM have been used to further enhance the predictability of the q-RASAR model. The prediction quality of the q-RASAR models outperforms the previously reported quasi-SMILEs-based QSAR model in terms of external correlation coefficient (Q2F1 SVM q-RASAR: 0.935 vs. Q2VLD QSAR: 0.89). In this research, the toxicity values of several new untested binary mixtures have been predicted with the new models, and the reliability of the PLS predictions has been validated by the prediction reliability indicator tool. The q-RASAR approach can be used as reliable, complementary, and integrative to the conventional experimental approaches of pesticide mixture risk assessment.

A sound assessment of in silico models and their applicability domain can support the use of new approach methodologies (NAMs) in chemical risk assessment and requires increasing the users' confidence in this approach. Several approaches have been proposed to evaluate the applicability domain of such models, but their prediction power still needs a thorough assessment. In this context, the VEGA tool capable of assessing the applicability domain of in silico models is examined for a range of toxicological endpoints. The VEGA tool evaluates chemical structures and other features related to the predicted endpoints and is efficient in measuring applicability domain, enabling the user to identify less accurate predictions. This is demonstrated with many models addressing different endpoints, towards toxicity of relevance to human health, ecotoxicological endpoints, environmental fate, physicochemical and toxicokinetic properties, for both regression models and classifiers.

In the European Union, the Chemicals Strategy for Sustainability (CSS) highlights the need to enhance the identification and assessment of substances of concern while reducing animal testing, thus fostering the development and use of New Approach Methodologies (NAMs) such as in silico, in vitro and in chemico. In the United States, the Tox21 strategy aims at shifting toxicological assessments away from traditional animal studies towards target-specific, mechanism-based and biological observations mainly obtained by using NAMs. Many other jurisdictions around the world are also increasing the use of NAMs. Hence, the provision of dedicated non-animal toxicological data and reporting formats as a basis for chemical risk assessment is necessary. Harmonising data reporting is crucial when aiming at re-using and sharing data for chemical risk assessment across jurisdictions. The OECD has developed a series of OECD Harmonised Templates (OHT), which are standard data formats designed for reporting information used for the risk assessment of chemicals relevant to their intrinsic properties, including effects on human health (e.g., toxicokinetics, skin sensitisation, repeated dose toxicity) and the environment (e.g., toxicity to test species and wildlife, biodegradation in soil, metabolism of residues in crops). The objective of this paper is to demonstrate the applicability of the OHT standard format for reporting information under various chemical risk assessment regimes, and to provide users with practical guidance on the use of OHT 201, in particular to report test results on intermediate effects and mechanistic information.

The EFSA Panel on Food Additives and Flavourings (FAF) was requested to evaluate the safety of the smoke flavouring Primary Product Fumokomp (SF-009), for which a renewal application was submitted in accordance with Article 12(1) of Regulation (EC) No 2065/2003 (in the renewal application the Primary Product is reported as 'Fumokomp Conc.'). This opinion refers to an assessment of data submitted on chemical characterisation, dietary exposure and genotoxicity of the Primary Product. Fumokomp Conc. is produced by pyrolysis of beech and hornbeam woods. Gas chromatography-mass spectrometry (GC-MS) was applied for both identification and quantification of the volatile constituents of the Primary Product. Given the limitations of the method, the Panel cannot judge with confidence whether the applied method meets the legal quality criterion that at least 80% of the volatile fraction shall be identified and quantified. Moreover, the Panel concluded that the absence of furan-2(5H)-one from the Primary Product was not convincingly demonstrated. At the maximum proposed use levels, dietary exposure estimates calculated with FAIM ranged from 0.04 to 0.9 mg/kg body weight (bw) per day at the mean and from 0.1 to 1.5 mg/kg bw per day at the 95th percentile. The information available on the 32 identified components of the Primary Product, although limited, did not indicate a concern for genotoxicity for any of these substances. However, whole mixture testing in an in vitro mouse lymphoma assay gave positive results which would require an adequate in vivo follow-up study. In addition, the potential for aneugenicity of the Primary Product has not been adequately investigated. Accordingly, the potential safety concern for genotoxicity of the Primary Product cannot be ruled out.(c) 2023 European Food Safety Authority.

Soil pollution is a critical environmental challenge: the substances released in the soil can adversely affect humans and the ecosystem. Several bioassays were developed to investigate the soil ecotoxicity of chemicals with soil microbes, plants, invertebrates and vertebrates. The 28-day collembolan reproduction test with the springtail Folsomia candida is a recently introduced bioassay described by OECD guideline 232. Although the importance of springtails for maintaining soil quality, toxicity data for Collembola are still limited. We have developed two QSAR models for the prediction of reproductive toxicity induced by organic compounds in Folsomia candida using 28 days NOEC data. We assembled a dataset with the highest number of compounds available so far: 54 compounds were collected from publicly available sources, including plant protection products, reactive intermediates and industrial chemicals, household and cosmetic ingredients, drugs, environmental transformation products and polycyclic aromatic hydrocarbons. The models were developed using partial least squares regression (PLS) and the Monte Carlo technique with respectively the open source tools Small Dataset Modeler and CORAL software. Both QSAR models gave good predictive performance even though based on a small dataset, so they could serve for the ecological risk assessment of chemicals for terrestrial organisms.

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, which support filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e. species sensitivity distributions and ultimately for landscape assessment i.e. landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application of such in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impact of single chemicals, multiple chemicals and multiple stressors on living organisms.

Amphibian populations are undergoing a global decline worldwide. Such decline has been attributed to their unique physiology, ecology, and exposure to multiple stressors including chemicals, temperature, and biological hazards such as fungi of the Batrachochytrium genus, viruses such as Ranavirus, and habitat reduction. There are limited toxicity data for chemicals available for amphibians and few quantitative structure-activity relationship (QSAR) models have been developed and are publicly available. Such QSARs provide important tools to assess the toxicity of chemicals particularly in a data poor context. QSARs provide important tools to assess the toxicity of chemicals particularly when no toxicological data are available. This manuscript provides a description and validation of a regression-based QSAR model to predict, in a quantitative manner, acute lethal toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica). QSAR models for acute median lethal molar concentrations (LC50-12 h) of waterborne chemicals using the Monte Carlo method were developed. The statistical characteristics of the QSARs were described as average values obtained from five random distributions into training and validation sets. Predictions from the model gave satisfactory results for the overall training set (R2 = 0.72 and RMSE = 0.33) and were even more robust for the validation set (R2 = 0.96 and RMSE = 0.11). Further development of QSAR models in amphibians, particularly for other life stages and species, are discussed.

Multiple stressors threaten bee health, a major one being pesticides. Bees are simultaneously exposed to multiple pesticides that can cause both lethal and sublethal effects. Risk assessment and most research on bee health, however, focus on lethal individual effects. Here, we performed a systematic literature review and meta-analysis that summarizes and re-interprets the available qualitative and quantitative information on the lethal, sublethal, and combined toxicity of a comprehensive range of pesticides on bees. We provide results (1970-2019) for multiple bee species (Bombus, Osmia, Megachile, Melipona, Partamona, Scaptotrigona), although most works focused on Apis mellifera L. (78 %). Our harmonised results document the lethal toxicity of pesticides in bees (n = 377 pesticides) and the types of sublethal testing methods and related effects that cause a sublethal effect (n = 375 sublethal experiments). We identified the most common combinations of pesticides and mode of actions tested, and summarize the experimental methods, magnitude of the interactions, and robustness of available data (n = 361 experiments). We provide open access searchable, comprehensive, and integrated list of pesticides and their levels causing lethal, sublethal, and combined effects. We report major data gaps related to pesticide's sublethal (71 %) and combined (e.g., ~99 %) toxicity. We identified pesticides and mode of actions of greatest concern in terms of sublethal (chlorothalonil, pymetrozine, glyphosate; neonicotinoids) and combined (tau-fluvalinate combinations; acetylcholinesterase inhibitors and neonicotinoids) effects. Although certain pesticides have faced regulatory restrictions in specific countries (chlorothalonil, pymetrozine, neonicotinoids), most are still widely used worldwide (e.g., glyphosate). This work aims at facilitating the implementation of more comprehensive and harmonised research and risk assessments, considering sublethal and combined effects. To ensure safeguarding pollinators and the environment, we advocate for a more refined and holistic assessment that do not only focus on lethality but uses harmonised methods to test sublethal and relevant combinations.

Since its creation in 2002, the European Food Safety Authority (EFSA) has produced risk assessments for over 5000 substances in >2000 Scientific Opinions, Statements and Conclusions through the work of its Scientific Panels, Units and Scientific Committee. OpenFoodTox is an open source toxicological database, available both for download and data visualisation which provides data for all substances evaluated by EFSA including substance characterisation, links to EFSA's outputs, applicable legislations regulations, and a summary of hazard identification and hazard characterisation data for human health, animal health and ecological assessments. The database has been structured using OECD harmonised templates for reporting chemical test summaries (OHTs) to facilitate data sharing with stakeholders with an interest in chemical risk assessment, such as sister agencies, international scientific advisory bodies, and others. This manuscript provides a description of OpenFoodTox including data model, content and tools to download and search the database. Examples of applications of OpenFoodTox in chemical risk assessment are discussed including new quantitative structure-activity relationship (QSAR) models, integration into tools (OECD QSAR Toolbox and AMBIT-2.0), assessment of environmental footprints and testing of threshold of toxicological concern (TTC) values for food related compounds. Finally, future developments for OpenFoodTox 2.0 include the integration of new properties, such as physico-chemical properties, exposure data, toxicokinetic information; and the future integration within in silico modelling platforms such as QSAR models and physiologically-based kinetic models. Such structured in vivo, in vitro and in silico hazard data provide different lines of evidence which can be assembled, weighed and integrated using harmonised Weight of Evidence approaches to support the use of New Approach Methodologies (NAMs) in chemical risk assessment and the reduction of animal testing.

The present document provides the summary of the activities undertaken during the fourth of the framework contract (OC/EFSA/SCER/2018/01) to maintain, update and further develop the OpenFoodTox database (“OpenFodTox 2.0”). OpenFoodTox has been developed to map hazards data published in EFSA documents (opinions, statements, and conclusions) on risk assessment of food and feed. The repository holds summary data on identification of chemicals, document descriptors, hazard identification, and hazard characterisation. Within OpenFoodTox 2.0, the collection and entry of all hazard data assessed by EFSA scientific panels was performed according to the existing data model. During the fourth year, 141 new substances and 304 new hazard assessments (collected from new 188 EFSA documents) were added to the database. The OpenFoodTox 2.0 now includes more than 10800 assessments for over 5890 chemicals (each assessment potentially including multiple (eco-) toxicity endpoints and/or hazard/risk characterisation data) extracted from the screening of about 2435 documents (opinions, statements, conclusions) published by EFSA from 2020 to August 2022. To maintain the database, its data model was further expanded to incorporate new data types, including physicochemical properties (OHT 1 to 23-5) and toxicokinetic data (OHT 58). Overall, OpenFoodTox was enriched with physicochemical properties and/or ADME/PK/TK data (including quantitative values for key toxicokinetics parameters, e.g., Cmax, AUC, T-half and T-max) for 1,263 substances from 664 EFSA outputs. In total, 16,611 database records with experimentally-derived physicochemical properties data were added to the database for 969 substances (collected from 605 EFSA outputs) as well as 7,699 ADME/PK/TK database records for 852 substances (from 577 documents) and are available for download under https://zenodo.org/records/8120114. Using VEGA predicted values of physiochemical properties and toxicity endpoints were added for 3980 compounds. The flexible summary wad tested on a data rich scenario using the data on conazoles. Finally, new QSAR models and a new read-across tools (VERA) are presented here.