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Daneshmand M, SalarAmoli J, BaghbanZadeh N. A QSAR study for predicting malformation in zebrafish embryo. Toxicol Mech Methods 2024; 34:743-749. [PMID: 38586962 DOI: 10.1080/15376516.2024.2338907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/30/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is in silico models. In this study, we aim to develop a quantitative structure-activity relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation. METHODS The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatics software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as gradient boosting model (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NNs) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew's correlation coefficient (MCC) and balanced accuracy (BAC) score, were used to compare the models. RESULTS A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient boosting was determined to be the best algorithm with 78% predictive power. CONCLUSIONS The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and GBM is the best model due to its MCC and BAC.
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Affiliation(s)
- Mahsa Daneshmand
- Department of Comparative Bioscience, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Jamileh SalarAmoli
- Department of Comparative Bioscience, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
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2
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Franco A, Vieira D, Clerbaux LA, Orgiazzi A, Labouyrie M, Köninger J, Silva V, van Dam R, Carnesecchi E, Dorne JLCM, Vuaille J, Lobo Vicente J, Jones A. Evaluation of the ecological risk of pesticide residues from the European LUCAS Soil monitoring 2018 survey. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2024; 20:1639-1653. [PMID: 38602265 DOI: 10.1002/ieam.4917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 02/01/2024] [Accepted: 03/07/2024] [Indexed: 04/12/2024]
Abstract
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).
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Affiliation(s)
- Antonio Franco
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Diana Vieira
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Laure-Alix Clerbaux
- European Commission, Joint Research Centre (JRC), Ispra, Italy
- Institute of Experimental and Clinical Research, University of Louvain, Louvain, Belgium
| | - Alberto Orgiazzi
- European Commission, Joint Research Centre (JRC), Ispra, Italy
- European Dynamics, Brussels, Belgium
| | - Maeva Labouyrie
- Plant-Soil-Interactions, Research Division Agroecology and Environment, Agroscope, Zürich, Switzerland
- Department of Plant and Microbial Biology, University of Zurich, Zürich, Switzerland
| | - Julia Köninger
- Departamento de Ecología y Biología Animal, Universidade de Vigo, Vigo, Spain
| | - Vera Silva
- Soil Physics and Land Management Group, Wageningen University & Research, Wageningen, The Netherlands
| | - Ruud van Dam
- Wageningen Food Safety Research (WFSR), Wageningen University & Research, Wageningen, The Netherlands
| | | | | | | | | | - Arwyn Jones
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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3
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Li X, Zhang F, Zheng L, Guo J. Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction. JOURNAL OF HAZARDOUS MATERIALS 2024; 475:134828. [PMID: 38876015 DOI: 10.1016/j.jhazmat.2024.134828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/09/2024] [Accepted: 06/03/2024] [Indexed: 06/16/2024]
Abstract
The prediction of ecological toxicity plays an increasingly important role in modern society. However, the existing models often suffer from poor performance and limited predictive capabilities. In this study, we propose a novel approach for ecological toxicity assessment based on pre-trained models. By leveraging pre-training techniques and graph neural network models, we establish a highperformance predictive model. Furthermore, we incorporate a variational autoencoder to optimize the model, enabling simultaneous discrimination of toxicity to bees and molecular degradability. Additionally, despite the low similarity between the endogenous hormones in bees and the compounds in our dataset, our model confidently predicts that these hormones are non-toxic to bees, which further strengthens the credibility and accuracy of our model. We also discovered the negative correlation between the degradation and bee toxicity of compounds. In summary, this study presents an ecological toxicity assessment model with outstanding performance. The proposed model accurately predicts the toxicity of chemicals to bees and their degradability capabilities, offering valuable technical support to relevant fields.
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Affiliation(s)
- Xinkang Li
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao
| | - Feng Zhang
- College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China
| | - Liangzhen Zheng
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; Zelixir Biotech Company Ltd. Shanghai, China.
| | - Jingjing Guo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao.
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4
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Daghighi A, Casanola-Martin GM, Iduoku K, Kusic H, González-Díaz H, Rasulev B. Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10116-10127. [PMID: 38797941 DOI: 10.1021/acs.est.4c01017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.
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Affiliation(s)
- Amirreza Daghighi
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, Zagreb 10000, Croatia
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa 48940, Spain
- BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, Leioa 48940, Spain
- IKERBASQUE, Basque Foundation for Science,Bilbao, Biscay 48011, Spain
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
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5
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Anandhi G, Iyapparaja M. Systematic approaches to machine learning models for predicting pesticide toxicity. Heliyon 2024; 10:e28752. [PMID: 38576573 PMCID: PMC10990867 DOI: 10.1016/j.heliyon.2024.e28752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 03/13/2024] [Accepted: 03/24/2024] [Indexed: 04/06/2024] Open
Abstract
Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.
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Affiliation(s)
- Ganesan Anandhi
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - M. Iyapparaja
- Department of Smart Computing, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
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6
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Dorne JLCM, Cortiñas‐Abrahantes J, Spyropoulos F, Darney K, Lautz L, Louisse J, Kass GEN, Carnesecchi E, Liem AKD, Tarazona JV, Billat P, Beaudoin R, Zeman F, Bodin C, Smith A, Nathanail A, Di Nicola MR, Kleiner J, Terron A, Parra‐Morte JM, Verloo D, Robinson T. TKPlate 1.0: An Open-access platform for toxicokinetic and toxicodynamic modelling of chemicals to implement new approach methodologies in chemical risk assessment. EFSA J 2023; 21:e211101. [PMID: 38027439 PMCID: PMC10644227 DOI: 10.2903/j.efsa.2023.e211101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023] Open
Abstract
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.
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7
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Fernandes DC, Tambourgi DV. Complement System Inhibitory Drugs in a Zebrafish ( Danio rerio) Model: Computational Modeling. Int J Mol Sci 2023; 24:13895. [PMID: 37762197 PMCID: PMC10530807 DOI: 10.3390/ijms241813895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
The dysregulation of complement system activation usually results in acute or chronic inflammation and can contribute to the development of various diseases. Although the activation of complement pathways is essential for innate defense, exacerbated activity of this system may be harmful to the host. Thus, drugs with the potential to inhibit the activation of the complement system may be important tools in therapy for diseases associated with complement system activation. The synthetic peptides Cp40 and PMX205 can be highlighted in this regard, given that they selectively inhibit the C3 and block the C5a receptor (C5aR1), respectively. The zebrafish (Danio rerio) is a robust model for studying the complement system. The aim of the present study was to use in silico computational modeling to investigate the hypothesis that these complement system inhibitor peptides interact with their target molecules in zebrafish, for subsequent in vivo validation. For this, we analyzed molecular docking interactions between peptides and target molecules. Our study demonstrated that Cp40 and the cyclic peptide PMX205 have positive interactions with their respective zebrafish targets, thus suggesting that zebrafish can be used as an animal model for therapeutic studies on these inhibitors.
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Affiliation(s)
| | - Denise V. Tambourgi
- Immunochemistry Laboratory, Butantan Institute, São Paulo 05503-900, Brazil;
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8
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Bo T, Lin Y, Han J, Hao Z, Liu J. Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse. JOURNAL OF HAZARDOUS MATERIALS 2023; 452:131344. [PMID: 37027914 DOI: 10.1016/j.jhazmat.2023.131344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/20/2023] [Accepted: 03/31/2023] [Indexed: 05/03/2023]
Abstract
Machine learning (ML) methods provide a new opportunity to build quantitative structure-activity relationship (QSAR) models for predicting chemicals' toxicity based on large toxicity data sets, but they are limited in insufficient model robustness due to poor data set quality for chemicals with certain structures. To address this issue and improve model robustness, we built a large data set on rat oral acute toxicity for thousands of chemicals, then used ML to filter chemicals favorable for regression models (CFRM). In comparison to chemicals not favorable for regression models (CNRM), CFRM accounted for 67% of chemicals in the original data set, and had a higher structural similarity and a smaller toxicity distribution in 2-4 log10 (mg/kg). The performance of established regression models for CFRM was greatly improved, with root-mean-square deviations (RMSE) in the range of 0.45-0.48 log10 (mg/kg). Classification models were built for CNRM using all chemicals in the original data set, and the area under receiver operating characteristic (AUROC) reached 0.75-0.76. The proposed strategy was successfully applied to a mouse oral acute data set, yielding RMSE and AUROC in the range of 0.36-0.38 log10 (mg/kg) and 0.79, respectively.
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Affiliation(s)
- Tao Bo
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China
| | - Yaohui Lin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China; Key Laboratory for Analytical Science of Food Safety and Biology of MOE, Fujian Provincial Key Lab of Analysis and Detection for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, Fujian 350116, China
| | - Jinglong Han
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Zhineng Hao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.
| | - Jingfu Liu
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.
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9
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Cattaneo I, Astuto MC, Binaglia M, Devos Y, Dorne JLCM, Ana FA, Fernandez DA, Garcia-Vello P, Kass GE, Lanzoni A, Liem AKD, Panzarea M, Paraskevopulos K, Parra Morte JM, Tarazona JV, Terron A. Implementing New Approach Methodologies (NAMs) in food safety assessments: Strategic objectives and actions taken by the European Food Safety Authority. Trends Food Sci Technol 2023. [DOI: 10.1016/j.tifs.2023.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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10
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Cattaneo I, Kalian AD, Di Nicola MR, Dujardin B, Levorato S, Mohimont L, Nathanail AV, Carnessechi E, Astuto MC, Tarazona JV, Kass GEN, Liem AKD, Robinson T, Manini P, Hogstrand C, Price PS, Dorne JLCM. Risk Assessment of Combined Exposure to Multiple Chemicals at the European Food Safety Authority: Principles, Guidance Documents, Applications and Future Challenges. Toxins (Basel) 2023; 15:40. [PMID: 36668860 PMCID: PMC9861867 DOI: 10.3390/toxins15010040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/21/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
Human health and animal health risk assessment of combined exposure to multiple chemicals use the same steps as single-substance risk assessment, namely problem formulation, exposure assessment, hazard assessment and risk characterisation. The main unique feature of combined RA is the assessment of combined exposure, toxicity and risk. Recently, the Scientific Committee of the European Food Safety Authority (EFSA) published two relevant guidance documents. The first one "Harmonised methodologies for the human health, animal health and ecological risk assessment of combined exposure to multiple chemicals" provides principles and explores methodologies for all steps of risk assessment together with a reporting table. This guidance supports also the default assumption that dose addition is applied for combined toxicity of the chemicals unless evidence for response addition or interactions (antagonism or synergism) is available. The second guidance document provides an account of the scientific criteria to group chemicals in assessment groups using hazard-driven criteria and prioritisation methods, i.e., exposure-driven and risk-based approaches. This manuscript describes such principles, provides a brief description of EFSA's guidance documents, examples of applications in the human health and animal health area and concludes with a discussion on future challenges in this field.
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Affiliation(s)
- Irene Cattaneo
- Methodology and Scientific Support Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Alexander D. Kalian
- Department of Nutritional Sciences, Faculty of Life Sciences & Medicine, King’s College London, Franklin-Wilkins Building, London SE1 9NH, UK
| | - Matteo R. Di Nicola
- Unit of Dermatology, IRCCS San Raffaele Hospital, Via Olgettin 60, 20132 Milan, Italy
| | - Bruno Dujardin
- Methodology and Scientific Support Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Sara Levorato
- Methodology and Scientific Support Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Luc Mohimont
- Plant Health and Pesticide Residues Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Alexis V. Nathanail
- Methodology and Scientific Support Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Edoardo Carnessechi
- iDATA Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Maria Chiara Astuto
- Methodology and Scientific Support Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Jose V. Tarazona
- Methodology and Scientific Support Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - George E. N. Kass
- Chief Scientist Office, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Antoine K. Djien Liem
- Methodology and Scientific Support Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Tobin Robinson
- Plant Health and Pesticide Residues Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Paola Manini
- Feed and Contaminants Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
| | - Christer Hogstrand
- Department of Nutritional Sciences, Faculty of Life Sciences & Medicine, King’s College London, Franklin-Wilkins Building, London SE1 9NH, UK
| | - Paul S. Price
- Retired United States Environmental Protection Agency (US EPA), 6408 Hoover Trail Road S.W., Cedar Rapids, IA 52404, USA
| | - Jean Lou C. M. Dorne
- Methodology and Scientific Support Unit, European Food Safety Authority, Via Carlo Magno 1A, 43216 Parma, Italy
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Chen Y, Dong Y, Li L, Jiao J, Liu S, Zou X. Toxicity Rank Order (TRO) As a New Approach for Toxicity Prediction by QSAR Models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:701. [PMID: 36613021 PMCID: PMC9819504 DOI: 10.3390/ijerph20010701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Quantitative Structure-Activity Relationship (QSAR) models are commonly used for risk assessment of emerging contaminants. The objective of this study was to use a toxicity rank order (TRO) as an integrating parameter to improve the toxicity prediction by QSAR models. TRO for each contaminant was calculated from collected toxicity data including acute toxicity concentration and no observed effect concentration. TRO values associated with toxicity mechanisms were used to classify pollutants into three modes of action consisting of narcosis, transition and reactivity. The selection principle of parameters for QSAR models was established and verified. It showed a reasonable prediction of toxicities caused by organophosphates and benzene derivatives, especially. Compared with traditional procedures, incorporating TRO showed an improved correlation coefficient of QSAR models by approximately 10%. Our study indicated that the proposed procedure can be used for screening modeling parameter data and improve the toxicity prediction by QSAR models, and this could facilitate prediction and evaluation of environmental contaminant toxicity.
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Affiliation(s)
- Yuting Chen
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Yuying Dong
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Le Li
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Jian Jiao
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Sitong Liu
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Xuejun Zou
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
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12
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Tosi S, Sfeir C, Carnesecchi E, vanEngelsdorp D, Chauzat MP. Lethal, sublethal, and combined effects of pesticides on bees: A meta-analysis and new risk assessment tools. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 844:156857. [PMID: 35760183 DOI: 10.1016/j.scitotenv.2022.156857] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 06/06/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
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.
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Affiliation(s)
- Simone Tosi
- Paris-Est University, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Laboratory for Animal Health, Maisons-Alfort, France; Department of Agricultural, Forest, and Food Sciences, University of Turin, Italy.
| | - Cynthia Sfeir
- Paris-Est University, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Laboratory for Animal Health, Maisons-Alfort, France
| | - Edoardo Carnesecchi
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508, TD, Utrecht, the Netherlands
| | - Dennis vanEngelsdorp
- Department of Entomology, University of Maryland, 4112 Plant Sciences Building, College Park, MD, 20742-4454, USA
| | - Marie-Pierre Chauzat
- Paris-Est University, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Laboratory for Animal Health, Maisons-Alfort, France; ANSES, Sophia Antipolis laboratory, Unit of Honey bee Pathology, European Reference Laboratory for Honeybee health, F-06902 Sophia Antipolis, France
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13
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Toropov AA, Di Nicola MR, Toropova AP, Roncaglioni A, Carnesecchi E, Kramer NI, Williams AJ, Ortiz-Santaliestra ME, Benfenati E, Dorne JLCM. A regression-based QSAR-model to predict acute toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica): Calibration, validation, and future developments to support risk assessment of chemicals in amphibians. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 830:154795. [PMID: 35341855 PMCID: PMC9535814 DOI: 10.1016/j.scitotenv.2022.154795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/16/2022] [Accepted: 03/20/2022] [Indexed: 04/15/2023]
Abstract
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.
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Affiliation(s)
- Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Matteo R Di Nicola
- Unit of Dermatology and Cosmetology, IRCCS San Raffaele Hospital, Via Olgettina 60, 20132 Milan, Italy; Toxicology Division, Wageningen University, PO Box 8000, 6700 EA Wageningen, the Netherlands.
| | - Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Alessandra Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Edoardo Carnesecchi
- Institute of Risk Assessment, Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Evidence Management Unit, European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy.
| | - Nynke I Kramer
- Toxicology Division, Wageningen University, PO Box 8000, 6700 EA Wageningen, the Netherlands; Institute of Risk Assessment, Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands.
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, Durham, USA.
| | - Manuel E Ortiz-Santaliestra
- Instituto de Investigación en Recursos Cinegéticos (IREC) UCLM-CSIC-JCCM, Ronda de Toledo 12, 13005 Ciudad Real, Spain.
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Jean-Lou C M Dorne
- Methodology and Scientific Support Unit, European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy.
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14
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Larras F, Charles S, Chaumot A, Pelosi C, Le Gall M, Mamy L, Beaudouin R. A critical review of effect modeling for ecological risk assessment of plant protection products. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:43448-43500. [PMID: 35391640 DOI: 10.1007/s11356-022-19111-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 02/03/2022] [Indexed: 06/14/2023]
Abstract
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).
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Affiliation(s)
- Floriane Larras
- INRAE, Directorate for Collective Scientific Assessment, Foresight and Advanced Studies, Paris, 75338, France
| | - Sandrine Charles
- University of Lyon, University Lyon 1, CNRS UMR 5558, Laboratory of Biometry and Evolutionary Biology, Villeurbanne Cedex, 69622, France
| | - Arnaud Chaumot
- INRAE, UR RiverLy, Ecotoxicology laboratory, Villeurbanne, F-69625, France
| | - Céline Pelosi
- Avignon University, INRAE, UMR EMMAH, Avignon, 84000, France
| | - Morgane Le Gall
- Ifremer, Information Scientifique et Technique, Bibliothèque La Pérouse, Plouzané, 29280, France
| | - Laure Mamy
- Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Thiverval-Grignon, 78850, France
| | - Rémy Beaudouin
- Ineris, Experimental Toxicology and Modelling Unit, UMR-I 02 SEBIO, Verneuil en Halatte, 65550, France.
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15
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Mukherjee RK, Kumar V, Roy K. Chemometric modeling of plant protection products (PPPs) for the prediction of acute contact toxicity against honey bees (A. mellifera): A 2D-QSAR approach. JOURNAL OF HAZARDOUS MATERIALS 2022; 423:127230. [PMID: 34844352 DOI: 10.1016/j.jhazmat.2021.127230] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 06/13/2023]
Abstract
Honey bees (Apis mellifera) are vital for economic, viable agriculture and for food safety. Although Plant Protection Products (PPPs) are of undeniable importance in the global agricultural system, these have become potential threats for non-target organisms like pollinators (e.g., honey bees etc.), resulting in the disruption of the ecological balance. In the current work, we have used the 113 PPP analogs to develop a 2D-QSAR model and explored the structural features modulating the toxic effects on honey bees, following the Organization for Economic Co-operation and Development (OECD) guidelines. The extensive validation of the developed model has been performed using internal and external validation metrics to make sure that the model is statistically sound and interpretable enough to be acceptable. The obtained results (R2 = 0.666, Q2 = 0.594, Q2F1 = 0.647 and Q2F2 = 0.646) determine the predictability and reliability of the developed model. This model should be useful for the predictions (acute contact toxicity (LD50)) of the new and untested compounds located inside the applicability domain of the developed model. Moreover, we have performed the in-silico prediction of toxicity against honey bees of a total of 709 compounds obtained from the pesticide properties database (PPDB) using the developed model.
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Affiliation(s)
- Rajendra Kumar Mukherjee
- Drug Theoretics and Cheminformatics (DTC) Laboratory,Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics (DTC) Laboratory,Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory,Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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16
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Shen C, Pan X, Wu X, Xu J, Dong F, Zheng Y. Ecological risk assessment for difenoconazole in aquatic ecosystems using a web-based interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) model. CHEMOSPHERE 2022; 289:133236. [PMID: 34896421 DOI: 10.1016/j.chemosphere.2021.133236] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/07/2021] [Accepted: 12/08/2021] [Indexed: 06/14/2023]
Abstract
Difenoconazole is a typical triazole fungicide that can inhibit demethylation during ergosterol synthesis. Due to its wide use, difenoconazole is frequently detected in surface water, paddy water, agricultural water, and other aquatic environments. Presently, an assessment of the ecological risk posed by difenoconazole in aquatic ecosystems is lacking. Here, a web-based interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) model was first applied to assess the ecological risk of difenoconazole in aquatic environments. Meanwhile, maximum acceptable concentration (MAC), maximum risk-free concentration (MRFC), and risk quotient (RQ) values were used to evaluate the potential risk of difenoconazole to aquatic organisms. Our results showed that an aquatic MAC value of 0.31 μg/L was acceptable for difenoconazole in aquatic environments. Further, the detected concentration of difenoconazole was lower than the MRFC value of 0.09 μg/L indicating no risk to aquatic organisms. Assessment data suggested that difenoconazole exhibited potential risks to eight studied aquatic ecosystems (including surface water, paddy water, and agricultural water) in different countries (RQ > 1), indicating that difenoconazole overuse could cause adverse effects to aquatic organisms in these aquatic ecosystems. Thus, restricted use and rational use of difenoconazole are recommended.
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Affiliation(s)
- Chao Shen
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, PR China
| | - Xinglu Pan
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, PR China
| | - Xiaohu Wu
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, PR China
| | - Jun Xu
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, PR China
| | - Fengshou Dong
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, PR China.
| | - Yongquan Zheng
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100193, PR China
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17
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Astuto MC, Di Nicola MR, Tarazona JV, Rortais A, Devos Y, Liem AKD, Kass GEN, Bastaki M, Schoonjans R, Maggiore A, Charles S, Ratier A, Lopes C, Gestin O, Robinson T, Williams A, Kramer N, Carnesecchi E, Dorne JLCM. In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives. Methods Mol Biol 2022; 2425:589-636. [PMID: 35188648 DOI: 10.1007/978-1-0716-1960-5_23] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
| | | | | | - A Rortais
- European Food Safety Authority, Parma, Italy
| | - Yann Devos
- European Food Safety Authority, Parma, Italy
| | | | | | | | | | | | | | | | | | | | | | - Antony Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, NC, USA
| | - Nynke Kramer
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
| | - Edoardo Carnesecchi
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
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18
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Wang Y, Zhu YC, Li W, Yao J, Reddy GVP, Lv L. Binary and ternary toxicological interactions of clothianidin and eight commonly used pesticides on honey bees (Apis mellifera). ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 223:112563. [PMID: 34343900 DOI: 10.1016/j.ecoenv.2021.112563] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/23/2021] [Accepted: 07/24/2021] [Indexed: 06/13/2023]
Abstract
Although many toxicological evaluations have been conducted for honey bees (Apis mellifera), most of these studies have only focused on the effects of individual chemicals. However, honey bees are usually exposed to pesticide mixtures under field conditions. In this study, we examined the effects of individual pesticides and mixtures of clothianidin (CLO) with eight other pesticides [carbaryl (CAR), thiodicarb (THI), chlorpyrifos (CHL), beta-cyfluthrin (BCY), gamma-cyhalothrin (GCY), tetraconazole (TET), spinosad (SPI) and indoxacarb (IND)] on honey bees using a feeding method. Toxicity tests of a 4-day exposure to individual pesticides revealed that CLO had the highest toxicity to A. mellifera, with an LC50 value of 0.24 μg a.i. mL-1, followed by IND and CHL with LC50 values of 3.40 and 3.56 μg a.i. mL-1, respectively. SPI and CAR had relatively low toxicities, with LC50 values of 7.19 and 8.42 μg a.i. mL-1, respectively. In contrast, TET exhibited the least toxicity, with an LC50 value of 258.7 μg a.i. mL-1. Most binary mixtures of CLO with other pesticides exerted additive and antagonistic effects. However, all the ternary mixtures containing CLO and TET (except for CLO+TET+THD) elicited synergistic responses to bees. Either increased numbers of components in the mixture or/and a unique mode of action appeared to be responsible for the higher toxicity of mixtures. Our findings emphasized the need for risk assessment of pesticide mixtures rather than the individual chemicals. Our data also provided information that might help growers avoid increased toxicity and unnecessary injury to pollinators.
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Affiliation(s)
- Yanhua Wang
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products / Key Laboratory of Detection for Pesticide Residue and Control of Zhejiang Province, Institute of Quality and Standard for Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, Zhejiang, PR China; United States Department of Agriculture, Agricultural Research Service (USDA-ARS), 141 Experiment Station Road, Stoneville, MS 38776, USA
| | - Yu-Cheng Zhu
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS), 141 Experiment Station Road, Stoneville, MS 38776, USA.
| | - Wenhong Li
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS), 141 Experiment Station Road, Stoneville, MS 38776, USA; Guizhou Institute of Plant Protection, Guizhou Academy of Agricultural Sciences, Guiyang 550006, Guizhou, PR China
| | - Jianxiu Yao
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS), 141 Experiment Station Road, Stoneville, MS 38776, USA; Kansas State University, Manhattan, KS 66506, USA
| | - Gadi V P Reddy
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS), 141 Experiment Station Road, Stoneville, MS 38776, USA
| | - Lu Lv
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products / Key Laboratory of Detection for Pesticide Residue and Control of Zhejiang Province, Institute of Quality and Standard for Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, Zhejiang, PR China
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19
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Ouyang Y, Huang JJ, Wang YL, Zhong H, Song BA, Hao GF. In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness? JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:10761-10773. [PMID: 34516106 DOI: 10.1021/acs.jafc.1c01460] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Unfavorable bioavailability is an important aspect underlying the failure of drug candidates. Computational approaches for evaluating drug-likeness can minimize these risks. Over the past decades, computational approaches for evaluating drug-likeness have sped up the process of drug development and were also quickly derived to pesticide-likeness. As a result of many critical differences between drugs and pesticides, many kinds of methods for drug-likeness cannot be used for pesticide-likeness. Therefore, it is crucial to comprehensively compare and analyze the differences between drug-likeness and pesticide-likeness, which may provide a basis for solving the problems encountered during the evaluation of pesticide-likeness. Here, we systematically collected the recent advances of drug-likeness and pesticide-likeness and compared their characteristics. We also evaluated the current lack of studies on pesticide-likeness, the molecular descriptors and parameters adopted, the pesticide-likeness model on pesticide target organisms, and comprehensive analysis tools. This work may guide researchers to use appropriate methods for developing pesticide-likeness models. It may also aid non-specialists to understand some important concepts in drug-likeness and pesticide-likeness.
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Affiliation(s)
- Yan Ouyang
- Guizhou Engineering Laboratory for Synthetic Drugs, Key Laboratory of Guizhou Fermentation Engineering and Biomedicine, School of Pharmaceutical Sciences, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
| | - Jun-Jie Huang
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
| | - Yu-Liang Wang
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Hang Zhong
- Guizhou Engineering Laboratory for Synthetic Drugs, Key Laboratory of Guizhou Fermentation Engineering and Biomedicine, School of Pharmaceutical Sciences, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
| | - Bao-An Song
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
| | - Ge-Fei Hao
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang, Guizhou 550025, People's Republic of China
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20
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Belsky J, Biddinger DJ, Joshi NK. Whole-Body Acute Contact Toxicity of Formulated Insecticide Mixtures to Blue Orchard Bees ( Osmia lignaria). TOXICS 2021; 9:toxics9030061. [PMID: 33802682 PMCID: PMC8002567 DOI: 10.3390/toxics9030061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 02/25/2021] [Accepted: 03/02/2021] [Indexed: 11/16/2022]
Abstract
Blue orchard bees, [Osmia lignaria (Say) (Hymenoptera: Megachilidae)], have been developed as an important pollinator for orchard crops in North America over the last 40 years. The toxicity of several pesticides to O. lignaria and other Osmia species has been previously reported. However, the field-realistic toxicity of formulated premix insecticides comprised of multiple active ingredients (each with a different mode of action) to O. lignaria has not been assessed. Here, we use a customized spray tower in a laboratory setting to assess adult male and female whole-body direct contact exposure to four formulated pesticide mixtures: thiamethoxam + lambda-cyhalothrin (TLC), imidacloprid + beta-cyfluthrin (IBC), chlorantraniliprole + lambda-cyhalothrin (CLC) and methoxyfenozide + spinetoram (MS) by directly spraying anesthetized bees in Petri dishes. Separately, adult male and female whole-body direct contact exposure to formulated imidacloprid (I), beta-cyfluthrin (BC) and their 1:1 binary combination (IBC) was assessed using the same experimental method. Resulting mortality in each study was screened up to 96 h post-treatment to determine acute whole-body contact toxicity. In the first study, TLC and IBC resulted in statistically higher mortality at 24 and 48 h than the two other insecticide combinations tested. The CLC and MS combinations were slower acting and the highest mortality for O. lignaria exposed to these mixtures was recorded at 96 h. We did observe significant differences in toxicity between CLC and MS. In the second study, exposure to the 1:1 binary combination of IBC caused overall significantly higher mortality than exposure to I or BC alone. Both active ingredients alone, however, demonstrated equivalent levels of mortality to the 1:1 binary combination treatment at the 96 h observation reading, indicating increased speed of kill, but not necessarily increased toxicity. Significant differences in the onset of mortality following acute contact whole-body exposure to the formulated insecticide mixtures and individual active ingredients tested were consistently observed across all experiments in both studies.
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Affiliation(s)
- Joseph Belsky
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA;
| | - David J. Biddinger
- Department of Entomology, The Pennsylvania State University, University Park, PA 16801, USA;
- Penn State Fruit Research and Extension Center, Biglerville, PA 17307, USA
| | - Neelendra K. Joshi
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA;
- Correspondence:
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21
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Dorne JLCM, Richardson J, Livaniou A, Carnesecchi E, Ceriani L, Baldin R, Kovarich S, Pavan M, Saouter E, Biganzoli F, Pasinato L, Zare Jeddi M, Robinson TP, Kass GEN, Liem AKD, Toropov AA, Toropova AP, Yang C, Tarkhov A, Georgiadis N, Di Nicola MR, Mostrag A, Verhagen H, Roncaglioni A, Benfenati E, Bassan A. EFSA's OpenFoodTox: An open source toxicological database on chemicals in food and feed and its future developments. ENVIRONMENT INTERNATIONAL 2021; 146:106293. [PMID: 33395940 DOI: 10.1016/j.envint.2020.106293] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 11/11/2020] [Accepted: 11/16/2020] [Indexed: 05/12/2023]
Abstract
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.
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Affiliation(s)
- J L C M Dorne
- European Food Safety Authority, Via Carlo Magno, 1A, 43126 Parma, Italy.
| | - J Richardson
- European Food Safety Authority, Via Carlo Magno, 1A, 43126 Parma, Italy
| | - A Livaniou
- European Food Safety Authority, Via Carlo Magno, 1A, 43126 Parma, Italy
| | - E Carnesecchi
- Istituto di Ricerche Farmacologico Mario Negri, Via La Masa 19, 20156 Milano, Italy; Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands
| | - L Ceriani
- S-IN Soluzioni Informatiche, Via Ferrari 14, 36100 Vicenza, Italy
| | - R Baldin
- S-IN Soluzioni Informatiche, Via Ferrari 14, 36100 Vicenza, Italy
| | - S Kovarich
- S-IN Soluzioni Informatiche, Via Ferrari 14, 36100 Vicenza, Italy
| | - M Pavan
- S-IN Soluzioni Informatiche, Via Ferrari 14, 36100 Vicenza, Italy
| | - E Saouter
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - F Biganzoli
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - L Pasinato
- European Food Safety Authority, Via Carlo Magno, 1A, 43126 Parma, Italy
| | - M Zare Jeddi
- European Food Safety Authority, Via Carlo Magno, 1A, 43126 Parma, Italy; Istituto di Ricerche Farmacologico Mario Negri, Via La Masa 19, 20156 Milano, Italy
| | - T P Robinson
- European Food Safety Authority, Via Carlo Magno, 1A, 43126 Parma, Italy
| | - G E N Kass
- European Food Safety Authority, Via Carlo Magno, 1A, 43126 Parma, Italy
| | - A K D Liem
- European Food Safety Authority, Via Carlo Magno, 1A, 43126 Parma, Italy
| | - A A Toropov
- Istituto di Ricerche Farmacologico Mario Negri, Via La Masa 19, 20156 Milano, Italy
| | - A P Toropova
- Istituto di Ricerche Farmacologico Mario Negri, Via La Masa 19, 20156 Milano, Italy
| | - C Yang
- MN-AM, 90411 Nürnberg, Germany
| | | | | | | | | | - H Verhagen
- European Food Safety Authority, Via Carlo Magno, 1A, 43126 Parma, Italy; University of Ulster, Coleraine, Northern Ireland, UK
| | - A Roncaglioni
- Istituto di Ricerche Farmacologico Mario Negri, Via La Masa 19, 20156 Milano, Italy
| | - E Benfenati
- Istituto di Ricerche Farmacologico Mario Negri, Via La Masa 19, 20156 Milano, Italy
| | - A Bassan
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands
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Madden JC, Enoch SJ, Paini A, Cronin MTD. A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications. Altern Lab Anim 2020; 48:146-172. [PMID: 33119417 DOI: 10.1177/0261192920965977] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.
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Affiliation(s)
- Judith C Madden
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Alicia Paini
- 99013European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
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