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Rahimi-Soujeh Z, Safaie N, Moradi S, Abbod M, Sharifi R, Mojerlou S, Mokhtassi-Bidgoli A. New binary mixtures of fungicides against Macrophomina phaseolina: machine learning-driven QSAR, read-across prediction, and molecular dynamics simulation. CHEMOSPHERE 2024:143533. [PMID: 39419329 DOI: 10.1016/j.chemosphere.2024.143533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Revised: 10/10/2024] [Accepted: 10/12/2024] [Indexed: 10/19/2024]
Abstract
Quantitative Structure-Activity Relationship (QSAR) analysis greatly enhances the development and research of pesticides. This study employed Multiple Linear Regression (MLR), machine learning (ML), and read-across (RA) approaches to investigate the combined effects of binary mixtures of fungicides on Macrophomina phaseolina. Using the Fixed Ratio Ray Design (FRRD) method, 75 binary mixtures of six frequently used fungicides were generated, with many exhibiting additive interactions as indicated by the Concentration Addition (CA) and Independent Action (IA) models. The QSAR analysis revealed that Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models were the most effective, outperforming the Least Squares Kernel (LSK), MLR, and RA methods. SVR achieved an outstanding R2 of 0.95 and Q2LMO of 0.84, whereas GPR demonstrated values of 0.93 and 0.81 for the same metrics. Internal and external validation confirmed the reliability and generalizability of these models, suggesting they could be applied to a wider array of data. Moreover, Molecular Dynamics (MD) simulations showed that the effects of the fungicides are linked to physiological mechanisms rather than intermolecular interactions within their formulations. This study establishes a robust framework for creating potent fungicide combinations that improve disease management efficacy while promoting environmental sustainability and reducing the chemical load to mitigate negative impacts.
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Affiliation(s)
- Zaniar Rahimi-Soujeh
- Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Naser Safaie
- Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
| | - Sajad Moradi
- Nano Drug Delivery Research Center, Health Technology Institute, Kermanshah University of Medical, Kermanshah, Iran
| | - Mohsen Abbod
- Department of Plant Protection, Faculty of Agriculture, Al-Baath University, Homs, Syria
| | - Rouhalah Sharifi
- Department of Plant Protection, Faculty of Agricultural Engineering, Razi University, Kermanshah, Iran
| | - Shideh Mojerlou
- Department of Horticulture and Plant Protection, Faculty of Agriculture, Shahrood University of Technology, Shahrood, Iran
| | - Ali Mokhtassi-Bidgoli
- Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
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2
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Sharifi M, Harwood GP, Harris M, Patel DM, Collison E, Lunsman T. Leveraging In Silico Structure-Activity Models to Predict Acute Honey Bee ( Apis mellifera) Toxicity for Agrochemicals. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:20775-20782. [PMID: 39258845 DOI: 10.1021/acs.jafc.4c02518] [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: 09/12/2024]
Abstract
In the realm of crop protection products, ensuring the safety of pollinators stands as a pivotal aspect of advancing sustainable solutions. Extensive research has been dedicated to this crucial topic as well as new approach methodologies in toxicity testing. Hence, within the agricultural and chemical industries, prioritizing pollinator safety remains a constant objective during the development of predictive tools. One of these tools includes computational models like quantitative structure-activity relationships (QSARs) that are valuable in predicting the toxicity of chemicals. This research uses bee toxicity data to develop artificial neural network classification models for predicting honey bee acute toxicity. Bee toxicity data from 1542 compounds were used to develop models; the sensitivity and specificity of the best model were 0.90 and 0.91, respectively. These in silico models can aid in the discovery of next-generation crop protection products. These tools can guide the screening and selection of next-generation crop protection molecules with high margins of safety to pollinators, and candidates with favorable sustainability profiles can be identified at the early discovery stage as precursors to in vivo data generation.
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Affiliation(s)
- Max Sharifi
- Predictive Safety Center, Regulatory and Stewardship, Corteva Agriscience, 9330 Zionsville Road, Indianapolis, Indiana 46268, United States
| | - Gyan P Harwood
- Predictive Safety Center, Regulatory and Stewardship, Corteva Agriscience, 9330 Zionsville Road, Indianapolis, Indiana 46268, United States
| | - Melissa Harris
- Predictive Safety Center, Regulatory and Stewardship, Corteva Agriscience, 9330 Zionsville Road, Indianapolis, Indiana 46268, United States
| | - Drew M Patel
- Predictive Safety Center, Regulatory and Stewardship, Corteva Agriscience, 9330 Zionsville Road, Indianapolis, Indiana 46268, United States
| | - Elizabeth Collison
- Corteva Agriscience Regulatory Innovation Centre, 101E Park Drive, Abingdon OX14 4RY, U.K
| | - Tamara Lunsman
- Predictive Safety Center, Regulatory and Stewardship, Corteva Agriscience, 9330 Zionsville Road, Indianapolis, Indiana 46268, United States
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3
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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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Gao YY, Zhao W, Huang YQ, Kumar V, Zhang X, Hao GF. In silico environmental risk assessment improves efficiency for pesticide safety management. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:167878. [PMID: 37858821 DOI: 10.1016/j.scitotenv.2023.167878] [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: 08/03/2023] [Revised: 10/09/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
Pesticides are indispensable to maintain crop quality and food production worldwide, but their use also poses environmental risks. Pesticide risk assessment involves a series of complex, expensive and time-consuming toxicity tests. To improve the efficiency and accuracy for assessing the environmental impact of pesticides, numerous computational tools have been developed. However, there is a notable deficiency in critical analysis or a systematic summary of environmental risk assessment tools and their applicable contexts. Here, many of the current approaches and tools for assessing environmental risks posed by pesticides are reviewed, and the question of whether these tools are fit for use on complex multicomponent scenarios is discussed. We analyze the adaptations of these tools to aquatic and terrestrial ecosystems, followed by the provision of resources for predicting pesticide concentrations in environmental medias, including air, soil and water. The successful application of computational tools for risk assessment and interpretation of predicted results will also be discussed. This assessment serves as a valuable resource, enabling scientists to utilize suitable models to enhance the robustness of pesticides risk assessments.
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Affiliation(s)
- Yang-Yang Gao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Wei Zhao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Yuan-Qin Huang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Vinit Kumar
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Xiao Zhang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China; National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan 430079, PR China.
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5
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Djoumbou-Feunang Y, Wilmot J, Kinney J, Chanda P, Yu P, Sader A, Sharifi M, Smith S, Ou J, Hu J, Shipp E, Tomandl D, Kumpatla SP. Cheminformatics and artificial intelligence for accelerating agrochemical discovery. Front Chem 2023; 11:1292027. [PMID: 38093816 PMCID: PMC10716421 DOI: 10.3389/fchem.2023.1292027] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/09/2023] [Indexed: 10/17/2024] Open
Abstract
The global cost-benefit analysis of pesticide use during the last 30 years has been characterized by a significant increase during the period from 1990 to 2007 followed by a decline. This observation can be attributed to several factors including, but not limited to, pest resistance, lack of novelty with respect to modes of action or classes of chemistry, and regulatory action. Due to current and projected increases of the global population, it is evident that the demand for food, and consequently, the usage of pesticides to improve yields will increase. Addressing these challenges and needs while promoting new crop protection agents through an increasingly stringent regulatory landscape requires the development and integration of infrastructures for innovative, cost- and time-effective discovery and development of novel and sustainable molecules. Significant advances in artificial intelligence (AI) and cheminformatics over the last two decades have improved the decision-making power of research scientists in the discovery of bioactive molecules. AI- and cheminformatics-driven molecule discovery offers the opportunity of moving experiments from the greenhouse to a virtual environment where thousands to billions of molecules can be investigated at a rapid pace, providing unbiased hypothesis for lead generation, optimization, and effective suggestions for compound synthesis and testing. To date, this is illustrated to a far lesser extent in the publicly available agrochemical research literature compared to drug discovery. In this review, we provide an overview of the crop protection discovery pipeline and how traditional, cheminformatics, and AI technologies can help to address the needs and challenges of agrochemical discovery towards rapidly developing novel and more sustainable products.
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Affiliation(s)
| | - Jeremy Wilmot
- Corteva Agriscience, Crop Protection Discovery and Development, Indianapolis, IN, United States
| | - John Kinney
- Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States
| | - Pritam Chanda
- Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States
| | - Pulan Yu
- Corteva Agriscience, Crop Protection Discovery and Development, Indianapolis, IN, United States
| | - Avery Sader
- Corteva Agriscience, Crop Protection Discovery and Development, Indianapolis, IN, United States
| | - Max Sharifi
- Corteva Agriscience, Regulatory and Stewardship, Indianapolis, IN, United States
| | - Scott Smith
- Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States
| | - Junjun Ou
- Corteva Agriscience, Crop Protection Discovery and Development, Indianapolis, IN, United States
| | - Jie Hu
- Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States
| | - Elizabeth Shipp
- Corteva Agriscience UK Limited, Regulation Innovation Center, Abingdon, United Kingdom
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6
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Chatterjee M, Banerjee A, Tosi S, Carnesecchi E, Benfenati E, Roy K. Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees. JOURNAL OF HAZARDOUS MATERIALS 2023; 460:132358. [PMID: 37634379 DOI: 10.1016/j.jhazmat.2023.132358] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 08/02/2023] [Accepted: 08/20/2023] [Indexed: 08/29/2023]
Abstract
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 q-RASAR 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.
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Affiliation(s)
- Mainak Chatterjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Simone Tosi
- Department of Agricultural, Forest, and Food Sciences, University of Turin, Turin, Italy
| | | | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, via Mario Negri 2, 20156 Milano, Italy
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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Straw EA, Cini E, Gold H, Linguadoca A, Mayne C, Rockx J, Brown MJF, Garratt MPD, Potts SG, Senapathi D. Neither sulfoxaflor, Crithidia bombi, nor their combination impact bumble bee colony development or field bean pollination. Sci Rep 2023; 13:16462. [PMID: 37777537 PMCID: PMC10542809 DOI: 10.1038/s41598-023-43215-6] [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: 01/09/2023] [Accepted: 09/21/2023] [Indexed: 10/02/2023] Open
Abstract
Many pollinators, including bumble bees, are in decline. Such declines are known to be driven by a number of interacting factors. Decreases in bee populations may also negatively impact the key ecosystem service, pollination, that they provide. Pesticides and parasites are often cited as two of the drivers of bee declines, particularly as they have previously been found to interact with one another to the detriment of bee health. Here we test the effects of an insecticide, sulfoxaflor, and a highly prevalent bumble bee parasite, Crithidia bombi, on the bumble bee Bombus terrestris. After exposing colonies to realistic doses of either sulfoxaflor and/or Crithidia bombi in a fully crossed experiment, colonies were allowed to forage on field beans in outdoor exclusion cages. Foraging performance was monitored, and the impacts on fruit set were recorded. We found no effect of either stressor, or their interaction, on the pollination services they provide to field beans, either at an individual level or a whole colony level. Further, there was no impact of any treatment, in any metric, on colony development. Our results contrast with prior findings that similar insecticides (neonicotinoids) impact pollination services, and that sulfoxaflor impacts colony development, potentially suggesting that sulfoxaflor is a less harmful compound to bee health than neonicotinoids insecticides.
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Affiliation(s)
- Edward A Straw
- Department of Botany, Trinity College Dublin, Dublin, D02 PN40, Ireland
- Department of Biological Sciences, Centre for Ecology, Evolution and Behaviour, School of Life Sciences and the Environment, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK
| | - Elena Cini
- Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading, RG6 6AR, UK.
| | - Harriet Gold
- The School of Archaeology, Geography and Environmental Sciences, University of Reading, Reading, RG6 6AB, UK
| | - Alberto Linguadoca
- Department of Biological Sciences, Centre for Ecology, Evolution and Behaviour, School of Life Sciences and the Environment, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK
- Pesticides Peer Review Unit, European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126, Parma, Italy
| | - Chloe Mayne
- School of Biological Sciences, University of Reading, Reading, RG6 6AS, UK
| | - Joris Rockx
- Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading, RG6 6AR, UK
| | - Mark J F Brown
- Department of Biological Sciences, Centre for Ecology, Evolution and Behaviour, School of Life Sciences and the Environment, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK
| | - Michael P D Garratt
- Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading, RG6 6AR, UK
| | - Simon G Potts
- Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading, RG6 6AR, UK
| | - Deepa Senapathi
- Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading, RG6 6AR, UK.
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8
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Yang YT, Ni HG. Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures. WATER RESEARCH 2023; 236:119981. [PMID: 37084578 DOI: 10.1016/j.watres.2023.119981] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/03/2023]
Abstract
As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.
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Affiliation(s)
- Yu-Ting Yang
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Hong-Gang Ni
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
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9
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Toropova AP, Toropov AA, Roncaglioni A, Benfenati E, Leszczynska D, Leszczynski J. CORAL: Model of Ecological Impact of Heavy Metals on Soils via the Study of Modification of Concentration of Biomolecules in Earthworms (Eisenia fetida). ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2023; 84:504-515. [PMID: 37202557 DOI: 10.1007/s00244-023-01001-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 04/25/2023] [Indexed: 05/20/2023]
Abstract
The traditional application for quantitative structure-property/activity relationships (QSPRs/QSARs) in the fields of thermodynamics, toxicology or drug design is predicting the impact of molecular features using data on the measurable characteristics of substances. However, it is often necessary to evaluate the influence of various exposure conditions and environmental factors, besides the molecular structure. Different enzyme-driven processes lead to the accumulation of metal ions by the worms. Heavy metals are sequestered in these organisms without being released back into the soil. In this study, we propose a novel approach for modeling the absorption of heavy metals, such as mercury and cobalt by worms. The models are based on optimal descriptors calculated for the so-called quasi-SMILES, which incorporate strings of codes reflecting experimental conditions. We modeled the impact on the levels of proteins, hydrocarbons, and lipids in an earthworm's body caused by different combinations of concentrations of heavy metals and exposure time observed over two months of exposure with a measurement interval of 15 days.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS, 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA
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10
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Toropova AP, Toropov AA, Roncaglioni A, Benfenati E. The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity. TOXICS 2023; 11:toxics11050419. [PMID: 37235234 DOI: 10.3390/toxics11050419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/11/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023]
Abstract
Removing a drug-like substance that can cause drug-induced liver injury from the drug discovery process is a significant task for medicinal chemistry. In silico models can facilitate this process. Semi-correlation is an approach to building in silico models representing the prediction in the active (1)-inactive (0) format. The so-called system of self-consistent models has been suggested as an approach for two tasks: (i) building up a model and (ii) estimating its predictive potential. However, this approach has been tested so far for regression models. Here, the approach is applied to building up and estimating a categorical hepatotoxicity model using the CORAL software. This new process yields good results: sensitivity = 0.77, specificity = 0.75, accuracy = 0.76, and Matthew correlation coefficient = 0.51 (all compounds) and sensitivity = 0.83, specificity = 0.81, accuracy = 0.83 and Matthew correlation coefficient = 0.63 (validation set).
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Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Andrey A Toropov
- 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
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
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11
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Zhu T, Chen Y, Tao C. Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159448. [PMID: 36252662 DOI: 10.1016/j.scitotenv.2022.159448] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/06/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
As an essential environmental property, the aqueous solubility quantifies the hydrophobicity of a compound. It could be further utilized to evaluate the ecological risk and toxicity of organic pollutants. Concerned about the proliferation of organic contaminants in water and the associated technical burden, researchers have developed QSPR models to predict aqueous solubility. However, there are no standard procedures or best practices on how to comprehensively evaluate models. Hence, the CRITIC-TOPSIS comprehensive assessment method was first-ever proposed according to a variety of statistical parameters in the environmental model research field. 39 models based on 13 ML algorithms (belonged to 4 tribes) and 3 descriptor screening methods, were developed to calculate aqueous solubility values (log Kws) for organic chemicals reliably and verify the effectiveness of the comprehensive assessment method. The evaluations were carried out for exhibiting better predictive accuracy and external competitiveness of the MLR-1, XGB-1, DNN-1, and kNN-1 models in contrast to other prediction models in each tribe. Further, XGB model based on SRM (XGB-1, C = 0.599) was selected as an optimal pathway for prediction of aqueous solubility. We hope that the proposed comprehensive evaluation approach could act as a promising tool for selecting the optimum environmental property prediction methods.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Ying Chen
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
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12
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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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13
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Chatterjee M, Roy K. Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri. CHEMOSPHERE 2022; 308:136463. [PMID: 36122748 DOI: 10.1016/j.chemosphere.2022.136463] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
Different classes of chemicals are present in the environment as mixtures. Among them, pharmaceuticals and pesticides are of major concern due to their improper use and disposal, and subsequent additive and non-additive effects. To assess the environmental risk posed by the mixtures of pharmaceuticals and pesticides, a quantitative structure-activity relationship (QSAR) model has been developed in this study using the pEC50 values of 198 binary and multi-component mixtures against the marine bacterium Aliivibrio fischeri. The developed partial least squares (PLS) model has been rigorously validated and proved to be a robust and extremely predictive one. To address the chances of overestimation of validation metrics, three cross-validation tests (mixtures out, compounds out, and everything out) have been applied, and the results were satisfactory. The use of simple 2-dimensional descriptors makes the prediction much quick, and also makes the model easily interpretable. A machine learning-based chemical read-across prediction has also been performed to justify the effectiveness of selected structural features in this study. In a nutshell, this study proves QSAR and chemical read-across as effective alternative approaches for the toxicity prediction of pharmaceutical and pesticide mixtures and also approves the use of mixture descriptors for modelling mixtures successfully.
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Affiliation(s)
- Mainak Chatterjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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14
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Tao C, Chen Y, Tao T, Cao Z, Chen W, Zhu T. Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 311:119857. [PMID: 35944777 DOI: 10.1016/j.envpol.2022.119857] [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: 05/26/2022] [Revised: 07/17/2022] [Accepted: 07/23/2022] [Indexed: 06/15/2023]
Abstract
The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., KXAD-A) are indispensable to obtain POPs concentration, and the KXAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R2adj and Q2ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R2adj = 0.991, Q2ext = 0.935, RMSEtra = 0.271 and RMSEext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.
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Affiliation(s)
- Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ying Chen
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Tianyun Tao
- College of Agriculture, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Zaizhi Cao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
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15
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Yang P, Henle EA, Fern XZ, Simon CM. Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels. J Chem Phys 2022; 157:034102. [DOI: 10.1063/5.0090573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a dataset of 382 molecules with toxicity labels from honey bee exposure experiments, we train a support vector machine (SVM) to predict the toxicity of pesticides to honey bees. We compare two representations of the pesticide molecules: (i) a random walk feature vector listing counts of length- L walks on the molecular graph with each vertex- and edge-label sequence and (ii) the Molecular ACCess System (MACCS) structural key fingerprint (FP), a bit vector indicating the presence/absence of a list of pre-defined subgraph patterns in the molecular graph. We explicitly construct the MACCS FPs but rely on the fixed-length- L random walk graph kernel (RWGK) in place of the dot product for the random walk representation. The L-RWGK-SVM achieves an accuracy, precision, recall, and F1 score (mean over 2000 runs) of 0.81, 0.68, 0.71, and 0.69, respectively, on the test data set—with L = 4 being the mode optimal walk length. The MACCS-FP-SVM performs on par/marginally better than the L-RWGK-SVM, lends more interpretability, but varies more in performance. We interpret the MACCS-FP-SVM by illuminating which subgraph patterns in the molecules tend to strongly push them toward the toxic/non-toxic side of the separating hyperplane.
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Affiliation(s)
- Ping Yang
- School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, Oregon 97331, USA
| | - E. Adrian Henle
- School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, Oregon 97331, USA
| | - Xiaoli Z. Fern
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, USA
| | - Cory M. Simon
- School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, Oregon 97331, USA
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16
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Kumar P, Kumar A, Singh D. CORAL: Development of a hybrid descriptor based QSTR model to predict the toxicity of dioxins and dioxin-like compounds with correlation intensity index and consensus modelling. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2022; 93:103893. [PMID: 35654373 DOI: 10.1016/j.etap.2022.103893] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/21/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
In the present study, ninety-five halogenated dioxins and related chemicals (dibenzo-p-dioxins, dibenzofurans, biphenyls, and naphthalene) with endpoint pEC50 were used to develop twelve quantitative structure toxicity relationship (QSTR) models using inbuilt Monte Carlo algorithm of CORAL software. The hybrid optimal descriptor of correlation weights (DCW) using a combination of SMILES and HSG (hydrogen suppressed graph) was employed to generate QSTR models. Three target functions i.e. TF1 (WIIC=WCII=0), TF2 (WIIC= 0.3 & WCII=0) and TF3 (WIIC= 0.0 &WCII=0.3) were employed to develop robust QSTR models and the statistical outcomes of each target function were compared with each other. The correlation intensity index (CII) was found a reliable benchmark of the predictive potential for QSTR models. The numerical value of the determination coefficient of the validation set of split 1 computed by TF3 was found highest (RValid2=0.8438). The fragments responsible for the toxicity of dioxins and related chemicals were also identified in terms of the promoter of increase/decrease for pEC50. Three random splits (Split 1, Split 2 and Split 4) were selected for the extraction of the promoter of increase/decrease for pEC50. In the last, consensus modelling was performed using the intelligent consensus tool of DTC lab (https://dtclab.webs.com/software-tools). The original consensus model, which was created by combining four distinct models employing the split 4 arrangement, was more predictive for the validation set and the numerical value of the determination coefficient of the test set (validation set) was increased from 0.8133 to 0.9725. For the validation set of split 4, the mean absolute error (MAE 100%) was also lowered from 0.513 to 0.2739.
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Affiliation(s)
- Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana 136119, India.
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, Haryana 125001, India.
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, Haryana 124001, India
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17
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Bunmahotama W, Vijver MG, Peijnenburg W. Development of a Quasi-Quantitative Structure-Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal-Based Nanomaterials. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2022; 41:1439-1450. [PMID: 35234298 PMCID: PMC9325417 DOI: 10.1002/etc.5322] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/17/2021] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
The conventional Hill equation model is suitable to fit dose-response data obtained from performing (eco)toxicity assays. Models based on quasi-quantitative structure-activity relationships (QSARs) to estimate the Hill coefficient ( n H ) ${n}_{{\rm{H}}})$ were developed with the aim of predicting the response of the invertebrate species Daphnia magna to exposure to metal-based nanomaterials. Descriptors representing the pristine properties of nanoparticles and media conditions were coded to a quasi-simplified molecular input line entry system and correlated to experimentally derived values of n H ${n}_{{\rm{H}}}$ . Monte Carlo optimization was used to model the set of n H ${n}_{{\rm{H}}}$ values, and the model was trained on the basis of reported dose-response relationships of 60 data sets (n = 367 individual response observations) of 11 metal-based nanomaterials as obtained from 20 literature reports. The model simulates the training data well, with only 2.3% deviation between experimental and modeled response data. The technique was employed to predict the dose-response relationships of 15 additional data sets (n = 72 individual observations) not included in model development of seven metal-based nanomaterials from 10 literature reports, with an average error of 3.5%. Combining the model output with either the median effective concentration value or any other known effect level as obtained from experimental data allows the prediction of full dose-response curves of D. magna immobilization. This model is an accurate screening tool that allows the determination of the shape and slope of dose-response curves, thereby greatly reducing experimental effort in case of novel advanced metal-based nanomaterials or the prediction of responses in altered exposure media. This screening model is compliant with the 3Rs (replacement, reduction, and refinement) principle, which is embraced by the scientific and regulatory communities dealing with nano-safety. Environ Toxicol Chem 2022;41:1439-1450. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Warisa Bunmahotama
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
| | - Martina G. Vijver
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
| | - Willie Peijnenburg
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
- Center for Safety of Substances and ProductsNational Institute of Public Health and the EnvironmentBilthovenThe Netherlands
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18
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Wang Z, Zhang F, Wang DG. Predicting joint toxicity of chemicals by incorporating a weighted descriptor into a mixture model: Cases for binary antibiotics and binary nanoparticles. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 236:113472. [PMID: 35390689 DOI: 10.1016/j.ecoenv.2022.113472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/21/2022] [Accepted: 03/27/2022] [Indexed: 06/14/2023]
Abstract
A prediction method that integrated a mixture descriptor with an established mixture toxicology method was proposed for the joint toxicity of chemical pollutants. A weighted descriptor derived from the single descriptor of each component was employed to calculate a mixture descriptor, which was successfully embedded into the generalized concentration addition (GCA) model named the extended GCA (XGCA) model. To develop and validate the proposed approach, binary antibiotic mixtures (ciprofloxacin and oxytetracycline) and metal-oxide (copper oxide and zinc oxide) nanoparticle mixtures were selected to study their toxicity to freshwater green algae. The results showed that concentration-response curve (CRC) derived from the XGCA model was closer to the observed CRC than those from the GCA, Concentration Addition (CA), and Independent Action (IA) models. The difference between effect concentrations predicted by the XGCA model and observed did not exceed a factor of 1.6. The XGCA model was relatively more accurate at predicting joint toxicity (in terms of effect concentrations and effect errors) than the reference models, independent of component types and mixture ratios. The XGCA model predicts the joint toxicity through molecular structural or nanostructural characters, thus modes of toxic action are not preconditions for predicting the toxicity of the mixtures. This result demonstrates the practicability of using the XGCA method in toxicity assessments of mixture pollutants with unknown modes of action.
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Affiliation(s)
- Zhuang Wang
- School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science and Technology, Nanjing 210044, PR China.
| | - Fan Zhang
- School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science and Technology, Nanjing 210044, PR China
| | - De-Gao Wang
- College of Environmental Science and Engineering, Dalian Maritime University, Dalian 116026, PR China
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19
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Zhu T, Tao C. Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor. JOURNAL OF HAZARDOUS MATERIALS 2022; 423:127037. [PMID: 34530267 DOI: 10.1016/j.jhazmat.2021.127037] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Polydimethylsiloxane-air partition coefficient (KPDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict KPDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the Radj2, QBOOT2 and Qext2 are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental KPDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
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20
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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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21
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Crisan L, Borota A, Bora A, Suzuki T, Funar-Timofei S. Application of Molecular Docking, Homology Modeling, and Chemometric Approaches to Neonicotinoid Toxicity against Aphis craccivora. Mol Inform 2021; 41:e2100058. [PMID: 34710288 DOI: 10.1002/minf.202100058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 10/15/2021] [Indexed: 11/08/2022]
Abstract
Neonicotinoids are known as effective pesticides against various insect species. They can harm useful insects including honeybees, with a relatively low threat to nontarget organisms and the environment. This paper presents combined methods to explore the insecticidal activity of neonicotinoids with diverse scaffolds, active against Aphis craccivora. Pharmacophore, molecular docking into the active site of nicotinic acetylcholine receptor homology model, and linear and non-linear regression approaches were used to find new insecticide candidates. The potential toxic effects against honeybees were evaluated using the molecular docking in the active site of the new Aphis mellifera homology model. Four new untested compounds were assigned as insecticide candidates, active against Aphis craccivora with less potential toxic effects for honeybees. This approach may be an effective strategy to design environmentally friendly insecticides against the cowpea aphid.
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Affiliation(s)
- Luminita Crisan
- Coriolan Dragulescu Institute of Chemistry of the Romanian Academy, 24 M. Viteazu Avenue, 300223, Timisoara, Romania
| | - Ana Borota
- Coriolan Dragulescu Institute of Chemistry of the Romanian Academy, 24 M. Viteazu Avenue, 300223, Timisoara, Romania
| | - Alina Bora
- Coriolan Dragulescu Institute of Chemistry of the Romanian Academy, 24 M. Viteazu Avenue, 300223, Timisoara, Romania
| | - Takahiro Suzuki
- TNatural Science Laboratory, Toyo University, 5-28-20 Hakusan, Bunkyo-ku, Tokyo, 112-8606, Japan
| | - Simona Funar-Timofei
- Coriolan Dragulescu Institute of Chemistry of the Romanian Academy, 24 M. Viteazu Avenue, 300223, Timisoara, Romania
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22
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Lotfi S, Ahmadi S, Kumar P. The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors. RSC Adv 2021; 11:33849-33857. [PMID: 35497322 PMCID: PMC9042335 DOI: 10.1039/d1ra06861j] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/11/2021] [Indexed: 12/17/2022] Open
Abstract
Ionic liquids (ILs) have captured intensive attention owing to their unique properties such as high thermal stability, negligible vapour pressure, high dissolution capacity and high ionic conductivity as well as their wide applications in various scientific fields including organic synthesis, catalysis, and industrial extraction processes. Many applications of ionic liquids (ILs) rely on the melting point (Tm). Therefore, in the present manuscript, the melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs. The Monte Carlo algorithm of CORAL software is applied to build up a robust QSPR model to calculate the values Tm of 353 imidazolium ILs. Using a combination of SMILES and hydrogen-suppressed molecular graphs (HSGs), the hybrid optimal descriptor is computed and used to generate the QSPR models. Internal and external validation parameters are also employed to evaluate the predictability and reliability of the QSPR model. Four splits are prepared from the dataset and each split is randomly distributed into four sets i.e. training set (≈33%), invisible training set (≈31%), calibration set (≈16%) and validation set (≈20%). In QSPR modelling, the numerical values of various statistical features of the validation sets such as RValidation2, QValidation2, and IICValidation are found to be in the range of 0.7846–0.8535, 0.7687–0.8423 and 0.7424–0.8982, respectively. For mechanistic interpretation, the structural attributes which are responsible for the increase/decrease of Tm are also extracted. The melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs.![]()
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Affiliation(s)
- Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU) 19395-4697 Tehran Iran
| | - Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University Tehran Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University Kurukshetra Haryana 136119 India
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Lotfi S, Ahmadi S, Kumar P. A hybrid descriptor based QSPR model to predict the thermal decomposition temperature of imidazolium ionic liquids using Monte Carlo approach. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116465] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Toropova AP, Toropov AA. Can the Monte Carlo method predict the toxicity of binary mixtures? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:39493-39500. [PMID: 33755888 DOI: 10.1007/s11356-021-13460-1] [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] [Received: 01/08/2021] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
Risk assessment of toxicants mainly is a result of experiments with single substances. However, toxicity in natural ecosystems typically does not result from single toxicant exposure but is rather a result of exposure to mixtures of toxicants. It is not surprising a mixture of toxicity is a subject of eco-toxicological interest for several decades. A quantitative structure-activity relationships (QSAR)-based approach is an attractive approach to assessing the joint effects in the binary mixtures. The validity of the proposed approach was demonstrated by comparing the predicted values against the experimentally determined values. Simplified molecular input-line entry system (SMILES) is used for the representation of the molecular structures of components of two-component mixtures to build up QSAR. The SMILES-based models are improving if the Monte Carlo optimization aimed to define 2D-optimal descriptors apply the so-called index of ideality of correlation (IIC), which is a mathematical function of both the correlation coefficient and mean absolute error calculated for the positive and negative difference between observed and calculated values of toxicity. The average statistical quality of these models (for the validation set) is n=25, R2=0.95, and RMSE=0.375.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy
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Toropov AA, Toropova AP. Quasi-SMILES as a basis for the development of models for the toxicity of ZnO nanoparticles. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 772:145532. [PMID: 33578164 DOI: 10.1016/j.scitotenv.2021.145532] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/07/2021] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
The application of nanomaterials is expanding. Therefore, it is necessary to investigate the relationship between the structure and toxicity of different nanomaterials. Quasi-SMILES is a line of symbols which are codes of corresponding conditions of experiments aimed to estimate the toxicity of ZnO nanoparticles towards the rat via intraperitoneal injections. By means of the Monte Carlo method, the so-called correlation weights for fragments of quasi-SMILES can be calculated. Having the numerical data on the correlation weights one can build up a one-variable model for the toxicity. The checking up of the approach with five random splits of all available data on results of thirty-six experiments into a sub-system of training and sub-system of validation has confirmed the significance of the statistical quality of models obtained with the above approach. The average determination coefficient equal to 0.957 (dispersion 0.010) and average root mean square error equal to 7.25 [mg/kg] (dispersion 0.59 [mg/kg]).
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Affiliation(s)
- Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
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Kasiotis KM, Zafeiraki E, Kapaxidi E, Manea-Karga E, Antonatos S, Anastasiadou P, Milonas P, Machera K. Pesticides residues and metabolites in honeybees: A Greek overview exploring Varroa and Nosema potential synergies. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 769:145213. [PMID: 33736246 DOI: 10.1016/j.scitotenv.2021.145213] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 01/10/2021] [Accepted: 01/11/2021] [Indexed: 06/12/2023]
Abstract
The aim of this study was to investigate reported cases of honeybee mortality incidents and the potential association to pesticide exposure and to their metabolites. The same honeybee samples were also assessed for Varroa mites, and Nosema microsporidia provoked infections to provide an integrated picture of all observable stressors that may impact bees' survival. Thus, honeybee samples from different areas of Greece (2014-2018) were analyzed for the presence of pesticide residues and metabolites. In this context, an existing LC-ESI-QqQ-MS multiresidue method of analytes of different chemical classes such as neonicotinoids, organophosphates, triazoles, carbamates, was enriched with additional active substances, developed and validated. A complementary GC-EI-QqQ-MS method was also exploited for the same scope covering pyrethroid compounds. Both methods monitored more than 150 active substances and metabolites and presented acceptable linearity over the ranges assayed. The calculated recoveries ranged from 65 to 120% for the three concentration levels, while the precision (RSD%) values ranged between 4 and 15%. Therefore, this approach proved sufficient to act as a monitoring tool for the determination of pesticide residues in cases of suspected honeybee poisoning incidents. From the analysis of 320 samples, the presence of 70 active substances and metabolites was confirmed with concentrations varying from 1.4 ng/g to 166 μg/g. Predominant detections were the acaricide coumaphos, several neonicotinoids exemplified by clothianidin, organophosporous compounds dimethoate and chlorpyrifos, and some pyrethroids. Metabolites of imidacloprid, chlorpyrifos, coumaphos, acetamiprid, fenthion and amitraz were also identified. Concerning Nosema and Varroa they were identified in 27 and 22% of samples examined, respectively, verifying their prevalence and coexistence with pesticides and their metabolites in honeybees.
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Affiliation(s)
- Konstantinos M Kasiotis
- Benaki Phytopathological Institute, Department of Pesticides Control and Phytopharmacy, Laboratory of Pesticides' Toxicology, 8 St. Delta Street, Kifissia, 14561 Athens, Greece.
| | - Effrosyni Zafeiraki
- Benaki Phytopathological Institute, Department of Pesticides Control and Phytopharmacy, Laboratory of Pesticides' Toxicology, 8 St. Delta Street, Kifissia, 14561 Athens, Greece
| | - Eleftheria Kapaxidi
- Benaki Phytopathological Institute, Department of Entomology & Agricultural Entomology, Laboratory of Acarology & Agricultural Zoology, Greece
| | - Elektra Manea-Karga
- Benaki Phytopathological Institute, Department of Pesticides Control and Phytopharmacy, Laboratory of Pesticides' Toxicology, 8 St. Delta Street, Kifissia, 14561 Athens, Greece
| | - Spyridon Antonatos
- Benaki Phytopathological Institute, Department of Entomology & Agricultural Entomology, Laboratory of Agricultural Entomology, Greece
| | - Pelagia Anastasiadou
- Benaki Phytopathological Institute, Department of Pesticides Control and Phytopharmacy, Laboratory of Pesticides' Toxicology, 8 St. Delta Street, Kifissia, 14561 Athens, Greece
| | - Panagiotis Milonas
- Benaki Phytopathological Institute, Department of Entomology & Agricultural Entomology, Biological Control Laboratory, Greece
| | - Kyriaki Machera
- Benaki Phytopathological Institute, Department of Pesticides Control and Phytopharmacy, Laboratory of Pesticides' Toxicology, 8 St. Delta Street, Kifissia, 14561 Athens, Greece.
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More S, Bampidis V, Benford D, Bragard C, Halldorsson T, Hernández‐Jerez A, Bennekou SH, Koutsoumanis K, Machera K, Naegeli H, Nielsen SS, Schlatter J, Schrenk D, Silano V, Turck D, Younes M, Arnold G, Dorne J, Maggiore A, Pagani S, Szentes C, Terry S, Tosi S, Vrbos D, Zamariola G, Rortais A. A systems-based approach to the environmental risk assessment of multiple stressors in honey bees. EFSA J 2021; 19:e06607. [PMID: 34025804 PMCID: PMC8135085 DOI: 10.2903/j.efsa.2021.6607] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The European Parliament requested EFSA to develop a holistic risk assessment of multiple stressors in honey bees. To this end, a systems-based approach that is composed of two core components: a monitoring system and a modelling system are put forward with honey bees taken as a showcase. Key developments in the current scientific opinion (including systematic data collection from sentinel beehives and an agent-based simulation) have the potential to substantially contribute to future development of environmental risk assessments of multiple stressors at larger spatial and temporal scales. For the monitoring, sentinel hives would be placed across representative climatic zones and landscapes in the EU and connected to a platform for data storage and analysis. Data on bee health status, chemical residues and the immediate or broader landscape around the hives would be collected in a harmonised and standardised manner, and would be used to inform stakeholders, and the modelling system, ApisRAM, which simulates as accurately as possible a honey bee colony. ApisRAM would be calibrated and continuously updated with incoming monitoring data and emerging scientific knowledge from research. It will be a supportive tool for beekeeping, farming, research, risk assessment and risk management, and it will benefit the wider society. A societal outlook on the proposed approach is included and this was conducted with targeted social science research with 64 beekeepers from eight EU Member States and with members of the EU Bee Partnership. Gaps and opportunities are identified to further implement the approach. Conclusions and recommendations are made on a way forward, both for the application of the approach and its use in a broader context.
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Zhu T, Gu L, Chen M, Sun F. Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches. CHEMOSPHERE 2021; 266:128962. [PMID: 33218721 DOI: 10.1016/j.chemosphere.2020.128962] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 06/11/2023]
Abstract
Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (KPUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the KPUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Liming Gu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Feng Sun
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
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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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Carnesecchi E, Toma C, Roncaglioni A, Kramer N, Benfenati E, Dorne JLCM. Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 735:139243. [PMID: 32480144 DOI: 10.1016/j.scitotenv.2020.139243] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 06/11/2023]
Abstract
Honey bees (Apis mellifera) provide key ecosystem services as pollinators bridging agriculture, the food chain and ecological communities, thereby ensuring food production and security. Ecological risk assessment of single Plant Protection Products (PPPs) requires an understanding of the exposure and toxicity. In silico tools such as QSAR models can play a major role for the prediction of structural, physico-chemical and pharmacokinetic properties of chemicals as well as toxicity of single and multiple chemicals. Here, the first integrative honey bee QSAR model has been developed for PPPs using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD50) and ii) to profile the Mode of Action (MoA) of pesticides active substances. Three different classification-based and four regression-based models were developed and tested for their performance, thus identifying two models providing the most reliable predictions based on k-NN algorithm. The two-category QSAR model (toxic/non-toxic; n = 411) was validated using sensitivity (=0.93), specificity (=0.85), balanced accuracy (=0.90), and Matthews correlation coefficient (MCC = 0.78) as statistical parameters. The regression-based model (n = 113) was validated for its reliability and robustness (R2 = 0.74; MAE = 0.52). Current study proposes the MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD50s data points from three different databases. The classification allows to further define MoAs and the target site of PPPs active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals. Relevant future perspectives are briefly addressed to integrate MoA, adverse outcome pathways (AOPs) and toxicokinetic information for the refinement of single-chemical/combined toxicity predictions and risk estimates at different levels of biological organization in the bee health context.
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Affiliation(s)
- Edoardo Carnesecchi
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy.
| | - Cosimo Toma
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Alessandra Roncaglioni
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Nynke Kramer
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands
| | - Emilio Benfenati
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
| | - Jean Lou C M Dorne
- European Food Safety Authority (EFSA), Scientific Committee and Emerging Risks Unit, Via Carlo Magno 1A, 43126 Parma, Italy
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Kumar P, Kumar A. In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:697-715. [PMID: 32878494 DOI: 10.1080/1062936x.2020.1806105] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/03/2020] [Indexed: 06/11/2023]
Abstract
Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because of poor affinity for the fibre and discharge into the water stream. The chemical aspects of 72 azo dyes towards cellulose fibre in terms of their affinity by QSPR have been explored in the present work. We have employed two approaches, namely balance of correlation without IIC (TF1) and balance of correlation with IIC (TF2), to generate 16 QSAR models from 8 splits. The determination coefficient of calibration and validation set was found higher when the QSPR models were developed using the index of ideality correlation (IIC) parameter (TF2). The model developed with TF2 for split 3 was considered as a prominent model because the determination coefficient of the validation set was maximum (r 2 = 0.9468). The applicability domain (AD) was also analysed based on 'statistical defect', d(A) for a SMILES attribute. The mechanistic interpretation was done by identifying the SMILES attributes responsible for the promoter of endpoint increase and promoter of endpoint decrease. These SMILES attributes were applied to design 15 new dyes with higher affinity for cellulose fibre.
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Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University , Kurukshetra, India
| | - A Kumar
- Department of Pharmaceutical Sciences, Guru Jambeshwar University of Science and Technology , Hisar, India
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Kumar A, Kumar P. Construction of pioneering quantitative structure activity relationship screening models for abuse potential of designer drugs using index of ideality of correlation in monte carlo optimization. Arch Toxicol 2020; 94:3069-3086. [DOI: 10.1007/s00204-020-02828-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 06/24/2020] [Indexed: 01/05/2023]
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Benfenati E, Carnesecchi E, Roncaglioni A, Baldin R, Ceriani L, Ciacci A, Kovarich S, Sartori L, Mostrag A, Magdziarz T, Yang C. Maintenance,update and further development of EFSA's Chemical Hazards: OpenFoodTox 2.0. ACTA ACUST UNITED AC 2020. [DOI: 10.2903/sp.efsa.2020.en-1822] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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