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Radivojević I, Stojilković N, Antonijević-Miljaković E, Đorđević AB, Baralić K, Ćurčić M, Marić Đ, Đukić-Ćosić D, Bulat Z, Durgo K, Antonijević B. In silico attempt to reveal the link between cancer development and combined exposure to the maize herbicides: Glyphosate, nicosulfuron, S-metolachlor and terbuthylazine. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 949:175187. [PMID: 39094656 DOI: 10.1016/j.scitotenv.2024.175187] [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: 05/26/2024] [Revised: 07/27/2024] [Accepted: 07/29/2024] [Indexed: 08/04/2024]
Abstract
Pesticides are crucial for crop protection and have seen a 50 % increase in use in the last decade. Besides preventing significant crop losses their use has raised health concerns due to consumer exposure through residues in food and water. The toxicity data from individual components is often used to assess overall mixture toxicity, but uncertainty persists in understanding the behaviors of individual chemicals within these mixtures. Assessing the risk of pesticide mixture exposure remains challenging, potentially leading to overestimation or underestimation of toxicity. This study aims to establish a possible link between exposure to a herbicide mixture and genotoxic effects, focusing on cancer development. Our analysis was focused on four herbicides glyphosate, nicosulfuron, S-metolachlor and terbuthylazine. To determine the link between genes associated with cancer development due to exposure to herbicide mixture, a CTD database tools were used. Through the ToppFun tool molecular function and biological process associated with genes common to the disease of interest and selected herbicides were evaluated. And finally, GeneMANIA was used in order to analyze the function and interaction between common genes of herbicide mixture. Among the 7 common genes for herbicide mixture and cancer development coexpression characteristics were dominant at 65.41 %, 22.14 % of annotated genes shared the same pathway and 7.88 % showed co-localization. Among six target genes involved in genetic disease development co-expression was dominant at 87.34 %, colocalization at 8.03 % and shared protein domains at 4.52 %. Comprehensive molecular analyses, encompassing genomics, proteomics, and pathway analysis, are essential to unravel the specific mechanisms involved in the context of the studied mixture and its potential carcinogenic effects.
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Affiliation(s)
- Ivana Radivojević
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia.
| | - Nikola Stojilković
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Evica Antonijević-Miljaković
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Aleksandra Buha Đorđević
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Katarina Baralić
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Marijana Ćurčić
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Đurđica Marić
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Danijela Đukić-Ćosić
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Zorica Bulat
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Ksenija Durgo
- Faculty of food technology and biotechnology, University of Zagreb, Pierottijeva street 6, 10000 Zagreb, Croatia
| | - Biljana Antonijević
- Department of Toxicology "Akademik Danilo Soldatović", Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
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2
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Iovine N, Toropova AP, Toropov AA, Roncaglioni A, Benfenati E. Models for the No-Observed-Effect Concentration (NOEC) and Maximal Half-Effective Concentration (EC50). TOXICS 2024; 12:425. [PMID: 38922105 PMCID: PMC11209108 DOI: 10.3390/toxics12060425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 06/04/2024] [Accepted: 06/11/2024] [Indexed: 06/27/2024]
Abstract
Typical in silico models for ecotoxicology focus on a few endpoints, but there is a need to increase the diversity of these models. This study proposes models using the NOEC for the harlequin fly (Chironomus riparius) and EC50 for swollen duckweed (Lemna gibba) for the first time. The data were derived from the EFSA OpenFoodTox database. The models were based on the correlation weights of molecular features used to calculate the 2D descriptor in CORAL software. The Monte Carlo method was used to calculate the correlation weights of the algorithms. The determination coefficients of the best models for the external validation set were 0.74 (NOAEC) and 0.85 (EC50).
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Affiliation(s)
| | | | | | | | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy; (N.I.); (A.P.T.); (A.A.T.); (A.R.)
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Hong Y, Xie H, Jin X, Naraginti S, Xu D, Guo C, Feng C, Wu F, Giesy JP. Prediction of HC 5s for phthalate esters by use of the QSAR-ICE model and ecological risk assessment in Chinese surface waters. JOURNAL OF HAZARDOUS MATERIALS 2024; 467:133642. [PMID: 38330644 DOI: 10.1016/j.jhazmat.2024.133642] [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: 10/30/2023] [Revised: 01/01/2024] [Accepted: 01/25/2024] [Indexed: 02/10/2024]
Abstract
Due to their endocrine-disrupting effects and the risks posed in surface waters, in particular by chronic low-dose exposure to aquatic organisms, phthalate esters (PAEs) have received significant attention. However, most assessments of risks posed by PAEs were performed at a selection level, and thus limited by empirical data on toxic effects and potencies. A quantitative structure activity relationship (QSAR) and interspecies correlation estimation (ICE) model was constructed to estimate hazardous concentrations (HCs) of selected PAEs to aquatic organisms, then they were used to conduct a multiple-level environmental risk assessment for PAEs in surface waters of China. Values of hazardous concentration for 5% of species (HC5s), based on acute lethality, estimated by use of the QSAR-ICE model were within 1.25-fold of HC5 values derived from empirical data on toxic potency, indicating that the QSAR-ICE model predicts the toxicity of these three PAEs with sufficient accuracy. The five selected PAEs may be commonly measured in China surface waters at concentrations between ng/L and μg/L. Risk quotients according to median concentrations of the five PAEs ranged from 3.24 for di(2-ethylhexhyl) phthalate (DEHP) to 4.10 × 10-3 for dimethyl phthalate (DMP). DEHP and dibutyl phthalate (DBP) had risks to the most vulnerable aquatic biota, with the frequency of exceedances of the predicted no-effect concentration (PNECs) of 75.5% and 38.0%, respectively. DEHP and DBP were identified as having "high" or "moderate" risks. Results of the joint probability curves (JPC) method indicated DEHP posed "intermediate" risk to freshwater species with a maximum risk product of 5.98%. The multiple level system introduced in this study can be used to prioritize chemicals and other new pollutant in the aquatic ecological.
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Affiliation(s)
- Yajun Hong
- School of Chemical and Environmental Engineering, Anhui Polytechnic University, Wuhu 241000, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Huiyu Xie
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Xiaowei Jin
- China National Environmental Monitoring Centre, Beijing 100012, China.
| | - Saraschandra Naraginti
- School of Chemical and Environmental Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Dayong Xu
- School of Chemical and Environmental Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Changsheng Guo
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Chenglian Feng
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - John P Giesy
- Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada; Department of Environmental Sciences, Baylor University, Waco, TX 76798-7266, USA; Department of Integrative Biology and Centre for Integrative Toxicology, Michigan State University, East Lansing, MI 48895, USA
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4
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Li F, Wang P, Fan T, Zhang N, Zhao L, Zhong R, Sun G. Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133410. [PMID: 38185092 DOI: 10.1016/j.jhazmat.2023.133410] [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: 11/19/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/09/2024]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.
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Affiliation(s)
- Feifan Li
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment, Beijing 100029, China
| | - Peng Wang
- Department of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Na Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
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Pandey V. Predictionof Environmental FateandToxicityofInsecticidesUsing Multi-Target QSAR Approach. Chem Biodivers 2024; 21:e202301213. [PMID: 38109053 DOI: 10.1002/cbdv.202301213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 12/03/2023] [Indexed: 12/19/2023]
Abstract
Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi-target-QSAR model established by a backpropagation neural network trained with the Levenberg-Marquardt (LM) algorithm was used to construct a statistically robust and easily interpretable Mt-QSAR model with high external predictability for the simultaneous prediction of the environmental fate in form of octanol-water partition coefficient (LogP), (BCF) and acute oral toxicity in mammals and birds (LD50rat ) and (LD50bird ) for a wide range of chemical structural classes of insecticides. Principal component analysis was performed on descriptors selected by the SW-MLR method, and the selected PCs were used for constructing the SW-MLR-PCA-ANN model. The developed well-trained model (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R2 =0.69) was statistically robust as indicated by the external validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, R2 =0.61). The AD of the developed Mt-QSAR model was also defined to identify the most reliable predictions. Finally, the missing values in the dataset for the aforementioned targets were predicted using the constructed Mt-QSAR model. The proposed approach can be used for simultaneous prediction of the environmental fate of new insecticides, especially ones that haven't been tested yet.
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Affiliation(s)
- Vandana Pandey
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
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6
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Yang Y, Zhong J, Shen S, Huang J, Hong Y, Qu X, Chen Q, Niu B. Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment. Med Chem 2024; 20:2-16. [PMID: 37038674 DOI: 10.2174/1573406419666230406091759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/10/2023] [Accepted: 01/23/2023] [Indexed: 04/12/2023]
Abstract
Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.
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Affiliation(s)
- Yunfeng Yang
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Junjie Zhong
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Songyu Shen
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Jiajun Huang
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Yihan Hong
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Xiaosheng Qu
- National Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, Goang Xi, China
| | - Qin Chen
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Bing Niu
- School of life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
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7
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Sun G, Bai P, Fan T, Zhao L, Zhong R, McElhinney RS, McMurry TBH, Donnelly DJ, McCormick JE, Kelly J, Margison GP. QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency. Pharmaceutics 2023; 15:2170. [PMID: 37631385 PMCID: PMC10458236 DOI: 10.3390/pharmaceutics15082170] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/16/2023] [Accepted: 08/19/2023] [Indexed: 08/27/2023] Open
Abstract
O6-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine O6-alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [3H]-methylated-DNA-based MGMT inactivation assays and calculated the IC50 values. Using the results of 370 compounds, we performed quantitative structure-activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC50 values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability (R2pr = 0.7474, Q2Fn = 0.7375-0.7437, CCCpr = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model (R2pr = 0.7528, Q2Fn = 0.7387-0.7449, CCCpr = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment.
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Affiliation(s)
- Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
| | - Peiying Bai
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
- Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (P.B.); (T.F.); (L.Z.); (R.Z.)
| | - R. Stanley McElhinney
- Chemistry Department, Trinity College, D02 PN40 Dublin, Ireland; (T.B.H.M.); (D.J.D.)
| | - T. Brian H. McMurry
- Chemistry Department, Trinity College, D02 PN40 Dublin, Ireland; (T.B.H.M.); (D.J.D.)
| | - Dorothy J. Donnelly
- Chemistry Department, Trinity College, D02 PN40 Dublin, Ireland; (T.B.H.M.); (D.J.D.)
| | - Joan E. McCormick
- Chemistry Department, Trinity College, D02 PN40 Dublin, Ireland; (T.B.H.M.); (D.J.D.)
| | - Jane Kelly
- Carcinogenesis Department, Paterson Institute for Cancer Research, Manchester M20 9BX, UK;
| | - Geoffrey P. Margison
- Carcinogenesis Department, Paterson Institute for Cancer Research, Manchester M20 9BX, UK;
- Epidemiology and Public Health Group, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PG, UK
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Zhang S, Han Y, Peng J, Chen Y, Zhan L, Li J. Human health risk assessment for contaminated sites: A retrospective review. ENVIRONMENT INTERNATIONAL 2023; 171:107700. [PMID: 36527872 DOI: 10.1016/j.envint.2022.107700] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Soil contamination is a serious global hazard as contaminants can migrate to the human body through the soil, water, air, and food, threatening human health. Human Health Risk Assessment (HHRA) is a commonly used method for estimating the magnitude and probability of adverse health effects in humans that may be exposed to contaminants in contaminated environmental media in the present or future. Such estimations have improved for decades with various risk assessment frameworks and well-established models. However, the existing literature does not provide a comprehensive overview of the methods and models of HHRA that are needed to grasp the current status of HHRA and future research directions. Thus, this paper aims to systematically review the HHRA approaches and models, particularly those related to contaminated sites from peer-reviewed literature and guidelines. The approaches and models focus on methods used in hazard identification, toxicity databases in dose-response assessment, approaches and fate and transport models in exposure assessment, risk characterization, and uncertainty characterization. The features and applicability of the most commonly used HHRA tools are also described. The future research trend for HHRA for contaminated sites is also forecasted. The transition from animal experiments to new methods in risk identification, the integration and update and sharing of existing toxicity databases, the integration of human biomonitoring into the risk assessment process, and the integration of migration and transformation models and risk assessment are the way forward for risk assessment in the future. This review provides readers with an overall understanding of HHRA and a grasp of its developmental direction.
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Affiliation(s)
- Shuai Zhang
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yingyue Han
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
| | - Jingyu Peng
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yunmin Chen
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China
| | - Liangtong Zhan
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China
| | - Jinlong Li
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China.
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9
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Banjare P, Singh J, Papa E, Roy PP. Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:10599-10612. [PMID: 36083366 DOI: 10.1007/s11356-022-22635-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q2LOO = 0.620-0.663) and externally (Q2Fn = 0.693-0.868, CCCext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO2, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.
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Affiliation(s)
- Purusottam Banjare
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Jagadish Singh
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Ester Papa
- Department of Theoretical and Applied Sciences (DiSTA), University of Insubria, Via J.H. Dunant 3, 21100, Varese, Italy
| | - Partha Pratim Roy
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India.
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Zhu T, Tao C, Cheng H, Cong H. Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 846:157455. [PMID: 35863580 DOI: 10.1016/j.scitotenv.2022.157455] [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: 05/25/2022] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (Kd). Almost all quantitative structure-property relationship (QSPR) models that describe Kd apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (KPE-w), PE-seawater (KPE-sw), PVC-water (KPVC-w) and PP-seawater (KPP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting Kd more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with KPE-w, KPE-sw, KPVC-w and KPP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (Radj2: 0.907-0.999), robustness (QBOOT2: 0.900-0.937) and predictability (Rext2: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.
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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
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Haibing Cong
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
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11
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Gu W, Zhao Y, Yang L, Du M, Li Q, Ren Z, Li X. A new perspective to improve the treatment of Lianhuaqingwen on COVID-19 and prevent the environmental health risk of medication. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:74208-74224. [PMID: 35635661 PMCID: PMC9148946 DOI: 10.1007/s11356-022-21125-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
Lianhuaqingwen (LH), one traditional Chinese medicine (TCM), has been used to treat the coronavirus disease 2019 (COVID-19), but its ecotoxicity with potential human health security has not been well investigated. To overcome such adverse effects and improve its medication efficacy, an intelligent multi-method integrated dietary scheme, screening, and performance evaluation approach was developed. Thirteen LH compounds were selected, and the main protease (Mpro) was used as the potential drug target. Resulted information showed that the more compounds of LH added, the higher medication efficacy obtained using multi-method integrated screening system, expert consultation method, and molecular dynamics simulation. Pharmacodynamic mechanism analysis showed that low total energy and polar surface area of LH active compound (i.e., β-sitosterol) will contribute to the best therapeutic effect on COVID-19 using quantitative structure-activity relationships (QSAR) and sensitivity models. Additionally, when mild COVID-19 patients take LH with the optimum dietary scheme (i.e., β-lactoglobulin, α-lactalbumin, vitamin A, vitamin B, vitamin C, carotene, and vitamin E), the medication efficacy were significantly improved (23.58%). Pharmacokinetics and toxicokinetics results showed that LH had certain human health risks and ecotoxicity. This study revealed the multi-compound interaction mechanism of LH treatment on COVID-19, and provided theoretical guidance for improving therapeutic effect, evaluating TCM safety, and preventing human health risk.
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Affiliation(s)
- Wenwen Gu
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206 China
| | - Yuanyuan Zhao
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206 China
| | - Luze Yang
- College of New Energy and Environment, Jilin University, Changchun, 130012 China
| | - Meijin Du
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206 China
| | - Qing Li
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206 China
| | - Zhixing Ren
- College of Forestry, Northeast Forestry University, No. 26 Hexing Road, Harbin, China
| | - Xixi Li
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5 Canada
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12
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Yu X, Zeng Q. Random forest algorithm-based classification model of pesticide aquatic toxicity to fishes. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 251:106265. [PMID: 36030712 DOI: 10.1016/j.aquatox.2022.106265] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/12/2022] [Accepted: 08/13/2022] [Indexed: 06/15/2023]
Abstract
Aquatic toxicity of pesticides can result in poisoning of many non-target organisms, of which various fishes are the most prominent one. It is a challenge to predict the toxicity (LC50) classes of organic pesticides to various fish species from global QSAR models with a larger applicability domain. In this paper, by applying the random forest (RF) algorithm for a two-class problem, only eight molecular descriptors were used to develop a quantitative structure-activity relationship (QSAR) model for 1106 toxicity data (96 h, LC50) of organic pesticides to various fish species including Oncorhynchus mykiss, Lepomis macrochirus, Pimephales promelas, Brachydanio rerio, Cyprinodon, Cyprinus carpio, etc. By the prediction of the optimal RF Model I (ntree =280, mtry = 3 and nodesize = 5), the training set (885 organic pesticides) has the prediction accuracies of 99.6% for Class 1 (LC50 ≤ 10) and 96.7% for Class 2 (LC50 > 10); the test set (221 organic pesticides) has the accuracies being 90.8% for Class 1 and 91.2% for Class 2. The optimal RF Model I is satisfactory compared with other QSAR model reported in the literature, although its descriptor subset is small.
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Affiliation(s)
- Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, China.
| | - Qun Zeng
- Department of Neurosurgery, Central Hospital of Xiangtan, Xiangtan, Hunan 411100, China
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13
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Shavalieva G, Papadokonstantakis S, Peters G. Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity. J Chem Inf Model 2022; 62:4018-4031. [PMID: 35998659 PMCID: PMC9472271 DOI: 10.1021/acs.jcim.1c01079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Indexed: 11/30/2022]
Abstract
Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models' performance.
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Affiliation(s)
- Gulnara Shavalieva
- Department
of Space, Earth and Environment, Division of Energy Technology, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Stavros Papadokonstantakis
- Department
of Space, Earth and Environment, Division of Energy Technology, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Institute
of Chemical, Environmental and Bioscience Engineering, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria
| | - Gregory Peters
- Department
of Technology Management and Economics, Chalmers University of Technology, SE-411 33 Gothenburg, Sweden
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14
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Hong Y, Feng C, Jin X, Xie H, Liu N, Bai Y, Wu F, Raimondo S. A QSAR-ICE-SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity. ENVIRONMENT INTERNATIONAL 2022; 167:107367. [PMID: 35944286 PMCID: PMC10015408 DOI: 10.1016/j.envint.2022.107367] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/04/2022] [Accepted: 06/18/2022] [Indexed: 05/26/2023]
Abstract
Alkylphenols (APs) are ubiquitous and generally present in higher residue levels in the environment. The present work focuses on the development of a set of in silico models to predict the aquatic toxicity of APs with incomplete/unknown toxicity data in aquatic environments. To achieve this, a QSAR-ICE-SSD model was constructed for aquatic organisms by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the hazardous concentrations (HCs) of selected APs. The research indicated that the keywords "alkylphenol" and "nonylphenol" were most commonly studied. The selected ICE models were robust (R2: 0.70-0.99; p-value < 0.01). All models had a high reliability cross- validation success rates (>75%), and the HC5 predicted with the QSAR-ICE-SSD model was 2-fold than that derived with measured experimental data. The HC5 values demonstrated nearly linear decreasing trend from 2-MP to 4-HTP, while the decreasing trend from 4-HTP to 4-DP became shallower, indicates that the toxicity of APs to aquatic organisms increases with the addition of alkyl carbon chain lengths. The ecological risks assessment (ERA) of APs revealed that aquatic organisms were at risk from exposure to 4-NP at most river stations (the highest risk quotient (RQ) = 1.51), with the highest relative risk associated with 2.9% of 4-NP detected in 82.9% of the sampling sites. The targeted APs posed potential ecological risks in the Yongding and Beiyun River according to the mixture ERA. The potential application of QSAR-ICE-SSD models could satisfy the immediate needs for HC5 derivations without the need for additional in vivo testing.
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Affiliation(s)
- Yajun Hong
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Chenglian Feng
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Xiaowei Jin
- China National Environmental Monitoring Centre, Beijing, 100012, China.
| | - Huiyu Xie
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Na Liu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yingchen Bai
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Sandy Raimondo
- United States Environmental Protection Agency, Gulf Ecosystem Measurement and Modeling Division, Gulf Breeze, Florida 32561, United States
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15
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Bruinen de Bruin Y, Franco A, Ahrens A, Morris A, Verhagen H, Kephalopoulos S, Dulio V, Slobodnik J, Sijm DTHM, Vermeire T, Ito T, Takaki K, De Mello J, Bessems J, Zare Jeddi M, Tanarro Gozalo C, Pollard K, McCourt J, Fantke P. Enhancing the use of exposure science across EU chemical policies as part of the European Exposure Science Strategy 2020-2030. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:513-525. [PMID: 34697409 PMCID: PMC9349036 DOI: 10.1038/s41370-021-00388-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 09/08/2021] [Accepted: 09/14/2021] [Indexed: 05/26/2023]
Abstract
BACKGROUND A scientific framework on exposure science will boost the multiuse of exposure knowledge across EU chemicals-related policies and improve risk assessment, risk management and communication across EU safety, security and sustainability domains. OBJECTIVE To stimulate public and private actors to align and strengthen the cross-policy adoption of exposure assessment data, methods and tools across EU legislation. METHODS By mapping and analysing the EU regulatory landscape making use of exposure information, policy and research challenges and key areas of action are identified and translated into opportunities enhancing policy and scientific efficiency. RESULTS Identified key areas of actions are to develop a common scientific exposure assessment framework, supported by baseline acceptance criteria and a shared knowledge base enhancing exchangeability and acceptability of exposure knowledge within and across EU chemicals-related policies. Furthermore, such framework will improve communication and management across EU chemical safety, security and sustainability policies comprising sourcing, manufacturing and global trade of goods and waste management. In support of building such a common framework and its effective use in policy and industry, exposure science innovation needs to be better embedded along the whole policymaking cycle, and be integrated into companies' safety and sustainability management systems. This will help to systemically improve regulatory risk management practices. SIGNIFICANCE This paper constitutes an important step towards the implementation of the EU Green Deal and its underlying policy strategies, such as the Chemicals Strategy for Sustainability.
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Affiliation(s)
- Yuri Bruinen de Bruin
- European Commission, Joint Research Centre, Directorate for Space, Security and Migration, Geel, Belgium.
- European Chemical Industry Council (Cefic), Brussels, Belgium.
| | - Antonio Franco
- European Commission, Joint Research Centre, Directorate on Health, Consumer and Reference Materials, Ispra, Italy
| | | | - Alick Morris
- European Commission, Directorate General Employment, Luxembourg, Luxembourg
| | - Hans Verhagen
- National Food Institute, Technical University of Denmark, Kgs. Lyngby, Denmark
- University of Ulster, Coleraine, Northern Ireland
| | - Stylianos Kephalopoulos
- European Commission, Joint Research Centre, Directorate on Health, Consumer and Reference Materials, Ispra, Italy
| | - Valeria Dulio
- INERIS - National Institute for Environment and Industrial Risks, Verneuil en Halatte, France
| | | | - Dick T H M Sijm
- Dutch Food and Consumer Product Safety Authority, Utrecht, The Netherlands
- University College Venlo, Campus Venlo, Maastricht University, Maastricht, The Netherlands
| | - Theo Vermeire
- RIVM - National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Takaaki Ito
- Organisation for Economic Co-operation and Development, Paris, France
| | - Koki Takaki
- Organisation for Economic Co-operation and Development, Paris, France
| | | | - Jos Bessems
- Flemish Institute for Technological Research, Mol, Belgium
| | - Maryam Zare Jeddi
- RIVM - National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | | | - Josephine McCourt
- European Commission, Joint Research Centre, Directorate for Space, Security and Migration, Geel, Belgium
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Kgs. Lyngby, Denmark
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16
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Larras F, Charles S, Chaumot A, Pelosi C, Le Gall M, Mamy L, Beaudouin R. A critical review of effect modeling for ecological risk assessment of plant protection products. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:43448-43500. [PMID: 35391640 DOI: 10.1007/s11356-022-19111-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 02/03/2022] [Indexed: 06/14/2023]
Abstract
A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).
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Affiliation(s)
- Floriane Larras
- INRAE, Directorate for Collective Scientific Assessment, Foresight and Advanced Studies, Paris, 75338, France
| | - Sandrine Charles
- University of Lyon, University Lyon 1, CNRS UMR 5558, Laboratory of Biometry and Evolutionary Biology, Villeurbanne Cedex, 69622, France
| | - Arnaud Chaumot
- INRAE, UR RiverLy, Ecotoxicology laboratory, Villeurbanne, F-69625, France
| | - Céline Pelosi
- Avignon University, INRAE, UMR EMMAH, Avignon, 84000, France
| | - Morgane Le Gall
- Ifremer, Information Scientifique et Technique, Bibliothèque La Pérouse, Plouzané, 29280, France
| | - Laure Mamy
- Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Thiverval-Grignon, 78850, France
| | - Rémy Beaudouin
- Ineris, Experimental Toxicology and Modelling Unit, UMR-I 02 SEBIO, Verneuil en Halatte, 65550, France.
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17
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Jia Q, Wang J, Yan F, Wang Q. A QSTR model for toxicity prediction of pesticides towards Daphnia magna. CHEMOSPHERE 2022; 291:132980. [PMID: 34813852 DOI: 10.1016/j.chemosphere.2021.132980] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/15/2021] [Accepted: 11/17/2021] [Indexed: 06/13/2023]
Abstract
Because of the large amount of pesticides discharged into rivers, adverse effects could be induced to aquatic organisms. Daphnia magna is often used as an indicator organism to evaluate the toxicity of pesticides. In this study, a quantitative structure-toxicity relationship (QSTR) model was established based on norm descriptors for predicting the acute toxicity of pesticides to Daphnia magna. The model results showed the good predictability (Rtraining2 = 0.8045, Rtesting2 = 0.8224). The validation results of internal validation, external validation, Y-randomization test and application domain analysis demonstrated the model's stability, reliability and robustness. Therefore, the above results indicate that norm descriptors might be universal for describing the relationship between the toxicity and structures of pesticides compounds. Moreover, some pesticides' toxicities without experimental data were also predicted by this model.
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Affiliation(s)
- Qingzhu Jia
- School of Marine and Environmental Science, Tianjin Marine Environmental Protection and Restoration Technology Engineering Center, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China
| | - Junli Wang
- School of Marine and Environmental Science, Tianjin Marine Environmental Protection and Restoration Technology Engineering Center, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China
| | - Fangyou Yan
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China.
| | - Qiang Wang
- School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China
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18
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Prediction of pEC50(M) and molecular docking study for the selective inhibition of arachidonate 5-lipoxygenase. UKRAINIAN BIOCHEMICAL JOURNAL 2021. [DOI: 10.15407/ubj93.06.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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19
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Zhu T, Chen W, Jafvert CT, Fu D, Cheng H, Chen M, Wang Y. Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 291:118223. [PMID: 34583266 DOI: 10.1016/j.envpol.2021.118223] [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: 06/23/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Knowledge about partitioning constants of hydrophobic organic compounds (HOCs) between the polymer and aqueous phases is critical for assessing chemical environmental fate and transport. The conventional experimental method is characterized by large discrepancies in the measured values due to the limited water solubility of HOCs and other associated issues. In the current work, a novel three-phase partitioning system was evaluated to determine accurate low-density polyethylene (LDPE)-water partition coefficients (KPE-w). By adding sufficient surfactant (Brij 30) to form the micellar pseudo-phase within the polymer/water system, the KPE-w values were obtained from a combination of two experimentally measured values, that is, the micelle-water partition coefficient (Kmic-w) and the LDPE-micelle partition coefficient (KPE-mic). The method presented here is capable of shortening the equilibration time to half a month, and avoiding defects of the traditional method with respect to directly measured aqueous phase concentrations. Herein, the KPE-w values were determined for HOCs with little errors. Meanwhile, based on the 120 experimental KPE-w data, several in silico models were also developed as valid extrapolation tools to estimate missing or uncertain values. Analysis of the underlying solubility interactions in the nonionic surfactant micelles were investigated, providing additional support for the reliability of the proposed method.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Chad T Jafvert
- Lyles School of Civil Engineering, and Environmental & Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Dafang Fu
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Yajun Wang
- School of Civil Engineering, Lanzhou University of Technology, 287 Langongping, Lanzhou, 730050, China
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20
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Zhu T, Chen W, Gu Y, Jafvert CT, Fu D. Polyethylene-water partition coefficients for polychlorinated biphenyls: Application of QSPR predictions models with experimental validation. WATER RESEARCH 2021; 207:117799. [PMID: 34731669 DOI: 10.1016/j.watres.2021.117799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 10/01/2021] [Accepted: 10/20/2021] [Indexed: 06/13/2023]
Abstract
The water environmental recalcitrance and ecotoxicity caused by polychlorinated biphenyls (PCBs) are international issues of common concern. The partition coefficients with PCBs between low-density polyethylene (LDPE) and water (KPE-w) are significant to assess their environmental transport and/or fate in aquatic environment. Even moderately hydrophobic PCBs, however, possess large KPE-w values, which makes directly experimental measurement labored. Here, based on the combination of quantitative structure-property relationships (QSPRs) and machine-learning algorithms, 10 in-silico models are developed to provide a quick estimate of KPE-w. These models exhibit good goodness-of-fit (R2adj: 0.919-0.975), robustness (Q2LOO: 0.870-0.954) and external prediction performances (Q2ext: 0.880-0.971), providing a speedy feasibility to close data gaps for limited or absent experimental information, especially the RF-2 model. Particularly, an additional experimental verification is performed for models by a rapid and accurate three-phase system (aqueous phase, surfactant micelles and LDPE). The results of the experiments for 16 PCBs show the modeling results agree well with experimental values, within or approaching the residuals of ± 0.3 log unit. Mechanism interpretations imply that the number of chlorine atoms and ortho-substituted chlorines are the great effect parameters for KPE-w. This result also heightens interest in measuring and predicting the KPE-w values of chemicals containing halogen atoms in water.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, P.R.China.
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, P.R.China
| | - Yuanyuan Gu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, P.R.China
| | - Chad T Jafvert
- Lyles School of Civil Engineering, and Environmental & Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Dafang Fu
- School of Civil Engineering, Southeast University, Nanjing, 210096, P.R.China
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21
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Yang L, Sang C, Wang Y, Liu W, Hao W, Chang J, Li J. Development of QSAR models for evaluating pesticide toxicity against Skeletonema costatum. CHEMOSPHERE 2021; 285:131456. [PMID: 34256203 DOI: 10.1016/j.chemosphere.2021.131456] [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: 03/14/2021] [Revised: 06/27/2021] [Accepted: 07/05/2021] [Indexed: 06/13/2023]
Abstract
Nowadays, the emergence of pesticides and its application in agriculture greatly improved the crop quality and food production. However, the resulted ecological problem caused by the widespread pesticide residues attracted more and more attention since the pesticides were harmful to most living organisms. Regulatory agencies such as Environmental Protection Agency (EPA) and European Chemicals Agency (ECHA) stipulated that a comprehensive pesticides risk assessment was essential and also underscored the application of computation method in evaluating pesticides. The present study aimed to use the Quantitative Structure-Activity Relationship (QSAR) method to establish models for quantitatively and qualitatively predicting the toxicity of pesticide against Skeletonema costatum. The regression model was developed using the Genetic Algorithm plus Multiple Linear Regression method and the classification model was established based on the Random Forest algorithm, respectively. Various internal and external validation metrics suggested that the obtained regression model was of good fitness (R2=0.722), robustness (QLOO2=0.653) and external predictive ability (QFn2:0.719-0.776, CCC = 0.878). The classification could correctly predict 79.4% of pesticides in the training set and 69.7% in the validation set. The relatively high sensitivity value of the classification model indicated its good performance in identifying high-toxic pesticides. It could be concluded from the selected modelling descriptors that molecular weight and polarizability impacted the toxicity the most. The atom-type E-state descriptors generally contributed negatively to the pesticide toxicity which verified the negative influence of molecular hydrophilicity. Moreover, the lipophilic, carbon-type, charge related descriptors demonstrated the important influence of lipophilicity and polarity on pesticide toxicity. The models presented in this work could be used to pre-evaluate the toxicity of pesticides within the applicability domain, thus focusing resources on the high-toxic pesticides and assessing the environmental risk of pesticides quickly and economically.
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Affiliation(s)
- Lu Yang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, NO. 12 Zhongguancun South Street, Haidian District, Beijing, 10081, China
| | - Cuihong Sang
- Plant Protective Station, Agriculture Agency of Minquan Country, Boai Road, Henan, 476800, China
| | - Yinghuan Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China.
| | - Wentao Liu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Weiyu Hao
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Jing Chang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
| | - Jianzhong Li
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, 100085, PR China
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22
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Colombo R, Souza AT. Degradation of ethyl butylacetylaminopropionate (IR3535) during chlorination: Tentative identification and toxicity prediction of its disinfection by-products. CHEMOSPHERE 2021; 280:130656. [PMID: 33933999 DOI: 10.1016/j.chemosphere.2021.130656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/13/2021] [Accepted: 04/20/2021] [Indexed: 06/12/2023]
Abstract
Studies have reported the presence of ethyl butylacetylaminopropionate (IR3535) in waters, and the content of this repellent is expected to rise significantly in the future. There are extremely scarce data in the literature regarding the behavior of IR3535 and its derivatives in water. The present work reports the results obtained from experiments conducted under controlled conditions aiming at investigating the transformation of IR3535 in chlorinated water, in addition to an attempt to identify its disinfection by-products (DBPs). The work also reports the findings of analyses conducted in swimming pool water samples which sought to investigate the presence and content of IR3535 and its targeted DBPs in these samples. The results obtained in the controlled experiments show that IR3535 is not completely degraded under the chlorinated conditions evaluated and 9 DBPs were tentatively identified. The presence of IR3535 was detected in both adults and children's pool water samples at concentrations ranging from 62 ng L-1 to 114 ng L-1. Some of the DBPs identified in the controlled experiments were also detected in the pool water samples. The toxicity of the 9 DBPs identified was evaluated using the QSAR model, where some by-products presented mutagenic and carcinogenic properties.
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Affiliation(s)
- Renata Colombo
- School of Arts, Sciences and Humanities, University of São Paulo, R. Arlindo Béttio, 100, Vila Guaraciaba, 03828-000, São Paulo, SP, Brazil.
| | - Alanis Tiozzo Souza
- School of Arts, Sciences and Humanities, University of São Paulo, R. Arlindo Béttio, 100, Vila Guaraciaba, 03828-000, São Paulo, SP, Brazil.
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23
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Evaluation of molecular structure based descriptors for the prediction of pEC50(M) for the selective adenosine A2A Receptor. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.130080] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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24
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Gu W, Li X, Du M, Ren Z, Li Q, Li Y. Identification and regulation of ecotoxicity of polychlorinated naphthalenes to aquatic food Chain (green algae-Daphnia magna-fish). AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 233:105774. [PMID: 33610856 DOI: 10.1016/j.aquatox.2021.105774] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/05/2021] [Accepted: 02/07/2021] [Indexed: 06/12/2023]
Abstract
Polychlorinated naphthalenes (PCNs) are widely distributed in the aquatic environment and can be transmitted through the food chain, which can amplify their toxic effects on human. To inhibit their transmission in the trophic level, this study aimed to predict the joint toxicity mechanism of polychlorinated naphthalenes (PCNs) to the key organisms and control scheme of its toxicity in the aquatic food chain (green algae-Daphnia magna-fish). The toxic effect grade and mode of action (MoA) of PCNs on the food chain were first predicted to guide the establishment of toxic mechanism model. QSAR models were constructed to quantify the mechanism of aquatic toxicity due to PCNs. The results showed the PCN compounds studied were highly toxic at all the trophic levels of the aquatic food chain. The binding ability of PCNs to the aquatic organisms was the main factor causing the toxicity of PCNs in the food chain, followed by electronic parameters EHOMO and ELUMO. Moreover, the binding ability between PCNs and food chain receptors was related to the molecular hydrophobicity, the hydrophobicity can be changed by adjusting the ability of PCNs to be adsorbed by sediment and their chlorine substituents, while the effect of PCNs electronic parameters (EHOMO and ELUMO) can be adjusted by their solvation effect. In addition, the macro-control scheme of PCN-based aquatic toxicity mechanism was established, and the molecular dynamics (MD) simulation confirmed its effectiveness and accessibility. The MD simulation showed the inhibition effect of nutrition-grade toxicity in the food chain was significant when the external stimulation conditions of solvation, anaerobic dechlorination and molecular adsorption were improved, with the decrease range of 66.26-263.16%, 198.93-323.98% and 189.24-549.48%, respectively. This work reveals new insights into the mechanism of PCNs joint toxicity to aquatic ecosystem food chain and develop appropriate strategies for its ecological risk management.
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Affiliation(s)
- Wenwen Gu
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206, China.
| | - Xixi Li
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University, St. John's, NL, A1B 3X5, Canada.
| | - Meijin Du
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206, China.
| | - Zhixing Ren
- College of Forestry, Northeast Forestry University, No. 26 Hexing Road, Harbin, China.
| | - Qing Li
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206, China.
| | - Yu Li
- MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206, China.
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25
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Shen C, Zhu K, Ruan J, Li J, Wang Y, Zhao M, He C, Zuo Z. Screening of potential oestrogen receptor α agonists in pesticides via in silico, in vitro and in vivo methods. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 270:116015. [PMID: 33352482 DOI: 10.1016/j.envpol.2020.116015] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/28/2020] [Accepted: 11/05/2020] [Indexed: 06/12/2023]
Abstract
In modern agricultural management, the use of pesticides is indispensable. Due to their massive use worldwide, pesticides represent a latent risk to both humans and the environment. In the present study, 1056 frequently used pesticides were screened for oestrogen receptor (ER) agonistic activity by using in silico methods. We found that 72 and 47 pesticides potentially have ER agonistic activity by the machine learning methods random forest (RF) and deep neural network (DNN), respectively. Among endocrine-disrupting chemicals (EDCs), 14 have been reported as EDCs or ER agonists by previous studies. We selected 3 reported and 7 previously unreported pesticides from 76 potential ER agonists to further assess ERα agonistic activity. All 10 selected pesticides exhibited ERα agonistic activity in human cells or zebrafish. In the dual-luciferase reporter gene assays, six pesticides exhibited ERα agonistic activity. Additionally, nine pesticides could induce mRNA expression of the pS2 and NRF1 genes in MCF-7 cells, and seven pesticides could induce mRNA expression of the vtg1 and vtg2 genes in zebrafish. Importantly, the remaining 48 out of 76 potential ER agonists, none of which have previously been reported to have endocrine-disrupting effects or oestrogenic activity, should be of great concern. Our screening results can inform environmental protection goals and play an important role in environmental protection and early warnings to human health.
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Affiliation(s)
- Chao Shen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Kongyang Zhu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Jinpeng Ruan
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Jialing Li
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Yi Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Meirong Zhao
- College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310014, China
| | - Chengyong He
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Zhenghong Zuo
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, Fujian, 361005, China.
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26
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Gilbert EPK, Edwin L. A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts. REVIEWS OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2021; 257:37-68. [PMID: 33932184 DOI: 10.1007/398_2020_64] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The lure of increased productivity and crop yield has caused the imprudent use of pesticides in great quantity that has unfavorably affected environmental health. Pesticides are chemicals intended for avoiding, eliminating, and mitigating any pests that affect the crop. Lack of awareness, improper management, and negligent disposal of pesticide containers have led to the permeation of pesticide residues into the food chain and other environmental pathways, leading to environmental degradation. Sufficient steps must be undertaken at various levels to monitor and ensure judicious use of pesticides. Development of prediction models for optimum use of pesticides, pesticide management, and their impact would be of great help in monitoring and controlling the ill effects of excessive use of pesticides. This paper aims to present an exhaustive review of the prediction models developed and modeling strategies used to optimize the use of pesticides.
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Affiliation(s)
- Edwin Prem Kumar Gilbert
- Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
| | - Lydia Edwin
- Department of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India
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