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Chen D, Liu Y, Liu Y, Zhao K, Zhang T, Gao Y, Wang Q, Song B, Hao G. ChemFREE: a one-stop comprehensive platform for ecological and environmental risk evaluation of chemicals in one health world. Nucleic Acids Res 2024; 52:W450-W460. [PMID: 38832633 PMCID: PMC11223831 DOI: 10.1093/nar/gkae446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/03/2024] [Accepted: 05/11/2024] [Indexed: 06/05/2024] Open
Abstract
Addressing health and safety crises stemming from various environmental and ecological issues is a core focus of One Health (OH), which aims to balance and optimize the health of humans, animals, and the environment. While many chemicals contribute significantly to our quality of life when properly used, others pose environmental and ecological health risks. Recently, assessing the ecological and environmental risks associated with chemicals has gained increasing significance in the OH world. In silico models may address time-consuming and costly challenges, and fill gaps in situations where no experimental data is available. However, despite their significant contributions, these assessment models are not web-integrated, leading to user inconvenience. In this study, we developed a one-stop comprehensive web platform for freely evaluating the eco-environmental risk of chemicals, named ChemFREE (Chemical Formula Risk Evaluation of Eco-environment, available in http://chemfree.agroda.cn/chemfree/). Inputting SMILES string of chemicals, users will obtain the assessment outputs of ecological and environmental risk, etc. A performance evaluation of 2935 external chemicals revealed that most classification models achieved an accuracy rate above 0.816. Additionally, the $Q_{F1}^2$ metric for regression models ranges from 0.618 to 0.898. Therefore, it will facilitate the eco-environmental risk evaluation of chemicals in the OH world.
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Affiliation(s)
- Dongyu Chen
- State 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, P. R. China
| | - Yingwei Liu
- State 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, P. R. China
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Yang Liu
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Kejun Zhao
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Tianhan Zhang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Yangyang Gao
- State 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, P. R. China
| | - Qi Wang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China
| | - Baoan Song
- State 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, P. R. China
| | - Gefei Hao
- State 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, P. R. China
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Yuan S, Yang J, Fu X, Yu H, Guo Y, Xie Y, Xiao Y, Cheng Y, Yao W. Effect of tannic acid binding on the thermal degradation behavior and product toxicity of boscalid. Food Chem 2024; 444:138654. [PMID: 38335685 DOI: 10.1016/j.foodchem.2024.138654] [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: 09/22/2023] [Revised: 01/12/2024] [Accepted: 01/31/2024] [Indexed: 02/12/2024]
Abstract
The effect of tannic acid (TA) binding on the thermal degradation of boscalid was studied in this work. The results revealed that TA binding has a significant impact on boscalid degradation. The degradation rate constant of bound boscalid was reduced, and its corresponding half-life was significantly prolonged compared to the free state. Four identical degradation products were detected in both states through UHPLC-Q-TOF-MS, indicating that degradation products were not affected by TA binding. Based on DFT and MS analysis, the degradation pathways of boscalid included hydroxyl substitution of chlorine atoms and cleavage of CN and CC bonds. The toxicity of B2 and B3 exceeded that of boscalid. In summary, the binding of TA and boscalid significantly affected the thermal degradation rate of boscalid while preserving the types of degradation products. This study contributed to a fundamental understanding of the degradation process of bound pesticide residues in complex food matrices.
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Affiliation(s)
- Shaofeng Yuan
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu Province, China
| | - Jian Yang
- China Academy of Launch Vehicle Technology, Beijing, China
| | - Xiaoyan Fu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu Province, China
| | - Hang Yu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu Province, China
| | - Yahui Guo
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu Province, China
| | - Yunfei Xie
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu Province, China
| | - Yuan Xiao
- School of Public Health, Wannan Medical College, Wuhu, Anhui, China
| | - Yuliang Cheng
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu Province, China
| | - Weirong Yao
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu Province, China.
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Daghighi A, Casanola-Martin GM, Iduoku K, Kusic H, González-Díaz H, Rasulev B. Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10116-10127. [PMID: 38797941 DOI: 10.1021/acs.est.4c01017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.
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Affiliation(s)
- Amirreza Daghighi
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, Zagreb 10000, Croatia
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa 48940, Spain
- BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, Leioa 48940, Spain
- IKERBASQUE, Basque Foundation for Science,Bilbao, Biscay 48011, Spain
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
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Kumar A, Ojha PK, Roy K. The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2024; 26:870-881. [PMID: 38652036 DOI: 10.1039/d4em00059e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a short/long time which depends on the nature and amount of chemicals consumed. Therefore, the maximum acceptable daily intake of chemicals must be calculated to prevent these risks. In the present work, regression-based quantitative structure-activity relationship (QSAR) models were developed using 39 pesticides with maximum acceptable daily intake (MADI) for humans as the endpoint. From the statistical results (R2 = 0.674-0.712, QLOO2 = 0.553-0.580, Q(F1)2 = 0.544-0.611, and Q(F2)2 = 0.531-0.599), it can be inferred that the developed models were robust, reliable, reproducible, accurate, and predictive. Intelligent Consensus Prediction (ICP) was employed to improve the external predictivity (Q(F1)2 =0.579-0.657 and Q(F2)2 = 0.563-0.647) of the models. Some of the chemical markers responsible for toxicity enhancement are the presence of unsaturated bonds, lipophilicity, presence of C< (double bond-single bond-single bonded carbon), and the presence of sulphur and phosphate bonds at the topological distances 1 and 6, while the presence of hydrophilic groups and short chain fragments reduces the toxicity. The Pesticide Properties Database (PPDB) (1694 pesticides) was also screened with the developed models. Hence, this research work will be helpful for the toxicity assessment of pesticides before their synthesis, the development of eco-friendly and safer pesticides, and data-gap filling reducing the time, cost, and animal experimentation. Thus, this study might hold promise for future potential MADI assessment of pesticides and provide a meaningful contribution to the field of risk assessment.
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Affiliation(s)
- Ankur Kumar
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
| | - Probir Kumar Ojha
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
| | - Kunal Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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Nakayama Y, Morishita S, Doi H, Hirano T, Kaneko H. Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning. ACS OMEGA 2024; 9:18488-18494. [PMID: 38680296 PMCID: PMC11044161 DOI: 10.1021/acsomega.4c00655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/27/2024] [Accepted: 03/27/2024] [Indexed: 05/01/2024]
Abstract
Pesticides are widely used to improve crop productivity by eliminating weeds and pests. Conventional pesticide development involves synthesizing compounds, testing their activities, and studying their effects on the ecosystem. However, as pesticide discovery has an extremely low success rate, many compounds must be synthesized and tested. To overcome the high human, financial, and time costs of this process, machine learning is attracting increasing attention. In this study, we used machine learning for the molecular design of novel seed compounds for herbicides and insecticides. Classification models were constructed by using compounds that had been tested as herbicides and insecticides, and an inverse analysis of the constructed models was conducted. In the molecular design of herbicides, we proposed 186 new samples as herbicides using ensemble learning and a method for expressing explanatory variables that consider the relationships among eight weed species. For the molecular design of insecticides, we used undersampling and ensemble learning for the analysis of unbalanced data. Based on approximately 340,000 compounds, 12 potential insecticides were proposed, of which 2 exhibited actual activity when tested. These results demonstrate the potential of the developed machine-learning method for rapidly identifying novel herbicides and insecticides.
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Affiliation(s)
- Yuki Nakayama
- Department
of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
| | - Saki Morishita
- Hokko
Chemical Industry Co., Ltd., 2165, Toda, Atsugi-shi, Kanagawa 243-0023, Japan
| | - Hayato Doi
- Hokko
Chemical Industry Co., Ltd., 2165, Toda, Atsugi-shi, Kanagawa 243-0023, Japan
| | - Tatsuya Hirano
- Hokko
Chemical Industry Co., Ltd., 2165, Toda, Atsugi-shi, Kanagawa 243-0023, Japan
| | - Hiromasa Kaneko
- Department
of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
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6
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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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7
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Kumar A, Ojha PK, Roy K. First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:241-263. [PMID: 38390626 DOI: 10.1080/1062936x.2024.2320143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results (n train = 53 - 62 , r 2 = 0.614 to 0.754, Q L O O 2 = 0.501 to 0.703 and Q F 1 2 = 0.531 to 0.718, Q F 2 2 = 0.523 - 0.713 ), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity (Q F 1 2 = 0.595 - 0.813 , Q F 2 2 = 0.573 - 0.809 ) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.
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Affiliation(s)
- A Kumar
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - P K Ojha
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - K Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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8
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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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9
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Yang L, Tian R, Li Z, Ma X, Wang H, Sun W. Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach. CHEMOSPHERE 2023; 328:138433. [PMID: 36963572 DOI: 10.1016/j.chemosphere.2023.138433] [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: 01/11/2023] [Revised: 03/02/2023] [Accepted: 03/15/2023] [Indexed: 06/18/2023]
Abstract
Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.
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Affiliation(s)
- Lu Yang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Ruya Tian
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhoujing Li
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiaomin Ma
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Hongyan Wang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Wei Sun
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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Wang K, Lv Y, He M, Tian L, Nie F, Shao Z, Wang Z. A Quantitative Structure-Activity Relationship Approach to Determine Biotoxicity of Amide Herbicides for Ecotoxicological Risk Assessment. ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2023; 84:214-226. [PMID: 36646954 DOI: 10.1007/s00244-023-00980-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Amide herbicides have been widely applied in agriculture and found to be widespread and affect nontarget organisms in the environment. To better understand the biotoxicity mechanisms and determine the toxicity to the nontarget organisms for the hazard and risk assessment, five QSAR models were developed for the biotoxicity prediction of amide herbicides toward five aquatic and terrestrial organisms (including algae, daphnia, fish, earthworm and avian species), based on toxicity concentration and quantitative molecular descriptors. The results showed that the developed models complied with OECD principles for QSAR validation and presented excellent performances in predictive ability. In combination, the investigated QSAR relationship led to the toxicity mechanisms that eleven electrical descriptors (EHOMO, ELUMO, αxx, αyy, αzz, μ, qN-, Qxx, Qyy, qH+, and q-), four thermodynamic descriptors (Cv, Sθ, Hθ, and ZPVE), and one steric descriptor (Vm) were strongly associated with the biotoxicity of amide herbicides. Electrical descriptors showed the greatest impacts on the toxicity of amide herbicides, followed by thermodynamic and steric descriptors.
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Affiliation(s)
- Kexin Wang
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
| | - Yangzhou Lv
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
| | - Mei He
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China.
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology, Beijing, 102200, China.
| | - Lei Tian
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China.
- School of Petroleum Engineering, Yangtze University, Wuhan, 430100, China.
| | - Fan Nie
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology, Beijing, 102200, China
| | - Zhiguo Shao
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology, Beijing, 102200, China
| | - Zhansheng Wang
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology, Beijing, 102200, China
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Li Z, Fantke P. Framework for defining pesticide maximum residue levels in feed: applications to cattle and sheep. PEST MANAGEMENT SCIENCE 2023; 79:748-759. [PMID: 36259312 PMCID: PMC10092036 DOI: 10.1002/ps.7241] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 10/12/2022] [Accepted: 10/19/2022] [Indexed: 06/12/2023]
Abstract
BACKGROUND Pesticide residues in animal feed can endanger animal health and compromise the safety of livestock products for human consumption. Even though policymakers such as the European Union and the World Health Organization have established maximum residue levels (MRLs) for pesticides in both human food and animal feed, there is no systematic management of pesticides in animal feed that considers the entire supply chain. In response, we propose a framework for defining consistent MRLs for pesticides in animal feed that assesses the impact of defined MRLs on upstream (e.g., MRLs in feed crops) and downstream (e.g., MRLs in livestock products) sectors of the livestock-product supply chain. RESULTS The MRLs determined for the selected pesticides in the feed of cattle and sheep as case study animals indicate that lipophilic pesticides tend to have lower MRLs than hydrophilic pesticides, primarily due to the relatively high toxicity and biotransfer factors of lipophilic pesticides. In addition, we observe that, primarily for lipophilic pesticides, upstream and downstream regulations are not aligned in terms of defining MRLs in feed using current MRLs in crops with relevance to feed and foods of animal origin. CONCLUSION Some of the current pesticide regulations in the livestock-product supply chain need to be re-evaluated to ensure that MRLs in the upstream sector (i.e., crops) do not result in unacceptable residues in the downstream sector (i.e., MRLs in livestock products affecting animal and human health). Finally, we provide recommendations for optimizing the derivation of MRLs in feed, including the evaluation of residue fate during feed and food manufacturing processes. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Zijian Li
- School of Public Health (Shenzhen)Sun Yat‐sen UniversityShenzhenChina
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Environmental and Resource EngineeringTechnical University of DenmarkKgs. LyngbyDenmark
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Li F, Sun G, Fan T, Zhang N, Zhao L, Zhong R, Peng Y. Ecotoxicological QSAR modelling of the acute toxicity of fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) against two aquatic organisms: Consensus modelling and comparison with ECOSAR. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 255:106393. [PMID: 36621240 DOI: 10.1016/j.aquatox.2022.106393] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 12/08/2022] [Accepted: 12/31/2022] [Indexed: 06/17/2023]
Abstract
Fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) are a type of organic compounds widely occurring in the environment that pose a potential hazard to ecosystem and public health, and thus receive extensive attention from various regulatory agencies. Here, quantitative structure-activity relationship (QSAR) models were constructed to model the ecotoxicity of FNFPAHs against two aquatic species, Daphnia magna and Oncorhynchus mykiss. According to the stringent OECD guidelines, we used genetic algorithm (GA) plus multiple linear regression (MLR) approach to establish QSAR models of the two aquatic toxicity endpoints: D. magna (48 h LC50) and O. mykiss (96 h LC50). The models were established using simple 2D descriptors with explicit physicochemical significance and evaluated using various internal/external validation metrics. The results clearly show that both models are statistically robust (QLOO2 = 0.7834 for D. magna and QLOO2 = 0.8162 for O. mykiss), have good internal fitness (R2 = 0.8159 for D. magna and R2 = 0.8626 for O. mykiss and external predictive ability (D. magna: Rtest2 = 0.8259, QFn2 = 0.7640∼0.8140, CCCtest = 0.8972; O. mykiss:Rtest2 = 0.8077, QFn2 = 0.7615∼0.7722, CCCtest = 0.8910). To prove the predictive performance of the developed models, an additional comparison with the standard ECOSAR tool obviously shows that our models have lower RMSE values. Subsequently, we utilized the best models to predict the true external set compounds collected from the PPDB database to further fill the toxicity data gap. In addition, consensus models (CMs) that integrate all validated individual models (IMs) were more externally predictive than IMs, of which CM2 has the best prediction performance towards the two aquatic species. Overall, the models presented here could be used to evaluate unknown FNFPAHs inside the domain of applicability (AD), thus being very important for environmental risk assessment under current regulatory frameworks.
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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
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, 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
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Beijing University of Technology, Beijing 100124, China
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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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Chen Y, Dong Y, Li L, Jiao J, Liu S, Zou X. Toxicity Rank Order (TRO) As a New Approach for Toxicity Prediction by QSAR Models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:701. [PMID: 36613021 PMCID: PMC9819504 DOI: 10.3390/ijerph20010701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Quantitative Structure-Activity Relationship (QSAR) models are commonly used for risk assessment of emerging contaminants. The objective of this study was to use a toxicity rank order (TRO) as an integrating parameter to improve the toxicity prediction by QSAR models. TRO for each contaminant was calculated from collected toxicity data including acute toxicity concentration and no observed effect concentration. TRO values associated with toxicity mechanisms were used to classify pollutants into three modes of action consisting of narcosis, transition and reactivity. The selection principle of parameters for QSAR models was established and verified. It showed a reasonable prediction of toxicities caused by organophosphates and benzene derivatives, especially. Compared with traditional procedures, incorporating TRO showed an improved correlation coefficient of QSAR models by approximately 10%. Our study indicated that the proposed procedure can be used for screening modeling parameter data and improve the toxicity prediction by QSAR models, and this could facilitate prediction and evaluation of environmental contaminant toxicity.
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Affiliation(s)
- Yuting Chen
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Yuying Dong
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Le Li
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Jian Jiao
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Sitong Liu
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
| | - Xuejun Zou
- College of Environment and Resource, Dalian Minzu University, Dalian 116600, China
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