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Mishra M. Daphnia magna as a Model Organism to Predict the Teratogenic Effect of Different Compounds. Methods Mol Biol 2024; 2753:261-281. [PMID: 38285344 DOI: 10.1007/978-1-0716-3625-1_13] [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] [Indexed: 01/30/2024]
Abstract
For aquatic ecosystem Daphnia magna is evolving as a model organism to check the teratogenicity of numerous compounds. D. magna can be easily cultured in the laboratory, and the teratogen effect of several compounds can be easily studied. The developmental stages are well studied in D. magna. All the developmental stages are transparent so the defect can be easily accessed. So, the postembryonic developmental changes can be easily studied after the exposure with teratogen. More importantly, D. magna also have a swimming behavioral phenotype. The behavioral defect can be easily accessed after teratogen exposure. The current chapter summarizes numerous protocols associated with embryo and adult staining and adult behavioral assays that can be used to access the teratogenicity of any unknown compound.
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Affiliation(s)
- Monalisa Mishra
- Neural Developmental Biology Lab, Department of Life Science, NIT Rourkela, Rourkela, Odisha, India.
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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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Nath A, Ojha PK, Roy K. QSAR assessment of aquatic toxicity potential of diverse agrochemicals. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023:1-20. [PMID: 37941423 DOI: 10.1080/1062936x.2023.2278074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 10/24/2023] [Indexed: 11/10/2023]
Abstract
The fast-increasing number of commercially produced chemicals challenges the experimental ecotoxicity assessment methods, which are costly, time-consuming, and dependent on the sacrifice of animals. In this regard, Quantitative Structure-Property/Activity Relationships (QSPR/QSAR) have led the way in developing ecotoxicity assessment models. In this study, QSAR models have been developed using the pEC50 values of 82 diverse agrochemicals or agro-molecules against a planktonic crustacean Daphnia magna with easily interpretable 2D descriptors. Moreover, a link among octanol-water partition coefficient (KOW), bio-concentration factor (BCF), and critical body residue (CBR) has been addressed, and their imputation for the prediction of the toxicity endpoint (EC50) has been done with an objective of the advanced exploration of several ecotoxicological parameters for toxic chemicals. The developed partial least squares (PLS) models were validated rigorously and proved to be robust, sound, and immensely well-predictive. The final Daphnia toxicity model derived from experimental derived properties along with computed descriptors emerged better in statistical quality and predictivity than those obtained solely from computed descriptors. Additionally, the pEC50 and other important properties (log KOW, log BCF, and log CBR) for a set of external agro-molecules, not employed in model development, were predicted to show the predictive ability of the models.
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Affiliation(s)
| | - P K Ojha
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - K Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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Hou Y, Bai Y, Lu C, Wang Q, Wang Z, Gao J, Xu H. Applying molecular docking to pesticides. PEST MANAGEMENT SCIENCE 2023; 79:4140-4152. [PMID: 37547967 DOI: 10.1002/ps.7700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/17/2023] [Accepted: 08/05/2023] [Indexed: 08/08/2023]
Abstract
Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yang Hou
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Yuqian Bai
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Chang Lu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Qiuchan Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Zishi Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Jinsheng Gao
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Hongliang Xu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
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Kang JK, Lee D, Muambo KE, Choi JW, Oh JE. Development of an embedded molecular structure-based model for prediction of micropollutant treatability in a drinking water treatment plant by machine learning from three years monitoring data. WATER RESEARCH 2023; 239:120037. [PMID: 37182312 DOI: 10.1016/j.watres.2023.120037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/25/2023] [Accepted: 05/01/2023] [Indexed: 05/16/2023]
Abstract
In this study, an autoencoder-based molecular structure embedding model was developed to predict treatability of micropollutant in a drinking water treatment plant (DWTP) by machine learning using 69 micropollutants monitoring data at 18 DWTPs for three years. The molecular structure, which contains physicochemical characteristics, was embedded as a fixed-length vector that is advantageous for data-driven analysis and machine learning. First, the molecular structure of the micropollutants was converted to a sequence of tokens using the simplified molecular-input line-entry system (SMILES) pair encoding tokenizer, a frequency-based tokenization method. It was then compressed into fixed-length vectors using an autoencoder trained on various molecular structures within the Chemical Entities of Biological Interest. To validate the proposed models, a binary classification of micropollutant treatability was performed using the embedded molecular structure of micropollutants with various external features, such as concentration, season, and the presence of specific drinking water treatment processes by machine learning. The accuracy of the developed model for the 69 micropollutants in this study was 0.86, and the molecular structure was determined to be the most important feature. Furthermore, an accuracy of 0.71 was obtained in external validation for pharmaceuticals and personal care products that were not used for training. This shows that the proposed embedding vector can be generalized to unseen molecules during the training process, which means that it reflects the characteristics of the molecular structures.
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Affiliation(s)
- Jin-Kyu Kang
- Institute for Environment and Energy, Pusan National University, Busan 46241, Republic of Korea
| | | | - Kimberly Etombi Muambo
- Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea
| | - Jae-Won Choi
- Department of Water Environmental Safety Management, K-water, Shintanjinro 200 Daeduck, Daejeon 34350, Republic of Korea
| | - Jeong-Eun Oh
- Institute for Environment and Energy, Pusan National University, Busan 46241, Republic of Korea; Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea.
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Huang T, Sun G, Zhao L, Zhang N, Zhong R, Peng Y. Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review. Int J Mol Sci 2021; 22:8557. [PMID: 34445263 PMCID: PMC8395302 DOI: 10.3390/ijms22168557] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/05/2021] [Accepted: 08/05/2021] [Indexed: 01/22/2023] Open
Abstract
Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.
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Affiliation(s)
- Tao Huang
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Guohui Sun
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Lijiao Zhao
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Na Zhang
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Rugang Zhong
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, College of Environmental and Chemical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China;
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