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Chen Y, Li M, Gao W, Guan Y, Hao Z, Liu J. Occurrence and risks of pharmaceuticals, personal care products, and endocrine-disrupting compounds in Chinese surface waters. J Environ Sci (China) 2024; 146:251-263. [PMID: 38969453 DOI: 10.1016/j.jes.2023.10.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/09/2023] [Accepted: 10/09/2023] [Indexed: 07/07/2024]
Abstract
The continuous and rapid increase of chemical pollution in surface waters has become a pressing and widely recognized global concern. As emerging contaminants (ECs) in surface waters, pharmaceutical and personal care products (PPCPs), and endocrine-disrupting compounds (EDCs) have attracted considerable attention due to their wide occurrence and potential threat to human health. Therefore, a comprehensive understanding of the occurrence and risks of ECs in Chinese surface waters is urgently required. This study summarizes and assesses the environmental occurrence concentrations and ecological risks of 42 pharmaceuticals, 15 personal care products (PCPs), and 20 EDCs frequently detected in Chinese surface waters. The ECs were primarily detected in China's densely populated and highly industrialized regions. Most detected PPCPs and EDCs had concentrations between ng/L to µg/L, whereas norfloxacin, caffeine, and erythromycin had relatively high contamination levels, even exceeding 2000 ng/L. Risk evaluation based on the risk quotient method revealed that 34 PPCPs and EDCs in Chinese surface waters did not pose a significant risk, whereas 4-nonylphenol, 4-tert-octylphenol, 17α-ethinyl estradiol, 17β-estradiol, and triclocarban did. This review provides a comprehensive summary of the occurrence and associated hazards of typical PPCPs and EDCs in Chinese surface waters over the past decade, and will aid in the regulation and control of these ECs in Chinese surface waters.
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Affiliation(s)
- Yuhang Chen
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, China
| | - Mengyuan Li
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Environmental Science and Engineering, China West Normal University, Nanchong 637009, China
| | - Weichun Gao
- College of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, China
| | - Yinyan Guan
- College of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, China
| | - Zhineng Hao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, China; College of Environmental Science and Engineering, China West Normal University, Nanchong 637009, China.
| | - Jingfu Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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Mei S. Transferring knowledge across aquatic species via clustering techniques to unravel patterns of pesticide toxicity. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 950:175385. [PMID: 39122048 DOI: 10.1016/j.scitotenv.2024.175385] [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: 05/13/2024] [Revised: 07/28/2024] [Accepted: 08/06/2024] [Indexed: 08/12/2024]
Abstract
In silico modelling takes the advantage of accelerating ecotoxicological assessments on hazardous chemicals without conducting risky in vivo experiments under ethic regulation. To date, the prevailing strategy of one model for one species cannot be well generalized to multi-species modelling. In this work, we propose a new strategy of one model for multiple species to facilitate knowledge transfer across aquatic species. The available lethal concentration values of 4952 pesticides on 651 fish species are aggregated into one toxicity response matrix, purely through which we attempt to unravel fish toxicosis-phylogenesis relationships and pesticide toxicity-structure relationships via clustering techniques including non-negative matrix factorization (NMF) and hierarchical clustering. The clustering results suggest that (1) close NMF weights indicate close species-toxicosis and pesticide-toxicity profiles; (2) and that species toxicosis patterns are related with species phylogenetic relationships; (3) and that close pesticide-toxicity profiles indicate similar atom-pair structural fingerprints. These environmental, chemical and biological insights can be used as expert knowledge for environmentalists to manually gain knowledge about untested species/pesticides from tested species/pesticides, and meanwhile provide support for us to build in silico models from species phylogenetic and pesticide structural points of view. Besides unravelling the mechanisms behind toxicity response, we also adopt stratified cross validation and external test to validate the reliability of using NMF to predict missing toxicity values. Independent test on external data shows that NMF achieves 0.8404-0.9397 R2 on four fish species. In the context of toxicity prediction, non-negative matrix factorization can be viewed as a model based on quantitative activity-activity relationships (QAAR), and provides an alternative approach of inferring toxicity values on untested species from tested species.
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Affiliation(s)
- Suyu Mei
- Software College, Shenyang Normal University, Shenyang 110034, China.
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Chen Z, Li N, Zhang P, Li Y, Li X. CardioDPi: An explainable deep-learning model for identifying cardiotoxic chemicals targeting hERG, Cav1.2, and Nav1.5 channels. JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134724. [PMID: 38805819 DOI: 10.1016/j.jhazmat.2024.134724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/08/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
The cardiotoxic effects of various pollutants have been a growing concern in environmental and material science. These effects encompass arrhythmias, myocardial injury, cardiac insufficiency, and pericardial inflammation. Compounds such as organic solvents and air pollutants disrupt the potassium, sodium, and calcium ion channels cardiac cell membranes, leading to the dysregulation of cardiac function. However, current cardiotoxicity models have disadvantages of incomplete data, ion channels, interpretability issues, and inability of toxic structure visualization. Herein, an interpretable deep-learning model known as CardioDPi was developed, which is capable of discriminating cardiotoxicity induced by the human Ether-à-go-go-related gene (hERG) channel, sodium channel (Na_v1.5), and calcium channel (Ca_v1.5) blockade. External validation yielded promising area under the ROC curve (AUC) values of 0.89, 0.89, and 0.94 for the hERG, Na_v1.5, and Ca_v1.5 channels, respectively. The CardioDPi can be freely accessed on the web server CardioDPipredictor (http://cardiodpi.sapredictor.cn/). Furthermore, the structural characteristics of cardiotoxic compounds were analyzed and structural alerts (SAs) can be extracted using the user-friendly CardioDPi-SAdetector web service (http://cardiosa.sapredictor.cn/). CardioDPi is a valuable tool for identifying cardiotoxic chemicals that are environmental and health risks. Moreover, the SA system provides essential insights for mode-of-action studies concerning cardiotoxic compounds.
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Affiliation(s)
- Zhaoyang Chen
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Na Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China.
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4
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Anand G, Koniusz P, Kumar A, Golding LA, Morgan MJ, Moghadam P. Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE). JOURNAL OF HAZARDOUS MATERIALS 2024; 472:134456. [PMID: 38703678 DOI: 10.1016/j.jhazmat.2024.134456] [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: 02/05/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024]
Abstract
Exposure to toxic chemicals threatens species and ecosystems. This study introduces a novel approach using Graph Neural Networks (GNNs) to integrate aquatic toxicity data, providing an alternative to complement traditional in vivo ecotoxicity testing. This study pioneers the application of GNN in ecotoxicology by formulating the problem as a relation prediction task. GRAPE's key innovation lies in simultaneously modelling 444 aquatic species and 2826 chemicals within a graph, leveraging relations from existing datasets where informative species and chemical features are augmented to make informed predictions. Extensive evaluations demonstrate the superiority of GRAPE over Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models, achieving remarkable improvements of up to a 30% increase in recall values. GRAPE consistently outperforms LR and MLP in predicting novel chemicals and new species. In particular, GRAPE showcases substantial enhancements in recall values, with improvements of ≥ 100% for novel chemicals and up to 13% for new species. Specifically, GRAPE correctly predicts the effects of novel chemicals (104 out of 126) and effects on new species (7 out of 8). Moreover, the study highlights the effectiveness of the proposed chemical features and induced network topology through GNN for accurately predicting metallic (74 out of 86) and organic (612 out of 674) chemicals, showcasing the broad applicability and robustness of the GRAPE model in ecotoxicological investigations. The code/data are provided at https://github.com/csiro-robotics/GRAPE.
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Affiliation(s)
- Gaurangi Anand
- Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Dutton Park 4102, QLD, Australia
| | - Piotr Koniusz
- Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Black Mountain 2601, ACT, Australia.
| | - Anupama Kumar
- Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Waite Campus 5064, SA, Australia
| | - Lisa A Golding
- Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Dutton Park 4102, QLD, Australia
| | - Matthew J Morgan
- Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Black Mountain 2601, ACT, Australia
| | - Peyman Moghadam
- Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Pullenvale 4069, QLD, Australia
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Lou S, Yu Z, Huang Z, Wang H, Pan F, Li W, Liu G, Tang Y. In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods. Chem Res Toxicol 2024; 37:513-524. [PMID: 38380652 DOI: 10.1021/acs.chemrestox.4c00012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.
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Affiliation(s)
- Shang Lou
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhuohang Yu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zejun Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Haoqiang Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Fei Pan
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Shiammala PN, Duraimutharasan NKB, Vaseeharan B, Alothaim AS, Al-Malki ES, Snekaa B, Safi SZ, Singh SK, Velmurugan D, Selvaraj C. Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors. Methods 2023; 219:82-94. [PMID: 37778659 DOI: 10.1016/j.ymeth.2023.09.010] [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: 08/07/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023] Open
Abstract
Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.
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Affiliation(s)
| | | | - Baskaralingam Vaseeharan
- Department of Animal Health and Management, Science Block, Alagappa University, Karaikudi, Tamil Nadu 630 003, India
| | - Abdulaziz S Alothaim
- Department of Biology, College of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia
| | - Esam S Al-Malki
- Department of Biology, College of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia
| | - Babu Snekaa
- Laboratory for Artificial Intelligence and Molecular Modelling, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu 600077, India
| | - Sher Zaman Safi
- Faculty of Medicine, Bioscience and Nursing, MAHSA University, Jenjarom 42610, Selangor, Malaysia
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Devadasan Velmurugan
- Department of Biotechnology, College of Engineering & Technology, SRM Institute of Science & Technology, Kattankulathur, Chennai, Tamil Nadu 603203, India
| | - Chandrabose Selvaraj
- Laboratory for Artificial Intelligence and Molecular Modelling, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu 600077, India; Laboratory for Artificial Intelligence and Molecular Modelling, Center for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu 602105, India.
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