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Chen C, Zhang D, Yuan A, Shen J, Wang L, Wang SL. A novel approach to predict the comprehensive EROD potency: Mechanism-based curve fitting of CYP1A1 activity by PAHs. Sci Total Environ 2022; 843:157052. [PMID: 35787903 DOI: 10.1016/j.scitotenv.2022.157052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/24/2022] [Accepted: 06/25/2022] [Indexed: 06/15/2023]
Abstract
Cytochrome P450 1A1 (CYP1A1) plays critical roles in polycyclic aromatic hydrocarbon (PAH) toxicity, including DNA adduction and ROS generation. Therefore, CYP1A1 activity quantified by the 7-ethoxyresorufin-O-deethylase (EROD) assay (named EROD potency) has been considered a typical biomarker of PAH exposure and toxicity. The EROD dose-response curve always presents a biphasic style, increasing at low concentrations and decreasing at high concentrations of PAHs, but relative effect potency (REP) commonly used in PAH risk assessment is only involved in the increasing phase. In this study, a full bell-shaped EROD curve fitting formula Eq. (1) was obtained by considering both CYP1A1 mRNA induction and enzyme inhibition to completely assess the EROD potency of PAHs. Correspondingly, in silico models of QSAR and docking methods successfully predicted the full EROD curves of PAHs, and the structure-activity relationship indicated that PAHs with heavy molecular weight and large diameter showed stronger EROD potency. Further EROD potency with predicted curve parameters (EC50,ind and area index) was confirmed by the reported REP (R2 = 0.697-0.977) and experimental data from human and mouse cells (R2 = 0.700-0.804). This study provides a novel curve fitting for the EROD dose-response relationship and a prediction model for PAH EROD potency.
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Affiliation(s)
- Chao Chen
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Di Zhang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Anjie Yuan
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Jiemiao Shen
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Li Wang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Shou-Lin Wang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; State Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China.
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Wang X, Teng Y, Ji C, Wu H, Li F. Critical target identification and human health risk ranking of metal ions based on mechanism-driven modeling. Chemosphere 2022; 301:134724. [PMID: 35487360 DOI: 10.1016/j.chemosphere.2022.134724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/15/2022] [Accepted: 04/22/2022] [Indexed: 06/14/2023]
Abstract
Huge amounts of metals have been released into environment due to various anthropogenic activities, such as smelting and processing of metals and subsequent application in construction, automobiles, batteries, optoelectronic devices, and so on, resulting in widespread detection in environmental media. However, some metal ions are considered as "Environmental health hazards", leading to serious human health concerns through affecting critical targets. Hence, it is necessary to quickly and effectively recognize the key target of metal ions in living organisms. Fortunately, the development of high-throughput analysis and in silico approaches offer a promising tool for target identification. In this study, the key oncogenic target (tumor suppressor protein, p53) was screened by network analysis based on the comparative toxicogenomics database (CTD). Some metal ions could bind to p53 core domain, impair its function and induce the development of cancer risk, but its mechanisms were still unclear. Therefore, a quantitative structure-activity relationship (QSAR) model was constructed to characterize the binding constants (Ka) between DNA binding domain of p53 (p53 DBD) and nine metal ions (Mg2+, Ca2+, Cu2+, Zn2+, Co2+, Ni2+, Mn2+, Fe3+ and Ba2+). It had good robustness and predictive ability, which could be used to predict the Ka values of other six metal ions (Li+, Ag+, Cs+, Cd2+, Hg2+ and Pb2+) within application domain. The results showed strong binding affinity between Cd2+/Hg2+/Pb2+ and p53 DBD. Subsequent mechanism analyses revealed that first hydrolysis constant (|logKOH|) and polarization force (Z2/r) were key metal ion-characteristic parameters. The metal ions with weak hydrolysis constants and strong polarization forces could readily interact with N-containing histidine and S-containing cysteine of p53 DBD, which resulted in high Ka values. This study identified p53 as potential target for metal ions, revealed the key characteristics affecting the actions and provide a basic understanding of metal ions-p53 DBD interaction.
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Affiliation(s)
- Xiaoqing Wang
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, 264003, PR China; University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Yuefa Teng
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, 264003, PR China; University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Chenglong Ji
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, 264003, PR China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, PR China
| | - Huifeng Wu
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, 264003, PR China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, PR China.
| | - Fei Li
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, PR China.
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Sun X, Zhang X, Wang L, Li Y, Muir DCG, Zeng EY. Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern. J Hazard Mater 2022; 421:126746. [PMID: 34388923 DOI: 10.1016/j.jhazmat.2021.126746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/24/2021] [Accepted: 07/24/2021] [Indexed: 06/13/2023]
Abstract
Deep convolutional neural network (DCNN) has proved to be a promising tool for identifying organic chemicals of environmental concern. However, the uncertainty associated with DCNN predictions remains to be quantified. The training process contains many random configurations, including dataset segmentation, input sequences, and initial weight, etc. Moreover, the DCNN working mechanism is non-linear and opaque. To increase confidence to use this novel approach, persistent, bioaccumulative, and toxic substances (PBTs) were utilized as representative chemicals of environmental concern to estimate the prediction uncertainty under five distinguished datasets and ten different molecular descriptor (MD) arrangements with 111,852 chemicals and 2424 available MDs. An internal correlation coefficient test indicated that the prediction confidence reached 0.98 when a mean of 50 DCNNs' predictions was used instead of a sing DCNN prediction. A threshold for PBT categorization was determined by considering costs between false-negative and false-positive predictions. As revealed by the guided backpropagation-class activation mapping (GBP-CAM) saliency images, only 12% of all selected MDs were activated by DCNN and influenced decision-making process. However, the activated MDs not only varied among chemical classes but also shifted with different DCNNs. Principal component analysis indicated that 2424 MDs could transform into 370 orthogonal variables. Both results suggest that redundancy exists among selected MDs. Yet, DCNN was found to adapt to redundant data by focusing on the most important information for better prediction performance.
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Affiliation(s)
- Xiangfei Sun
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou 511443, China
| | - Xianming Zhang
- Department of Chemistry and Biochemistry, Concordia University, Montreal, Quebec H4B 1R6, Canada
| | - Luyao Wang
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou 511443, China
| | - Yuanxin Li
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou 511443, China
| | - Derek C G Muir
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou 511443, China; Environment and Climate Change Canada, Aquatic Contaminants Research Division, 867 Lakeshore Road, Burlington, Ontario L7S 1A1, Canada
| | - Eddy Y Zeng
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou 511443, China.
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Huang Y, Wang J, Wang S, Xu X, Qin W, Wen Y, Zhao YH, Martyniuk CJ. Discrimination of active and inactive substances in cytotoxicity based on Tox21 10K compound library: Structure alert and mode of action. Toxicology 2021; 462:152948. [PMID: 34530041 DOI: 10.1016/j.tox.2021.152948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/28/2021] [Accepted: 09/08/2021] [Indexed: 10/20/2022]
Abstract
In vitro cytotoxicity assay is an ideal alternative method for the in vivo toxicity in the risk assessment of pollutants in environment. However, modes of action (MOAs) of cytotoxicity have not been investigated for a wide range of compounds. In this paper, binomial and recursive partitioning analysis were carried out between the cytotoxicity and molecular descriptors for 8981 compounds. The results showed that cytotoxicity is strongly related to the chemical hydrophobicity and excess molar refraction, indicating the bio-uptake and chemical-receptor interaction through π and n electron pair play important roles in the cytotoxicity. The decision tree derived from recursive partitioning analysis revealed that the studied compounds could be divided into 25 groups and their structural characteristics could be used as structure alert to identify active and inactive compounds in cytotoxicity. The descriptors used in the decision tree revealed that chemical ionization and bioavailability could affect the cytotoxicity for ionizable and highly hydrophobic compounds. Comparison of MOAs based on Verhaar's classification scheme showed that many inert or less inert compounds were inactive substance, and many reactive or specifically-acting compounds were active substances in the cytotoxicity. In vitro toxicity assay instead of in vivo toxicity assay can be used in the environmental hazard and risk assessment of organic pollutants. The descriptors used in the binomial equation and decision tree reveal that chemical hydrophobicity, ionization and solubility play very important roles for identification of active and inactive compounds. The results obtained in this paper are valuable for understanding the modes of action in cytotoxicity and in vivo-in vitro toxicity relationship.
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Affiliation(s)
- Ying Huang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Jia Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Shuo Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Xiaotian Xu
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Weichao Qin
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Yang Wen
- Key Laboratory of Environmental Materials and Pollution Control, The Education Department of Jilin Province, School of Environmental Science and Engineering, Jilin Normal University, Siping, Jilin 136000, PR China.
| | - Yuan H Zhao
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China.
| | - Christopher J Martyniuk
- Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine, UF Genetics Institute, Interdisciplinary Program in Biomedical Sciences Neuroscience, University of Florida, Gainesville, FL, 32611, USA
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Cho CW, Pham TPT, Zhao Y, Stolte S, Yun YS. Review of the toxic effects of ionic liquids. Sci Total Environ 2021; 786:147309. [PMID: 33975102 DOI: 10.1016/j.scitotenv.2021.147309] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/15/2021] [Accepted: 04/18/2021] [Indexed: 05/11/2023]
Abstract
Interest in ionic liquids (ILs), called green or designer solvents, has been increasing because of their excellent properties such as thermal stability and low vapor pressure; thus, they can replace harmful organic chemicals and help several industrial fields e.g., energy-storage materials production and biomaterial pretreatment. However, the claim that ILs are green solvents should be carefully considered from an environmental perspective. ILs, given their minimal vapor pressure, may not directly cause atmospheric pollution. However, they have the potential to cause adverse effects if leaked into the environment, for instance if they are spilled due to human mistakes or technical errors. To estimate the risks of ILs, numerous ILs have had their toxicity assessed toward several micro- and macro-organisms over the past few decades. Since the toxic effects of ILs depend on the method of estimating toxicity, it is necessary to briefly summarize and comprehensively discuss the biological effects of ILs according to their structure and toxicity testing levels. This can help simplify our understanding of the toxicity of ILs. Therefore, in this review, we discuss the key findings of toxicological information of ILs, collect some toxicity data of ILs to different species, and explain the influence of IL structure on their toxic properties. In the discussion, we estimated two different sensitivity values of toxicity testing levels depending on the experiment condition, which are theoretical magnitudes of the inherent sensitivity of toxicity testing levels in various conditions and their changes in biological response according to the change in IL structure. Finally, some perspectives, future research directions, and limitations to toxicological research of ILs, presented so far, are discussed.
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Affiliation(s)
- Chul-Woong Cho
- Department of Bioenergy Science and Technology, Chonnam National University, Gwangju, South Korea.
| | - Thi Phuong Thuy Pham
- Faculty of Biotechnology, HoChiMihn University of Food Industry, Ho Chi Minh City, Viet Nam
| | - Yufeng Zhao
- College of Resource and Environmental Science, South-Central University for Nationalities, Wuhan 430074, Hubei Province, China
| | - Stefan Stolte
- Technische Universität Dresden, Faculty of Environmental Sciences, Department of Hydrosciences, Institute of Water Chemistry, Bergstraße 66, 01062 Dresden, Germany
| | - Yeoung-Sang Yun
- School of Chemical Engineering, Chonbuk National University, 567 Beakje-dearo, Deokjin-gu, Jeonju, Jeonbuk 561-756, South Korea.
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Chatterjee M, Roy K. Prediction of aquatic toxicity of chemical mixtures by the QSAR approach using 2D structural descriptors. J Hazard Mater 2021; 408:124936. [PMID: 33387719 DOI: 10.1016/j.jhazmat.2020.124936] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/17/2020] [Accepted: 12/20/2020] [Indexed: 06/12/2023]
Abstract
The rapid industrialization has led to the generation of various organic chemicals and multi-component mixtures which affect the environment adversely. Although organic chemicals are often exposed to the environment as a form of chemical mixtures rather than individual compounds, there is insufficient toxicity data available for the chemical mixtures due to the associated complexities. Most importantly, the nature of toxicity of mixtures is completely different from the individual chemicals, which makes the evaluation more difficult and challenging. In this paper, we have developed QSAR models for various individual and mixture data sets for the prediction of the aquatic toxicity. We have used Partial Least Squares (PLS) regression as a statistical tool to build the models. The various structural features of the individual chemicals and the mixture components have been modeled against the toxicity end point pEC50 (negative logarithm of median effective concentration in molar scale) of the aquatic organisms Photobacterium phosphoreum (marine bacterium) and Selenastrum capricornutum (freshwater algae). The mixture descriptors have been calculated by the weighted descriptor generation approach. The models were developed in accordance with OECD guidelines, and the quality of each model has been adjudged by strict validation parameters. The final models are robust, extremely predictive and interpretable mechanistically which can be used for the prediction of toxicity of untested chemical mixtures under the domain of applicability of the developed models.
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Affiliation(s)
- Mainak Chatterjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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Pandey SK, Roy K. QSPR modeling of octanol-water partition coefficient and organic carbon normalized sorption coefficient of diverse organic chemicals using Extended Topochemical Atom (ETA) indices. Ecotoxicol Environ Saf 2021; 208:111411. [PMID: 33080425 DOI: 10.1016/j.ecoenv.2020.111411] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/21/2020] [Accepted: 09/23/2020] [Indexed: 06/11/2023]
Abstract
Octanol-water partition coefficient (logKow) and soil organic carbon content normalized sorption coefficient (logKoc) values are two important physicochemical properties in the context of bioaccumulation and environmental fate of organic compounds and their environmental risk assessment. Simple, interpretable and easy-to-derive extended topochemical atom (ETA) indices obtained from 2D structural representation of compounds were used for quantitative structure-property relationship (QSPR) modeling of these two endpoints. Linear regression based models developed using only ETA indices show encouraging statistical and validation results. Based on the information obtained from developed QSPR models, we may conclude that molecular volume, branching pattern, presence of hydrophobic Cl atoms, cyclicity/fusion, polar environment, electron density, unsaturation content, hydrogen bonding propensity or hydrogen bond donor atoms, local topology, presence of heteroatoms and aromaticity are crucial factors in controlling the logKow and logKoc values of the compounds. The suggested explanatory features for different classes of chemicals or the whole diverse set can help in safer designing of chemicals, which is one of the primary agenda of the "Green Chemistry" program.
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Affiliation(s)
- Sapna Kumari Pandey
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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Zhang H, Gu X, Meng C, Zhou D, Chen G, Wang J, Liu Y, Li N. Computational investigation of 4,5-diphenyl-1H-pyrrole-3-carboxylic acid derivatives as B-cell lymphoma-extra large (Bcl-xL) inhibitors by using 3D-QSAR, molecular docking, and molecular dynamics simulations. Struct Chem 2020. [DOI: 10.1007/s11224-020-01631-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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