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Sun H, Yao J, Long Z, Luo R, Wang J, Liu SS, Tang L, Wu M. A new parameter for quantitatively characterizing antibiotic hormesis: QSAR construction and joint toxic action judgment. JOURNAL OF HAZARDOUS MATERIALS 2024; 479:135767. [PMID: 39255662 DOI: 10.1016/j.jhazmat.2024.135767] [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: 06/25/2024] [Revised: 08/14/2024] [Accepted: 09/05/2024] [Indexed: 09/12/2024]
Abstract
Antibiotics usually induce the hormetic effects on bacteria, featured by low-dose stimulation and high-dose inhibition, which challenges the central belief in toxicity assessment and environmental risk assessment of antibiotics. However, there are currently no ideal parameters to quantitatively characterize hormesis. In this study, an effective area in hormesis (AH) was developed to quantify the biphasic dose-responses of single antibiotics (sulfonamides (SAs), sulfonamides potentiators (SAPs), and tetracyclines (TCs)) and binary mixtures (SAs-SAPs, SAs-TCs, and SAs-SAs) to the bioluminescence of Aliivibrio fischeri. Using Ebind (the lowest interaction energy between antibiotic and target protein) and Kow (octanol-water partition coefficient) as the structural descriptors, the reliable quantitative structure-activity relationship (QSAR) models were constructed for the AH values of test antibiotics and mixtures. Furthermore, a novel method based on AH was established to judge the joint toxic actions of binary antibiotics, which mainly exhibited synergism. The results also indicated that SAPs (or TCs) contributed more than SAs in the hormetic effects of antibiotic mixtures. This study proposes a new quantitative parameter for characterizing and predicting antibiotic hormesis, and considers hormesis as an integrated whole to reveal the combined effects of antibiotics, which will promote the development of risk evaluation for antibiotics and their mixtures.
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Affiliation(s)
- Haoyu Sun
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Jingyi Yao
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Zhenheng Long
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Ruijia Luo
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Jiajun Wang
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Shu-Shen Liu
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Liang Tang
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China.
| | - Minghong Wu
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; College of Environment & Safety Engineering, Fuzhou University, Fuzhou 350108, Fujian, China
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2
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Qin LT, Zhang JY, Nong QY, Xu XCL, Zeng HH, Liang YP, Mo LY. Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 360:124565. [PMID: 39033842 DOI: 10.1016/j.envpol.2024.124565] [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: 05/10/2024] [Revised: 07/13/2024] [Accepted: 07/15/2024] [Indexed: 07/23/2024]
Abstract
Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics and fungicides against Auxenochlorella pyrenoidosa. Both regression and classification models for these mixtures were developed using machine learning models: random forest (RF), k-nearest neighbors (KNN), and kernel k-nearest neighbors (KKNN). The KKNN model emerged as the best regression model with high values of determination coefficient (R2 = 0.977), explained variance in prediction leave-one-out (Q2LOO = 0.894), and explained variance in external prediction (Q2F1 = 0.929, Q2F2 = 0.929, and Q2F3 = 0.923). The RF model, the leading classifier, exhibited high accuracy (accuracy = 1 for the training set and 0.905 for the test set) in distinguishing the synergistic and antagonistic mixtures. These results provide crucial value for the risk assessment of mixtures.
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Affiliation(s)
- Li-Tang Qin
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China; Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin, 541004, China; Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin, 541004, China
| | - Jun-Yao Zhang
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China
| | - Qiong-Yuan Nong
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China
| | - Xia-Chang-Li Xu
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China
| | - Hong-Hu Zeng
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China; Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin, 541004, China; Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin, 541004, China
| | - Yan-Peng Liang
- College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China; Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin, 541004, China; Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin, 541004, China.
| | - Ling-Yun Mo
- Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin, 541004, China; Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin, 541004, China; Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Nanjing, China.
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3
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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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4
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Keshavarz MH, Shirazi Z, Jafari M, Oliaeei A. Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters. CHEMOSPHERE 2024; 357:142046. [PMID: 38636913 DOI: 10.1016/j.chemosphere.2024.142046] [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: 01/19/2024] [Revised: 04/01/2024] [Accepted: 04/12/2024] [Indexed: 04/20/2024]
Abstract
Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure-activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.
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Affiliation(s)
| | - Zeinab Shirazi
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Iran
| | - Mohammad Jafari
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Iran
| | - Ahmadreza Oliaeei
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Iran
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5
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Long Z, Yao J, Wu M, Liu SS, Tang L, Lei B, Wang J, Sun H. Acute toxicity of binary mixtures for quorum sensing inhibitors and sulfonamides against Aliivibrio fischeri: QSAR investigations and joint toxic actions. Curr Res Toxicol 2024; 6:100172. [PMID: 38803613 PMCID: PMC11128832 DOI: 10.1016/j.crtox.2024.100172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Quorum sensing inhibitors (QSIs), as a kind of ideal antibiotic substitutes, have been recommended to be used in combination with traditional antibiotics in medical and aquaculture fields. Due to the co-existence of QSIs and antibiotics in environmental media, it is necessary to evaluate their joint risk. However, there is little information about the acute toxicity of mixtures for QSIs and antibiotics. In this study, 10 QSIs and 3 sulfonamides (SAs, as the representatives for traditional antibiotics) were selected as the test chemicals, and their acute toxic effects were determined using the bioluminescence of Aliivibrio fischeri (A. fischeri) as the endpoint. The results indicated that SAs and QSIs all induced S-shaped dose-responses in A. fischeri bioluminescence. Furthermore, SAs possessed greater acute toxicity than QSIs, and luciferase (Luc) might be the target protein of test chemicals. Based on the median effective concentration (EC50) for each test chemical, QSI-SA mixtures were designed according to equitoxic (EC50(QSI):EC50(SA) = 1:1) and non-equitoxic ratios (EC50(QSI):EC50(SA) = 1:10, 1:5, 1:0.2, and 1:0.1). It could be observed that with the increase of QSI proportion, the acute toxicity of QSI-SA mixtures enhanced while the corresponding TU values decreased. Furthermore, QSIs contributed more to the acute toxicity of test binary mixtures. The joint toxic actions of QSIs and SAs were synergism for 23 mixtures, antagonism for 12 mixtures, and addition for 1 mixture. Quantitative structure-activity relationship (QSAR) models for the acute toxicity QSIs, SAs, and their binary mixtures were then constructed based on the lowest CDOCKER interaction energy (Ebind-Luc) between Luc and each chemical and the component proportion in the mixture. These models exhibited good robustness and predictive ability in evaluating the toxicity data and joint toxic actions of QSIs and SAs. This study provides reference data and applicable QSAR models for the environmental risk assessment of QSIs, and gives a new perspective for exploring the joint effects of QSI-antibiotic mixtures.
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Affiliation(s)
- Zhenheng Long
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Jingyi Yao
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Minghong Wu
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Shu-shen Liu
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Liang Tang
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Bo Lei
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Jiajun Wang
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Haoyu Sun
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
- Shandong Key Laboratory of Environmental Processes and Health, School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China
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6
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Lee JW, Shim I, Park K. Proposing Effective Ecotoxicity Test Species for Chemical Safety Assessment in East Asia: A Review. TOXICS 2023; 12:30. [PMID: 38250986 PMCID: PMC10819827 DOI: 10.3390/toxics12010030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 12/25/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024]
Abstract
East Asia leads the global chemical industry, but environmental chemical risk in these countries is an emerging concern. Despite this, only a few native species that are representative of East Asian environments are listed as test species in international guidelines compared with those native to Europe and America. This review suggests that Zacco platypus, Misgurnus anguillicaudatus, Hydrilla verticillata, Neocaridina denticulata spp., and Scenedesmus obliquus, all resident to East Asia, are promising test species for ecotoxicity tests. The utility of these five species in environmental risk assessment (ERA) varies depending on their individual traits and the state of ecotoxicity research, indicating a need for different applications of each species according to ERA objectives. Furthermore, the traits of these five species can complement each other when assessing chemical effects under diverse exposure scenarios, suggesting they can form a versatile battery for ERA. This review also analyzes recent trends in ecotoxicity studies and proposes emerging research issues, such as the application of alternative test methods, comparative studies using model species, the identification of specific markers for test species, and performance of toxicity tests under environmentally relevant conditions. The information provided on the utility of the five species and alternative issues in toxicity tests could assist in selecting test species suited to study objectives for more effective ERA.
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Affiliation(s)
- Jin Wuk Lee
- Research of Environmental Health, National Institute of Environmental Research, Incheon 404-708, Republic of Korea; (I.S.); (K.P.)
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7
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Hamaguchi M, Miwake H, Nakatake R, Arai N. Predicting the Performance of Functional Materials Composed of Polymeric Multicomponent Systems Using Artificial Intelligence-Formulations of Cleansing Foams as an Example. Polymers (Basel) 2023; 15:4216. [PMID: 37959896 PMCID: PMC10650783 DOI: 10.3390/polym15214216] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/12/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Cleansing foam is a common multicomponent polymeric functional material. It contains ingredients in innumerable combinations, which makes formulation optimization challenging. In this study, we used artificial intelligence (AI) with machine learning to develop a cleansing capability prediction system that considers the effects of self-assembled structures and chemical properties of ingredients. Over 500 cleansing foam samples were prepared and tested. Molecular descriptors and Hansen solubility index were used to estimate the cleansing capabilities of each formulation set. We used five machine-learning models to predict the cleansing capability. In addition, we employed an in silico formulation by generating virtual formulations and predicting their cleansing capabilities using an established AI model. The achieved accuracy was R2 = 0.770. Our observations revealed that mixtures of cosmetic ingredients exhibit complex interactions, resulting in nonlinear behavior, which adds to the complexity of predicting cleansing performance. Nevertheless, accurate chemical property descriptors, along with the aid of in silico formulations, enabled the identification of potential ingredients. We anticipate that our system will efficiently predict the chemical properties of polymer-containing blends.
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Affiliation(s)
- Masugu Hamaguchi
- Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Kanagawa, Japan;
- Kirin Central Research Institute, Kirin Holdings, 26-1, Muraoka-Higashi 2-Chome, Fujisawa 251-8555, Kanagawa, Japan
| | - Hideki Miwake
- Research Institute, Fancl Corporation, 12-13 Kamishinano, Totsuka-ku, Yokohama 244-0806, Kanagawa, Japan
| | - Ryoichi Nakatake
- Research Institute, Fancl Corporation, 12-13 Kamishinano, Totsuka-ku, Yokohama 244-0806, Kanagawa, Japan
| | - Noriyoshi Arai
- Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Kanagawa, Japan;
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8
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Chatterjee M, Banerjee A, Tosi S, Carnesecchi E, Benfenati E, Roy K. Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees. JOURNAL OF HAZARDOUS MATERIALS 2023; 460:132358. [PMID: 37634379 DOI: 10.1016/j.jhazmat.2023.132358] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 08/02/2023] [Accepted: 08/20/2023] [Indexed: 08/29/2023]
Abstract
We have reported here a quantitative read-across structure-activity relationship (q-RASAR) model for the prediction of binary mixture toxicity (acute contact toxicity) in honey bees. Both the quantitative structure-activity relationship (QSAR) and the similarity-based read-across algorithms are used simultaneously for enhancing the predictability of the model. Several similarity and error-based parameters, obtained from the read-across prediction tool, have been put together with the structural and physicochemical descriptors to develop the final q-RASAR model. The calculated statistical and validation metrics indicate the goodness-of-fit, robustness, and good predictability of the partial least squares (PLS) regression model. Machine learning algorithms like ridge regression, linear support vector machine (SVM), and non-linear SVM have been used to further enhance the predictability of the q-RASAR model. The prediction quality of the q-RASAR models outperforms the previously reported quasi-SMILEs-based QSAR model in terms of external correlation coefficient (Q2F1 SVM q-RASAR: 0.935 vs. Q2VLD QSAR: 0.89). In this research, the toxicity values of several new untested binary mixtures have been predicted with the new models, and the reliability of the PLS predictions has been validated by the prediction reliability indicator tool. The q-RASAR approach can be used as reliable, complementary, and integrative to the conventional experimental approaches of pesticide mixture risk assessment.
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Affiliation(s)
- Mainak Chatterjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Simone Tosi
- Department of Agricultural, Forest, and Food Sciences, University of Turin, Turin, Italy
| | | | - Emilio Benfenati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, via Mario Negri 2, 20156 Milano, Italy
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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Chatterjee M, Roy K. "Data fusion" quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species. JOURNAL OF HAZARDOUS MATERIALS 2023; 459:132129. [PMID: 37506640 DOI: 10.1016/j.jhazmat.2023.132129] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/28/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023]
Abstract
Antibiotics are often found in the environment as pollutants. They are usually found as mixtures in the environment and may produce toxicity against different ecological species due to joint exposure in the sub-optimal range. Sometimes the degradation products of parent chemicals also interact with it and cause mixture toxicity. In this study, we have developed three different mixture-Quantitative Structure-Activity Relationship (mixture-QSAR) models for three different bacterial species (Vibrio fischeri, Escherichia coli, and Bacillus subtilis). The toxicity data were collected from a previous experimental report in the literature, which comprised binary and ternary mixtures of sulfonamides (SAs), sulfonamide potentiators (SAPs), and tetracyclines (TCs). We have also explored the interspecies modeling to find inter-correlation among the toxicity of these studied organisms and have developed quantitative structure activity-activity relationship (QSAAR) models by employing the "data fusion" quantitative read-across structure-activity-activity relationship (q-RASAAR) and partial least squares (PLS) regression algorithms. All the models are strictly validated using both internal and external validation tests as suggested in the OECD guidelines. Three different mixing rules have been used in this study for descriptor computations to incorporate the additive and interaction effects among the mixture components. To the best of our knowledge, this is the first report of interspecies mixture toxicity models which can predict the cellular toxicity of binary and ternary mixtures against any of the three above-mentioned organisms.
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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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Fjodorova N, Novič M, Venko K, Rasulev B, Türker Saçan M, Tugcu G, Sağ Erdem S, Toropova AP, Toropov AA. Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives. Int J Mol Sci 2023; 24:14160. [PMID: 37762462 PMCID: PMC10531479 DOI: 10.3390/ijms241814160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)-as a known target of toxins in fathead minnows and Daphnia magna, causing the inhibition of AChE-was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure-activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions.
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Affiliation(s)
- Natalja Fjodorova
- Laboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Marjana Novič
- Laboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Katja Venko
- Laboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia; (M.N.); (K.V.)
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, NDSU Dept 2510, P.O. Box 6050, Fargo, ND 58108, USA;
| | - Melek Türker Saçan
- Ecotoxicology and Chemometrics Lab, Institute of Environmental Sciences, Bogazici University, Hisar Campus, 34342 Istanbul, Turkey;
| | - Gulcin Tugcu
- Department of Toxicology, Faculty of Pharmacy, Yeditepe University, Atasehir, 34755 Istanbul, Turkey;
| | - Safiye Sağ Erdem
- Department of Chemistry, Marmara University, 34722 Istanbul, Turkey;
| | - Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Via Mario Negri 2, 20156 Milano, Italy; (A.P.T.); (A.A.T.)
| | - Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Via Mario Negri 2, 20156 Milano, Italy; (A.P.T.); (A.A.T.)
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11
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Yang YT, Ni HG. Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures. WATER RESEARCH 2023; 236:119981. [PMID: 37084578 DOI: 10.1016/j.watres.2023.119981] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/03/2023]
Abstract
As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.
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Affiliation(s)
- Yu-Ting Yang
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Hong-Gang Ni
- School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
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12
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Paul R, Chatterjee M, Roy K. First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:88302-88317. [PMID: 35829883 DOI: 10.1007/s11356-022-21937-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
Soil invertebrates serve as an outstanding biological indicator of the terrestrial ecosystem and overall soil quality, considering their high sensitivity when compared to other indicators of soil quality. In this study, the available soil ecotoxicity data (pEC50) against the soil invertebrate Folsomia candida (C. name: Springtail) (n = 45) were collated from the database of ECOTOX (cfpub.epa.gov/ecotox) and subjected to QSAR analysis using 2D descriptors. Four partial least squares (PLS) models were built based on the features selected through genertic algorithm followed by the best subset selection. These four models were then used as inputs for Intelligent Consensus Predictor version 1.2 (PLS version) to get the final consensus predictions, using the best selection of predictions (compound-wise) from four "qualified" individual models. Both internal and external validations metrics of the consensus predictions are well- balanced and within the acceptable range as per the OECD criteria. The consensus model was found to be better than the previous developed models for this endpoint. Predictions were also made using the Chemical Read-across approach, which showed even better external validation metric values than the consensus predictions. From the selected features in the QSAR models, it has been found out that molecular weight and presence of a di-thiophosphate group, electron donor groups, and polyhalogen substitutions have a significant impact on the soil ecotoxicity. The soil ecotoxicological risk assessment of organic chemicals can therefore be prioritized by these features. The models developed from diverse structural organic compounds can be applied to any new query compound for data gap filling.
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Affiliation(s)
- Rahul Paul
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - 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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13
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Chatterjee M, Roy K. Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri. CHEMOSPHERE 2022; 308:136463. [PMID: 36122748 DOI: 10.1016/j.chemosphere.2022.136463] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
Different classes of chemicals are present in the environment as mixtures. Among them, pharmaceuticals and pesticides are of major concern due to their improper use and disposal, and subsequent additive and non-additive effects. To assess the environmental risk posed by the mixtures of pharmaceuticals and pesticides, a quantitative structure-activity relationship (QSAR) model has been developed in this study using the pEC50 values of 198 binary and multi-component mixtures against the marine bacterium Aliivibrio fischeri. The developed partial least squares (PLS) model has been rigorously validated and proved to be a robust and extremely predictive one. To address the chances of overestimation of validation metrics, three cross-validation tests (mixtures out, compounds out, and everything out) have been applied, and the results were satisfactory. The use of simple 2-dimensional descriptors makes the prediction much quick, and also makes the model easily interpretable. A machine learning-based chemical read-across prediction has also been performed to justify the effectiveness of selected structural features in this study. In a nutshell, this study proves QSAR and chemical read-across as effective alternative approaches for the toxicity prediction of pharmaceutical and pesticide mixtures and also approves the use of mixture descriptors for modelling mixtures successfully.
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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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14
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Quantitative Measurements of Pharmacological and Toxicological Activity of Molecules. CHEMISTRY 2022. [DOI: 10.3390/chemistry4040097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Toxicity and pharmacological activity scales of molecules, in particular toxicants, xenobiotics, drugs, nutraceuticals, etc., are described by multiples indicators, and the most popular is the median lethal dose (LD50). At the molecular level, reversible inhibition or binding constants provide unique information on the potential activity of molecules. The important problem concerning the meaningfulness of IC50 for irreversible ligands/inhibitors is emphasized. Definitions and principles for determination of these quantitative parameters are briefly introduced in this article. Special attention is devoted to the relationships between these indicators. Finally, different approaches making it possible to link pharmacological and toxicological properties of molecules in terms of molecular interactions (or chemical reactions) with their biological targets are briefly examined. Experimental trends for future high-throughput screening of active molecules are pointed out.
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15
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Lin W, Zhao B, Ping S, Zhang X, Ji Y, Ren Y. Ultraviolet oxidative degradation of typical antidepressants: Pathway, product toxicity, and DFT theoretical calculation. CHEMOSPHERE 2022; 305:135440. [PMID: 35753423 DOI: 10.1016/j.chemosphere.2022.135440] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/27/2022] [Accepted: 06/19/2022] [Indexed: 06/15/2023]
Abstract
The ubiquity of antidepressants in the environment has posed a potential threat to eco-systematic safety. In this study, six kinds of antidepressants including fluoxetine (FLU), paroxetine (PAR), sertraline (SER), fluvoxamine (FLX), citalopram (CTP), and venlafaxine (VEN) were selected to explore their degrading kinetics, transformation pathways, and the acute toxicity of the reaction solution during UV oxidation. The results showed that the order of the photodegradation rate was FLU > PAR > SER > CTP > FLX > VEN. The calculation results of density functional theory (DFT) and molecular orbital theory showed that it was positively correlated with the frontier electron density of drugs and negatively correlated with the HOMO-LUMO gap, respectively. Intermediates were identified with UHPLC-Q-TOF/MS/MS to propose the possible degradation pathways of the drugs and the most likely directions of the reactions were determined by the single point energy calculation. The results of toxicity tests indicated that the acute toxicity of the reaction solution of PAR did not change significantly. The photolysates toxicity of FLU, SER, and FLX decreased at the end of the reaction, while that of CTP and VEN was increased by 1.5 and 1.3 times compared with the parent compound, respectively. Toxicity predictions by the quantitative structure activity relationship (QSAR) model showed that except FLU-162, FLX-174, and VEN-230, other degradation products have developmental toxicity. The results revealed the transformation pathways of these drugs under the UV disinfection process in wastewater treatment plants, especially the formation of toxic by-products during the disinfection process.
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Affiliation(s)
- Wenting Lin
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, PR China
| | - Baocong Zhao
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong-Hong Kong, Macao Joint Laboratory for Contaminants Exposure and Health, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou, 510006, China; Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Key Laboratory of City Cluster Environmental Safety and Green Development, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou, 510006, China
| | - Senwen Ping
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, PR China
| | - Xiaohan Zhang
- Shenzhen Shenshui Water Resources Consulting Co..Ltd, Shenzhen, 518003, China
| | - Yuemeng Ji
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong-Hong Kong, Macao Joint Laboratory for Contaminants Exposure and Health, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou, 510006, China; Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Key Laboratory of City Cluster Environmental Safety and Green Development, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou, 510006, China
| | - Yuan Ren
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, PR China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, China; The Key Laboratory of Environmental Protection and Eco-Remediation of Guangdong Regular Higher Education Institutions, China.
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16
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Ji M, Zhang L, Zhuang X, Tian C, Luan F, Cordeiro MNDS. Toxicity Assessment of the Binary Mixtures of Aquatic Organisms Based on Different Hypothetical Descriptors. Molecules 2022; 27:molecules27196389. [PMID: 36234923 PMCID: PMC9571779 DOI: 10.3390/molecules27196389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/07/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
Modern industrialization has led to the creation of a wide range of organic chemicals, especially in the form of multicomponent mixtures, thus making the evaluation of environmental pollution more difficult by normal methods. In this paper, we attempt to use forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNN) to establish quantitative structure–activity relationship models (QSARs) to predict the toxicity of 79 binary mixtures of aquatic organisms using different hypothetical descriptors. To search for the proper mixture descriptors, 11 mixture rules were performed and tested based on preliminary modeling results. The statistical parameters of the best derived MLR model were Ntrain = 62, R2 = 0.727, RMS = 0.494, F = 159.537, Q2LOO = 0.727, and Q2pred = 0.725 for the training set; and Ntest = 17, R2 = 0.721, RMS = 0.508, F = 38.773, and q2ext = 0.720 for the external test set. The RBFNN model gave the following statistical results: Ntrain = 62, R2 = 0.956, RMS = 0.199, F = 1279.919, Q2LOO = 0.955, and Q2pred = 0.855 for the training set; and Ntest = 17, R2 = 0.880, RMS = 0.367, F = 110.980, and q2ext = 0.853 for the external test set. The quality of the models was assessed by validating the relevant parameters, and the final results showed that the developed models are predictive and can be used for the toxicity prediction of binary mixtures within their applicability domain.
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Affiliation(s)
- Meng Ji
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China
| | - Lihong Zhang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China
| | - Xuming Zhuang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China
| | - Chunyuan Tian
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China
| | - Feng Luan
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China
- Correspondence:
| | - Maria Natália D. S. Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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17
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Wang X, Li F, Chen J, Teng Y, Ji C, Wu H. Critical features identification for chemical chronic toxicity based on mechanistic forecast models. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 307:119584. [PMID: 35688391 DOI: 10.1016/j.envpol.2022.119584] [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: 01/03/2022] [Revised: 05/03/2022] [Accepted: 06/03/2022] [Indexed: 06/15/2023]
Abstract
Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors - random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.
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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
| | - 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.
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian, 116024, 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; 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; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, PR China
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18
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Xu M, Yang H, Liu G, Tang Y, Li W. In Silico Prediction of Chemical Aquatic Toxicity by Multiple Machine Learning and Deep Learning Approaches. J Appl Toxicol 2022; 42:1766-1776. [PMID: 35653511 DOI: 10.1002/jat.4354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/16/2022] [Accepted: 05/31/2022] [Indexed: 11/08/2022]
Abstract
Fish is one of the model animals used to evaluate the adverse effects of a chemical exposed to the ecosystem. However, its low throughput and relevantly high expense make it impossible to test all new chemicals in manufacture. Hence using in silico models to prioritize compounds to be tested has been widely applied in environmental risk assessment and drug discovery. In this study, we constructed the local predictive models for four fish species, including bluegill sunfish, rainbow trout, fathead minnow, and sheepshead minnow, and the global models with all four fish data. A total of 1874 unique compounds with their labels, i.e. toxic (LC50 < 10 ppm) or nontoxic were collected from ECOTOX and literature. Both conventional machine learning methods and the deep learning architecture, graph convolutional network (GCN), were used to build predictive models. The classification accuracy of the best local model for each fish species was higher than 0.83. For the global models, two strategies including consistency prediction and probability threshold were adopted to improve the predictive capability at the cost of limiting applicability domain. For 63% of compounds in domain, the accuracy was around 0.97. By comparison of the deep learning and machine learning methods, we found that the single-task GCN showed specific advantages in performance and multi-task GCN showed no advantages over the conventional machine learning methods. The data and models are available on GitHub (https://github.com/ChemPredict/ChemicalAquaticToxicity).
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Affiliation(s)
- Minjie Xu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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19
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Chatterjee M, Roy K. Application of cross-validation strategies to avoid overestimation of performance of 2D-QSAR models for the prediction of aquatic toxicity of chemical mixtures. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:463-484. [PMID: 35638563 DOI: 10.1080/1062936x.2022.2081255] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
The quantitative structure-activity relationship (QSAR) modelling of mixtures is not as simple as that for individual chemicals, and it needs additional care to avoid overestimation of the performance. In this research, we have developed a 2D-QSAR model using only 2D interpretable and reproducible descriptors to predict the aquatic toxicity of mixtures of polar and non-polar narcotic substances present in the environment. Partial least squares (PLS) regression has been used to model the response variable (log 1/EC50 against Photobacterium phosphoreum) and the structural features of 84 binary mixtures of polar and nonpolar narcotic toxicants complying with the Organization of Economic Co-operation and Development (OECD) protocols. The model was cross-validated by mixtures-out and compounds-out cross-validation to nullify the developmental bias. The reliability of prediction of the model has been judged by the Prediction Reliability Indicator (PRI) tool using a newly designed set. The new model is robust, reproducible, extremely predictive, easily interpretable, and can be used for reliable prediction of aquatic toxicity of any untested chemical mixtures within the applicability domain. We have additionally used a machine learning-based chemical read-across algorithm in this study to improve the quality of predictions for the toxicity of the mixtures with the modelled descriptors.
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Affiliation(s)
- M Chatterjee
- 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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20
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Sedykh A, Choksi NY, Allen DG, Casey WM, Shah R, Kleinstreuer NC. Mixtures-Inclusive In Silico Models of Ocular Toxicity Based on United States and International Hazard Categories. Chem Res Toxicol 2022; 35:992-1000. [PMID: 35549170 DOI: 10.1021/acs.chemrestox.1c00443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Computational modeling grounded in reliable experimental data can help design effective non-animal approaches to predict the eye irritation and corrosion potential of chemicals. The National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) has compiled and curated a database of in vivo eye irritation studies from the scientific literature and from stakeholder-provided data. The database contains 810 annotated records of 593 unique substances, including mixtures, categorized according to UN GHS and US EPA hazard classifications. This study reports a set of in silico models to predict EPA and GHS hazard classifications for chemicals and mixtures, accounting for purity by setting thresholds of 100% and 10% concentration. We used two approaches to predict classification of mixtures: conventional and mixture-based. Conventional models evaluated substances based on the chemical structure of its major component. These models achieved balanced accuracy in the range of 68-80% and 87-96% for the 100% and 10% test concentration thresholds, respectively. Mixture-based models, which accounted for all known components in the substance by weighted feature averaging, showed similar or slightly higher accuracy of 72-79% and 89-94% for the respective thresholds. We also noted a strong trend between the pH feature metric calculated for each substance and its activity. Across all the models, the calculated pH of inactive substances was within one log10 unit of neutral pH, on average, while for active substances, pH varied from neutral by at least 2 log10 units. This pH dependency is especially important for complex mixtures. Additional evaluation on an external test set of 673 substances obtained from ECHA dossiers achieved balanced accuracies of 64-71%, which suggests that these models can be useful in screening compounds for ocular irritation potential. Negative predictive value was particularly high and indicates the potential application of these models in a bottom-up approach to identify nonirritant substances.
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Affiliation(s)
- Alexander Sedykh
- Sciome LLC, 1920 E NC 54 Hwy, Suite 510, Durham, North Carolina 27713, United States
| | - Neepa Y Choksi
- Integrated Laboratory Systems Inc, 601 Keystone Park Drive, Suite 200, Morrisville, North Carolina 27560, United States
| | - David G Allen
- Integrated Laboratory Systems Inc, 601 Keystone Park Drive, Suite 200, Morrisville, North Carolina 27560, United States
| | - Warren M Casey
- NIH/NIEHS/DNTP/NICEATM, 530 Davis Drive, Morrisville, North Carolina 27560, United States
| | - Ruchir Shah
- Sciome LLC, 1920 E NC 54 Hwy, Suite 510, Durham, North Carolina 27713, United States
| | - Nicole C Kleinstreuer
- NIH/NIEHS/DNTP/NICEATM, 530 Davis Drive, Morrisville, North Carolina 27560, United States
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21
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Rani P, Dutta K, Kumar V. Artificial intelligence techniques for prediction of drug synergy in malignant diseases: Past, present, and future. Comput Biol Med 2022; 144:105334. [DOI: 10.1016/j.compbiomed.2022.105334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/13/2022] [Accepted: 02/13/2022] [Indexed: 12/22/2022]
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22
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Computational Modeling of Mixture Toxicity. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2425:561-587. [PMID: 35188647 DOI: 10.1007/978-1-0716-1960-5_22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Environmental pollution has become an inevitable problem and a relevant global issue of the twenty-first century. The fast industrial growth has caused the production and release of various chemical species and multicomponent mixtures to the environment which affect the entire living world adversely. Various industrial regulatory agencies are working in this domain to regulate the production of chemical entities, proper release of chemical wastes, and the risk assessment of the industrial and hazardous chemicals; however, they mostly rely upon the single chemical risk assessment instead of considering the toxicity of multicomponent mixtures. In this era of chemical advances, single chemical exposure is a myth. The entire living world is always being exposed to the environmental chemical mixtures but the scarcity of toxicity data of chemical mixtures is a serious concern. The nature of toxicity of mixtures is entirely different and complex from the individual chemicals because of the interactions (synergism/antagonism) among the mixture components. Various regulatory authorities and the scientific world have come up with a handful of methodologies and guidelines for evaluating the harmful effects of the multicomponent mixtures, though there is no such significant, standard, and reliable approach for the toxicity evaluation of chemical mixtures and their management across diverse fields. Toxicity experimentations on laboratory animals are troublesome, time-consuming, costly, and unethical. Thus, to reduce the animal experimentations, the scientific communities, regulatory agencies, and the industries are now depending upon the already proven computational alternatives. The computational approaches are capable of predicting toxicities, prioritizing chemicals, and their risk assessment. Besides these, the in silico methods are cost-effective, less time-consuming, and easy to understand. It has been found out that most of the in silico toxicity predictions are on single chemicals and till date there are very few computational studies available for chemical mixtures in the scientific literature. Therefore, the current chapter illustrates the importance of determination of toxicity of mixtures, the conventional methods for toxicity evaluation of chemical mixtures, and the role of in silico methods to assess the toxicity, followed by the types of various computational methods used for such purpose. Additionally, few successful applications of computational tools in toxicity prediction of mixtures have been discussed in detail. At the end of this chapter, we have discussed some future perspectives toward the role and applications of in silico techniques for toxicity prediction of mixtures.
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Kar S, Leszczynski J. Computational Approaches in Assessments of Mixture Toxicity. CURRENT OPINION IN TOXICOLOGY 2022. [DOI: 10.1016/j.cotox.2022.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Li M, Wang Y, Ma L, Yan X, Lei Q. Dose-effect and structure-activity relationships of haloquinoline toxicity towards Vibrio fischeri. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:10858-10864. [PMID: 34528206 DOI: 10.1007/s11356-021-16388-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Many quinoline (QL) derivatives are present in the environment and pose potential threats to human health and ecological safety. The acute toxicity of 30 haloquinolines (HQs) was examined using the photobacterium Vibrio fischeri. IC50 values (inhibitory concentration for 50% luminescence elimination) were in the range 5.52 to >200 mg·L-1. The derivative 5-BrQL exhibited the highest toxicity, with 3-ClQL, 3-BrQL, 4-BrQL, 5-BrQL, 6-BrQL, and 6-IQL all having IC50 values below 10 mg·L-1. Comparative molecular field analysis modeling based on the steric and electrostatic field properties of the HQs was used to quantify the impact of halogen substituents on their toxicity. QL derivative rings with larger substituents at the 2/8-positions and less negative charge at the 4/5/6/8-positions were positively correlated with acute toxicity towards V. fischeri.
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Affiliation(s)
- Min Li
- College of Biological Science and Engineering, North Minzu University, Yinchuan, 750021, Ningxia Province, People's Republic of China.
- Key Laboratory of Ecological Protection of Agro-pastoral Ecotones in the Yellow River Basin, National Ethnic Affairs Commission of the People's Republic of China, Yinchuan, 750021, Ningxia Province, People's Republic of China.
| | - Yayao Wang
- College of Biological Science and Engineering, North Minzu University, Yinchuan, 750021, Ningxia Province, People's Republic of China
| | - Lu Ma
- College of Biological Science and Engineering, North Minzu University, Yinchuan, 750021, Ningxia Province, People's Republic of China
| | - Xingfu Yan
- College of Biological Science and Engineering, North Minzu University, Yinchuan, 750021, Ningxia Province, People's Republic of China
- Key Laboratory of Ecological Protection of Agro-pastoral Ecotones in the Yellow River Basin, National Ethnic Affairs Commission of the People's Republic of China, Yinchuan, 750021, Ningxia Province, People's Republic of China
| | - Qian Lei
- College of Biological Science and Engineering, North Minzu University, Yinchuan, 750021, Ningxia Province, People's Republic of China
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25
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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. JOURNAL OF HAZARDOUS MATERIALS 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] [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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Zhu T, Chen W, Jafvert CT, Fu D, Cheng H, Chen M, Wang Y. Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 291:118223. [PMID: 34583266 DOI: 10.1016/j.envpol.2021.118223] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Knowledge about partitioning constants of hydrophobic organic compounds (HOCs) between the polymer and aqueous phases is critical for assessing chemical environmental fate and transport. The conventional experimental method is characterized by large discrepancies in the measured values due to the limited water solubility of HOCs and other associated issues. In the current work, a novel three-phase partitioning system was evaluated to determine accurate low-density polyethylene (LDPE)-water partition coefficients (KPE-w). By adding sufficient surfactant (Brij 30) to form the micellar pseudo-phase within the polymer/water system, the KPE-w values were obtained from a combination of two experimentally measured values, that is, the micelle-water partition coefficient (Kmic-w) and the LDPE-micelle partition coefficient (KPE-mic). The method presented here is capable of shortening the equilibration time to half a month, and avoiding defects of the traditional method with respect to directly measured aqueous phase concentrations. Herein, the KPE-w values were determined for HOCs with little errors. Meanwhile, based on the 120 experimental KPE-w data, several in silico models were also developed as valid extrapolation tools to estimate missing or uncertain values. Analysis of the underlying solubility interactions in the nonionic surfactant micelles were investigated, providing additional support for the reliability of the proposed method.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Chad T Jafvert
- Lyles School of Civil Engineering, and Environmental & Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Dafang Fu
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Yajun Wang
- School of Civil Engineering, Lanzhou University of Technology, 287 Langongping, Lanzhou, 730050, China
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Luo D, Tong JB, Zhang X, Xiao XC, Bian S. Computational strategies towards developing novel SARS-CoV-2 M pro inhibitors against COVID-19. J Mol Struct 2021; 1247:131378. [PMID: 34483363 PMCID: PMC8398673 DOI: 10.1016/j.molstruc.2021.131378] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 11/25/2022]
Abstract
The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains to be a serious threat due to the lack of a specific therapeutic agent. Computational methods are particularly suitable for rapidly fight against SARS-CoV-2. This present research aims to systematically explore the interaction mechanism of a series of novel bicycloproline-containing SARS-CoV-2 Mpro inhibitors through integrated computational approaches. We designed six structurally modified novel SARS-CoV-2 Mpro inhibitors based on the QSAR study. The four designed compounds with higher docking scores were further explored through molecular docking, molecular dynamics (MD) simulations, free energy calculations, and residual energy contributions estimated by the MM-PBSA approach, with comparison to compound 23(PDB entry 7D3I). This research not only provides robust QSAR models as valuable screening tools for the development of anti-COVID-19 drugs, but also proposes the newly designed SARS-CoV-2 Mpro inhibitors with nanomolar activities that can be potentially used for further characterization to treat SARS-CoV-2 virus.
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Affiliation(s)
- Ding Luo
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Jian-Bo Tong
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Xing Zhang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Xue-Chun Xiao
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Shuai Bian
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.,Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
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