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Lv X, He M, Wei J, Li Q, Nie F, Shao Z, Wang Z, Tian L. Development of an effective QSAR-based hazard threshold prediction model for the ecological risk assessment of aromatic hydrocarbon compounds. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:47220-47236. [PMID: 38990260 DOI: 10.1007/s11356-024-34016-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 06/12/2024] [Indexed: 07/12/2024]
Abstract
The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure-activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC5) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and Vm, displayed excellent performance, as reflected by an optimal goodness of fit (R2adj = 0.918), robustness (Q2LOO = 0.869), and external prediction ability (Q2F1 = 0.760, Q2F2 = 0.782, and Q2F3 = 0.774). The hazard thresholds estimated using the optimal QSAR model were approximately close to the published water quality criteria developed by different countries and regions. The quantitative structure-toxicity relationship demonstrated that the molecular descriptors associated with electrophilicity and topological and electrotopological properties were important factors that affected the risks of AHCs. A new and reliable approach to estimate the hazard threshold of ecological risk assessment for various aromatic hydrocarbon pollutants was provided in this study, which can be widely popularised to similar contaminants with diverse structures.
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Affiliation(s)
- Xiudi Lv
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
- School of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Mei He
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
- School of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Jiajia Wei
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
- School of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Qiang Li
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China
- School of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Fan Nie
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology Co., Ltd, Beijing, 102206, China
| | - Zhiguo Shao
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology Co., Ltd, Beijing, 102206, China
| | - Zhansheng Wang
- State Key Laboratory of Petroleum Pollution Control, CNPC Research Institute of Safety and Environmental Technology Co., Ltd, Beijing, 102206, China
| | - Lei Tian
- Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China.
- School of Petroleum Engineering, Yangtze University, Wuhan, 430100, China.
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Gallagher A, Kar S. Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR. CHEMOSPHERE 2024; 349:140810. [PMID: 38029938 DOI: 10.1016/j.chemosphere.2023.140810] [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: 10/14/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 12/01/2023]
Abstract
Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA's ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 h to 4 h), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S-Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.
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Affiliation(s)
- Andrea Gallagher
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ, 07083, USA
| | - Supratik Kar
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ, 07083, USA.
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Liu Z, Dang K, Gao J, Fan P, Li C, Wang H, Li H, Deng X, Gao Y, Qian A. Toxicity prediction of 1,2,4-triazoles compounds by QSTR and interspecies QSTTR models. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 242:113839. [PMID: 35816839 DOI: 10.1016/j.ecoenv.2022.113839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/09/2022] [Accepted: 06/30/2022] [Indexed: 06/15/2023]
Abstract
1,2,4-triazole derivatives exhibit various biological activities, including antibacterial and antifungal properties. On the other hand, these chemicals may have unique cumulative and harmful effects on living organisms. The goal of this work is to use quantitative structure-toxicity relationship (QSTR) and interspecies quantitative toxicity-toxicity relationship (iQSTTR) models to predict the acute toxicity of 1,2,4-triazole derivatives. The QSTR models were generated by multiple linear regression (MLR) following the OECD recommendations for QSAR model development and validation. The iQSTTR models were constructed using data on acute oral toxicity in rats and mice, as well as the 2D descriptor. The application domain (AD) analysis was used to identify model outliers and determine if the forecast was credible. Six QSTR models were successfully constructed in rats and mice using various delivery methods, and the scatter plots demonstrated excellent consistency across training and test sets. According to external and internal validation criteria, all six QSTR models may be broadly accepted; however, the orally administered mice model was the optimum one among the six species. Several chemicals with leverage values above the requirements were identified as response or structural outliers in the training sets for six QSTR and two iQSTTR models. All outliers, however, fell slightly outside the threshold or had low prediction errors, which may have had little impact on the capacity to forecast and were therefore preserved in the final models. In fact, neither the QSTR nor the iQSTTR test sets contained any response outliers. Additionally, all external and internal validation results for the iQSTTR models were approved, with the iQSTTR models outperforming the comparable QSTR models, which are deemed more dependable. The QSTR and iQSTTR models performed well in predicting toxicity using test sets, which would be beneficial in evaluating and synthesizing newly discovered 1,2,4-triazoles derivatives with low toxicity and environmental hazard.
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Affiliation(s)
- Zhiyong Liu
- Lab for Bone Metabolism, Xi'an Key Laboratory of Special Medicine and Health Engineering, Key Lab for Space Biosciences and Biotechnology, Research Center for Special Medicine and Health Systems Engineering, NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Toxicology Research Center, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Kai Dang
- Lab for Bone Metabolism, Xi'an Key Laboratory of Special Medicine and Health Engineering, Key Lab for Space Biosciences and Biotechnology, Research Center for Special Medicine and Health Systems Engineering, NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Junhong Gao
- Toxicology Research Center, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Peng Fan
- Toxicology Research Center, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Cunzhi Li
- Toxicology Research Center, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Hong Wang
- Toxicology Research Center, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Huan Li
- Toxicology Research Center, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Xiaoni Deng
- Lab for Bone Metabolism, Xi'an Key Laboratory of Special Medicine and Health Engineering, Key Lab for Space Biosciences and Biotechnology, Research Center for Special Medicine and Health Systems Engineering, NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Yongchao Gao
- Toxicology Research Center, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Airong Qian
- Lab for Bone Metabolism, Xi'an Key Laboratory of Special Medicine and Health Engineering, Key Lab for Space Biosciences and Biotechnology, Research Center for Special Medicine and Health Systems Engineering, NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China.
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Li F, Fan T, Sun G, Zhao L, Zhong R, Peng Y. Systematic QSAR and iQCCR modelling of fused/non-fused aromatic hydrocarbons (FNFAHs) carcinogenicity to rodents: reducing unnecessary chemical synthesis and animal testing. GREEN CHEMISTRY 2022; 24:5304-5319. [DOI: 10.1039/d2gc00986b] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
The prediction of new or untested FNFAHs will reduce unnecessary chemical synthesis and animal testing, and contribute to the design of safer chemicals for production activities.
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Affiliation(s)
- Feifan Li
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
- Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
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Banjare P, Singh J, Roy PP. Predictive classification-based QSTR models for toxicity study of diverse pesticides on multiple avian species. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:17992-18003. [PMID: 33410022 DOI: 10.1007/s11356-020-11713-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 11/16/2020] [Indexed: 06/12/2023]
Abstract
Protection and restoration of different endangered bird species from pesticide exposure is crucial from the point of safety assessment of ecosystem. Toxicity predictions or risk assessment of pesticides by chemometric tools is one of the challenging fields in recent era. In the present study, classification-based quantitative structure toxicity relationship (QSTR) models were developed for a large dataset (516) of diverse pesticides on multiple avian species mallard duck, bobwhite quail, and zebra finch according to the Organization for Economic Co-operation and Development guidelines. The QSTR models were developed by linear discriminant analysis method with genetic algorithm for feature selection from 2D descriptors using QSAR-Co software. Different statistical metrics assured the reliability and robustness of the developed models. External compound prediction highlighted predictive nature of the models. The mechanistic interpretation suggested that presence of phosphate, halogens (Cl, Br), ether linkage, and NCOO influence the avian toxicity. Furthermore, model reliability was checked by the application of the standardization approach of the applicability domain (AD). Finally, the developed models provided a priori toxic and non-toxic classification for unknown pesticides (inside AD), with particular emphasis on organophosphate pesticides. The interspecies toxicity correlation and predictions encouraged for their further applicability for the fulfilment of data gaps in vital missing species.
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Affiliation(s)
- Purusottam Banjare
- Department of Medicinal and Pharmaceutical Chemistry, Institute of Pharmaceutical Sciences, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Jagadish Singh
- Department of Medicinal and Pharmaceutical Chemistry, Institute of Pharmaceutical Sciences, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Partha Pratim Roy
- Department of Medicinal and Pharmaceutical Chemistry, Institute of Pharmaceutical Sciences, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009, India.
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6
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Kar S, Leszczynski J. Is intraspecies QSTR model answer to toxicity data gap filling: Ecotoxicity modeling of chemicals to avian species. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 738:139858. [PMID: 32526407 DOI: 10.1016/j.scitotenv.2020.139858] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/29/2020] [Accepted: 05/29/2020] [Indexed: 06/11/2023]
Abstract
Interspecies model represents an established approach for the response data gap filling for regulatory agencies and researchers. We propose a novel approach of intraspecies modeling within the animals of the same species, instead of animals from different species. The proposed intraspecies model is capable of more precise extrapolation of data than the interspecies model, as animals under the same species share a similar mechanism of action (MOA) and target sites for the response. Along with the advantage of better prediction over the interspecies model, the intraspecies model has all the significant features like recognition of MOA, species-specific toxicity, reduction of animal experimentation, and money and time. To establish and test the intraspecies modeling approach, we have modeled ecotoxicity of organic chemicals to three avian species: Anas platyrhynchos, Colinus virginianus, and Phasianus colchicus. The intraspecies models offer to identify the mechanistic interpretation of the ecotoxicity of the studied chemicals along with the toxicity data gap filling. The success of the intraspecies modeling relies on connecting the missing dots of toxicity for the regulatory purposes, especially when there is a scarcity of ecotoxicity experimental data and in silico models for avian species.
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Affiliation(s)
- Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson MS-39217, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson MS-39217, USA.
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7
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Benfenati E, Chaudhry Q, Gini G, Dorne JL. Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy. ENVIRONMENT INTERNATIONAL 2019; 131:105060. [PMID: 31377600 DOI: 10.1016/j.envint.2019.105060] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 06/26/2019] [Accepted: 07/25/2019] [Indexed: 06/10/2023]
Abstract
In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.
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Affiliation(s)
- Emilio Benfenati
- Department of Environmental and Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, Milano, Italy.
| | - Qasim Chaudhry
- University of Chester, Parkgate Road, Chester CH1 4BJ, United Kingdom
| | | | - Jean Lou Dorne
- Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, Parma, Italy
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Toropov AA, Toropova AP. Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints. Toxicol Mech Methods 2018; 29:43-52. [PMID: 30064284 DOI: 10.1080/15376516.2018.1506851] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The CORAL software is a tool to build up quantitative structure-property/activity relationships (QSPRs/QSARs). The project of updated version of the CORAL software is discussed in terms of practical applications for building up various models. The updating is the possibility to improve the predictive potential of models using the so-called Index of Ideality of Correlation (IIC) as a criterion of the predictive potential for QSPR/QSAR models. Efficacy of the IIC is examined with three examples of building up QSARs: (i) models for anticancer activity; (ii) models for mutagenicity; and (iii) models for toxicity of psychotropic drugs. The validation of these models has been carried out with several splits into the training, invisible training, calibration, and validation sets. The ability of IIC to be an indicator of predictive potential of QSAR models is confirmed. The updated version of the CORAL software (CORALSEA-2017, http://www.insilico.eu/coral ) is available on the Internet.
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Affiliation(s)
- Andrey A Toropov
- a Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology , Istituto di Ricerche Farmacologiche Mario Negri IRCCS , Milano , Italy
| | - Alla P Toropova
- a Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology , Istituto di Ricerche Farmacologiche Mario Negri IRCCS , Milano , Italy
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Relić D, Héberger K, Sakan S, Škrbić B, Popović A, Đorđević D. Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods. CHEMOSPHERE 2018; 198:103-110. [PMID: 29421718 DOI: 10.1016/j.chemosphere.2017.12.200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Revised: 12/29/2017] [Accepted: 12/31/2017] [Indexed: 06/08/2023]
Abstract
This study aims to compare three extraction techniques of four sequential element extraction steps from soil and sediment samples that were taken from the location of the Pančevo petrochemical industry (Serbia). Elements were extracted using three different techniques: conventional, microwave and ultrasound extraction. A novel procedure - sum of the ranking differences (SRD) - was able to rank the techniques and elements, to see whether this method is a suitable tool to reveal the similarities and dissimilarities in element extraction techniques, provided that a proper ranking reference is available. The concentrations of the following elements Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Sn, Sr, V and Zn were determined through ICP OES. The different efficiencies and recovery values of element concentrations using each of the three extraction techniques were examined by the CRM BCR-701. By using SRD, we obtained a better separation between the different extraction techniques and steps when we rank their differences among the samples while lower separation was obtained according to analysed elements. Appling this method for ordering the elements could be useful for three purposes: (i) to find possible associations among the elements; (ii) to find possible elements that have outlier concentrations or (iii) detect differences in geochemical origin or behaviour of elements. Cross-validation of the SRD values in combination with cluster and principal component analysis revealed the same groups of extraction steps and techniques.
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Affiliation(s)
- Dubravka Relić
- University of Belgrade, Faculty of Chemistry, Studentski trg 12-16, Belgrade, 11158, Serbia.
| | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1117 Budapest, Magyar tudósok krt. 2, Hungary
| | - Sanja Sakan
- Centre of Excellence in Environmental Chemistry and Engineering, ICTM, University of Belgrade, Njegoševa 12, Belgrade, 11158, Serbia
| | - Biljana Škrbić
- University of Novi Sad, Faculty of Technology, Bulevar cara Lazara 1, 21000, Novi Sad, Serbia
| | - Aleksandar Popović
- University of Belgrade, Faculty of Chemistry, Studentski trg 12-16, Belgrade, 11158, Serbia
| | - Dragana Đorđević
- Centre of Excellence in Environmental Chemistry and Engineering, ICTM, University of Belgrade, Njegoševa 12, Belgrade, 11158, Serbia.
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Hamadache M, Benkortbi O, Hanini S, Amrane A. QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:896-907. [PMID: 29067614 DOI: 10.1007/s11356-017-0498-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 10/16/2017] [Indexed: 06/07/2023]
Abstract
Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD50) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q 2 and [Formula: see text] are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD50 of 20 other pesticides. A good correlation between predicted and experimental values was found (R 2 = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.
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Affiliation(s)
- Mabrouk Hamadache
- Département du génie des procédés et environnement, Faculté de technologie, Université de Médéa, 26000, Médéa, Algeria.
| | - Othmane Benkortbi
- Département du génie des procédés et environnement, Faculté de technologie, Université de Médéa, 26000, Médéa, Algeria
| | - Salah Hanini
- Département du génie des procédés et environnement, Faculté de technologie, Université de Médéa, 26000, Médéa, Algeria
| | - Abdeltif Amrane
- Ecole Nationale Supérieure de Chimie de Rennes, CNRS, UMR 6226, Université de Rennes 1, 11 allée de Beaulieu, 35708, Rennes Cedex 7, CS 50837, France
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11
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Chen G, Vijver MG, Xiao Y, Peijnenburg WJGM. A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials. MATERIALS 2017; 10:ma10091013. [PMID: 28858269 PMCID: PMC5615668 DOI: 10.3390/ma10091013] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Revised: 08/08/2017] [Accepted: 08/28/2017] [Indexed: 11/16/2022]
Abstract
Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.
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Affiliation(s)
- Guangchao Chen
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Martina G Vijver
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Yinlong Xiao
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Willie J G M Peijnenburg
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
- Centre for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands.
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12
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Toropov AA, Toropova AP, Benfenati E, Nicolotti O, Carotti A, Nesmerak K, Veselinović AM, Veselinović JB, Duchowicz PR, Bacelo D, Castro EA, Rasulev BF, Leszczynska D, Leszczynski J. QSPR/QSAR Analyses by Means of the CORAL Software. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Pablo R. Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Argentina
| | | | - Eduardo A. Castro
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Argentina
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Song F, Zhang A, Liang H, Cui L, Li W, Si H, Duan Y, Zhai H. QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:E1141. [PMID: 27854309 PMCID: PMC5129351 DOI: 10.3390/ijerph13111141] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 10/21/2016] [Accepted: 10/24/2016] [Indexed: 11/27/2022]
Abstract
A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method.
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Affiliation(s)
- Fucheng Song
- Department of Public Health, Qingdao University Medical College, Qingdao 266071, China.
| | - Anling Zhang
- Modern Educational Technology Center, Qingdao University, Qingdao 266071, China.
| | - Hui Liang
- Department of Public Health, Qingdao University Medical College, Qingdao 266071, China.
| | - Lianhua Cui
- Department of Public Health, Qingdao University Medical College, Qingdao 266071, China.
| | - Wenlian Li
- Department of Public Health, Qingdao University Medical College, Qingdao 266071, China.
| | - Hongzong Si
- Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, The Growing Base for State Key Laboratory, Qingdao University, Ningxia Road 308, Qingdao 266071, China.
| | - Yunbo Duan
- Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, The Growing Base for State Key Laboratory, Qingdao University, Ningxia Road 308, Qingdao 266071, China.
| | - Honglin Zhai
- Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
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Zhang H, Cao ZX, Li M, Li YZ, Peng C. Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals. Food Chem Toxicol 2016; 97:141-149. [PMID: 27597133 DOI: 10.1016/j.fct.2016.09.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 08/02/2016] [Accepted: 09/01/2016] [Indexed: 02/05/2023]
Abstract
The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (MW), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development.
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Affiliation(s)
- Hui Zhang
- College of Life Science, Northwest Normal University, Lanzhou, Gansu, 730070, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, 610041, PR China.
| | - Zhi-Xing Cao
- Pharmacy College, Chengdu University of Traditional Chinese Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province-key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chendu, Sichuan, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, 610041, PR China
| | - Meng Li
- College of Life Science, Northwest Normal University, Lanzhou, Gansu, 730070, PR China
| | - Yu-Zhi Li
- Pharmacy College, Chengdu University of Traditional Chinese Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province-key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chendu, Sichuan, PR China
| | - Cheng Peng
- Pharmacy College, Chengdu University of Traditional Chinese Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province-key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chendu, Sichuan, PR China
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15
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Das RN, Roy K, Popelier PLA. Interspecies quantitative structure-toxicity-toxicity (QSTTR) relationship modeling of ionic liquids. Toxicity of ionic liquids to V. fischeri, D. magna and S. vacuolatus. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2015; 122:497-520. [PMID: 26414597 DOI: 10.1016/j.ecoenv.2015.09.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 09/08/2015] [Accepted: 09/09/2015] [Indexed: 06/05/2023]
Abstract
Considering the increasing uses of ionic liquids (ILs) in various industrial processes and chemical engineering operations, a complete assessment of their hazardous profile is essential. In the absence of adequate experimental data, in silico modeling might be helpful in filling data gaps for the toxicity of ILs towards various ecological indicator organisms. Using the rationale of taxonomic relatedness, the development of predictive quantitative structure-toxicity-toxicity relationship (QSTTR) models allows predicting the toxicity of ILs to a particular species using available experimental toxicity data towards a different species. Such studies may employ, along with the available experimental toxicity data to a species, molecular structure features and physicochemical properties of chemicals as independent variables for prediction of the toxicity profile against another closely related species. A few such interspecies toxicity correlation models have been reported in the literature for diverse chemicals in general, but this approach has been rarely applied to the class of ionic liquids. The present study involves the use of IL toxicity data towards the bacteria Vibrio fischeri along with molecular structure derived information or computational descriptors like extended topochemical atom (ETA) indices, quantum topological molecular similarity (QTMS) descriptors and computed lipophilicity measure (logk0) for the interspecies exploration of the toxicity data towards green algae S. vacuolatus and crustacea Daphnia magna, separately. This modeling study has been performed in accordance with the OECD guidelines. Finally, predictions for a true external set have been performed to fill the data gap of toxicity towards daphnids and algae using the Vibrio toxicity data and molecular structure attributes.
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Affiliation(s)
- Rudra Narayan Das
- 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; Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, UK.
| | - Paul L A Popelier
- Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, UK.
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16
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Toropov AA, Toropova AP, Benfenati E, Nicolotti O, Carotti A, Nesmerak K, Veselinović AM, Veselinović JB, Duchowicz PR, Bacelo D, Castro EA, Rasulev BF, Leszczynska D, Leszczynski J. QSPR/QSAR Analyses by Means of the CORAL Software. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS IN DRUG DESIGN, PREDICTIVE TOXICOLOGY, AND RISK ASSESSMENT 2015. [DOI: 10.4018/978-1-4666-8136-1.ch015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Pablo R. Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Argentina
| | | | - Eduardo A. Castro
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Argentina
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17
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Mridha P, Pal P, Roy K. Chemometric modelling of triphenylmethyl derivatives as potent anticancer agents. MOLECULAR SIMULATION 2014. [DOI: 10.1080/08927022.2013.854897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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18
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19
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Kleandrova VV, Luan F, González-Díaz H, Ruso JM, Melo A, Speck-Planche A, Cordeiro MNDS. Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. ENVIRONMENT INTERNATIONAL 2014; 73:288-94. [PMID: 25173945 DOI: 10.1016/j.envint.2014.08.009] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 07/10/2014] [Accepted: 08/09/2014] [Indexed: 05/14/2023]
Abstract
Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and biological/chemical properties. However, the potential toxic effects of nanoparticles to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanoparticles against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle-nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results of this study thus provide a single valuable tool toward an efficient prediction of the ecotoxicity of nanoparticles under multiple experimental conditions.
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Affiliation(s)
- Valeria V Kleandrova
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
| | - Feng Luan
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal; Department of Applied Chemistry, Yantai University, Yantai 264005, People's Republic of China
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940 Bilbao, Spain; IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
| | - Juan M Ruso
- Department of Applied Physics, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain
| | - André Melo
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
| | - Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal; Department of Applied Physics, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain.
| | - M Natália D S Cordeiro
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
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20
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Luan F, Kleandrova VV, González-Díaz H, Ruso JM, Melo A, Speck-Planche A, Cordeiro MNDS. Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. NANOSCALE 2014; 6:10623-10630. [PMID: 25083742 DOI: 10.1039/c4nr01285b] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Nowadays, the interest in the search for new nanomaterials with improved electrical, optical, catalytic and biological properties has increased. Despite the potential benefits that can be gathered from the use of nanoparticles, only little attention has been paid to their possible toxic effects that may affect human health. In this context, several assays have been carried out to evaluate the cytotoxicity of nanoparticles in mammalian cells. Owing to the cost in both resources and time involved in such toxicological assays, there has been a considerable increase in the interest towards alternative computational methods, like the application of quantitative structure-activity/toxicity relationship (QSAR/QSTR) models for risk assessment of nanoparticles. However, most QSAR/QSTR models developed so far have predicted cytotoxicity against only one cell line, and they did not provide information regarding the influence of important factors rather than composition or size. This work reports a QSTR-perturbation model aiming at simultaneously predicting the cytotoxicity of different nanoparticles against several mammalian cell lines, and also considering different times of exposure of the cell lines, as well as the chemical composition of nanoparticles, size, conditions under which the size was measured, and shape. The derived QSTR-perturbation model, using a dataset of 1681 cases (nanoparticle-nanoparticle pairs), exhibited an accuracy higher than 93% for both training and prediction sets. In order to demonstrate the practical applicability of our model, the cytotoxicity of different silica (SiO2), nickel (Ni), and nickel(ii) oxide (NiO) nanoparticles were predicted and found to be in very good agreement with experimental reports. To the best of our knowledge, this is the first attempt to simultaneously predict the cytotoxicity of nanoparticles under multiple experimental conditions by applying a single unique QSTR model.
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Affiliation(s)
- Feng Luan
- Department of Applied Chemistry, Yantai University, Yantai 264005, People's Republic of China
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21
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Nandy A, Kar S, Roy K. Development and validation of regression-based QSAR models for quantification of contributions of molecular fragments to skin sensitization potency of diverse organic chemicals. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:1009-1023. [PMID: 23988224 DOI: 10.1080/1062936x.2013.821422] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In our present work, we have developed regression-based QSAR models for skin sensitization potential of 51 diverse organic chemicals. The objective behind the present work is to determine the influence of different molecular features on the skin sensitizing potential of chemicals. Among several models developed, the two best ones are discussed to unveil specific information regarding the contribution of different structural and physicochemical features towards the property of skin sensitization. The QSAR models suggested that aromatic compounds are more skin sensitizing than aliphatic ones, but in the case of carbonyl compounds, aliphatic ones are more skin sensitizing than aromatic ones. Other descriptors such as LUMO and <2-Atype_H-47> signify the importance of the electrophilic and hydrophilic character respectively of the molecules for showing skin sensitizing property. Another two descriptors, <Dipole-mag-2.72> and (3)χC also exert significant contributions towards the skin sensitization potential of the chemicals. Further, it is observed that the nitrogen atoms (nN), triple bonds (nTB) and also the fragment Al-C(=X)-Al (Atype_C38) are responsible for skin sensitizing property. All the above information provides additional support for further research involving identification of the skin sensitization potential of new chemicals.
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Affiliation(s)
- A Nandy
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata , India
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22
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Singh KP, Gupta S, Rai P. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches. Toxicol Appl Pharmacol 2013; 272:465-75. [PMID: 23856075 DOI: 10.1016/j.taap.2013.06.029] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2013] [Accepted: 06/22/2013] [Indexed: 01/31/2023]
Abstract
Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes.
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Affiliation(s)
- Kunwar P Singh
- Academy of Scientific and Innovative Research, Council of Scientific & Industrial Research, New Delhi, India; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, India.
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Nandy A, Kar S, Roy K. Development of classification- and regression-based QSAR models andin silicoscreening of skin sensitisation potential of diverse organic chemicals. MOLECULAR SIMULATION 2013. [DOI: 10.1080/08927022.2013.801076] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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24
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Comparison of comet assay parameters for estimation of genotoxicity by sum of ranking differences. Anal Bioanal Chem 2013; 405:4879-85. [DOI: 10.1007/s00216-013-6909-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Revised: 02/01/2013] [Accepted: 03/08/2013] [Indexed: 11/30/2022]
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25
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Yuan J, Pu Y, Yin L. QSAR study of liver specificity of carcinogenicity of N-nitroso compounds. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2012; 84:282-292. [PMID: 22910279 DOI: 10.1016/j.ecoenv.2012.07.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2012] [Revised: 07/21/2012] [Accepted: 07/24/2012] [Indexed: 06/01/2023]
Abstract
The quantitative structure-activity relationship (QSAR) of N-nitroso compounds (NOCs) for rat liver was developed by a topological sub-structural molecular-descriptors (TOPS-MODE) approach to predict non-liver-carcinogenic and liver-carcinogenic N-nitroso compounds based on a data set of 108 NOCs. Three descriptors calculated solely from the molecular structures of the compounds were selected by enhanced replacement method (ERM) and were weighted, respectively, with atomic weight, bond dipole moments and Abraham solute descriptor partition between water and aqueous solvent systems to indicate the importance of their roles in liver specificity. A detailed discussion on these three descriptors was carried out, and the contributions of different fragments to rat-liver specificity and the interactions among fragments were analyzed. Such results can offer some useful theoretical references for understanding the chemical structural and biological factors related to the liver-specific biological activity of NOCs.
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Affiliation(s)
- Jintao Yuan
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, 87 Dingjiaqiao, Nanjing 210009, China.
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Kar S, Deeb O, Roy K. Development of classification and regression based QSAR models to predict rodent carcinogenic potency using oral slope factor. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2012; 82:85-95. [PMID: 22698880 DOI: 10.1016/j.ecoenv.2012.05.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Revised: 02/06/2012] [Accepted: 05/21/2012] [Indexed: 06/01/2023]
Abstract
Carcinogenicity is among the toxicological endpoints posing the highest concern for human health. Oral slope factors (OSFs) are used to estimate quantitatively the carcinogenic potency or the risk associated with exposure to the chemical by oral route. Regulatory agencies in food and drug administration and environmental protection are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. In this background, we have developed quantitative structure-carcinogenicity regression models for rodents based on the carcinogenic potential of 70 chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the Organization for Economic Cooperation and Development (OECD) principles for the validation of QSAR models. We have also attempted to develop a carcinogenicity classification model based on Linear Discriminant Analysis (LDA). Developed regression and LDA models are rigorously validated internally as well as externally. Our in silico studies make it possible to obtain a quantitative interpretation of the structural information of carcinogenicity along with identification of the discriminant functions between lower and higher carcinogenic compounds by LDA. Pharmacological distribution diagrams (PDDs) are used as a visualizing technique for the identification and selection of chemicals with lower carcinogenicity. Constructive, informative and comparable interpretations have been observed in both cases of classification and regression based modeling.
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Affiliation(s)
- Supratik Kar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
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