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Stankovic B, Marinkovic F. A novel procedure for selection of molecular descriptors: QSAR model for mutagenicity of nitroaromatic compounds. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:54603-54617. [PMID: 39207617 DOI: 10.1007/s11356-024-34800-x] [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: 04/19/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024]
Abstract
Nitroaromatic compounds (NACs) stand out as pervasive organic pollutants, prompting an imperative need to investigate their hazardous effects. Computational chemistry methods play a crucial role in this exploration, offering a safer and more time-efficient approach, mandated by various legislations. In this study, our focus lay on the development of transparent, interpretable, reproducible, and publicly available methodologies aimed at deriving quantitative structure-activity relationship models and testing them by modelling the mutagenicity of NACs against the Salmonella typhimurium TA100 strain. Descriptors were selected from Mordred and RDKit molecular descriptors, along with several quantum chemistry descriptors. For that purpose, the genetic algorithm (GA), as the most widely used method in the literature, and three alternative algorithms (Boruta, Featurewiz, and ForwardSelector) combined with the forward stepwise selection technique were used. The construction of models utilized the multiple linear regression method, with subsequent scrutiny of fitting and predictive performance, reliability, and robustness through various statistical validation criteria. The models were ranked by the Multi-Criteria Decision Making procedure. Findings have revealed that the proposed methodology for descriptor selection outperforms GA, with Featurewiz showing a slight advantage over Boruta and ForwardSelector. These constructed models can serve as valuable tools for the quick and reliable prediction of NACs mutagenicity.
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Affiliation(s)
- Branislav Stankovic
- Department for Nuclear and Plasma Physics, Vinča Institute of Nuclear Sciences -National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia.
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Hao Y, Fan T, Sun G, Li F, Zhang N, Zhao L, Zhong R. Environmental toxicity risk evaluation of nitroaromatic compounds: Machine learning driven binary/multiple classification and design of safe alternatives. Food Chem Toxicol 2022; 170:113461. [PMID: 36243219 DOI: 10.1016/j.fct.2022.113461] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/11/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022]
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Daghighi A, Casanola-Martin GM, Timmerman T, Milenković D, Lučić B, Rasulev B. In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach. TOXICS 2022; 10:toxics10120746. [PMID: 36548579 PMCID: PMC9786026 DOI: 10.3390/toxics10120746] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/10/2022] [Accepted: 11/28/2022] [Indexed: 06/02/2023]
Abstract
In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD50). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R2 = 0.88), validation set (R2 = 0.95), and true external test set (R2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.
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Affiliation(s)
- Amirreza Daghighi
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58105, USA
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | | | - Troy Timmerman
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA
| | - Dejan Milenković
- Department of Science, Institute for Information Technologies, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Bono Lučić
- NMR Centre, Ruđer Bošković Institute, 10000 Zagreb, Croatia
| | - Bakhtiyor Rasulev
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58105, USA
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
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Sun G, Zhang Y, Pei L, Lou Y, Mu Y, Yun J, Li F, Wang Y, Hao Z, Xi S, Li C, Chen C, Zhao L, Zhang N, Zhong R, Peng Y. Chemometric QSAR modeling of acute oral toxicity of Polycyclic Aromatic Hydrocarbons (PAHs) to rat using simple 2D descriptors and interspecies toxicity modeling with mouse. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 222:112525. [PMID: 34274838 DOI: 10.1016/j.ecoenv.2021.112525] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/07/2021] [Accepted: 07/13/2021] [Indexed: 06/13/2023]
Abstract
The information of the acute oral toxicity for most polycyclic aromatic hydrocarbons (PAHs) in mammals are lacking due to limited experimental resources, leading to a need to develop reliable in silico methods to evaluate the toxicity endpoint. In this study, we developed the quantitative structure-activity relationship (QSAR) models by genetic algorithm (GA) and multiple linear regression (MLR) for the rat acute oral toxicity (LD50) of PAHs following the strict validation principles of QSAR modeling recommended by OECD. The best QSAR model comprised eight simple 2D descriptors with definite physicochemical meaning, which showed that maximum atom-type electrotopological state, van der Waals surface area, mean atomic van der Waals volume, and total number of bonds are main influencing factors for the toxicity endpoint. A true external set (554 compounds) without rat acute oral toxicity values, and 22 limit test compounds, were firstly predicted along with reliability assessment. We also compared our proposed model with the OPERA predictions and recently published literature to prove the prediction reliability. Furthermore, the interspecies toxicity (iST) models of PAHs between rat and mouse were also established, validated and employed for filling data gap. Overall, our developed models should be applicable to new or untested or not yet synthesized PAHs falling within the applicability domain (AD) of the models for rapid acute oral toxicity prediction, thus being important for environmental or personal exposure risk assessment under regulatory frameworks.
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Affiliation(s)
- Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China.
| | - Yifan Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Luyu Pei
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Yuqing Lou
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Yao Mu
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Jiayi Yun
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Feifan Li
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Yachen Wang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Zhaoqi Hao
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Sha Xi
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Chen Li
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Chuhan Chen
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China.
| | - Na Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, College of Environmental and Chemical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China
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Huang T, Sun G, Zhao L, Zhang N, Zhong R, Peng Y. Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review. Int J Mol Sci 2021; 22:8557. [PMID: 34445263 PMCID: PMC8395302 DOI: 10.3390/ijms22168557] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/05/2021] [Accepted: 08/05/2021] [Indexed: 01/22/2023] Open
Abstract
Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.
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Affiliation(s)
- Tao Huang
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Guohui Sun
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Lijiao Zhao
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Na Zhang
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Rugang Zhong
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, College of Environmental and Chemical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China;
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Toropova AP, Toropov AA. Can the Monte Carlo method predict the toxicity of binary mixtures? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:39493-39500. [PMID: 33755888 DOI: 10.1007/s11356-021-13460-1] [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: 01/08/2021] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
Risk assessment of toxicants mainly is a result of experiments with single substances. However, toxicity in natural ecosystems typically does not result from single toxicant exposure but is rather a result of exposure to mixtures of toxicants. It is not surprising a mixture of toxicity is a subject of eco-toxicological interest for several decades. A quantitative structure-activity relationships (QSAR)-based approach is an attractive approach to assessing the joint effects in the binary mixtures. The validity of the proposed approach was demonstrated by comparing the predicted values against the experimentally determined values. Simplified molecular input-line entry system (SMILES) is used for the representation of the molecular structures of components of two-component mixtures to build up QSAR. The SMILES-based models are improving if the Monte Carlo optimization aimed to define 2D-optimal descriptors apply the so-called index of ideality of correlation (IIC), which is a mathematical function of both the correlation coefficient and mean absolute error calculated for the positive and negative difference between observed and calculated values of toxicity. The average statistical quality of these models (for the validation set) is n=25, R2=0.95, and RMSE=0.375.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy
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Hao Y, Sun G, Fan T, Tang X, Zhang J, Liu Y, Zhang N, Zhao L, Zhong R, Peng Y. In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse. JOURNAL OF HAZARDOUS MATERIALS 2020; 399:122981. [PMID: 32534390 DOI: 10.1016/j.jhazmat.2020.122981] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 05/14/2020] [Accepted: 05/16/2020] [Indexed: 06/11/2023]
Abstract
Nitroaromatic compounds (NACs) in the environment can cause serious public health and environmental problems due to their potential toxicity. This study established quantitative structure-toxicity relationship (QSTR) models for the acute oral toxicity of NACs towards rats following the stringent OECD principles for QSTR modelling. All models were assessed by various internationally accepted validation metrics and the OECD criteria. The best QSTR model contains seven simple and interpretable 2D descriptors with defined physicochemical meaning. Mechanistic interpretation indicated that van der Waals surface area, presence of C-F at topological distance 6, heteroatom content and frequency of C-N at topological distance 9 are main factors responsible for the toxicity of NACs. This proposed model was successfully applied to a true external set (295 compounds), and prediction reliability was analysed and discussed. Moreover, the rat-mouse and mouse-rat interspecies quantitative toxicity-toxicity relationship (iQTTR) models were also constructed, validated and employed in toxicity prediction for true external sets consisting of 67 and 265 compounds, respectively. These models showed good external predictivity that can be used to rapidly predict the rat oral acute toxicity of new or untested NACs falling within the applicability domain of the models, thus being beneficial in environmental risk assessment and regulatory purposes.
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Affiliation(s)
- Yuxing Hao
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Xiaoyu Tang
- College of Environmental and Energy Engineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Jing Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Yongdong Liu
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Na Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Engineering Research Center of Beijing, Beijing University of Technology, Beijing 100124, PR China.
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Kumar A, Kumar P. Identification of good and bad fragments of tricyclic triazinone analogues as potential PKC-θ inhibitors through SMILES–based QSAR and molecular docking. Struct Chem 2020. [DOI: 10.1007/s11224-020-01629-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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