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Oksel C, Ma CY, Liu JJ, Wilkins T, Wang XZ. Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 947:103-142. [PMID: 28168667 DOI: 10.1007/978-3-319-47754-1_5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.
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Affiliation(s)
- Ceyda Oksel
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Cai Y Ma
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Jing J Liu
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Terry Wilkins
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Xue Z Wang
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK.
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510641, China.
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Oksel C, Winkler DA, Ma CY, Wilkins T, Wang XZ. Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches. Nanotoxicology 2016; 10:1001-12. [DOI: 10.3109/17435390.2016.1161857] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Ceyda Oksel
- School of Chemical and Process Engineering, University of Leeds, Leeds, UK,
| | - David A. Winkler
- CSIRO Manufacturing Flagship, Clayton South, MDC, Melbourne, Australia,
- Monash Institute of Pharmaceutical Sciences, Parkville, Melbourne, Australia,
- Latrobe Institute for Molecular Science, Bundoora, Melbourne, Australia, and
- School of Chemical and Physical Sciences, Flinders University, Bedford Park, Adelaide, Australia
| | - Cai Y. Ma
- School of Chemical and Process Engineering, University of Leeds, Leeds, UK,
| | - Terry Wilkins
- School of Chemical and Process Engineering, University of Leeds, Leeds, UK,
| | - Xue Z. Wang
- School of Chemical and Process Engineering, University of Leeds, Leeds, UK,
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Judson P. The application of structure-activity relationships to the prediction of the mutagenic activity of chemicals. Methods Mol Biol 2012; 817:1-19. [PMID: 22147565 DOI: 10.1007/978-1-61779-421-6_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Prediction of mutagenicity by computer is now routinely used in research and by regulatory authorities. Broadly, two different approaches are in wide use. The first is based on statistical analysis of data to find patterns associated with mutagenic activity. The resultant models are generally termed quantitative structure-activity relationships (QSAR). The second is based on capturing human knowledge about the causes of mutagenicity and applying it in ways that mimic human reasoning. These systems are generally called knowledge-based system. Other methods for finding patterns in data, such as the application of neural networks, are in use but less widely so.
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Affiliation(s)
- Philip Judson
- Lhasa Limited, 22-23 Blenheim Terrace, Woodhouse Lane, Leeds, LS2 9HD, UK.
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Data flow modeling, data mining and QSAR in high-throughput discovery of functional nanomaterials. Comput Chem Eng 2011. [DOI: 10.1016/j.compchemeng.2010.04.018] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Jiao J, Tan SM, Luo RM, Zhou YP. A Robust Boosting Regression Tree with Applications in Quantitative Structure−Activity Relationship Studies of Organic Compounds. J Chem Inf Model 2011; 51:816-28. [DOI: 10.1021/ci100429u] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jian Jiao
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Shi-Miao Tan
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Rui-Ming Luo
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
| | - Yan-Ping Zhou
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China
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Ma CY, Wang XZ. Inductive data mining based on genetic programming: Automatic generation of decision trees from data for process historical data analysis. Comput Chem Eng 2009. [DOI: 10.1016/j.compchemeng.2009.04.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Zhou YP, Tang LJ, Jiao J, Song DD, Jiang JH, Yu RQ. Modified Particle Swarm Optimization Algorithm for Adaptively Configuring Globally Optimal Classification and Regression Trees. J Chem Inf Model 2009; 49:1144-53. [DOI: 10.1021/ci800374h] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yan-Ping Zhou
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China, and State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P. R. China
| | - Li-Juan Tang
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China, and State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P. R. China
| | - Jian Jiao
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China, and State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P. R. China
| | - Dan-Dan Song
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China, and State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P. R. China
| | - Jian-Hui Jiang
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China, and State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P. R. China
| | - Ru-Qin Yu
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China, and State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P. R. China
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Watson P. Naïve Bayes Classification Using 2D Pharmacophore Feature Triplet Vectors. J Chem Inf Model 2008; 48:166-78. [DOI: 10.1021/ci7003253] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Paul Watson
- Arena Pharmaceuticals, 6166 Nancy Ridge Drive, San Diego, California 92121
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Inductive data mining: Automatic generation of decision trees from data for QSAR modelling and process historical data analysis. COMPUTER AIDED CHEMICAL ENGINEERING 2008. [DOI: 10.1016/s1570-7946(08)80102-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Wang XZ, Buontempo FV, Young A, Osborn D. Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2006; 17:451-71. [PMID: 17050186 DOI: 10.1080/10629360600933723] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Recent literature has demonstrated the applicability of genetic programming to induction of decision trees for modelling toxicity endpoints. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node that will necessarily miss regions of the search space, the genetic programming based approach can overcome the problem. However, the method still requires the discretization of the often continuous-valued toxicity endpoints prior to the tree induction. A novel extension of this method, YAdapt, is introduced in this work which models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretization prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built.
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Affiliation(s)
- X Z Wang
- Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, LS2 9JT, UK.
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Mwense M, Wang XZ, Buontempo FV, Horan N, Young A, Osborn D. QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2006; 17:53-73. [PMID: 16513552 DOI: 10.1080/10659360600562202] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The principle of using a singe model to predict the toxicity of mixtures of chemicals based on the characterisation of the degrees of similarity and dissimilarity of the constituent chemicals using descriptors has been demonstrated in a previous work. The current study introduces a feature extraction technique, independent component analysis, to the method to remove the correlations and dependencies between descriptors and reduce the dimension prior to similarity and dissimilarity calculations. In addition, a goal attainment multi-objective optimisation technique is used for the determination of the fuzzy membership function parameters. For three mixtures, which include a new mixture and two previously studied mixtures that all inhibit reproduction (via different mechanisms of action) in green freshwater algae scenedesmus vacuolatus, the approach showed better or equivalent prediction performance than either concentration addition or independent action models. Unlike QSARs for pure compounds that require large collections of data, the new approach for mixtures only requires one mixture at a particular composition to determine the necessary fuzzy membership function parameter values. These values can then be used to predict the toxicity of the mixture at any other compositions. This could potentially lead to a reduction in the frequency of bioassay tests. Use of the fuzzy membership functions and parameter values obtained for one mixture when used to predict the toxicity of a completely different mixture is also tested and it is found that the approach also gives prediction results with good accuracy.
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Affiliation(s)
- M Mwense
- School of Process, Environmental and Materials Engineering, Institute of Particle Science and Engineering
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