Advances in Biomechanics

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Density Functional Theory (DFT) Based Quantitative Structure Toxicity Relationship (QSTR) Modelling of the Acute Toxicity of Phenols

Received: Apr. 26, 2017    Accepted: May 09, 2017    Published: Jul. 06, 2017
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Abstract

A toxicity data set of 58 phenols to Tetrahymena pyriformis expressed as pEC50 (Log to base 10 of effective concentration, EC50) was taken from literature and the molecular structure of each molecule was optimized to obtain their minimum energy geometry. The descriptors of each optimized molecule were computed and subsequently used to build QSTR models. The best QSTR model hinted that the toxicity of phenol was dominantly influenced by the descriptors; molecular complexity (FMF), valence path cluster (VPC) and topological diameter (topo). The results of the statistical analysis of the tri- parametric model include; n = 41, Lack of fit (LOF) score = 0.06566, R2 = 0.7629, R2adj.= 0.7437, Q2LOO = 0.7, F-value = 39.69. The generated QSTR model has been proven to possess statistical significance, high predictive power and wide applicability domain. Thus, it is recommended for environmental risk assessment of phenols.

DOI 10.11648/j.abm.20170102.13
Published in Advances in Biomechanics ( Volume 1, Issue 2, October 2017 )
Page(s) 42-46
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

QSTR, Phenols, EC50, Model, XlogP, LOF

References
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[2] Bukowska, B., Kowalska, S. (2004) Phenol and catechol induce prehemolytic and hemolytic changes in human erythrocytes. Toxicol. Lett., 152: 73–84.
[3] Cunningham, W. P., Cunningham, M. A., Saigo, B. (2005) A Global Concern, Mc Graw-Hill Education. New York: Environmental Science.
[4] Kušić, H. (2009). Prediction of rate constants for radical degradation of aromatic pollutants in water matrix: A QSAR study. Chemosphere, 75; 1128–1134.
[5] Michałowicz, J., Duda, W. (2007). Phenols – Sources and Toxicity. Polish J. of Environ. Stud. 16 (3): 347-362.
[6] Gosselin, R. E., Smith, R. P., Hodge, H. C. (1984). Clinical Toxicology of Commercial Products, 5th ed.; Williams and Wilkings: Baltimore, pp. III -192-346.
[7] Basak, S. C., Grunwald, G. D., Gute, B. D., Balasubramanian, K., Opit, D. (2000). Use of statistical and neural net approaches in predicting toxicity of chemicals, J. Chem. Inf. Comput. Sci. 40: 885–890.
[8] Auer, C. M., Nabholz, J. V., Baetcke, K. P. (1990). Mode of action and the assessment of chemical hazards in the presence of limited data: Use of structure–activity relationships (SAR) under TSCA Section 5, Environ. Health. Perspect., 87: 183–197.
[9] Walker, J. D. (2003). Applications of QSAR in toxicology: a US Government perspective, J. Mol. Struct. (Theochem)., 622: 167–184.
[10] Luís, C., Gonçalo, B., Carrera, V. S. M., Aires-de-Sousa, J., Martin, I. L., Frade, R., Afonso, C. A. (2016). Physico-Chemical Properties of Task-Specific Ionic Liquids. Retrieved January 09, 2016, from http://cdn.intechopen.com/pdfs/13913.pdf.
[11] Jionghao HX, Zhang YT. A QSAR Study of Halogen Phenols Toxicity to the Tetrahymena Pyriformis, Computers and Applied Chemistry, 2007; 24: 87-90.
[12] Peng YF, Liu TB. (2009). QSAR Study of Halogen Phenols Toxicity to Tetrahymena Pyriformis, Chinese Journal of Structure Chemistry, 28: 218-222.
[13] He G, Feng L, Chen H. A QSAR Study of the Acute Toxicity of Halogenated Phenols. Procedia Engineering 2012; 43, 204 – 20.
[14] Jiang D. X., Li Y., Li J., Wang G. X. (2011). Prediction of the Aquatic Toxicity of Phenols to Tetrahymena Pyriformis from Molecular Descriptors. International Journal of Environmental Research, 5 (4), 923-938.
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    Ibraheem Wasiu Aderemi, Ameji Philip John, Awor George Okorn, Racheal U. Akpa, Raji Saheed Akinleye. (2017). Density Functional Theory (DFT) Based Quantitative Structure Toxicity Relationship (QSTR) Modelling of the Acute Toxicity of Phenols. Advances in Biomechanics, 1(2), 42-46. https://doi.org/10.11648/j.abm.20170102.13

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    ACS Style

    Ibraheem Wasiu Aderemi; Ameji Philip John; Awor George Okorn; Racheal U. Akpa; Raji Saheed Akinleye. Density Functional Theory (DFT) Based Quantitative Structure Toxicity Relationship (QSTR) Modelling of the Acute Toxicity of Phenols. Adv. Biomech. 2017, 1(2), 42-46. doi: 10.11648/j.abm.20170102.13

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    AMA Style

    Ibraheem Wasiu Aderemi, Ameji Philip John, Awor George Okorn, Racheal U. Akpa, Raji Saheed Akinleye. Density Functional Theory (DFT) Based Quantitative Structure Toxicity Relationship (QSTR) Modelling of the Acute Toxicity of Phenols. Adv Biomech. 2017;1(2):42-46. doi: 10.11648/j.abm.20170102.13

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  • @article{10.11648/j.abm.20170102.13,
      author = {Ibraheem Wasiu Aderemi and Ameji Philip John and Awor George Okorn and Racheal U. Akpa and Raji Saheed Akinleye},
      title = {Density Functional Theory (DFT) Based Quantitative Structure Toxicity Relationship (QSTR) Modelling of the Acute Toxicity of Phenols},
      journal = {Advances in Biomechanics},
      volume = {1},
      number = {2},
      pages = {42-46},
      doi = {10.11648/j.abm.20170102.13},
      url = {https://doi.org/10.11648/j.abm.20170102.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.abm.20170102.13},
      abstract = {A toxicity data set of 58 phenols to Tetrahymena pyriformis expressed as pEC50 (Log to base 10 of effective concentration, EC50) was taken from literature and the molecular structure of each molecule was optimized to obtain their minimum energy geometry. The descriptors of each optimized molecule were computed and subsequently used to build QSTR models. The best QSTR model hinted that the toxicity of phenol was dominantly influenced by the descriptors; molecular complexity (FMF), valence path cluster (VPC) and topological diameter (topo). The results of the statistical analysis of the tri- parametric model include; n = 41, Lack of fit (LOF) score = 0.06566, R2 = 0.7629, R2adj.= 0.7437, Q2LOO = 0.7, F-value = 39.69. The generated QSTR model has been proven to possess statistical significance, high predictive power and wide applicability domain. Thus, it is recommended for environmental risk assessment of phenols.},
     year = {2017}
    }
    

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    T1  - Density Functional Theory (DFT) Based Quantitative Structure Toxicity Relationship (QSTR) Modelling of the Acute Toxicity of Phenols
    AU  - Ibraheem Wasiu Aderemi
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    JO  - Advances in Biomechanics
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    UR  - https://doi.org/10.11648/j.abm.20170102.13
    AB  - A toxicity data set of 58 phenols to Tetrahymena pyriformis expressed as pEC50 (Log to base 10 of effective concentration, EC50) was taken from literature and the molecular structure of each molecule was optimized to obtain their minimum energy geometry. The descriptors of each optimized molecule were computed and subsequently used to build QSTR models. The best QSTR model hinted that the toxicity of phenol was dominantly influenced by the descriptors; molecular complexity (FMF), valence path cluster (VPC) and topological diameter (topo). The results of the statistical analysis of the tri- parametric model include; n = 41, Lack of fit (LOF) score = 0.06566, R2 = 0.7629, R2adj.= 0.7437, Q2LOO = 0.7, F-value = 39.69. The generated QSTR model has been proven to possess statistical significance, high predictive power and wide applicability domain. Thus, it is recommended for environmental risk assessment of phenols.
    VL  - 1
    IS  - 2
    ER  - 

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Author Information
  • Department of Chemistry, Ahmadu Bello University, Zaria, Kaduna, Nigeria

  • Department of Chemistry, Ahmadu Bello University, Zaria, Kaduna, Nigeria

  • Department of Chemistry, Ahmadu Bello University, Zaria, Kaduna, Nigeria

  • Department of Chemistry, Ahmadu Bello University, Zaria, Kaduna, Nigeria

  • Chemical Engineering Department, Ladoke Akintola University of Technology, Ogbomosho, Nigeria

  • Section