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Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives
The environmental pollution of quinolone antibiotics (QAs) has caused rising public concern due to their widespread usage. In this study, Gaussian 09 software was used to obtain the infrared spectral intensity (IRI) and ultraviolet spectral intensity (UVI) of 24 QAs based on the Density Functional T...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246649/ https://www.ncbi.nlm.nih.gov/pubmed/32384726 http://dx.doi.org/10.3390/ijerph17093239 |
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author | Li, Xixi Zhang, Baiyu Huang, Wendy Cantwell, Cuirin Chen, Bing |
author_facet | Li, Xixi Zhang, Baiyu Huang, Wendy Cantwell, Cuirin Chen, Bing |
author_sort | Li, Xixi |
collection | PubMed |
description | The environmental pollution of quinolone antibiotics (QAs) has caused rising public concern due to their widespread usage. In this study, Gaussian 09 software was used to obtain the infrared spectral intensity (IRI) and ultraviolet spectral intensity (UVI) of 24 QAs based on the Density Functional Theory (DFT). Rather than using two single-factor inputs, a fuzzy matter-element method was selected to calculate the combined effects of infrared and ultraviolet spectra (CI). The Comparative Molecular Field Analysis (CoMFA) was then used to construct a three-dimensional quantitative structure–activity relationship (3D-QSAR) with QAs’ molecular structure as the independent variable and CI as the dependent variable. Using marbofloxacin and levofloxacin as target molecules, the molecular design of 87 QA derivatives was carried out. The developed models were further used to determine the stability, functionality (genetic toxicity), and the environmental effects (bioaccumulation, biodegradability) of these designed QA derivatives. Results indicated that all QA derivatives are stable in the environment with their IRI, UVI, and CI enhanced. Meanwhile, the genetic toxicity of the 87 QA derivatives increased by varying degrees (0.24%–29.01%), among which the bioaccumulation and biodegradability of 43 QA derivatives were within the acceptable range. Through integration of fuzzy matter-element method and 3D-QSAR, this study advanced the QAs research with the enhanced CI and helped to generate the proposed environmentally friendly quinolone derivatives so as to aid the management of this class of antibiotics. |
format | Online Article Text |
id | pubmed-7246649 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72466492020-06-10 Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives Li, Xixi Zhang, Baiyu Huang, Wendy Cantwell, Cuirin Chen, Bing Int J Environ Res Public Health Article The environmental pollution of quinolone antibiotics (QAs) has caused rising public concern due to their widespread usage. In this study, Gaussian 09 software was used to obtain the infrared spectral intensity (IRI) and ultraviolet spectral intensity (UVI) of 24 QAs based on the Density Functional Theory (DFT). Rather than using two single-factor inputs, a fuzzy matter-element method was selected to calculate the combined effects of infrared and ultraviolet spectra (CI). The Comparative Molecular Field Analysis (CoMFA) was then used to construct a three-dimensional quantitative structure–activity relationship (3D-QSAR) with QAs’ molecular structure as the independent variable and CI as the dependent variable. Using marbofloxacin and levofloxacin as target molecules, the molecular design of 87 QA derivatives was carried out. The developed models were further used to determine the stability, functionality (genetic toxicity), and the environmental effects (bioaccumulation, biodegradability) of these designed QA derivatives. Results indicated that all QA derivatives are stable in the environment with their IRI, UVI, and CI enhanced. Meanwhile, the genetic toxicity of the 87 QA derivatives increased by varying degrees (0.24%–29.01%), among which the bioaccumulation and biodegradability of 43 QA derivatives were within the acceptable range. Through integration of fuzzy matter-element method and 3D-QSAR, this study advanced the QAs research with the enhanced CI and helped to generate the proposed environmentally friendly quinolone derivatives so as to aid the management of this class of antibiotics. MDPI 2020-05-06 2020-05 /pmc/articles/PMC7246649/ /pubmed/32384726 http://dx.doi.org/10.3390/ijerph17093239 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Xixi Zhang, Baiyu Huang, Wendy Cantwell, Cuirin Chen, Bing Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives |
title | Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives |
title_full | Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives |
title_fullStr | Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives |
title_full_unstemmed | Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives |
title_short | Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives |
title_sort | integration of fuzzy matter-element method and 3d-qsar model for generation of environmentally friendly quinolone derivatives |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246649/ https://www.ncbi.nlm.nih.gov/pubmed/32384726 http://dx.doi.org/10.3390/ijerph17093239 |
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