Cargando…

Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties

BACKGROUND: Advanced structure-activity relationship (SAR) modeling can be used as an alternative tool for identification of skin sensitizers and in improvement of the medical diagnosis and more effective practical measures to reduce the causative chemical exposures. It can also circumvent ethical c...

Descripción completa

Detalles Bibliográficos
Autores principales: Ouyang, Qin, Wang, Lirong, Mu, Ying, Xie, Xiang-Qun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4298069/
https://www.ncbi.nlm.nih.gov/pubmed/25539579
http://dx.doi.org/10.1186/2050-6511-15-76
_version_ 1782353216936083456
author Ouyang, Qin
Wang, Lirong
Mu, Ying
Xie, Xiang-Qun
author_facet Ouyang, Qin
Wang, Lirong
Mu, Ying
Xie, Xiang-Qun
author_sort Ouyang, Qin
collection PubMed
description BACKGROUND: Advanced structure-activity relationship (SAR) modeling can be used as an alternative tool for identification of skin sensitizers and in improvement of the medical diagnosis and more effective practical measures to reduce the causative chemical exposures. It can also circumvent ethical concern of using animals in toxicological tests, and reduce time and cost. Compounds with aniline or phenol moieties represent two large classes of frequently skin sensitizing chemicals but exhibiting very variable, and difficult to predict, potency. The mechanisms of action are not well-understood. METHODS: A group of mechanistically hard-to-be-classified aniline and phenol chemicals were collected. An in silico model was established by statistical analysis of quantum descriptors for the determination of the relationship between their chemical structures and skin sensitization potential. The sensitization mechanisms were investigated based on the features of the established model. Then the model was utilized to analyze a subset of FDA approved drugs containing aniline and/or phenol groups for prediction of their skin sensitization potential. RESULTS AND DISCUSSION: A linear discriminant model using the energy of the highest occupied molecular orbital (ϵ(HOMO)) as the descriptor yielded high prediction accuracy. The contribution of ϵ(HOMO) as a major determinant may suggest that autoxidation or free radical binding could be involved. The model was further applied to predict allergic potential of a subset of FDA approved drugs containing aniline and/or phenol moiety. The predictions imply that similar mechanisms (autoxidation or free radical binding) may also play a role in the skin sensitization caused by these drugs. CONCLUSIONS: An accurate and simple quantum mechanistic model has been developed to predict the skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol chemicals. The model could be useful for the skin sensitization potential predictions of a subset of FDA approved drugs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/2050-6511-15-76) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4298069
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-42980692015-01-20 Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties Ouyang, Qin Wang, Lirong Mu, Ying Xie, Xiang-Qun BMC Pharmacol Toxicol Research Article BACKGROUND: Advanced structure-activity relationship (SAR) modeling can be used as an alternative tool for identification of skin sensitizers and in improvement of the medical diagnosis and more effective practical measures to reduce the causative chemical exposures. It can also circumvent ethical concern of using animals in toxicological tests, and reduce time and cost. Compounds with aniline or phenol moieties represent two large classes of frequently skin sensitizing chemicals but exhibiting very variable, and difficult to predict, potency. The mechanisms of action are not well-understood. METHODS: A group of mechanistically hard-to-be-classified aniline and phenol chemicals were collected. An in silico model was established by statistical analysis of quantum descriptors for the determination of the relationship between their chemical structures and skin sensitization potential. The sensitization mechanisms were investigated based on the features of the established model. Then the model was utilized to analyze a subset of FDA approved drugs containing aniline and/or phenol groups for prediction of their skin sensitization potential. RESULTS AND DISCUSSION: A linear discriminant model using the energy of the highest occupied molecular orbital (ϵ(HOMO)) as the descriptor yielded high prediction accuracy. The contribution of ϵ(HOMO) as a major determinant may suggest that autoxidation or free radical binding could be involved. The model was further applied to predict allergic potential of a subset of FDA approved drugs containing aniline and/or phenol moiety. The predictions imply that similar mechanisms (autoxidation or free radical binding) may also play a role in the skin sensitization caused by these drugs. CONCLUSIONS: An accurate and simple quantum mechanistic model has been developed to predict the skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol chemicals. The model could be useful for the skin sensitization potential predictions of a subset of FDA approved drugs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/2050-6511-15-76) contains supplementary material, which is available to authorized users. BioMed Central 2014-12-24 /pmc/articles/PMC4298069/ /pubmed/25539579 http://dx.doi.org/10.1186/2050-6511-15-76 Text en © Ouyang et al.; licensee BioMed Central. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Ouyang, Qin
Wang, Lirong
Mu, Ying
Xie, Xiang-Qun
Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties
title Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties
title_full Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties
title_fullStr Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties
title_full_unstemmed Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties
title_short Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties
title_sort modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4298069/
https://www.ncbi.nlm.nih.gov/pubmed/25539579
http://dx.doi.org/10.1186/2050-6511-15-76
work_keys_str_mv AT ouyangqin modelingskinsensitizationpotentialofmechanisticallyhardtobeclassifiedanilineandphenolcompoundswithquantummechanisticproperties
AT wanglirong modelingskinsensitizationpotentialofmechanisticallyhardtobeclassifiedanilineandphenolcompoundswithquantummechanisticproperties
AT muying modelingskinsensitizationpotentialofmechanisticallyhardtobeclassifiedanilineandphenolcompoundswithquantummechanisticproperties
AT xiexiangqun modelingskinsensitizationpotentialofmechanisticallyhardtobeclassifiedanilineandphenolcompoundswithquantummechanisticproperties