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Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model

Terpenoids, including monoterpenoids (C(10)), norisoprenoids (C(13)), and sesquiterpenoids (C(15)), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terp...

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Autores principales: Perestrelo, Rosa, Silva, Catarina, Fernandes, Miguel X., Câmara, José S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6963511/
https://www.ncbi.nlm.nih.gov/pubmed/31805724
http://dx.doi.org/10.3390/foods8120628
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author Perestrelo, Rosa
Silva, Catarina
Fernandes, Miguel X.
Câmara, José S.
author_facet Perestrelo, Rosa
Silva, Catarina
Fernandes, Miguel X.
Câmara, José S.
author_sort Perestrelo, Rosa
collection PubMed
description Terpenoids, including monoterpenoids (C(10)), norisoprenoids (C(13)), and sesquiterpenoids (C(15)), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50–100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov’s partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q(2)) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.
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spelling pubmed-69635112020-01-30 Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model Perestrelo, Rosa Silva, Catarina Fernandes, Miguel X. Câmara, José S. Foods Article Terpenoids, including monoterpenoids (C(10)), norisoprenoids (C(13)), and sesquiterpenoids (C(15)), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50–100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov’s partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q(2)) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products. MDPI 2019-12-01 /pmc/articles/PMC6963511/ /pubmed/31805724 http://dx.doi.org/10.3390/foods8120628 Text en © 2019 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
Perestrelo, Rosa
Silva, Catarina
Fernandes, Miguel X.
Câmara, José S.
Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model
title Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model
title_full Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model
title_fullStr Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model
title_full_unstemmed Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model
title_short Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model
title_sort prediction of terpenoid toxicity based on a quantitative structure–activity relationship model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6963511/
https://www.ncbi.nlm.nih.gov/pubmed/31805724
http://dx.doi.org/10.3390/foods8120628
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