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A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database
The liver and the kidney are the most common targets of chemical toxicity, due to their major metabolic and excretory functions. However, since the liver is directly involved in biotransformation, compounds in many currently and normally used drugs could affect it adversely. Most chemical compounds...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407788/ https://www.ncbi.nlm.nih.gov/pubmed/28448553 http://dx.doi.org/10.1371/journal.pone.0176284 |
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author | Rueda-Zárate, Héctor A. Imaz-Rosshandler, Iván Cárdenas-Ovando, Roberto A. Castillo-Fernández, Juan E. Noguez-Monroy, Julieta Rangel-Escareño, Claudia |
author_facet | Rueda-Zárate, Héctor A. Imaz-Rosshandler, Iván Cárdenas-Ovando, Roberto A. Castillo-Fernández, Juan E. Noguez-Monroy, Julieta Rangel-Escareño, Claudia |
author_sort | Rueda-Zárate, Héctor A. |
collection | PubMed |
description | The liver and the kidney are the most common targets of chemical toxicity, due to their major metabolic and excretory functions. However, since the liver is directly involved in biotransformation, compounds in many currently and normally used drugs could affect it adversely. Most chemical compounds are already labeled according to FDA-approved labels using DILI-concern scale. Drug Induced Liver Injury (DILI) scale refers to an adverse drug reaction. Many compounds do not exhibit hepatotoxicity at early stages of development, so it is important to detect anomalies at gene expression level that could predict adverse reactions in later stages. In this study, a large collection of microarray data is used to investigate gene expression changes associated with hepatotoxicity. Using TG-GATEs a large-scale toxicogenomics database, we present a computational strategy to classify compounds by toxicity levels in human and animal models through patterns of gene expression. We combined machine learning algorithms with time series analysis to identify genes capable of classifying compounds by FDA-approved labeling as DILI-concern toxic. The goal is to define gene expression profiles capable of distinguishing the different subtypes of hepatotoxicity. The study illustrates that expression profiling can be used to classify compounds according to different hepatotoxic levels; to label those that are currently labeled as undertemined; and to determine if at the molecular level, animal models are a good proxy to predict hepatotoxicity in humans. |
format | Online Article Text |
id | pubmed-5407788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54077882017-05-14 A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database Rueda-Zárate, Héctor A. Imaz-Rosshandler, Iván Cárdenas-Ovando, Roberto A. Castillo-Fernández, Juan E. Noguez-Monroy, Julieta Rangel-Escareño, Claudia PLoS One Research Article The liver and the kidney are the most common targets of chemical toxicity, due to their major metabolic and excretory functions. However, since the liver is directly involved in biotransformation, compounds in many currently and normally used drugs could affect it adversely. Most chemical compounds are already labeled according to FDA-approved labels using DILI-concern scale. Drug Induced Liver Injury (DILI) scale refers to an adverse drug reaction. Many compounds do not exhibit hepatotoxicity at early stages of development, so it is important to detect anomalies at gene expression level that could predict adverse reactions in later stages. In this study, a large collection of microarray data is used to investigate gene expression changes associated with hepatotoxicity. Using TG-GATEs a large-scale toxicogenomics database, we present a computational strategy to classify compounds by toxicity levels in human and animal models through patterns of gene expression. We combined machine learning algorithms with time series analysis to identify genes capable of classifying compounds by FDA-approved labeling as DILI-concern toxic. The goal is to define gene expression profiles capable of distinguishing the different subtypes of hepatotoxicity. The study illustrates that expression profiling can be used to classify compounds according to different hepatotoxic levels; to label those that are currently labeled as undertemined; and to determine if at the molecular level, animal models are a good proxy to predict hepatotoxicity in humans. Public Library of Science 2017-04-27 /pmc/articles/PMC5407788/ /pubmed/28448553 http://dx.doi.org/10.1371/journal.pone.0176284 Text en © 2017 Rueda-Zárate et al http://creativecommons.org/licenses/by/4.0/ 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 author and source are credited. |
spellingShingle | Research Article Rueda-Zárate, Héctor A. Imaz-Rosshandler, Iván Cárdenas-Ovando, Roberto A. Castillo-Fernández, Juan E. Noguez-Monroy, Julieta Rangel-Escareño, Claudia A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database |
title | A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database |
title_full | A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database |
title_fullStr | A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database |
title_full_unstemmed | A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database |
title_short | A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database |
title_sort | computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407788/ https://www.ncbi.nlm.nih.gov/pubmed/28448553 http://dx.doi.org/10.1371/journal.pone.0176284 |
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