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A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database

dc.creatorJuana Julieta Noguez Monroy
dc.date2017
dc.date.accessioned2018-10-19T13:37:33Z
dc.date.available2018-10-19T13:37:33Z
dc.identifier.issn19326203
dc.identifier.doi10.1371/journal.pone.0176284
dc.identifier.urihttp://hdl.handle.net/11285/630611
dc.descriptionThe 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. © 2017 Rueda-Zárate et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.languageeng
dc.publisherPublic Library of Science
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85018256480&doi=10.1371%2fjournal.pone.0176284&partnerID=40&md5=83b012af09a54d86e76001438bf68e0d
dc.relationInvestigadores
dc.relationEstudiantes
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourcePLoS ONE
dc.subject1 naphthyl isothiocyanate
dc.subjectacetylsalicylic acid
dc.subjectallopurinol
dc.subjectallyl alcohol
dc.subjectamiodarone
dc.subjectazathioprine
dc.subjectbenzbromarone
dc.subjectbromobenzene
dc.subjectcarbamazepine
dc.subjectcarbon tetrachloride
dc.subjectchlorpromazine
dc.subjectcimetidine
dc.subjectclofibrate
dc.subjectcoumarin
dc.subjectcyclophosphamide
dc.subjectcytochrome P450 family 1
dc.subjectdiazepam
dc.subjectdiclofenac
dc.subjectdoxepin
dc.subjectethionine
dc.subjectfluphenazine
dc.subjectflutamide
dc.subjectgemfibrozil
dc.subjectglibenclamide
dc.subjectgriseofulvin
dc.subjecthaloperidol
dc.subjecthexachlorobenzene
dc.subjectindometacin
dc.subjectparacetamol
dc.subjectunindexed drug
dc.subjectcytotoxin
dc.subjectalgorithm
dc.subjectArticle
dc.subjectcluster analysis
dc.subjectdata base
dc.subjectgene expression
dc.subjectgenomics
dc.subjecthuman
dc.subjectliver cell
dc.subjectliver toxicity
dc.subjectmachine learning
dc.subjectmicroarray analysis
dc.subjectnonhuman
dc.subjectskin absorption
dc.subjecttoxic hepatitis
dc.subjectxenobiotic metabolism
dc.subjectanimal
dc.subjectDNA microarray
dc.subjectdose response
dc.subjectdrug effects
dc.subjectgenetic database
dc.subjectgenetics
dc.subjectgenomics
dc.subjectliver
dc.subjectmetabolism
dc.subjectmouse
dc.subjectpreclinical study
dc.subjectprocedures
dc.subjecttime factor
dc.subjecttoxicogenetics
dc.subjectunsupervised machine learning
dc.subjectAnimals
dc.subjectChemical and Drug Induced Liver Injury
dc.subjectCytotoxins
dc.subjectDatabases, Genetic
dc.subjectDose-Response Relationship, Drug
dc.subjectDrug Evaluation, Preclinical
dc.subjectGenomics
dc.subjectHumans
dc.subjectLiver
dc.subjectMice
dc.subjectOligonucleotide Array Sequence Analysis
dc.subjectTime Factors
dc.subjectToxicogenetics
dc.subjectUnsupervised Machine Learning
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleA computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database
dc.typeArtículo
dc.identifier.volume12
dc.identifier.issue4
refterms.dateFOA2018-10-19T13:37:33Z


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