Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data

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• dc.contributor.author Carbonell, Pabloca
• dc.contributor.author López, Oriolca
• dc.contributor.author Amberg, Alexanderca
• dc.contributor.author Pastor Maeso, Manuelca
• dc.contributor.author Sanz, Ferranca
• dc.date.accessioned 2017-07-04T07:58:45Z
• dc.date.available 2017-07-04T07:58:45Z
• dc.date.issued 2017
• dc.description.abstract The present study applies a systems biology approach for the in silico predictive modeling of drug toxicity on the basis of high-quality preclinical drug toxicity data with the aim of increasing the mechanistic understanding of toxic effects of compounds at different levels (pathway, cell, tissue, organ). The model development was carried out using 77 compounds for which gene expression data for treated primary human hepatocytes is available in the LINCS database and for which rodent in vivo hepatotoxicity information is available in the eTOX database. The data from LINCS were used to determine the type and number of pathways disturbed by each compound and to estimate the extent of disturbance (network perturbation elasticity), and were used to analyze the correspondence with the in vivo information from eTOX. Predictive models were developed through this integrative analysis, and their specificity and sensitivity were assessed. The quality of the predictions was determined on the basis of the area under the curve (AUC) of plots of true positive vs. false positive rates (ROC curves). The ROC AUC reached values of up to 0.9 (out of 1.0) for some hepatotoxicity endpoints. Moreover, the most frequently disturbed metabolic pathways were determined across the studied toxicants. They included, e.g., mitochondrial beta-oxidation of fatty acids and amino acid metabolism. The process was exemplified by successful predictions on various statins. In conclusion, an entirely new approach linking gene expression alterations to the prediction of complex organ toxicity was developed and evaluated.
• dc.description.sponsorship The research leading to these results has received support from the Innovative Medicines Initiative (IMI) Joint Undertaking under grant agreement nº 115002 (eTOX), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/200-2013) and EFPIA companie's in kind contributions.
• dc.format.mimetype application/pdfca
• dc.identifier.citation Carbonell P, Lopez O, Amberg A, Pastor Maeso M, Sanz F, et al. Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data. Altex. 2017;34(2):219-34. DOI: 10.14573/altex.1602071
• dc.identifier.doi http://dx.doi.org/10.14573/altex.1602071
• dc.identifier.issn 0946-7785
• dc.identifier.uri http://hdl.handle.net/10230/32495
• dc.language.iso eng
• dc.publisher ALTEX Editionca
• dc.relation.ispartof Altex. 2017;34(2):219-34
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/115002
• dc.rights This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is appropriately cited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Systems biology
• dc.subject.keyword Predictive modeling
• dc.subject.keyword Drug toxicity
• dc.subject.keyword Hepatotoxicity
• dc.subject.keyword Gene regulation
• dc.title Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression dataca
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion