Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data.
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.
Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.|Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, Manchester, UK.
Pablo
P
Carbonell
Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
Oriol
O
Lopez
Sanofi Aventis Deutschland GmbH, Preclinical Safety, Frankfurt am Main, Germany.
Alexander
A
Amberg
Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
Manuel
M
Pastor
Research Programme on Biomedical Informatics (GRIB), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
Ferran
F
Sanz
D000826:Animal Testing Alternatives; D000818:Animals; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical / Q000379:methods; D064420:Drug-Related Side Effects and Adverse Reactions / Q000235:genetics; D005786:Gene Expression Regulation / Q000235:genetics*; D022781:Hepatocytes / Q000187:drug effects*; D006801:Humans; D066298:In Vitro Techniques; D008099:Liver / Q000187:drug effects; D053858:Metabolic Networks and Pathways / Q000187:drug effects* / Q000235:genetics; D015233:Models, Statistical; D051381:Rats; D012680:Sensitivity and Specificity
drug toxicity; gene regulation; hepatotoxicity; predictive modeling; systems biology