Abstract
The present method describes a systems biology approach for the in silico predictive modeling of drug toxicity. 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). Moreover, the most frequently disturbed metabolic pathways and reactions were determined across the studied toxicants. 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.
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Acknowledgment
This work was supported by 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 (FP7/2007-2013) and EFPIA companies’ in-kind contributions.
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López-Massaguer, O., Pastor, M., Sanz, F., Carbonell, P. (2018). Hepatotoxicity Prediction by Systems Biology Modeling of Disturbed Metabolic Pathways Using Gene Expression Data. In: Nicolotti, O. (eds) Computational Toxicology. Methods in Molecular Biology, vol 1800. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7899-1_23
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DOI: https://doi.org/10.1007/978-1-4939-7899-1_23
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