López-Massaguer Oriol, Pastor Manuel, Sanz Ferran, Carbonell Pablo
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 Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Methods Mol Biol. 2018;1800:505-518. doi: 10.1007/978-1-4939-7899-1_23.
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.
本方法描述了一种用于药物毒性计算机预测建模的系统生物学方法。利用LINCS的数据来确定每种化合物干扰的途径类型和数量,并估计干扰程度(网络扰动弹性)。此外,还确定了在所研究的毒物中最常受到干扰的代谢途径和反应。通过对各种他汀类药物的成功预测举例说明了这一过程。总之,开发了一种将基因表达改变与复杂器官毒性预测联系起来的全新方法。