Insights into mechanisms and severity of drug-induced liver injury via computational systems toxicology approach.

Liver is the central place for drug metabolism. Drug-induced liver injury (DILI) is hence inevitable, and has become one of the leading causes for drug failure in development and drug withdrawal from the market. Due to lack of reliable preclinical and in vivo toxicology test conditions, it is time-consuming, laborious and costly to interpret the mechanisms of DILI through bioassays. In this paper, we developed a computational systems toxicology approach to investigate the molecular mechanisms of DILI. Totally 1478 DILI compounds were collected, together with 1067 known targets for 896 DILI compounds. Then, 173 new potential targets of these compounds were predicted by our bSDTNBI (balanced substructure-drug-target network-based inference) method. After network analysis, 145 primary genes were found to relate with hepatotoxicity and have higher expression in liver, among which 26 genes were predicted by our method, such as CYP2E1, GSTA1, EPHX1, ADH1B, ADH1C, ALDH2, F7, and IL2. A scoring function, DILI-Score, was further proposed to assess the hepatotoxic severity of a given compound. Finally, as case studies, we analyzed the mechanisms of DILI from the perspective of off-targets, and found out the pivotal genes for liver injuries induced by tyrosine kinase inhibitors and TAK-875. This work would be helpful for better understanding mechanisms of DILI and provide clues for reducing risk of DILI.