Semi-automated approach for generation of biological networks on drug-induced cholestasis, steatosis, hepatitis, and cirrhosis

Abstract

Drug-induced liver injury (DILI) is one of the leading reasons for discontinuation of a new drug development project. Diverse machine learning or deep learning models have been developed to predict DILI. However, these models have not provided an adequate understanding of the mechanisms leading to DILI. The development of safer drugs requires novel computational approaches that enable the prompt understanding of the mechanism of DILI. In this study, the mechanisms leading to the development of cholestasis, steatosis, hepatitis, and cirrhosis were explored using a semi-automated approach for data gathering and associations. Diverse data from ToxCast, Comparative Toxicogenomic Database (CTD), Reactome, and Open TG-GATEs on reference molecules leading to the development of the respective diseases were extracted. The data were used to create biological networks of the four diseases. As expected, the four networks had several common pathways, and a joint DILI network was assembled. Such biological networks could be used in drug discovery to identify possible molecules of concern as they provide a better understanding of the disease-specific key events. The events can be target-tested to provide indications for potential DILI effects.