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

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s43188-022-00124-6.

Integrated analysis of in vitro data and the adverse outcome pathway framework for prioritization and regulatory applications: An exploratory case study using publicly available data on piperonyl butoxide and liver models.

The integration of existing knowledge to support the risk assessment of chemicals is an ongoing challenge for scientists, risk assessors and risk managers. In addition, European Union regulations limiting the use of new animal testing in cosmetics makes already existing information even more valuable. Applying a previous SEURAT-1 program framework to derive predictions of in vivo toxicity responses for a compound, we selected piperonyl butoxide (PBO) as a case study for identification of knowledge and methodology gaps in understanding a compound's effects on the human liver. This is investigated through integration of data from human in vitro transcriptomics studies, biological pathway analysis, chemical and disease associations, and adverse outcome pathway (AOP) information. The outcomes of the analysis are used to generate AOPs of liver-related endpoints, identifying areas of concern for risk assessors and regulators. We demonstrate that integration of data through already existing and publicly available tools can produce outcomes comparable to those that may be found through more conventional time- and resource-intensive methods. It is also expected that, with more refinement, this approach could in the future provide evidence to support chemical risk assessment, while also identifying data gaps for which additional testing may be needed.