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

Hyun Kil Shin,Barry Hardy,Oana Florean,Myung-Gyun Kang,Tatyana Doktorova

doi:10.1007/s43188-022-00124-6

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.

Highlights

To protect human health and the environment from undesired adverse effects, safety information on the toxic potential of new molecules has to be generated and revised to regulate their use [1]
The expression of each gene was individually examined and compared to the negative control group to identify whether these genes could serve as biomarkers to characterize and identify cholestasis-specific mechanisms
This further refinement showed that most of the identified genes were deregulated in a similar fashion in the negative control group, with the exception of five genes (BHLHE40: basic helix-loop-helix family member e40, CSRP1: cysteine and glycine rich protein 1, NQO1: NAD(P) H quinone dehydrogenase 1, SLC16A10: solute carrier family 16 member 10, and AGT: angiotensinogen) that were consistently deregulated in the positive group as opposed to in the negative group (Fig. 4)

Summary

Introduction

To protect human health and the environment from undesired adverse effects, safety information on the toxic potential of new molecules has to be generated and revised to regulate their use [1]. Safety assessments usually involve a substantial amount of animal experimentation This is financially burdensome and time-consuming [2], is not always ethically performed, and sometimes, the relevance for humans is questionable. Among the numerous alternative approaches being developed and tested, those using toxicogenomics or computational toxicology in general are considered especially promising because they will likely facilitate faster hypothesis generation covering data-rich historical sources as input information. This will provide a more detailed and precise understanding of the mechanisms of toxicity

Methods

Results

Discussion

Conclusion