期刊
BIOMARKERS IN MEDICINE
卷 8, 期 2, 页码 201-213出版社
FUTURE MEDICINE LTD
DOI: 10.2217/bmm.13.146
关键词
biomarker; drug label; drug safety; drug-induced liver injury; predictive model
资金
- German Federal Ministry of Education and Research as part of the Virtual Liver Network initiative [031 6154]
- ORISE of the US FDA
Drug-induced liver injury (DILI) is a frequent cause for the termination of drug development programs and a leading reason of drug withdrawal from the marketplace. Unfortunately, the current preclinical testing strategies, including the regulatory-required animal toxicity studies or simple in vitro tests, are insufficiently powered to predict DILI in patients reliably. Notably, the limited predictive power of such testing strategies is mostly attributed to the complex nature of DILI, a poor understanding of its mechanism, a scarcity of human hepatotoxicity data and inadequate bioinformatics capabilities. With the advent of high-content screening assays, toxicogenomics and bioinformatics, multiple end points can be studied simultaneously to improve prediction of clinically relevant DILIs. This review focuses on the current state of efforts in developing predictive models from diverse data sources for potential use in detecting human hepatotoxicity, and also aims to provide perspectives on how to further improve DILI prediction.
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