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7313152 
Journal Article 
Quantitative Structure-Activity Relationship Models for Predicting Risk of Drug-Induced Liver Injury in Humans 
Hong, H; Zhu, J; Chen, M; Gong, P; Zhang, C; Tong, W 
2018 
1 
Methods in Pharmacology and Toxicology
ISSN: 1557-2153
EISSN: 1940-6053 
Humana Press Inc. 
Methods in Pharmacology and Toxicology 
9781493976768 
77-100 
English 
WOS:000441294200007 
Drug-induced liver injury (DILI) risk in humans is a complicated safety concern due to diverse mechanisms, various severity levels, variation in population groups, and difficulty in annotation of drugs, especially for the drugs that have been on the market for a short period of time. DILI remains a challenge for the industry and regulatory agencies. Assessing DILI risk in humans is important to assist drug development for the industry and to inform decision making on safety evaluation of drug products in regulatory science. Though various experimental methods have been used in current practices for assessment of DILI risk, in silico methods have been adopted in the field as an alternative because the development and validation of in silico models are much faster and cheaper. Many quantitative structure–activity relationship (QSAR) DILI prediction models have been reported. To better understand the QSAR models reported and to foster development of more reliable QSAR models, this chapter provides an instruction to the principals and the components of QSAR modeling, a summary on some popular algorithms and tools for QSAR modeling, and a review of QSAR models developed for prediction of DILI. © 2018, Springer Science+Business Media, LLC, part of Springer Nature. 
Drug-induced liver injury; DILI; Quantitative structure-activity relationship; QSAR; Prediction; Chemical descriptors; Algorithm; Drug; Safety