Quantitative Structure–Activity Relationship Models for Predicting Risk of Drug-Induced Liver Injury in Humans

  • Huixiao HongEmail author
  • Jieqiang Zhu
  • Minjun Chen
  • Ping Gong
  • Chaoyang Zhang
  • Weida Tong
Part of the Methods in Pharmacology and Toxicology book series (MIPT)


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.

Key words

Drug-induced liver injury DILI Quantitative structure–activity relationship QSAR Prediction Chemical descriptors Algorithm Drug Safety 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Huixiao Hong
    • 1
    Email author
  • Jieqiang Zhu
    • 1
  • Minjun Chen
    • 1
  • Ping Gong
    • 2
  • Chaoyang Zhang
    • 3
  • Weida Tong
    • 1
  1. 1.Division of Bioinformatics and Biostatistics, National Center for Toxicological ResearchU.S. Food and Drug AdministrationJeffersonUSA
  2. 2.Environmental LaboratoryU.S. Army Engineer Research and Development CenterVicksburgUSA
  3. 3.School of Computer ScienceUniversity of Southern MississippiHattiesburgUSA

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