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Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling

  • Minjun ChenEmail author
  • Jieqiang Zhu
  • Kristin Ashby
  • Leihong Wu
  • Zhichao Liu
  • Ping Gong
  • Chaoyang (Joe) Zhang
  • Jürgen Borlak
  • Huixiao Hong
  • Weida Tong
Chapter
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 30)

Abstract

Drug-induced liver injury (DILI) is a significant challenge to clinicians, drug developers, as well as regulators. There is an unmet need to reliably predict risk for DILI. Developing a risk management plan to improve the prediction of a drug’s hepatotoxic potential is a long-term effort of the research community. Robust predictive models or biomarkers are essential for assessing the risk for DILI in humans, while an improved DILI annotation is vital and largely affects the accuracy and utility of the developed predictive models. In this chapter, we will focus on the DILI research efforts at the National Center for Toxicological Research of the US Food and Drug Administration. We will first introduce our drug label-based approach to annotate the DILI risk associated with individual drugs and then upon these annotations we developed a series of predictive models that could be used to assess the potential of DILI risk, including the “rule-of-two” model, DILI score model, and conventional and modified Quantitative structure–activity relationship (QSAR) models.

Keywords

Modeling Risk management QSAR Rule-of-two DILI score 

Abbreviations

DF

Decision Forest

DILI

Drug-Induced Liver Injury

DILIN

Drug-Induced Liver Injury Network

EMA

European Medicines Agency

FDA

Food and Drug Administration

LTKB

Liver Toxicity Knowledge Base

MOA

Mode of Action

QSAR

Quantitative Structure–Activity Relationship

RM

Reactive Metabolites

Notes

Disclaimer

This article reflects the views of the authors and should not be construed to represent FDA’s views or policies.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Minjun Chen
    • 1
    Email author
  • Jieqiang Zhu
    • 1
  • Kristin Ashby
    • 1
  • Leihong Wu
    • 1
  • Zhichao Liu
    • 1
  • Ping Gong
    • 2
  • Chaoyang (Joe) Zhang
    • 3
  • Jürgen Borlak
    • 4
  • Huixiao Hong
    • 1
  • Weida Tong
    • 1
  1. 1.Division of Bioinformatics and BiostatisticsNational Center for Toxicological Research, US Food and Drug Administration (FDA)JeffersonUSA
  2. 2.Environmental LaboratoryUS Army Engineer Research and Development CenterVicksburgUSA
  3. 3.School of Computing Sciences and Computer EngineeringUniversity of Southern MississippiHattiesburgUSA
  4. 4.Hannover Medical SchoolCenter of Pharmacology and ToxicologyHannoverGermany

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