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

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Advances in Computational Toxicology

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 30))

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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.

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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

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

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Authors and Affiliations

  1. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration (FDA), Jefferson, AR, USA

    Minjun Chen, Jieqiang Zhu, Kristin Ashby, Leihong Wu, Zhichao Liu, Huixiao Hong & Weida Tong

  2. Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, 39180, USA

    Ping Gong

  3. School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, 39406, USA

    Chaoyang (Joe) Zhang

  4. Hannover Medical School, Center of Pharmacology and Toxicology, Hannover, Germany

    Jürgen Borlak

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  1. Minjun Chen
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  2. Jieqiang Zhu
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  3. Kristin Ashby
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  4. Leihong Wu
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  5. Zhichao Liu
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  7. Chaoyang (Joe) Zhang
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  9. Huixiao Hong
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  10. Weida Tong
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Correspondence to Minjun Chen .

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  1. National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA

    Huixiao Hong

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Chen, M. et al. (2019). Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling. In: Hong, H. (eds) Advances in Computational Toxicology. Challenges and Advances in Computational Chemistry and Physics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-16443-0_13

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