Chapter 13
Predicting the Risks of Drug-Induced
Liver Injury in Humans Utilizing
Computational Modeling
Minjun Chen, Jieqiang Zhu, Kristin Ashby, Leihong Wu, Zhichao Liu,
Ping Gong, Chaoyang (Joe) Zhang, Jürgen Borlak, Huixiao Hong
and Weida Tong
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
M. Chen (B) · J. Zhu · K. Ashby · L. Wu · Z. Liu · H. Hong · W. Tong
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US
Food and Drug Administration (FDA), Jefferson, AR, USA
e-mail: Minjun.Chen@fda.hhs.gov
J. Zhu
e-mail: Jieqiang.Zhu@fda.hhs.gov
K. Ashby
e-mail: Kristin.McEuen@fda.hhs.gov
L. Wu
e-mail: Leihong.Wu@fda.hhs.gov
Z. Liu
e-mail: Zhichao.Liu@fda.hhs.gov
H. Hong
e-mail: Huixiao.Hong@fda.hhs.gov
W. Tong
e-mail: Weida.Tong@fda.hhs.gov
P. Gong
Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg,
MS 39180, USA
e-mail: Ping.Gong@usace.army.mil
C. (Joe) Zhang
School of Computing Sciences and Computer Engineering, University of Southern Mississippi,
Hattiesburg, MS 39406, USA
e-mail: Chaoyang.Zhang@usm.edu
J. Borlak
Hannover Medical School, Center of Pharmacology and Toxicology, Hannover, Germany
e-mail: Borlak.Juergen@mh-hannover.de
© This is a U.S. government work and not under copyright protection in the U.S.; foreign
copyright protection may apply 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_13
259
Predicting the Risks of Drug-Induced
Liver Injury in Humans Utilizing
Computational Modeling
Minjun Chen, Jieqiang Zhu, Kristin Ashby, Leihong Wu, Zhichao Liu,
Ping Gong, Chaoyang (Joe) Zhang, Jürgen Borlak, Huixiao Hong
and Weida Tong
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
M. Chen (B) · J. Zhu · K. Ashby · L. Wu · Z. Liu · H. Hong · W. Tong
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US
Food and Drug Administration (FDA), Jefferson, AR, USA
e-mail: Minjun.Chen@fda.hhs.gov
J. Zhu
e-mail: Jieqiang.Zhu@fda.hhs.gov
K. Ashby
e-mail: Kristin.McEuen@fda.hhs.gov
L. Wu
e-mail: Leihong.Wu@fda.hhs.gov
Z. Liu
e-mail: Zhichao.Liu@fda.hhs.gov
H. Hong
e-mail: Huixiao.Hong@fda.hhs.gov
W. Tong
e-mail: Weida.Tong@fda.hhs.gov
P. Gong
Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg,
MS 39180, USA
e-mail: Ping.Gong@usace.army.mil
C. (Joe) Zhang
School of Computing Sciences and Computer Engineering, University of Southern Mississippi,
Hattiesburg, MS 39406, USA
e-mail: Chaoyang.Zhang@usm.edu
J. Borlak
Hannover Medical School, Center of Pharmacology and Toxicology, Hannover, Germany
e-mail: Borlak.Juergen@mh-hannover.de
© This is a U.S. government work and not under copyright protection in the U.S.; foreign
copyright protection may apply 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_13
259