260
M. Chen et al.
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
13.1 Introduction
Drug-induced liver injury (DILI) poses a significant challenge to the medical and
pharmaceutical communities as well as regulatory agencies. Many drugs have failed
during clinical trials, and over 50 drugs were withdrawn from the worldwide market
due to the concern of DILI risk [1]. Because of its significant impact on public health,
a series of guidances were published by regulatory agencies to request that the pharmaceutical industry better assesses DILI risk during drug development, including the
US Food and Drug Administration (FDA)’s guidance “Drug-Induced Liver Injury:
Premarketing Clinical Evaluation” and the European Medicines Agency (EMA)’s
“Non-clinical guidance on drug-induced hepatotoxicity” [2].
One significant challenge encountered by drug developers and regulators stems
from the lack of sensitive screening methodologies to identify DILI signals at the
early stage of drug development, especially before the first-in-human testing [3].
While animal studies remain the “gold standard” of testing strategies in preventing
M. Chen et al.
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
13.1 Introduction
Drug-induced liver injury (DILI) poses a significant challenge to the medical and
pharmaceutical communities as well as regulatory agencies. Many drugs have failed
during clinical trials, and over 50 drugs were withdrawn from the worldwide market
due to the concern of DILI risk [1]. Because of its significant impact on public health,
a series of guidances were published by regulatory agencies to request that the pharmaceutical industry better assesses DILI risk during drug development, including the
US Food and Drug Administration (FDA)’s guidance “Drug-Induced Liver Injury:
Premarketing Clinical Evaluation” and the European Medicines Agency (EMA)’s
“Non-clinical guidance on drug-induced hepatotoxicity” [2].
One significant challenge encountered by drug developers and regulators stems
from the lack of sensitive screening methodologies to identify DILI signals at the
early stage of drug development, especially before the first-in-human testing [3].
While animal studies remain the “gold standard” of testing strategies in preventing