Biology Reference
In-Depth Information
Approaches
Study Objectives
Examples
•Fatty acid β -oxidation
• Glutathione depletion
• Mitochondrial homeostasis
Tr anscriptomics in conjunction
with conventional toxicological
endpoints
Mechanisms of DILI
•Macrophage activator
•PPA R- α
• Oxidative stressor
•Reactive metabolites
Novel and sensitive biomarker
from short-term and in vitro
studies
Prediction of DILI
Characterization of
idiosyncratic event
LPS-potentiated rat model as two-hit
model for idiosyncratic DILI
Improvement of
non-clinical model
Gene expression-based prediction
model generated from short time
to predict non-genotoxic
hepatocarcinogens
Prediction analysis of
microarray classifier
Replace 2-year
carcinogenicity assay
FIGURE 6.1 Toxicogenomics applications exemplified by study of drug-induced liver injury (DILI) and
liver carcinogenicity in drug discovery and development. (1) Microarray analysis helps elucidate mechanisms of
DILI with gene expression profiling coupled with conventional toxicology endpoints. For example: gene expres-
sion analysis has been reported to reveal the mechanisms of hepatic steatosis. (2) Toxicogenomics represents an
approach to develop novel biomarkers that can be used as sensitive indicators of DILI. For example, macrophage
activator, PPAR-α, oxidative stressor (OS) or reactive metabolites (RM), all produce oxidative stress and liver injury
in rat, but through different mechanisms and resulting in different toxicological outcomes. (3) Genomics tools
have been used to investigate the mechanisms underlying various idiosyncratic DILI. For example, toxicogenomic
approaches have been used to evaluate the LPS-potentiated rat model. This model is a two-hit model for idiosyn-
cratic toxicity. It shows that genomics analysis is a valuable method for hypothesizing mechanisms and discover-
ing specific transcript changes that can be exploited as biomarkers for idiosyncratic DILI. (4) Prediction analysis of
microarray classifier generated from short-term animal study can be used to predict potential non-genotoxic hepa-
tocarcinogenicity. It was thought that a mechanism-based strategy should be employed in order to obtain useful
biomarker genes for hepatocarcinogenicity. This shed light on the possibility of replacing two-year carcinogenicity
study with toxicogenomics.
understanding of DILI could result in safer drugs and significantly reduce the cost of drug
development. However, the complexity of the hepatotoxicity endpoint makes it very difficult
to be predicted with the current approaches for pre-clinical assessment.
While toxicogenomics has been applied in the diverse areas of toxicology, the majority of
applications (or data) so far have focused on the study of hepatotoxicity. Along with the rea-
sons stated above, liver toxicity alone accounts for 40% of drug failures in clinical trials and
27% of market withdrawals [127-129] . DILI has become the most common cause of fulmi-
nant hepatic failure in patients, both in the United States and Europe [130-133] . It accounts
for approximately one half of acute liver failure and transplantation in Western countries
[134,135] . DILI also has frequently been the single reason for denial of new drug approvals
by the United States FDA [136-138] . Failures due to liver toxicity span the pre-clinical and
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