13 Predicting the Risks of Drug-Induced Liver Injury …
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potentially toxic drug candidates from entering clinical trials [4–6], it is not perfect
and sometimes fails to detect hepatotoxic drug candidates; a retrospective analysis
revealed that such tests failed in about 45% of DILI cases found in clinical trials [7].
In one notorious example, five subjects in a phase 2 clinical trial experienced fatal
hepatotoxicity induced by fialuridine, while this investigational nucleoside analogue
showed no liver damage in animal studies [8]. There is unmet need to more reliably
predict risk for DILI in humans and to overcome current limitations.
Many worldwide efforts have been launched to better understand and address
DILI issues. In the USA, the drug-induced liver injury network (DILIN) was funded
by National Institute of Healthy since the year of 1995 and is still today actively
collecting and analyzing cases of severe liver injury caused by prescription drugs,
over-the-counter drugs, and alternative medicines, such as herbal products and supplements. Similar government supported drug-induced liver injury network efforts
were recently established in Europe funded by European cooperation in Science and
Technology (http://www.cost.eu/COST_Actions/ca/CA17112). The US FDA has a
long-term effort to improve drug safety by better assessing pre-marketing and postmarketing data for identifying signs of toxicity. At the National Center for Toxicological Research, we have developed the Liver Toxicity Knowledge Base (LTKB)
which contains diverse liver-related data such as drug properties, DILI mechanisms,
and drug metabolism. that can be utilized to develop new models for assessing the
risks for DILI in humans [1, 5, 9–21]. In this chapter, we will introduce our continuing efforts toward the development of computational models for the prediction
of DILI risks in humans. First, we will present the drug label-based approach to
annotate the risk for DILI associated with individual drugs, and then based on these
annotations, we developed a panel of predictive models that could be used to assess
drug candidates for their potential to cause DILI risk before human testing or during
clinical trials.
13.2 Annotation of DILI Risk for Marketed Drugs
Annotation of DILI risk for drugs is challenging. Drugs could cause significantly
different scales of DILI risk even when their chemical structures are similar. For
example, alpidem and zolpidem both are anxiolytic drugs derived from the imidazopyridine family used as sleeping medication. These two drugs have similar chemical structures but distinct hepatotoxicity (Fig. 13.1): Alpidem was withdrawn due
to hepatotoxicity while zolpidem is still widely used in clinical practice with rare
hepatotoxicity observed. Drugs withdrawn from market due to hepatotoxicity and
those without hepatotoxicity observed represent two extremes within the spectrum of
the risk for humans. Most drugs are located within the middle of spectrum depending
on the associated DILI risk.
The DILI annotation discussed here refers to the classification of risks of DILI
exposure to the human population associated with the drug treatment for various
diseases. An improved annotation of DILI is vital and largely affects the accuracy and
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potentially toxic drug candidates from entering clinical trials [4–6], it is not perfect
and sometimes fails to detect hepatotoxic drug candidates; a retrospective analysis
revealed that such tests failed in about 45% of DILI cases found in clinical trials [7].
In one notorious example, five subjects in a phase 2 clinical trial experienced fatal
hepatotoxicity induced by fialuridine, while this investigational nucleoside analogue
showed no liver damage in animal studies [8]. There is unmet need to more reliably
predict risk for DILI in humans and to overcome current limitations.
Many worldwide efforts have been launched to better understand and address
DILI issues. In the USA, the drug-induced liver injury network (DILIN) was funded
by National Institute of Healthy since the year of 1995 and is still today actively
collecting and analyzing cases of severe liver injury caused by prescription drugs,
over-the-counter drugs, and alternative medicines, such as herbal products and supplements. Similar government supported drug-induced liver injury network efforts
were recently established in Europe funded by European cooperation in Science and
Technology (http://www.cost.eu/COST_Actions/ca/CA17112). The US FDA has a
long-term effort to improve drug safety by better assessing pre-marketing and postmarketing data for identifying signs of toxicity. At the National Center for Toxicological Research, we have developed the Liver Toxicity Knowledge Base (LTKB)
which contains diverse liver-related data such as drug properties, DILI mechanisms,
and drug metabolism. that can be utilized to develop new models for assessing the
risks for DILI in humans [1, 5, 9–21]. In this chapter, we will introduce our continuing efforts toward the development of computational models for the prediction
of DILI risks in humans. First, we will present the drug label-based approach to
annotate the risk for DILI associated with individual drugs, and then based on these
annotations, we developed a panel of predictive models that could be used to assess
drug candidates for their potential to cause DILI risk before human testing or during
clinical trials.
13.2 Annotation of DILI Risk for Marketed Drugs
Annotation of DILI risk for drugs is challenging. Drugs could cause significantly
different scales of DILI risk even when their chemical structures are similar. For
example, alpidem and zolpidem both are anxiolytic drugs derived from the imidazopyridine family used as sleeping medication. These two drugs have similar chemical structures but distinct hepatotoxicity (Fig. 13.1): Alpidem was withdrawn due
to hepatotoxicity while zolpidem is still widely used in clinical practice with rare
hepatotoxicity observed. Drugs withdrawn from market due to hepatotoxicity and
those without hepatotoxicity observed represent two extremes within the spectrum of
the risk for humans. Most drugs are located within the middle of spectrum depending
on the associated DILI risk.
The DILI annotation discussed here refers to the classification of risks of DILI
exposure to the human population associated with the drug treatment for various
diseases. An improved annotation of DILI is vital and largely affects the accuracy and