13 Predicting the Risks of Drug-Induced Liver Injury …
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ciated with liver injury [58–60]. Furthermore, a large-scale retrospective analysis
demonstrated that the level of covalent binding has no correlation with incidence of
liver toxicity observed in vivo in preclinical studies [57]. Even though, considering
the possible toxic implications, industry still strongly recommend to minimize the
potential of RM formation for drug [61–63] with a target threshold of <50 pmol of
RM bound to 1 mg protein [64].
We applied logistic regression analysis to investigate the association between daily
dose, logP, RM formation, and DILI risk by using N = 192 FDA-approved drugs. The
multivariate regression analysis suggested that daily dose, logP, and RM formation
all contributed independently to predicting DILI risk, and their contributions were
ranked by the order of RM > daily dose/Cmax > logP per the regression coefficients.
Consequently, we developed a DILI score model [12] derived from daily dose, logP,
and RM: 0.608 * log e (daily dose/mg) + 0.227 * logP + 2.833 * (RM formation);
here, RM was assigned as 1 or 0 based on whether a drug could produce reactive
metabolites. As an example, alpidem given at a daily dose of 150 mg/day has a logP
of 5.6 and produces RM which resulted in a DILI score of 0.608 * log e (150) +
0.216 * 5.6 + 2.833 * 1 = 7.15. Meanwhile, zolpidem (a drug with the same mode
of action, similar chemical structure, and preclinical safety profile but with distinct
liver toxicity) has a logP of 1.20 and is given at a daily dose of 10 mg, which resulted
in a DILI score of 4.51.
The developed DILI score model was evaluated by three independently published
datasets assessing its capability to predict the severity of DILI risk in humans. The
first dataset was derived from the LTKB-BD with a total of N = 354 drug annotated
with DILI potential, including 124 most-DILI-concern drugs, 162 less-DILI-concern
drugs, and 68 with no-DILI-concern. The second dataset with N = 227 drugs retrieved
from Greene et al. [24] had N = 130 human hepatotoxicity drugs, N = 44 drugs with
weak evidence, and N = 53 drugs with no evidence. The third dataset comes from
Suzuki et al. [26] and considered the severity of human hepatotoxicity, of which a
total of 182 drugs were obtained consisting of N = 35 withdrawn drugs, N = 61
with reported acute liver failure cases, and N = 86 general DILI drugs. Overall,
an increased DILI score significantly correlates with the severity of liver injury. In
the first dataset, the DILI risk score decreased in the order of most-DILI-concern >
less-DILI-concern > no-DILI-concern [1], and each of the subsequent comparisons
was statistically significant (P < 0.001). In Greene et al. [24] dataset, DILI score
also correctly predicted drugs with evidence for overt human hepatotoxicity having
significantly higher DILI scores than those with weak evidence (P < 0.001) and not
unexpectedly followed those without any evidence for developing DILI (P < 0.001).
For the data from Suzuki et al. [26], the algorithm also correctly predicted severe
DILI cases (P < 0.001).
Furthermore, the DILI score model was applied to N = 165 clinical cases collected from NIH LiverTox database (https://livertox.nih.gov/), and it was demonstrated that the DILI score correlated with the severity of clinical outcome.
The DILI score model was also applied to successfully distinguish some drug
pairs such as minocycline/doxycycline, trovafloxacin/moxifloxacin, and benzbro-
267
ciated with liver injury [58–60]. Furthermore, a large-scale retrospective analysis
demonstrated that the level of covalent binding has no correlation with incidence of
liver toxicity observed in vivo in preclinical studies [57]. Even though, considering
the possible toxic implications, industry still strongly recommend to minimize the
potential of RM formation for drug [61–63] with a target threshold of <50 pmol of
RM bound to 1 mg protein [64].
We applied logistic regression analysis to investigate the association between daily
dose, logP, RM formation, and DILI risk by using N = 192 FDA-approved drugs. The
multivariate regression analysis suggested that daily dose, logP, and RM formation
all contributed independently to predicting DILI risk, and their contributions were
ranked by the order of RM > daily dose/Cmax > logP per the regression coefficients.
Consequently, we developed a DILI score model [12] derived from daily dose, logP,
and RM: 0.608 * log e (daily dose/mg) + 0.227 * logP + 2.833 * (RM formation);
here, RM was assigned as 1 or 0 based on whether a drug could produce reactive
metabolites. As an example, alpidem given at a daily dose of 150 mg/day has a logP
of 5.6 and produces RM which resulted in a DILI score of 0.608 * log e (150) +
0.216 * 5.6 + 2.833 * 1 = 7.15. Meanwhile, zolpidem (a drug with the same mode
of action, similar chemical structure, and preclinical safety profile but with distinct
liver toxicity) has a logP of 1.20 and is given at a daily dose of 10 mg, which resulted
in a DILI score of 4.51.
The developed DILI score model was evaluated by three independently published
datasets assessing its capability to predict the severity of DILI risk in humans. The
first dataset was derived from the LTKB-BD with a total of N = 354 drug annotated
with DILI potential, including 124 most-DILI-concern drugs, 162 less-DILI-concern
drugs, and 68 with no-DILI-concern. The second dataset with N = 227 drugs retrieved
from Greene et al. [24] had N = 130 human hepatotoxicity drugs, N = 44 drugs with
weak evidence, and N = 53 drugs with no evidence. The third dataset comes from
Suzuki et al. [26] and considered the severity of human hepatotoxicity, of which a
total of 182 drugs were obtained consisting of N = 35 withdrawn drugs, N = 61
with reported acute liver failure cases, and N = 86 general DILI drugs. Overall,
an increased DILI score significantly correlates with the severity of liver injury. In
the first dataset, the DILI risk score decreased in the order of most-DILI-concern >
less-DILI-concern > no-DILI-concern [1], and each of the subsequent comparisons
was statistically significant (P < 0.001). In Greene et al. [24] dataset, DILI score
also correctly predicted drugs with evidence for overt human hepatotoxicity having
significantly higher DILI scores than those with weak evidence (P < 0.001) and not
unexpectedly followed those without any evidence for developing DILI (P < 0.001).
For the data from Suzuki et al. [26], the algorithm also correctly predicted severe
DILI cases (P < 0.001).
Furthermore, the DILI score model was applied to N = 165 clinical cases collected from NIH LiverTox database (https://livertox.nih.gov/), and it was demonstrated that the DILI score correlated with the severity of clinical outcome.
The DILI score model was also applied to successfully distinguish some drug
pairs such as minocycline/doxycycline, trovafloxacin/moxifloxacin, and benzbro-