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M. Chen et al.
robust predictive models for prediction of DILI risk in humans; however, appropriate
annotation is not a trivial task. We utilized the FDA-approved drug labels to annotate
a given drug’s risk for DILI in humans, which was demonstrated to be robust and
consistent across different types of drugs. The schema was further improved by
weighing evidence of case reports and was applied to 1036 FDA-approved drugs
to classified into three verified DILI groups (i.e.,
v Most-,
v Less-, and
v No-DILIconcern) with an additional group of drugs with DILI concern but without verified
causality (ambiguous annotation).
Besides the improved DILI annotations, we could develop better models by utilizing the relevant contributing factors and advanced modeling technologies. We
have developed a series of computational predictive models that use in silico or
physicochemical methods, including the “rule-of-two” model, DILI score model,
conventional QSAR model for the prediction of two classes and multiple classes
of DILI, and modified QSAR model including MOA-DILI model and DILI prediction systems model. Some models such as “rule-of-two” were independently validated and successfully identified drugs with significant hepatotoxicity. In the future,
some emerging technologies (e.g., high-throughput screening or high-content assay,
induced Pluripotent Stem Cells (iPSCs), engineered human liver cocultures, and 3D
cell culture) [74–78] could be incorporated into predictive models for a better identification of DILI risk liability at the early stage of drug development. In addition to
the drug properties we discussed above, host factors and their interactions with drug
properties [79, 80] should be considered and this information should be incorporated
into current drug-based models to improve prediction of DILI.
Disclaimer This article reflects the views of the authors and should not be construed to represent
FDA’s views or policies.
References
1. Chen M et al (2011) FDA-approved drug labeling for the study of drug-induced liver injury.
Drug Discov Today 16(15–16):697–703
2. Avigan MI, Muñoz MA (2018) Perspectives on the regulatory and clinical science of druginduced liver injury (DILI). In: Drug-induced liver toxicity. Springer, Berlin, pp 367–393
3. Noureddin N, Kaplowitz N (2018) Overview of mechanisms of drug-induced liver injury (DILI)
and key challenges in DILI research. In: Drug-induced liver toxicity. Springer, Berlin, pp 3–18
4. Fielden MR, Kolaja KL (2008) The role of early in vivo toxicity testing in drug discovery
toxicology. Expert Opin Drug Saf 7(2):107–110
5. Zhang M, Chen M, Tong W (2012) Is toxicogenomics a more reliable and sensitive biomarker
than conventional indicators from rats to predict drug-induced liver injury in humans? Chem
Res Toxicol 25(1):122–129
6. Khan I, Hausner E (2018) Regulatory toxicological studies: identifying drug-induced liver
injury using nonclinical studies. In: Drug-induced liver toxicity. Springer, Berlin, pp 395–410
7. Olson H et al (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals.
Regul Toxicol Pharmacol 32(1):56–67
M. Chen et al.
robust predictive models for prediction of DILI risk in humans; however, appropriate
annotation is not a trivial task. We utilized the FDA-approved drug labels to annotate
a given drug’s risk for DILI in humans, which was demonstrated to be robust and
consistent across different types of drugs. The schema was further improved by
weighing evidence of case reports and was applied to 1036 FDA-approved drugs
to classified into three verified DILI groups (i.e.,
v Most-,
v Less-, and
v No-DILIconcern) with an additional group of drugs with DILI concern but without verified
causality (ambiguous annotation).
Besides the improved DILI annotations, we could develop better models by utilizing the relevant contributing factors and advanced modeling technologies. We
have developed a series of computational predictive models that use in silico or
physicochemical methods, including the “rule-of-two” model, DILI score model,
conventional QSAR model for the prediction of two classes and multiple classes
of DILI, and modified QSAR model including MOA-DILI model and DILI prediction systems model. Some models such as “rule-of-two” were independently validated and successfully identified drugs with significant hepatotoxicity. In the future,
some emerging technologies (e.g., high-throughput screening or high-content assay,
induced Pluripotent Stem Cells (iPSCs), engineered human liver cocultures, and 3D
cell culture) [74–78] could be incorporated into predictive models for a better identification of DILI risk liability at the early stage of drug development. In addition to
the drug properties we discussed above, host factors and their interactions with drug
properties [79, 80] should be considered and this information should be incorporated
into current drug-based models to improve prediction of DILI.
Disclaimer This article reflects the views of the authors and should not be construed to represent
FDA’s views or policies.
References
1. Chen M et al (2011) FDA-approved drug labeling for the study of drug-induced liver injury.
Drug Discov Today 16(15–16):697–703
2. Avigan MI, Muñoz MA (2018) Perspectives on the regulatory and clinical science of druginduced liver injury (DILI). In: Drug-induced liver toxicity. Springer, Berlin, pp 367–393
3. Noureddin N, Kaplowitz N (2018) Overview of mechanisms of drug-induced liver injury (DILI)
and key challenges in DILI research. In: Drug-induced liver toxicity. Springer, Berlin, pp 3–18
4. Fielden MR, Kolaja KL (2008) The role of early in vivo toxicity testing in drug discovery
toxicology. Expert Opin Drug Saf 7(2):107–110
5. Zhang M, Chen M, Tong W (2012) Is toxicogenomics a more reliable and sensitive biomarker
than conventional indicators from rats to predict drug-induced liver injury in humans? Chem
Res Toxicol 25(1):122–129
6. Khan I, Hausner E (2018) Regulatory toxicological studies: identifying drug-induced liver
injury using nonclinical studies. In: Drug-induced liver toxicity. Springer, Berlin, pp 395–410
7. Olson H et al (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals.
Regul Toxicol Pharmacol 32(1):56–67