13 Predicting the Risks of Drug-Induced Liver Injury …
275
58. Stepan AF et al (2011) Structural alert/reactive metabolite concept as applied in medicinal
chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical
examination of trends in the top 200 drugs marketed in the United States. Chem Res Toxicol
24(9):1345–1410
59. Smith DA, Obach RS (2006) Metabolites and safety: what are the concerns, and how should
we address them? Chem Res Toxicol 19(12):1570–1579
60. Uetrecht J (2006) Evaluation of which reactive metabolite, if any, is responsible for a specific
idiosyncratic reaction*. Drug Metab Rev 38(4):745–753
61. Srivastava A et al (2010) Role of reactive metabolites in drug-induced hepatotoxicity. In:
Adverse drug reactions. Springer, Berlin, pp 165–194
62. Park B et al (2011) Drug bioactivation and protein adduct formation in the pathogenesis of
drug-induced toxicity. Chem Biol Interact 192(1):30–36
63. Kalgutkar AS et al (2005) A comprehensive listing of bioactivation pathways of organic functional groups. Curr Drug Metab 6(3):161–225
64. Evans DC et al (2004) Drug-protein adducts: an industry perspective on minimizing the potential for drug bioactivation in drug discovery and development. Chem Res Toxicol 17(1):3–16
65. Chen M et al (2012) A decade of toxicogenomic research and its contribution to toxicological
science. Toxicol Sci 130(2):217–228
66. Hong H et al (2018) Quantitative structure–activity relationship models for predicting risk
of drug-induced liver injury in humans. In: Drug-induced liver toxicity. Springer, Berlin, pp
77–100
67. Fisk L, Greene N, Naven R (2018) Physicochemical properties and structural alerts. In: Druginduced liver toxicity. Springer, pp 61–76
68. Hong H et al (2017) Development of decision forest models for prediction of drug-induced
liver injury in humans using a large set of FDA-approved drugs. Sci Rep 7(1):17311
69. Wu L et al (2017) Integrating drug’s mode of action into quantitative structure-activity relationships for improved prediction of drug-induced liver injury. J Chem Inf Model 57(4):1000–1006
70. Tice RR et al (2013) Improving the human hazard characterization of chemicals: a Tox21
update. Environ Health Perspect 121(7):756
71. Hong H et al (2008) Mold2, molecular descriptors from 2D structures for chemoinformatics
and toxicoinformatics. J Chem Inf Model 48(7):1337–1344
72. Liu Z et al (2011) Translating clinical findings into knowledge in drug safety evaluation—drug
induced liver injury prediction system (DILIps). PLoS Comput Biol 7(12):e1002310
73. Kuhn M et al (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst
Biol 6:343
74. Wong MW et al (2018) Status and use of Induced Pluripotent Stem Cells (iPSCs) in toxicity
testing. In: Drug-induced liver toxicity. Springer, Berlin, pp 199–212
75. Monckton CP, Khetani SR (2018) Engineered human liver cocultures for investigating druginduced liver injury. In: Drug-induced liver toxicity. Springer, Berlin, pp 213–248
76. Otieno MA, Gan J, Proctor W (2018) Status and future of 3D cell culture in toxicity testing.
In: Drug-induced liver toxicity. Springer, Berlin, pp 249–261
77. Persson M (2018) High content screening for prediction of human drug-induced liver injury.
In: Drug-induced liver toxicity. Springer, Berlin, pp 331–343
78. Porceddu M et al (2018) In vitro assessment of mitochondrial toxicity to predict drug-induced
liver injury. In: Drug-induced liver toxicity. Springer, Berlin, pp 283–300
79. Chen M et al (2015) Drug-induced liver injury: Interactions between drug properties and host
factors. J Hepatol 63(2):503–514
80. Stephens C, Lucena MI, Andrade RJ (2018) Host risk modifiers in idiosyncratic drug-induced
liver injury (DILI) and its interplay with drug properties. In: Drug-induced liver toxicity.
Springer, Berlin, pp 477–496
Minjun Chen is a principal investigator working at the Division of Bioinformatics and Biostatics of the US FDA’s National Center for Toxicological Research and serve as the adjunct faculty
275
58. Stepan AF et al (2011) Structural alert/reactive metabolite concept as applied in medicinal
chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical
examination of trends in the top 200 drugs marketed in the United States. Chem Res Toxicol
24(9):1345–1410
59. Smith DA, Obach RS (2006) Metabolites and safety: what are the concerns, and how should
we address them? Chem Res Toxicol 19(12):1570–1579
60. Uetrecht J (2006) Evaluation of which reactive metabolite, if any, is responsible for a specific
idiosyncratic reaction*. Drug Metab Rev 38(4):745–753
61. Srivastava A et al (2010) Role of reactive metabolites in drug-induced hepatotoxicity. In:
Adverse drug reactions. Springer, Berlin, pp 165–194
62. Park B et al (2011) Drug bioactivation and protein adduct formation in the pathogenesis of
drug-induced toxicity. Chem Biol Interact 192(1):30–36
63. Kalgutkar AS et al (2005) A comprehensive listing of bioactivation pathways of organic functional groups. Curr Drug Metab 6(3):161–225
64. Evans DC et al (2004) Drug-protein adducts: an industry perspective on minimizing the potential for drug bioactivation in drug discovery and development. Chem Res Toxicol 17(1):3–16
65. Chen M et al (2012) A decade of toxicogenomic research and its contribution to toxicological
science. Toxicol Sci 130(2):217–228
66. Hong H et al (2018) Quantitative structure–activity relationship models for predicting risk
of drug-induced liver injury in humans. In: Drug-induced liver toxicity. Springer, Berlin, pp
77–100
67. Fisk L, Greene N, Naven R (2018) Physicochemical properties and structural alerts. In: Druginduced liver toxicity. Springer, pp 61–76
68. Hong H et al (2017) Development of decision forest models for prediction of drug-induced
liver injury in humans using a large set of FDA-approved drugs. Sci Rep 7(1):17311
69. Wu L et al (2017) Integrating drug’s mode of action into quantitative structure-activity relationships for improved prediction of drug-induced liver injury. J Chem Inf Model 57(4):1000–1006
70. Tice RR et al (2013) Improving the human hazard characterization of chemicals: a Tox21
update. Environ Health Perspect 121(7):756
71. Hong H et al (2008) Mold2, molecular descriptors from 2D structures for chemoinformatics
and toxicoinformatics. J Chem Inf Model 48(7):1337–1344
72. Liu Z et al (2011) Translating clinical findings into knowledge in drug safety evaluation—drug
induced liver injury prediction system (DILIps). PLoS Comput Biol 7(12):e1002310
73. Kuhn M et al (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst
Biol 6:343
74. Wong MW et al (2018) Status and use of Induced Pluripotent Stem Cells (iPSCs) in toxicity
testing. In: Drug-induced liver toxicity. Springer, Berlin, pp 199–212
75. Monckton CP, Khetani SR (2018) Engineered human liver cocultures for investigating druginduced liver injury. In: Drug-induced liver toxicity. Springer, Berlin, pp 213–248
76. Otieno MA, Gan J, Proctor W (2018) Status and future of 3D cell culture in toxicity testing.
In: Drug-induced liver toxicity. Springer, Berlin, pp 249–261
77. Persson M (2018) High content screening for prediction of human drug-induced liver injury.
In: Drug-induced liver toxicity. Springer, Berlin, pp 331–343
78. Porceddu M et al (2018) In vitro assessment of mitochondrial toxicity to predict drug-induced
liver injury. In: Drug-induced liver toxicity. Springer, Berlin, pp 283–300
79. Chen M et al (2015) Drug-induced liver injury: Interactions between drug properties and host
factors. J Hepatol 63(2):503–514
80. Stephens C, Lucena MI, Andrade RJ (2018) Host risk modifiers in idiosyncratic drug-induced
liver injury (DILI) and its interplay with drug properties. In: Drug-induced liver toxicity.
Springer, Berlin, pp 477–496
Minjun Chen is a principal investigator working at the Division of Bioinformatics and Biostatics of the US FDA’s National Center for Toxicological Research and serve as the adjunct faculty