Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling

  • Minjun ChenEmail author
  • Jieqiang Zhu
  • Kristin Ashby
  • Leihong Wu
  • Zhichao Liu
  • Ping Gong
  • Chaoyang (Joe) Zhang
  • Jürgen Borlak
  • Huixiao Hong
  • Weida Tong
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 30)


Drug-induced liver injury (DILI) is a significant challenge to clinicians, drug developers, as well as regulators. There is an unmet need to reliably predict risk for DILI. Developing a risk management plan to improve the prediction of a drug’s hepatotoxic potential is a long-term effort of the research community. Robust predictive models or biomarkers are essential for assessing the risk for DILI in humans, while an improved DILI annotation is vital and largely affects the accuracy and utility of the developed predictive models. In this chapter, we will focus on the DILI research efforts at the National Center for Toxicological Research of the US Food and Drug Administration. We will first introduce our drug label-based approach to annotate the DILI risk associated with individual drugs and then upon these annotations we developed a series of predictive models that could be used to assess the potential of DILI risk, including the “rule-of-two” model, DILI score model, and conventional and modified Quantitative structure–activity relationship (QSAR) models.


Modeling Risk management QSAR Rule-of-two DILI score 



Decision Forest


Drug-Induced Liver Injury


Drug-Induced Liver Injury Network


European Medicines Agency


Food and Drug Administration


Liver Toxicity Knowledge Base


Mode of Action


Quantitative Structure–Activity Relationship


Reactive Metabolites



This article reflects the views of the authors and should not be construed to represent FDA’s views or policies.


  1. 1.
    Chen M et al (2011) FDA-approved drug labeling for the study of drug-induced liver injury. Drug Discov Today 16(15–16):697–703CrossRefGoogle Scholar
  2. 2.
    Avigan MI, Muñoz MA (2018) Perspectives on the regulatory and clinical science of drug-induced liver injury (DILI). In: Drug-induced liver toxicity. Springer, Berlin, pp 367–393Google Scholar
  3. 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 3–18Google Scholar
  4. 4.
    Fielden MR, Kolaja KL (2008) The role of early in vivo toxicity testing in drug discovery toxicology. Expert Opin Drug Saf 7(2):107–110CrossRefGoogle Scholar
  5. 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):122–129CrossRefGoogle Scholar
  6. 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 395–410Google Scholar
  7. 7.
    Olson H et al (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32(1):56–67CrossRefGoogle Scholar
  8. 8.
    Lewis W et al (1996) Fialuridine and its metabolites inhibit DNA polymerase gamma at sites of multiple adjacent analog incorporation, decrease mtDNA abundance, and cause mitochondrial structural defects in cultured hepatoblasts. Proc Natl Acad Sci 93(8):3592–3597CrossRefGoogle Scholar
  9. 9.
    Chen M et al (2013) Liver Toxicity Knowledge Base (LTKB)—a systems approach to a complex end point. Clin Pharmacol Ther 95(5):409–412CrossRefGoogle Scholar
  10. 10.
    Chen M et al (2014) Toward predictive models for drug-induced liver injury in humans: are we there yet? Biomark Med 8(2):201–213CrossRefGoogle Scholar
  11. 11.
    Chen M, Borlak J, Tong W (2013) High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury. Hepatology 58(1):388–396CrossRefGoogle Scholar
  12. 12.
    Chen M, Borlak J, Tong W (2016) A model to predict severity of drug-induced liver injury in humans. Hepatology 64(3):931–940CrossRefGoogle Scholar
  13. 13.
    Chen M et al (2013) Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs. Toxicol Sci 136(1):242–249CrossRefGoogle Scholar
  14. 14.
    Chen M et al (2015) Drug-induced liver injury: interactions between drug properties and host factors. J Hepatol 63(2):503–514CrossRefGoogle Scholar
  15. 15.
    Chen M et al (2016) DILIrank—the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans. Drug Discov Today 21(4):648–653CrossRefGoogle Scholar
  16. 16.
    Chen M et al (2014) An improved testing strategy to predict risk for drug-induced liver injury in humans using high-content screening assays and the ‘Rule-of-two’ model. Arch Toxicol 88(7):1439–1449CrossRefGoogle Scholar
  17. 17.
    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):e1002310CrossRefGoogle Scholar
  18. 18.
    Mishra P, Chen M (2017) Application of “Rule-of-two” model to direct-acting antivirals for treatment of chronic hepatitis C: can it predict potential for hepatotoxicity? Gastroenterology 152(6):1270–1274CrossRefGoogle Scholar
  19. 19.
    Wang Y et al (2013) A unifying ontology to integrate histological and clinical observations for drug-induced liver injury. Am J Pathol 182(4):1180–1187CrossRefGoogle Scholar
  20. 20.
    Yu K et al (2014) High daily dose and being a substrate of cytochrome P450 enzymes are two important predictors of drug-induced liver injury. Drug Metab Dispos 42(4):744–750CrossRefGoogle Scholar
  21. 21.
    Yu K et al (2014) Mining hidden knowledge for drug safety assessment: topic modeling of LiverTox as a case study. BMC Bioinform 15(Suppl 17):S6CrossRefGoogle Scholar
  22. 22.
    Przybylak KR, Cronin MT (2012) In silico models for drug-induced liver injury—current status. Expert Opin Drug Metab Toxicol 8(2):201–217CrossRefGoogle Scholar
  23. 23.
    Abboud G, Kaplowitz N (2007) Drug-induced liver injury. Drug Saf 30(4):277–294CrossRefGoogle Scholar
  24. 24.
    Greene N et al (2010) Developing structure-activity relationships for the prediction of hepatotoxicity. Chem Res Toxicol 23(7):1215–1222CrossRefGoogle Scholar
  25. 25.
    Gustafsson F et al (2013) A correlation between the in vitro drug toxicity of drugs to cell lines which express human P450s and their propensity to cause liver injury in humans. Toxicol Sci 137(1):189–211CrossRefGoogle Scholar
  26. 26.
    Suzuki A et al (2010) Drugs associated with hepatotoxicity and their reporting frequency of liver adverse events in VigiBase: unified list based on international collaborative work. Drug Saf 33(6):503–522CrossRefGoogle Scholar
  27. 27.
    Ursem CJ et al (2009) Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans. Part A: use of FDA post-market reports to create a database of hepatobiliary and urinary tract toxicities. Regul Toxicol Pharmacol 54(1):1–22CrossRefGoogle Scholar
  28. 28.
    Rodgers AD et al (2010) Modeling liver-related adverse effects of drugs using k nearest neighbor quantitative structure-activity relationship method. Chem Res Toxicol 23(4):724–732CrossRefGoogle Scholar
  29. 29.
    Zhu X, Kruhlak NL (2014) Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data. Toxicology 321:62–72CrossRefGoogle Scholar
  30. 30.
    Sakatis MZ et al (2012) Preclinical strategy to reduce clinical hepatotoxicity using in vitro bioactivation data for >200 compounds. Chem Res Toxicol 25(10):2067–2082CrossRefGoogle Scholar
  31. 31.
    Lammert C et al (2008) Relationship between daily dose of oral medications and idiosyncratic drug-induced liver injury: search for signals. Hepatology 47(6):2003–2009CrossRefGoogle Scholar
  32. 32.
    Weng Z et al (2015) A comprehensive study of the association between drug hepatotoxicity and daily dose, liver metabolism, and lipophilicity using 975 oral medications. Oncotarget 6(19):17031–17038CrossRefGoogle Scholar
  33. 33.
    Xu JJ et al (2008) Cellular imaging predictions of clinical drug-induced liver injury. Toxicol Sci 105(1):97–105CrossRefGoogle Scholar
  34. 34.
    Atienzar FA, Nicolas J-M (2018) Prediction of human liver toxicity using in vitro assays: limitations and opportunities. In: Drug-induced liver toxicity. Springer, Berlin, pp 125–150Google Scholar
  35. 35.
    Thakkar S et al (2018) The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury. Expert Rev Gastroenterol Hepatol 12(1):31–38CrossRefGoogle Scholar
  36. 36.
    Chen M et al (2014) Toward predictive models for drug-induced liver injury in humans: are we there yet? Biomark Med 8(2):201–213CrossRefGoogle Scholar
  37. 37.
    Chen M et al (2016) DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans. Drug Discov Today 21(4):648–653CrossRefGoogle Scholar
  38. 38.
    Thakkar S et al (2018) Drug-induced liver injury (DILI) classification and its application on human DILI risk prediction. In: Drug-induced liver toxicity. Springer, Berlin, pp 45–59Google Scholar
  39. 39.
    Regev A (2014) Drug-induced liver injury and drug development: industry perspective. Semin Liver Dis 34(02):227–239CrossRefGoogle Scholar
  40. 40.
    García-Cortés M et al (2011) Causality assessment methods in drug induced liver injury: strengths and weaknesses. J Hepatol 55(3):683–691CrossRefGoogle Scholar
  41. 41.
    Light DS, Aleo MD, Kenna JG (2018) Interpretation, integration, and implementation of in vitro assay data: the predictive toxicity challenge. In: Drug-induced liver toxicity. Springer, Berlin, pp 345–364Google Scholar
  42. 42.
    Ballet F (2010) Back to basics for idiosyncratic drug-induced liver injury: dose and metabolism make the poison. Gastroenterol Clin Biol 34(6–7):348–350CrossRefGoogle Scholar
  43. 43.
    Walgren JL, Mitchell MD, Thompson DC (2005) Role of metabolism in drug-induced idiosyncratic hepatotoxicity. Crit Rev Toxicol 35(4):325–361CrossRefGoogle Scholar
  44. 44.
    Uetrecht JP (1999) New concepts in immunology relevant to idiosyncratic drug reactions: the “danger hypothesis” and innate immune system. Chem Res Toxicol 12(5):387–395CrossRefGoogle Scholar
  45. 45.
    Senior JR (2008) What is idiosyncratic hepatotoxicity? What is it not? Hepatology 47(6):1813–1815CrossRefGoogle Scholar
  46. 46.
    Uetrecht J (2001) Prediction of a new drug’s potential to cause idiosyncratic reactions. Curr Opin Drug Discov Dev 4(1):55–59Google Scholar
  47. 47.
    Lammert C et al (2010) Oral medications with significant hepatic metabolism at higher risk for hepatic adverse events. Hepatology 51(2):615–620CrossRefGoogle Scholar
  48. 48.
    Leeson PD, Springthorpe B (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6(11):881–890CrossRefGoogle Scholar
  49. 49.
    Waring MJ (2010) Lipophilicity in drug discovery. Expert Opin Drug Discov 5(3):235–248CrossRefGoogle Scholar
  50. 50.
    Leeson PD (2018) Impact of physicochemical properties on dose and hepatotoxicity of oral drugs. Chem Res Toxicol 31(6):494–505CrossRefGoogle Scholar
  51. 51.
    Shah F et al (2015) Setting clinical exposure levels of concern for drug-induced liver injury (DILI) using mechanistic in vitro assays. Toxicol Sci 147(2):500–514CrossRefGoogle Scholar
  52. 52.
    Mishra P, Chen M (2017) Direct-acting antivirals for chronic hepatitis C: can drug properties signal potential for liver injury? Gastroenterology 152(6):1270–1274CrossRefGoogle Scholar
  53. 53.
    Kaplowitz N (2013) Avoiding idiosyncratic DILI: two is better than one. Hepatology 58(1):15–17CrossRefGoogle Scholar
  54. 54.
    Lewis JH (2014) Drug-induced liver injury, dosage, and drug disposition: is idiosyncrasy really unpredictable? Clin Gastroenterol Hepatol 4(1):4–8Google Scholar
  55. 55.
    Aleo MD et al (2014) Human drug-induced liver injury severity is highly associated to dual inhibition of liver mitochondrial function and bile salt export pump. Hepatology 60(3):1015–1022CrossRefGoogle Scholar
  56. 56.
    Stephens C, Andrade RJ, Lucena MI (2014) Mechanisms of drug-induced liver injury. Curr Opin Allergy Clin Immunol 14(4):286–292CrossRefGoogle Scholar
  57. 57.
    Park BK et al (2011) Managing the challenge of chemically reactive metabolites in drug development. Nat Rev Drug Discov 10(4):292–306CrossRefGoogle Scholar
  58. 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):1345–1410CrossRefGoogle Scholar
  59. 59.
    Smith DA, Obach RS (2006) Metabolites and safety: what are the concerns, and how should we address them? Chem Res Toxicol 19(12):1570–1579CrossRefGoogle Scholar
  60. 60.
    Uetrecht J (2006) Evaluation of which reactive metabolite, if any, is responsible for a specific idiosyncratic reaction*. Drug Metab Rev 38(4):745–753CrossRefGoogle Scholar
  61. 61.
    Srivastava A et al (2010) Role of reactive metabolites in drug-induced hepatotoxicity. In: Adverse drug reactions. Springer, Berlin, pp 165–194Google Scholar
  62. 62.
    Park B et al (2011) Drug bioactivation and protein adduct formation in the pathogenesis of drug-induced toxicity. Chem Biol Interact 192(1):30–36CrossRefGoogle Scholar
  63. 63.
    Kalgutkar AS et al (2005) A comprehensive listing of bioactivation pathways of organic functional groups. Curr Drug Metab 6(3):161–225CrossRefGoogle Scholar
  64. 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):3–16CrossRefGoogle Scholar
  65. 65.
    Chen M et al (2012) A decade of toxicogenomic research and its contribution to toxicological science. Toxicol Sci 130(2):217–228CrossRefGoogle Scholar
  66. 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 77–100Google Scholar
  67. 67.
    Fisk L, Greene N, Naven R (2018) Physicochemical properties and structural alerts. In: Drug-induced liver toxicity. Springer, pp 61–76Google Scholar
  68. 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):17311CrossRefGoogle Scholar
  69. 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):1000–1006CrossRefGoogle Scholar
  70. 70.
    Tice RR et al (2013) Improving the human hazard characterization of chemicals: a Tox21 update. Environ Health Perspect 121(7):756CrossRefGoogle Scholar
  71. 71.
    Hong H et al (2008) Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model 48(7):1337–1344CrossRefGoogle Scholar
  72. 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):e1002310CrossRefGoogle Scholar
  73. 73.
    Kuhn M et al (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 6:343CrossRefGoogle Scholar
  74. 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 199–212Google Scholar
  75. 75.
    Monckton CP, Khetani SR (2018) Engineered human liver cocultures for investigating drug-induced liver injury. In: Drug-induced liver toxicity. Springer, Berlin, pp 213–248Google Scholar
  76. 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 249–261Google Scholar
  77. 77.
    Persson M (2018) High content screening for prediction of human drug-induced liver injury. In: Drug-induced liver toxicity. Springer, Berlin, pp 331–343Google Scholar
  78. 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 283–300Google Scholar
  79. 79.
    Chen M et al (2015) Drug-induced liver injury: Interactions between drug properties and host factors. J Hepatol 63(2):503–514CrossRefGoogle Scholar
  80. 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 477–496Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Minjun Chen
    • 1
    Email author
  • Jieqiang Zhu
    • 1
  • Kristin Ashby
    • 1
  • Leihong Wu
    • 1
  • Zhichao Liu
    • 1
  • Ping Gong
    • 2
  • Chaoyang (Joe) Zhang
    • 3
  • Jürgen Borlak
    • 4
  • Huixiao Hong
    • 1
  • Weida Tong
    • 1
  1. 1.Division of Bioinformatics and BiostatisticsNational Center for Toxicological Research, US Food and Drug Administration (FDA)JeffersonUSA
  2. 2.Environmental LaboratoryUS Army Engineer Research and Development CenterVicksburgUSA
  3. 3.School of Computing Sciences and Computer EngineeringUniversity of Southern MississippiHattiesburgUSA
  4. 4.Hannover Medical SchoolCenter of Pharmacology and ToxicologyHannoverGermany

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