Skip to main content
SpringerLink
Log in

Detection, Elimination, Mitigation, and Prediction of Drug-Induced Liver Injury in Drug Discovery

  • Protocol
  • First Online:
Book cover Drug-Induced Liver Toxicity

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

Abstract

Despite being among the most efficiently detected and managed toxicity during preclinical drug development, drug-induced liver injury (DILI) remains a major hurdle and is recognized to be a major cause of drug attrition and market withdrawal. DILI impacts many different sectors of society including patients, public health systems, health insurers and the pharmaceutical industry. Animal models are very efficient at detecting direct, dose-dependent and species-independent toxicity to the liver, the so-called intrinsic DILI. Compounds inducing mild liver signals can be developed as drugs if they exhibit a positive therapeutic benefit and are deemed to be superior to the currently available standard of care/medications. These cases are well managed as opposed to the unpredictable, dose-independent, individual-specific idiosyncratic toxicities, which are typically not detected in preclinical phases of drug development. Considerable efforts are dedicated to the detection and understanding of idiosyncratic DILI, and to the prediction of intrinsic DILI. Ever more complex and biologically relevant in vitro models are emerging for compound prescreening purposes. These data are also being used to the development of in silico algorithms which, when combined with compound chemical properties, in vivo observations and human-based post-marketing data, yield analytical and potentially predictive systems. In addition, the recent emergence of viable humanized liver animal models should bring forth a new battery of assays for accurately predicting compound-induced intrinsic liver toxicity in patients, and may also pave the way toward a better understanding of idiosyncratic DILI reactions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Regev A (2014) Drug-induced liver injury and drug development: industry perspective. Semin Liver Dis 34(2):227–239. https://doi.org/10.1055/s-0034-1375962

    Article  CAS  PubMed  Google Scholar 

  2. van Tonder JJ, Steenkamp V, Gulumi M (2013) Pre-clinical assessment of the potential intrinsic hepatotoxicity of candidate drugs. In: Gowder S (ed) New insights into toxicity and drug testing. InTech, London, pp 3–28. https://doi.org/10.5772/54792

    Chapter  Google Scholar 

  3. Galie N, Hoeper MM, Gibbs JS, Simonneau G (2011) Liver toxicity of sitaxentan in pulmonary arterial hypertension. Eur Respir J 37(2):475–476. https://doi.org/10.1183/09031936.00194810

    Article  CAS  PubMed  Google Scholar 

  4. Jaeschke H (2007) Troglitazone hepatotoxicity: are we getting closer to understanding idiosyncratic liver injury? Toxicol Sci 97(1):1–3. https://doi.org/10.1093/toxsci/kfm021

    Article  CAS  PubMed  Google Scholar 

  5. Watkins PB (2005) Insight into hepatotoxicity: the troglitazone experience. Hepatology 41(2):229–230. https://doi.org/10.1002/hep.20598

    Article  PubMed  Google Scholar 

  6. Olson H, Betton G, Stritar J, Robinson D (1998) The predictivity of the toxicity of pharmaceuticals in humans from animal data—an interim assessment. Toxicol Lett 102–103:535–538

    Article  PubMed  Google Scholar 

  7. Olson H, Betton G, Robinson D, Thomas K, Monro A, Kolaja G, Lilly P, Sanders J, Sipes G, Bracken W, Dorato M, Van Deun K, Smith P, Berger B, Heller A (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32(1):56–67. https://doi.org/10.1006/rtph.2000.1399

    Article  CAS  PubMed  Google Scholar 

  8. van Tonder JJ, Steenkamp V, Gulumian M (2013) Pre-clinical assessment of the potential intrinsic hepatotoxicity of candidate drugs. In: Gowder S (ed) New insights into toxicity and drug testing. InTech, London, pp 3–28. https://doi.org/10.5772/54792

    Chapter  Google Scholar 

  9. Kaplowitz N (2005) Idiosyncratic drug hepatotoxicity. Nat Rev Drug Discov 4(6):489–499. https://doi.org/10.1038/nrd1750

    Article  CAS  PubMed  Google Scholar 

  10. Chalasani NP, Hayashi PH, Bonkovsky HL, Navarro VJ, Lee WM, Fontana RJ, Gastroenterol AC (2014) ACG clinical guideline: the diagnosis and management of idiosyncratic drug-induced liver injury. Am J Gastroenterol 109(7):950–966. https://doi.org/10.1038/ajg.2014.131

    Article  PubMed  Google Scholar 

  11. Chen MJ, Suzuki A, Borlak J, Andrade RJ, Lucena MI (2015) Drug-induced liver injury: interactions between drug properties and host factors. J Hepatol 63(2):503–514

    Article  CAS  PubMed  Google Scholar 

  12. Briggs K, Cases M, Heard DJ, Pastor M, Pognan F, Sanz F, Schwab CH, Steger-Hartmann T, Sutter A, Watson DK, Wichard JD (2012) Inroads to predict in vivo toxicology-an introduction to the eTOX Project. Int J Mol Sci 13(3):3820–3846. https://doi.org/10.3390/ijms13033820

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cases M, Briggs K, Steger-Hartmann T, Pognan F, Marc P, Kleinoder T, Schwab CH, Pastor M, Wichard J, Sanz F (2014) The eTOX data-sharing project to advance in silico drug-induced toxicity prediction. Int J Mol Sci 15(11):21136–21154. https://doi.org/10.3390/ijms151121136

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Steger-Hartmann T, Pognan F (2017) The eTOX Consortium: to improve the safety assessment of new drug candidates. Pharm Med 19(1):4–13

    Google Scholar 

  15. Hall AP, Elcombe CR, Foster JR, Harada T, Kaufmann W, Knippel A, Kuttler K, Malarkey DE, Maronpot RR, Nishikawa A, Nolte T, Schulte A, Strauss V, York MJ (2012) Liver hypertrophy: a review of adaptive (adverse and non-adverse) changes—conclusions from the 3rd international ESTP expert workshop. Toxicol Pathol 40(7):971–994. https://doi.org/10.1177/0192623312448935

    Article  CAS  PubMed  Google Scholar 

  16. Muller PY, Milton MN (2012) The determination and interpretation of the therapeutic index in drug development. Nat Rev Drug Discov 11(10):751–761. https://doi.org/10.1038/nrd3801

    Article  CAS  PubMed  Google Scholar 

  17. Yengi LG, Leung L, Kao J (2007) The evolving role of drug metabolism in drug discovery and development. Pharm Res 24(5):842–858. https://doi.org/10.1007/s11095-006-9217-9

    Article  CAS  PubMed  Google Scholar 

  18. Pelkonen O, Turpeinen M, Uusitalo J, Rautio A, Raunio H (2005) Prediction of drug metabolism and interactions on the basis of in vitro investigations. Basic Clin Pharmacol 96(3):167–175. https://doi.org/10.1111/j.1742-7843.2005.pto960305.x

    Article  CAS  Google Scholar 

  19. Park BK, Boobis A, Clarke S, Goldring CEP, Jones D, Kenna JG, Lambert C, Laverty HG, Naisbitt DJ, Nelson S, Nicoll-Griffith DA, Obach RS, Routledge P, Smith DA, Tweedie DJ, Vermeulen N, Williams DP, Wilson ID, Baillie TA (2011) Managing the challenge of chemically reactive metabolites in drug development. Nat Rev Drug Discov 10(4):292–306. https://doi.org/10.1038/nrd3408

    Article  CAS  PubMed  Google Scholar 

  20. Kirchmair J, Goller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G (2015) Predicting drug metabolism: experiment and/or computation? Nat Rev Drug Discov 14(6):387–404. https://doi.org/10.1038/nrd4581

    Article  CAS  PubMed  Google Scholar 

  21. Corsini A, Ganey P, Ju C, Kaplowitz N, Pessayre D, Roth R, Watkins PB, Albassam M, Liu BL, Stancic S, Suter L, Bortolini M (2012) Current challenges and controversies in drug-induced liver injury. Drug Saf 35(12):1099–1117

    Article  CAS  PubMed  Google Scholar 

  22. Weiler S, Merz M, Kullak-Ublick GA (2015) Drug-induced liver injury: the dawn of biomarkers? F1000Prime Rep 7:34. 10.12703/P7-34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fontana RJ (2014) Pathogenesis of idiosyncratic drug-induced liver injury and clinical perspectives. Gastroenterology 146(4):914–U437. https://doi.org/10.1053/j.gastro.2013.12.032

    Article  CAS  PubMed  Google Scholar 

  24. Thakral S, Ghoshal K (2015) miR-122 is a unique molecule with great potential in diagnosis, prognosis of liver disease, and therapy both as miRNA mimic and antimir. Curr Gene Ther 15(2):142–150. https://doi.org/10.2174/1566523214666141224095610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yang RZ, Park S, Reagan WJ, Goldstein R, Zhong S, Lawton M, Rajamohan F, Qian K, Liu L, Gong DW (2009) Alanine aminotransferase isoenzymes: molecular cloning and quantitative analysis of tissue expression in rats and serum elevation in liver toxicity. Hepatology 49(2):598–607. https://doi.org/10.1002/hep.22657

    Article  CAS  PubMed  Google Scholar 

  26. Radi ZA, Koza-Taylor PH, Bell RR, Obert LA, Runnels HA, Beebe JS, Lawton MP, Sadis S (2011) Increased serum enzyme levels associated with kupffer cell reduction with no signs of hepatic or skeletal muscle injury. Am J Pathol 179(1):240–247. https://doi.org/10.1016/j.ajpath.2011.03.029

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wang T, Papoutsi M, Wiesmann M, DeCristofaro M, Keselica MC, Skuba E, Spaet R, Markovits J, Wolf A, Moulin P, Pognan F, Vancutsem P, Petryk L, Sutton J, Chibout SD, Kluwe W (2011) Investigation of correlation among safety biomarkers in serum, histopathological examination, and toxicogenomics. Int J Toxicol 30(3):300–312. https://doi.org/10.1177/1091581811401920

    Article  CAS  PubMed  Google Scholar 

  28. Moggs J, Moulin P, Pognan F, Brees D, Leonard M, Busch S, Cordier A, Heard DJ, Kammuller M, Merz M, Bouchard P, Chibout SD (2012) Investigative safety science as a competitive advantage for Pharma. Expert Opin Drug Metab Toxicol 8(9):1071–1082. https://doi.org/10.1517/17425255.2012.693914

    Article  CAS  PubMed  Google Scholar 

  29. Kramer JA, Sagartz JE, Morris DL (2007) The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates. Nat Rev Drug Discov 6(8):636–649. https://doi.org/10.1038/nrd2378

    Article  CAS  PubMed  Google Scholar 

  30. Raies AB, Bajic VB (2016) In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci 6(2):147–172. https://doi.org/10.1002/wcms.1240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Combes RD (2012) In silico methods for toxicity prediction. Adv Exp Med Biol 745:96–116. https://doi.org/10.1007/978-1-4614-3055-1

    Article  CAS  PubMed  Google Scholar 

  32. Zhang C, Cheng FX, Li WH, Liu GX, Lee PW, Tang Y (2016) In silico prediction of drug induced liver toxicity using substructure pattern recognition method. Mol Inform 35(3–4):136–144. https://doi.org/10.1002/minf.201500055

    Article  CAS  PubMed  Google Scholar 

  33. Hewitt M, Enoch SJ, Madden JC, Przybylak KR, Cronin MTD (2013) Hepatotoxicity: a scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action. Crit Rev Toxicol 43(7):537–558. https://doi.org/10.3109/10408444.2013.811215

    Article  CAS  PubMed  Google Scholar 

  34. Combes RD (2011) Challenges for computational structure-activity modelling for predicting chemical toxicity: future improvements? Expert Opin Drug Metab Toxicol 7(9):1129–1140

    Article  PubMed  Google Scholar 

  35. Baker M (2016) 1,500 scientists lift the lid on reproducibility. Nature 533(7604):452–454. https://doi.org/10.1038/533452a

    Article  CAS  PubMed  Google Scholar 

  36. Novick PA, Ortiz OF, Poelman J, Abdulhay AY, Pande VS (2013) SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery. PLoS One 8(11):ARTN e79568. https://doi.org/10.1371/journal.pone.0079568

    Article  CAS  Google Scholar 

  37. Lo B (2015) Sharing clinical trial data: maximizing benefits, minimizing risk. JAMA 313(8):793–794. https://doi.org/10.1001/jama.2015.292

    Article  CAS  PubMed  Google Scholar 

  38. Goldacre B, Gray J (2016) OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials 17:ARTN 164. https://doi.org/10.1186/s13063-016-1290-8

    Article  Google Scholar 

  39. Bonini S, Eichler HG, Wathion N, Rasi G (2014) Transparency and the European Medicines Agency—sharing of clinical trial data. N Engl J Med 371(26):2452–2455. https://doi.org/10.1056/NEJMp1409464

    Article  PubMed  Google Scholar 

  40. Ravagli C, Pognan F, Marc P (2016) OntoBrowser: a collaborative tool for curation of ontologies by subject matter experts. Bioinformatics. https://doi.org/10.1093/bioinformatics/btw579

  41. Hewitt M, Ellison CM, Cronin MT, Pastor M, Steger-Hartmann T, Munoz-Muriendas J, Pognan F, Madden JC (2015) Ensuring confidence in predictions: a scheme to assess the scientific validity of in silico models. Adv Drug Deliv Rev. https://doi.org/10.1016/j.addr.2015.03.005

  42. Lin Z, Will Y (2012) Evaluation of drugs with specific organ toxicities in organ-specific cell lines. Toxicol Sci 126(1):114–127. https://doi.org/10.1093/toxsci/kfr339

    Article  CAS  PubMed  Google Scholar 

  43. Atienzar FA, Blomme EA, Chen MJ, Hewitt P, Kenna JG, Labbe G, Moulin F, Pognan F, Roth AB, Suter-Dick L, Ukairo O, Weaver RJ, Will Y, Dambach DM (2016) Key challenges and opportunities associated with the use of in vitro models to detect human DILI: integrated risk assessment and mitigation plans. Biomed Res Int 2016:ARTN 9737920. https://doi.org/10.1155/2016/9737920

    Article  CAS  Google Scholar 

  44. Hartung T, Daston G (2009) Are in vitro tests suitable for regulatory use? Toxicol Sci 111(2):233–237. https://doi.org/10.1093/toxsci/kfp149

    Article  CAS  PubMed  Google Scholar 

  45. Yoon M, Campbell JL, Andersen ME, Clewell HJ (2012) Quantitative in vitro to in vivo extrapolation of cell-based toxicity assay results. Crit Rev Toxicol 42(8):633–652. https://doi.org/10.3109/10408444.2012.692115

    Article  CAS  PubMed  Google Scholar 

  46. Astashkina A, Mann B, Grainger DW (2012) A critical evaluation of in vitro cell culture models for high-throughput drug screening and toxicity. Pharmacol Therapeut 134(1):82–106. https://doi.org/10.1016/j.pharmthera.2012.01.001

    Article  CAS  Google Scholar 

  47. Allen DD, Caviedes R, Cardenas AM, Shimahara T, Segura-Aguilar J, Caviedes PA (2005) Cell lines as in vitro models for drug screening and toxicity studies. Drug Dev Ind Pharm 31(8):757–768. https://doi.org/10.1080/03639040500216246

    Article  CAS  PubMed  Google Scholar 

  48. Peck Y, Wang DA (2013) Three-dimensionally engineered biomimetic tissue models for in vitro drug evaluation: delivery, efficacy and toxicity. Expert Opin Drug Deliv 10(3):369–383. https://doi.org/10.1517/17425247.2013.751096

    Article  CAS  PubMed  Google Scholar 

  49. Csobonyeiova M, Polak S, Danisovic L (2016) Toxicity testing and drug screening using iPSC-derived hepatocytes, cardiomyocytes, and neural cells. Can J Physiol Pharmacol 94(7):687–694. https://doi.org/10.1139/cjpp-2015-0459

    Article  CAS  PubMed  Google Scholar 

  50. Horvath P, Aulner N, Bickle M, Davies AM, Del Nery E, Ebner D, Montoya MC, Ostling P, Pietiainen V, Price LS, Shorte SL, Turcatti G, von Schantz C, Carragher NO (2016) Screening out irrelevant cell-based models of disease. Nat Rev Drug Discov 15(11):751–769. https://doi.org/10.1038/nrd.2016.175

    Article  CAS  PubMed  Google Scholar 

  51. Bale SS, Geerts S, Jindal R, Yarmush ML (2016) Isolation and co-culture of rat parenchymal and non-parenchymal liver cells to evaluate cellular interactions and response. Sci Rep UK 6:ARTN 25329. https://doi.org/10.1038/srep25329

    Article  CAS  Google Scholar 

  52. Bhatia SN, Ingber DE (2014) Microfluidic organs-on-chips. Nat Biotechnol 32(8):760–772. https://doi.org/10.1038/nbt.2989

    Article  CAS  PubMed  Google Scholar 

  53. Edmondson R, Broglie JJ, Adcock AF, Yang LJ (2014) Three-dimensional cell culture systems and their applications in drug discovery and cell-based biosensors. Assay Drug Dev Technol 12(4):207–218. https://doi.org/10.1089/adt.2014.573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Esch MB, Prot JM, Wang YI, Miller P, Llamas-Vidales JR, Naughton BA, Applegate DR, Shuler ML (2015) Multi-cellular 3D human primary liver cell culture elevates metabolic activity under fluidic flow. Lab Chip 15(10):2269–2277. https://doi.org/10.1039/c5lc00237k

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fatehullah A, Tan SH, Barker N (2016) Organoids as an in vitro model of human development and disease. Nat Cell Biol 18(3):246–254

    Article  PubMed  Google Scholar 

  56. Fennema E, Rivron N, Rouwkema J, van Blitterswijk C, de Boer J (2013) Spheroid culture as a tool for creating 3D complex tissues. Trends Biotechnol 31(2):108–115. https://doi.org/10.1016/j.tibtech.2012.12.003

    Article  CAS  PubMed  Google Scholar 

  57. Griffith LG, Swartz MA (2006) Capturing complex 3D tissue physiology in vitro. Nat Rev Mol Cell Biol 7(3):211–224. https://doi.org/10.1038/nrm1858

    Article  CAS  PubMed  Google Scholar 

  58. Pampaloni F, Reynaud EG, Stelzer EHK (2007) The third dimension bridges the gap between cell culture and live tissue. Nat Rev Mol Cell Biol 8(10):839–845. https://doi.org/10.1038/nrm2236

    Article  CAS  PubMed  Google Scholar 

  59. Germano D, Uteng M, Pognan F, Chibout SD, Wolf A (2015) Determination of liver specific toxicities in rat hepatocytes by high content imaging during 2-week multiple treatment. Toxicol In Vitro 30(1 Pt A):79–94. https://doi.org/10.1016/j.tiv.2014.05.009

    Article  CAS  PubMed  Google Scholar 

  60. Uteng M, Germano D, Balavenkatraman K, Pognan F, Wolf A (2014) High content imaging approaches for in vitro toxicology. In: Bal-Price A, Jennings P (eds) In vitro toxicol systems. Methods in pharmacology and toxicology. Springer, New York, NY, pp 377–397

    Chapter  Google Scholar 

  61. Stierum R, Aarts J, Boorsma A, Bosgra S, Caiment F, Ezendam J, Greupink R, Hendriksen P, Soeteman-Hernandez LG, Jennen D, Kleinjans J, Kroese D, Kuper F, van Loveren H, Monshouwer M, Russel F, van Someren E, Tsamou M, Groothuis G (2014) Assuring safety without animal testing concept (ASAT). Integration of human disease data with in vitro data to improve toxicology testing. Toxicol Lett 229:S4–S4. https://doi.org/10.1016/j.toxlet.2014.06.041

    Article  Google Scholar 

  62. Aeby P, Ashikaga T, Bessou-Touya S, Schepky A, Gerberick F, Kern P, Marrec-Fairley M, Maxwell G, Ovigne JM, Sakaguchi H, Reisinger K, Tailhardat M, Martinozzi-Teissier S, Winkler P (2010) Identifying and characterizing chemical skin sensitizers without animal testing: Colipa’s research and method development program. Toxicol In Vitro 24(6):1465–1473. https://doi.org/10.1016/j.tiv.2010.07.005

    Article  CAS  PubMed  Google Scholar 

  63. Sison-Young RL, Lauschke VM, Johann E, Alexandre E, Antherieu S, Aerts H, Gerets HHJ, Labbe G, Hoet D, Dorau M, Schofield CA, Lovatt CA, Holder JC, Stahl SH, Richert L, Kitteringham NR, Jones RP, Elmasry M, Weaver RJ, Hewitt PG, Ingelman-Sundberg M, Goldring CE, Park BK (2017) A multicenter assessment of single-cell models aligned to standard measures of cell health for prediction of acute hepatotoxicity. Arch Toxicol 91(3):1385–1400. https://doi.org/10.1007/s00204-016-1745-4

    Article  CAS  PubMed  Google Scholar 

  64. Gieseck RL, Hannan NRF, Bort R, Hanley NA, Drake RAL, Cameron GWW, Wynn TA, Vallier L (2014) Maturation of induced pluripotent stem cell derived hepatocytes by 3D-culture. PLoS One 9(1):ARTN e86372. https://doi.org/10.1371/journal.pone.0086372

    Article  CAS  Google Scholar 

  65. Song ZH, Cai J, Liu YX, Zhao DX, Yong J, Duo SG, Song XJ, Guo YS, Zhao Y, Qin H, Yin XL, Wu C, Che J, Lu SC, Ding MX, Deng HK (2009) Efficient generation of hepatocyte-like cells from human induced pluripotent stem cells. Cell Res 19(11):1233–1242. https://doi.org/10.1038/cr.2009.107

    Article  PubMed  Google Scholar 

  66. Yi F, Qu J, Li M, Suzuki K, Kim NY, Liu GH, Belmonte JC (2012) Establishment of hepatic and neural differentiation platforms of Wilson’s disease specific induced pluripotent stem cells. Protein Cell 3(11):855–863. https://doi.org/10.1007/s13238-012-2064-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Eggenschwiler R, Loya K, Sgodda M, Andre F, Cantz T (2011) Hepatic differentiation of murine disease-specific induced pluripotent stem cells allows disease modelling in vitro. Stem Cells Int 2011:924782. https://doi.org/10.4061/2011/924782

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ghodsizadeh A, Taei A, Totonchi M, Seifinejad A, Gourabi H, Pournasr B, Aghdami N, Malekzadeh R, Almadani N, Salekdeh GH, Baharvand H (2010) Generation of liver disease-specific induced pluripotent stem cells along with efficient differentiation to functional hepatocyte-like cells. Stem Cell Rev 6(4):622–632. https://doi.org/10.1007/s12015-010-9189-3

    Article  Google Scholar 

  69. Ohshita H, Tateno C (2017) Propagation of human hepatocytes in uPA/SCID mice: producing chimeric mice with humanized liver. Methods Mol Biol 1506:91–100. https://doi.org/10.1007/978-1-4939-6506-9_6

    Article  CAS  PubMed  Google Scholar 

  70. Uetrecht J, Naisbitt DJ (2013) Idiosyncratic adverse drug reactions: current concepts. Pharmacol Rev 65(2):779–808. https://doi.org/10.1124/pr.113.007450

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Strom SC, Davila J, Grompe M (2010) Chimeric mice with humanized liver: tools for the study of drug metabolism, excretion, and toxicity. Methods Mol Biol 640:491–509. https://doi.org/10.1007/978-1-60761-688-7_27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Walsh NC, Kenney LL, Jangalwe S, Aryee KE, Greiner DL, Brehm MA, Shultz LD (2017) Humanized mouse models of clinical disease. Annu Rev Pathol 12:187–215. https://doi.org/10.1146/annurev-pathol-052016-100332

    Article  CAS  PubMed  Google Scholar 

  73. Grompe M, Strom S (2013) Mice with human livers. Gastroenterology 145(6):1209–1214. https://doi.org/10.1053/j.gastro.2013.09.009

    Article  PubMed  Google Scholar 

  74. Ito R, Takahashi T, Katano I, Ito M (2012) Current advances in humanized mouse models. Cell Mol Immunol 9(3):208–214. https://doi.org/10.1038/cmi.2012.2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Tateno C, Kawase Y, Tobita Y, Hamamura S, Ohshita H, Yokomichi H, Sanada H, Kakuni M, Shiota A, Kojima Y, Ishida Y, Shitara H, Wada NA, Tateishi H, Sudoh M, Nagatsuka S, Jishage K, Kohara M (2015) Generation of novel chimeric mice with humanized livers by using hemizygous cDNA-uPA/SCID mice. PLoS One 10(11):ARTN e0142145. https://doi.org/10.1371/journal.pone.0142145

    Article  CAS  Google Scholar 

  76. Foster JR, Jacobsen M, Kenna G, Schulz-Utermoehl T, Morikawa Y, Salmu J, Wilson ID (2012) Differential effect of troglitazone on the human bile acid transporters, MRP2 and BSEP, in the PXB hepatic chimeric mouse. Toxicol Pathol 40(8):1106–1116. https://doi.org/10.1177/0192623312447542

    Article  CAS  PubMed  Google Scholar 

  77. Shultz LD, Brehm MA, Garcia-Martinez JV, Greiner DL (2012) Humanized mice for immune system investigation: progress, promise and challenges. Nat Rev Immunol 12(11):786–798. https://doi.org/10.1038/nri3311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Wilson EM, Bial J, Tarlow B, Bial G, Jensen B, Greiner DL, Brehm MA, Grompe M (2014) Extensive double humanization of both liver and hematopoiesis in FRGN mice. Stem Cell Res 13(3):404–412. https://doi.org/10.1016/j.scr.2014.08.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Qi ZP, Li L, Wang XF, Gao X, Wang X, Wei HM, Zhang J, Sun R, Tian ZG (2014) Bone marrow transplantation concurrently reconstitutes donor liver and immune system across host species barrier in mice. PLoS One 9(9):ARTN e106791. https://doi.org/10.1371/journal.pone.0106791

    Article  CAS  Google Scholar 

  80. Hirode M, Omura K, Kiyosawa N, Uehara T, Shimuzu T, Ono A, Miyagishima T, Nagao T, Ohno Y, Urushidani T (2009) Gene expression profiling in rat liver treated with various hepatotoxic-compounds inducing coagulopathy. J Toxicol Sci 34(3):281–293

    Article  CAS  PubMed  Google Scholar 

  81. Mohr SE, Smith JA, Shamu CE, Neumuller RA, Perrimon N (2014) RNAi screening comes of age: improved techniques and complementary approaches. Nat Rev Mol Cell Biol 15(9):591–600. https://doi.org/10.1038/nrm3860

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Housden BE, Muhar M, Gemberling M, Gersbach CA, Stainier DYR, Seydoux G, Mohr SE, Zuber J, Perrimon N (2017) Loss-of-function genetic tools for animal models: cross-species and cross-platform differences. Nat Rev Genet 18(1):24–40. https://doi.org/10.1038/nrg.2016.118

    Article  CAS  PubMed  Google Scholar 

  83. Doyle A, McGarry MP, Lee NA, Lee JJ (2012) The construction of transgenic and gene knockout/knockin mouse models of human disease. Transgenic Res 21(2):327–349. https://doi.org/10.1007/s11248-011-9537-3

    Article  CAS  PubMed  Google Scholar 

  84. Ott J, Wang J, Leal SM (2015) Genetic linkage analysis in the age of whole-genome sequencing. Nat Rev Genet 16(5):275–284. https://doi.org/10.1038/nrg3908

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Watson RE, Goodman JI (2002) Epigenetics and DNA methylation come of age in toxicology. Toxicol Sci 67(1):11–16

    Article  CAS  PubMed  Google Scholar 

  86. Miousse IR, Currie R, Datta K, Ellinger-Ziegelbauer H, French JE, Harrill AH, Koturbash I, Lawton M, Mann D, Meehan RR, Moggs JG, O'Lone R, Rasoulpour RJ, Pera RAR, Thompson K (2015) Importance of investigating epigenetic alterations for industry, and regulators: an appraisal of current efforts by the Health and Environmental Sciences Institute. Toxicology 335:11–19. https://doi.org/10.1016/j.tox.2015.06.009

    Article  CAS  PubMed  Google Scholar 

  87. Chen M, Zhang J, Wang Y, Liu Z, Kelly R, Zhou G, Fang H, Borlak J, Tong W (2013) The liver toxicity knowledge base: a systems approach to a complex end point. Clin Pharmacol Ther 93(5):409–412. https://doi.org/10.1038/clpt.2013.16

    Article  CAS  PubMed  Google Scholar 

  88. Bujold D, Morais DAD, Gauthier C, Cote C, Caron M, Kwan T, Chen KC, Laperle J, Markovits AN, Pastinen T, Caron B, Veilleux A, Jacques PE, Bourque G (2016) The international human epigenome consortium data portal. Cell Syst 3(5):496. https://doi.org/10.1016/j.cels.2016.10.019

    Article  CAS  PubMed  Google Scholar 

  89. Braeuning A, Gavrilov A, Brown S, Wolf CR, Henderson CJ, Schwarz M (2014) Phenobarbital-mediated tumor promotion in transgenic mice with humanized CAR and PXR. Toxicol Sci 140(2):259–270. https://doi.org/10.1093/toxsci/kfu099

    Article  CAS  PubMed  Google Scholar 

  90. Terranova R, Vitobello A, Del Rio EA, Wolf CR, Schwartz M, Thomson J, Meehan R, Moggs J (2017) Progress in identifying epigenetic mechanisms of xenobiotic-induced non-genotoxic carcinogenesis. Curr Opin Toxicol 3:626–670

    Google Scholar 

  91. Sanz F, Carrio P, Lopez O, Capoferri L, Kooi DP, Vermeulen NPE, Geerke DP, Montanari F, Ecker GF, Schwab CH, Kleinoder T, Magdziarz T, Pastor M (2015) Integrative modeling strategies for predicting drug toxicities at the eTOX Project. Mol Inform 34(6–7):477–484. https://doi.org/10.1002/minf.201400193

    Article  CAS  PubMed  Google Scholar 

  92. Low YS, Sedykh AY, Rusyn I, Tropsha A (2014) Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Curr Top Med Chem 14(11):1356–1364. 10.14573/altex.1603091

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Hartung T (2016) Making big sense from big data in toxicology by read-across. Altex-Altern Anim Ex 33(2):83–93

    Google Scholar 

  94. Miyamoto SW, Henderson S, Young HM, Pande A, Han JJ (2016) Tracking health data is not enough: a qualitative exploration of the role of healthcare partnerships and mHealth technology to promote physical activity and to sustain behavior change. Jmir Mhealth Uhealth 4(1):44–55. https://doi.org/10.2196/mhealth.4814

    Article  Google Scholar 

  95. Alemayehu D, Berger ML (2016) Big data: transforming drug development and health policy decision making. Health Serv Outcome 16(3):92–102. https://doi.org/10.1007/s10742-016-0144-x

    Article  Google Scholar 

  96. Soroushmehr SMR, Najarian K (2016) Transforming big data into computational models for personalized medicine and health care. Dialogues Clin Neurosci 18(3):339–343

    Google Scholar 

  97. Raschi E, De Ponti F (2015) Drug- and herb-induced liver injury: progress, current challenges and emerging signals of post-marketing risk. World J Hepatol 7(13):1761–1771. https://doi.org/10.4254/wjh.v7.i13.1761

    Article  PubMed  PubMed Central  Google Scholar 

  98. Menachemi N, Collum TH (2011) Benefits and drawbacks of electronic health record systems. Risk Manag Healthc Policy 4:47–55. https://doi.org/10.2147/RMHP.S12985

    Article  PubMed  PubMed Central  Google Scholar 

  99. Maciejewski M, Lounkine E, Whitebread S, Farmer P, DuMouchel W, Shoichet BK, Urban L, (2017) Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets. eLife 6

    Google Scholar 

  100. Outcome Sciences I, A Quintiles Company, Cambridge, MA (2014) Interfacing registries with electronic health records. In: Gliklich RE, Dreyer NA, Leavy MB (eds) Registries for evaluating patient outcomes: a user’s guide, 3rd edn. AHRQ methods for effective health care, 3rd edn. AHRQ, Rockville, MD

    Google Scholar 

  101. Luo J, Wu M, Gopukumar D, Zhao Y (2016) Big data application in biomedical research and health care: a literature review. Biomed Inform Insights 8:1–10. https://doi.org/10.4137/BII.S31559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Kruse CS, Goswamy R, Raval Y, Marawi S (2016) Challenges and opportunities of big data in health care: a systematic review. JMIR Med Inform 4(4):e38. https://doi.org/10.2196/medinform.5359

    Article  PubMed  PubMed Central  Google Scholar 

  103. Zhao J, Henriksson A, Asker L, Bostrom H (2015) Predictive modeling of structured electronic health records for adverse drug event detection. BMC Med Inform Decis Mak 15(Suppl 4):S1. https://doi.org/10.1186/1472-6947-15-S4-S1

    Article  PubMed  PubMed Central  Google Scholar 

  104. Blumenthal D, Tavenner M (2010) The “Meaningful Use” regulation for electronic health records. New Engl J Med 363(6):501–504. https://doi.org/10.1056/NEJMp1006114

    Article  CAS  PubMed  Google Scholar 

  105. Lin C, Karlson EW, Dligach D, Ramirez MP, Miller TA, Mo H, Braggs NS, Cagan A, Gainer V, Denny JC, Savova GK (2015) Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record. J Am Med Inform Assn 22(E1):E151–E161. https://doi.org/10.1136/amiajnl-2014-002642

    Article  Google Scholar 

  106. Miotto R, Li L, Kidd BA, Dudley JT (2016) Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 6:26094. https://doi.org/10.1038/srep26094

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, Bernal-Delgado E, Blomberg N, Bock C, Conesa A, Del Signore S, Delogne C, Devilee P, Di Meglio A, Eijkemans M, Flicek P, Graf N, Grimm V, Guchelaar HJ, Guo YK, Gut IG, Hanbury A, Hanif S, Hilgers RD, Honrado A, Hose DR, Houwing-Duistermaat J, Hubbard T, Janacek SH, Karanikas H, Kievits T, Kohler M, Kremer A, Lanfear J, Lengauer T, Maes E, Meert T, Muller W, Nickel D, Oledzki P, Pedersen B, Petkovic M, Pliakos K, Rattray M, Mas JRI, Schneider R, Sengstag T, Serra-Picamal X, Spek W, Vaas LAI, van Batenburg O, Vandelaer M, Varnai P, Villoslada P, Vizcaino JA, Wubbe JPM, Zanetti G (2016) Making sense of big data in health research: towards an EU action plan. Genome Med 8:ARTN 71. https://doi.org/10.1186/s13073-016-0323-y

    Article  CAS  Google Scholar 

  108. Coloma PM, Schuemie MJ, Trifiro G, Gini R, Herings R, Hippisley-Cox J, Mazzaglia G, Giaquinto C, Corrao G, Pedersen L, van der Lei J, Sturkenboom M, Consortium E-A (2011) Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol Drug Saf 20(1):1–11. https://doi.org/10.1002/pds.2053

    Article  PubMed  Google Scholar 

  109. Chiauzzi E, Rodarte C, DasMahapatra P (2015) Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med 13:ARTN 77. https://doi.org/10.1186/s12916-015-0319-2

    Article  Google Scholar 

  110. Redmond SJ, Lovell NH, Yang GZ, Horsch A, Lukowicz P, Murrugarra L, Marschollek M (2014) What does big data mean for wearable sensor systems? contribution of the IMIA wearable sensors in healthcare WG. Yearb Med Inform 9:135–142. 10.15265/IY-2014-0019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Schukat M, McCaldin D, Wang K, Schreier G, Lovell NH, Marschollek M, Redmond SJ (2016) Unintended consequences of wearable sensor use in healthcare. Contribution of the IMIA wearable sensors in healthcare WG. Yearb Med Inform 1:73–86. 10.15265/IY-2016-025

    Article  Google Scholar 

Download references

Acknowledgments

Dr. Jonathan Moggs is warmly thanked for his thorough and insightful review of the manuscript.

Author information

Authors and Affiliations

  1. Discovery Investigative Safety, PreClinical Safety, Novartis Pharmaceutical AG, Basel, Switzerland

    Francois Pognan

Authors
  1. Francois Pognan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francois Pognan .

Editor information

Editors and Affiliations

  1. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA

    Minjun Chen

  2. Drug Safety Research and Development, Pfizer Inc., Groton, CT, USA

    Yvonne Will

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Pognan, F. (2018). Detection, Elimination, Mitigation, and Prediction of Drug-Induced Liver Injury in Drug Discovery. In: Chen, M., Will, Y. (eds) Drug-Induced Liver Toxicity. Methods in Pharmacology and Toxicology. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-7677-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7677-5_2

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-7676-8

  • Online ISBN: 978-1-4939-7677-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation