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Regulatory Toxicological Studies: Identifying Drug-Induced Liver Injury Using Nonclinical Studies

  • Elizabeth HausnerEmail author
  • Imran KhanEmail author
Protocol
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

This chapter presents an overview of nonclinical safety assessment and the types of information that may be generated by drug developers for review by the US Food and Drug Administration (FDA). Every new drug molecule entering clinical development undergoes the process of safety assessment, a relatively standardized series of in vitro and in vivo examinations of the intrinsic properties of the proposed therapeutic. The goals are hazard characterization, identification of target organs, and determination of a theoretical margin of safety. These studies are usually conducted according to the guidances of the FDA and International Conference on Harmonization (ICH) and to a large extent conducted to the standards of Good Laboratory Practice. The ICH and FDA’s guidances allow investigators to modify safety assessment studies on a case by case basis when scientifically justified.

The components of nonclinical safety assessment include in vitro assessment of affinity for off-target receptors and enzymes, evaluation of the absorption, distribution, metabolism, and excretion (ADME), safety pharmacology, and repeat-dose animal studies and may include computer assisted analysis of the chemical structure. Standard in vivo toxicology studies alone are not sufficient to identify the potential for human drug-induced liver injury (DILI). A weight-of-evidence approach (WOE) is recommended. We discuss how each of the components of nonclinical investigation may be used in conjunction to help identify the potential for adverse hepatobiliary effects. Given the scientific flexibility offered by the FDA and ICH guidances, it is important to remember that repeat-dose animal studies may be modified to explore any identified safety signals. This allows for evaluation of possible reactive metabolites and other more subtle signals of drug-induced liver injury that might otherwise be missed in a standard single- or repeat-dose safety assessment study. Because of efforts to reduce, refine, and replace animal work, it becomes especially important to ensure that animal work is optimized. Judicious data-driven modification of the repeat-dose animal studies has the potential to increase the safety of clinical trial participants, to optimize the information obtained and to increase the translational value for the clinic.

Key words

Hepatobiliary Toxicology GLP Nonclinical QSAR Adverse Translational Regulatory 

Notes

Disclaimer

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

References

  1. 1.
    Peters TS (2005) Do preclinical testing strategies help predict human hepatotoxic potentials? Toxicol Pathol 33(1):146–154CrossRefGoogle Scholar
  2. 2.
    Everitt JI (2015) The future of preclinical animal models in pharmaceutical discovery and development: a need to bring in cerebro to the in vivo discussions. Toxicol Pathol 43(1):70–77CrossRefGoogle Scholar
  3. 3.
    Owens AH Jr (1962) Predicting anticancer drug effects in man from laboratory animal studies. J Chronic Dis 15:223–228CrossRefGoogle Scholar
  4. 4.
    Schein PS, Davis RD, Carter S, Newman J, Schein DR, Rall DP (1970) The evaluation of anticancer drugs in dogs and monkeys for the prediction of qualitative toxicities in man. Clin Pharmacol Ther 11(1):3–40CrossRefGoogle Scholar
  5. 5.
    Hayes AW, Fedorowski T, Balazs T, Carlton WW, Fowler BA, Gilman MR, Heyman I, Jackson BA, Kennedy GL, Shapiro RE, Smith CC, Tardiff RG, Weil CS (1982) Correlation of human hepatotoxicants with hepatic damage in animals. Fundam Appl Toxicol 2(2):55–66CrossRefGoogle Scholar
  6. 6.
    Igarashi T, Nakane S, Kitagawa T (1995) Predictability of clinical adverse reactions of drugs by general pharmacology studies. J Toxicol Sci 20(2):77–92CrossRefGoogle Scholar
  7. 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):56–67CrossRefGoogle Scholar
  8. 8.
    Tamaki C, Nagayama T, Hashiba M, Fujiyoshi M, Hizue M, Kodaira H, Nishida M, Suzuki K, Takashima Y, Ogino Y, Yasugi D, Yasuo Yoneta Y, Hisada S, Ohkura T, Nakamura K (2013) Potentials and limitations of nonclinical safety assessment for predicting clinical adverse drug reactions: correlation analysis of 142 approved drugs in Japan (PDF download available). J Toxicol Sci 38(4):581–598CrossRefGoogle Scholar
  9. 9.
    Chen M, Suzuki A, Borlak J, Andrade RJ, Lucena MI (2015) Drug-induced liver injury: interactions between drug properties and host factors. J Hepatol 63(2):503–514CrossRefGoogle Scholar
  10. 10.
    Obach RS, Kalgutkar AS, Soglia JR, Zhao SX (2008) Can in vitro metabolism-dependent covalent binding data in liver microsomes distinguish hepatotoxic from nonhepatotoxic drugs? An analysis of 18 drugs with consideration of intrinsic clearance and daily dose. Chem Res Toxicol 21(9):1814–1822CrossRefGoogle Scholar
  11. 11.
    Papoian T, Chiu HJ, Elayan I, Gowraganahalli J, Khan I, Laniyonu AA, Xinguang CL, Saulnier M, Simpson N, Yang B (2015) Secondary pharmacology data to assess potential off-target activity of new drugs: a regulatory perspective. Nat Rev Drug Discov 14(4):294. Epub 2015 Mar 20CrossRefGoogle Scholar
  12. 12.
    Roth AD, Lee, M-Y (2017) Idiosyncratic drug-induced liver injury (IDILI): potential mechanisms and predictive assays. Biomed Res Int. ePub 4 Jan 2017Google Scholar
  13. 13.
    Jan YH, Heck DE, Dragomir AC, Gardner CR, Laskin DL, Laskin JD (2014) Acetaminophen reactive intermediates target hepatic thioredoxin reductase. Chem Res Toxicol 27(5):882–894CrossRefGoogle Scholar
  14. 14.
    Kaplowitz N (2004) Drug-induced liver injury. Clin Infect Dis 38(Suppl 2):S44–S48CrossRefGoogle Scholar
  15. 15.
    Cho T, Uetrecht J (2017) How reactive metabolites induce an immune response that sometimes leads to an idiosyncratic drug reaction. Chem Res Toxicol 30:295–314CrossRefGoogle Scholar
  16. 16.
    Everds NE (2017) Deciphering sources of variability in clinical pathology—it’s not just about the numbers. Toxicol Pathol 45(2):275–280CrossRefGoogle Scholar
  17. 17.
    Boone L, Meyer D, Cusick P, Ennulat D, Provencher Bolliger A, Everds N, Meador V, Elliott G, Honor D, Bounous D, Jordan H (2005) Selection and interpretation of clinical pathology indicators of hepatic injury in preclinical studies. Vet Clin Pathol 34(3):182–188CrossRefGoogle Scholar
  18. 18.
    Fahey JR, Katoh H, Malcolm R, Perez AV (2013) The case for genetic monitoring of mice and rats used in biomedical research. Mamm Genome 24(3–4):89–94CrossRefGoogle Scholar
  19. 19.
    Calabrese EJ, Calabrese EJ, Bachmann KA, Bailer AJ, Bolger PM, Borak J, Cai L, Cedergreen N, Cherian MG, Chiueh CC, Clarkson TW, Cook RR, Diamond DM, Doolittle DJ, Dorato MA, Duke SO, Feinendegen L, Gardner DE, Hart RW, Hastings KL, Hayes AW, Hoffmann GR, Ives JA, Jaworowski Z, Johnson TE, Jonas WB, Kaminski NE, Keller JG, Klaunig JE, Knudsen TB, Kozumbo WJ, Lettieri T, Liu SZ, Maisseu A, Maynard KI, Masoro EJ, McClellan RO, Mehendale HM, Mothersill C, Newlin DB, Nigg HN, Oehme FW, Phalen RF, Philbert MA, Rattan SI, Riviere JE, Rodricks J, Sapolsky RM, Scott BR, Seymour C, Sinclair DA, Smith-Sonneborn J, Snow ET, Spear L, Stevenson DE, Thomas Y, Tubiana M, Williams GM, Mattson MP (2007) Biological stress response terminology: integrating the concepts of adaptive response and preconditioning stress within a hormetic dose-response framework. Toxicol Appl Pharmacol 222(1):122–128CrossRefGoogle Scholar
  20. 20.
    Robles-Diaz M, Medina-Caliz I, Stephens C, Andrade RJ, Lucena MI (2016) Biomarkers in DILI: one more step forward. Front Pharmacol 7:267CrossRefGoogle Scholar
  21. 21.
    Thulin P, Hornby RJ, Auli M, Nordhal G, Antoine DJ, Lewis PS, Goldring CE, Park BK, Prats N, Glinghammar B, Schippe-Koisten I (2017) A longitudinal assessment of miR-122 and GLDH as biomarkers of drug-induced liver injury in the rat. Biomarkers 22(5):461–469CrossRefGoogle Scholar
  22. 22.
    Church RJ, Watkin PB (2017) The transformation in biomarker detection and management of drug-induced liver injury. Liver Int 37:1582–1590CrossRefGoogle Scholar
  23. 23.
    Scaffidi P, Misteli T, Bianchi ME (2002) Release of chromatin protein HMGB1 by necrotic cells triggers inflammation. Nature 418(6894):191–195CrossRefGoogle Scholar
  24. 24.
    Bonaldi T, Talamo F, Scaffidi P, Ferrera D, Porto A, Bachi A, Rubartelli A, Agresti A, Bianchi ME (2003) Monocytic cells hyperacetylate chromatin protein HMGB1 to redirect it towards secretion. EMBO J 22(20):5551–5560CrossRefGoogle Scholar
  25. 25.
    Ku NO, Strnad P, Zhong B-H, Tao G-H, Omary MB (2007) Keratins let liver live: mutations predispose to liver disease and crosslinking generates Mallory-Denk bodies. Hepatology 46(5):1639–1649CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Division of Cardiovascular and Renal Products, Center for Drug Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringUSA
  2. 2.Division of Anesthesia, Analgesia and Addiction Products, Center for Drug Evaluationand and ResearchU.S. Food and Drug AdministrationSilver SpringUSA

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