Aysel Sabuncu Brain Research Center (BAM)

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    Advanced preclinical models for evaluation of drug-induced liver injury – consensus statement by the European Drug-Induced Liver Injury Network [PRO-EURO-DILI-NET]
    (Elsevier, 2021-06-24) Fernandez-Checa, J. C.; Bagnaninchi, P.; Ye, H.; Sancho-Bru, P.; Falcon-Perez, J. M.; Royo, F.; Garcia-Ruiz, C.; Konu, Özlen; Miranda, J.; Lunov, O.; Dejneka, A.; Elfick, A.; McDonald, A.; Sullivan, G. J.; Aithal, G. P.; Lucena, M. I.; Andrade, R. J.; Fromenty, B.; Kranendonk, M.; Cubero, F. J.; Nelson, L. J.
    Drug-induced liver injury (DILI) is a major cause of acute liver failure (ALF) and one of the leading indications for liver transplantation in Western societies. Given the wide use of both prescribed and over the counter drugs, DILI has become a major health issue for which there is a pressing need to find novel and effective therapies. Although significant progress has been made in understanding the molecular mechanisms underlying DILI, our incomplete knowledge of its pathogenesis and inability to predict DILI is largely due to both discordance between human and animal DILI in preclinical drug development and a lack of models that faithfully recapitulate complex pathophysiological features of human DILI. This is exemplified by the hepatotoxicity of acetaminophen (APAP) overdose, a major cause of ALF because of its extensive worldwide use as an analgesic. Despite intensive efforts utilising current animal and in vitro models, the mechanisms involved in the hepatotoxicity of APAP are still not fully understood. In this expert Consensus Statement, which is endorsed by the European Drug-Induced Liver Injury Network, we aim to facilitate and outline clinically impactful discoveries by detailing the requirements for more realistic human-based systems to assess hepatotoxicity and guide future drug safety testing. We present novel insights and discuss major players in APAP pathophysiology, and describe emerging in vitro and in vivo pre-clinical models, as well as advanced imaging and in silico technologies, which may improve prediction of clinical outcomes of DILI.