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Benesic, Andreas; Leitl, Alexandra; Gerbes, Alexander L. (2016): Monocyte-derived hepatocyte-like cells for causality assessment of idiosyncratic drug-induced liver injury. In: Gut, Vol. 65, No. 9: pp. 1555-1563
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Background Idiosyncratic drug-induced liver injury (iDILI) is a frequent cause of acute liver injury and a serious problem in late stage drug-development. Its diagnosis is one of the most challenging in hepatology, since it is done by exclusion and relies on expert opinion. Until now no reliable in vitro test exists to support the diagnosis of iDILI. In some instances it is impossible to determine the causative drug in polymedicated patients. Aim To investigate if monocyte-derived hepatocyte-like (MH) cells might be a tool supporting clinical judgment for iDILI diagnosis and causality assessment. Methods This prospective study included 54 patients with acute liver injury and intake of at least one drug. Thirty-one patients were diagnosed with iDILI based on causality likelihood. MH cells were generated from every patient and in vitro toxicity of the respective drugs was assessed by lactate-dehydrogenase release. The results from MH cells and RUCAM, the most widely used scoring system as methods to support clinical judgement were compared. Results MH cells showed enhanced toxicity in 29 of the 31 patients with iDILI, similar to RUCAM score. MH cells exhibited negative results in the 23 non-DILI cases, whereas RUCAM indicated possible iDILI in six cases. Analysis of the comedications also showed superior specificity of MH cells. No MH cell toxicity of the drugs showing toxicity in patients with iDILI was observed in MH cells of healthy donors. Conclusions In this pilot study in vitro testing using MH cells derived from patients with acute liver injury was able to identify patients with iDILI with an excellent sensitivity and a higher specificity than RUCAM, the most widely used current causality assessment score. Therefore, MH cells could be useful to identify the causative drugs even in polymedicated patients by adding objective data to causality assessment.