Z Gastroenterol 2016; 54 - KV195
DOI: 10.1055/s-0036-1586971

Identification of novel Biomarkers for idiosyncratic drug-induced liver injury by combining MetaHeps® and Proteomics

A Benesic 1, 2, D Dragoi 1, SM Ehrlich 1, G Pichler 3, 4, NA Kulak 3, 4, AL Gerbes 1
  • 1Klinikum der LMU München, Campus Großhadern, Medizinische Klinik II, Leber Centrum München, München, Deutschland
  • 2MetaHeps GmbH, Martinsried, Deutschland
  • 3Max Planck Institute for Biochemistry, Department of Proteomics and Signal Transduction, Martinsried, Deutschland
  • 4Preomics GmbH, Gauting, Deutschland

Background: Drug-induced liver injury (DILI) is the most frequent cause of acute liver failure and the single most important reason for regulatory actions on drugs. In contrast to dose-related hepatotoxicity, idiosyncratic DILI (iDILI) is not predictable by preclinical models. Diagnosis of iDILI relies on the exclusion of other causes for liver injury and it may be impossible to identify the causative agent in polymedication. These difficulties in precise case characterization impede with efficient development of novel iDILI biomarkers. We have developed a method to diagnose or exclude iDILI using hepatocyte-like cells derived from peripheral monocytes (MetaHeps®) allowing improved causality assessment in polymedication.

Aim of this study was to investigate whether proteomics analyses of patient derived MetaHeps® allow the identification of novel iDILI biomarkers.

Methods: MetaHeps® were generated from donors who underwent diclofenac (diclo) -treatment: a) healthy donors who tolerated diclo (healthy; n = 3), b) patients who suffered iDILI by diclo (DicloDILI, n = 4), c) patients with iDILI caused by another drug (DILI nonDiclo, n = 2) and d) patients with acute liver injury of non-drug cause (nonDILI; n = 3). MetaHeps® were treated with diclofenac in vitro and analysis of MetaHeps® was performed using mass spectrometry (MS)-based proteomics.

Results: Our “proof of concept” study shows that MS-based proteomics allows to identify > 3.000 proteins from MetaHeps®. Relevant differences in protein expression patterns were found between the single donors. Using Heat-Plot visualization as well as principal component analysis (PCA) we could show that MetaHeps® from patients with similar diagnoses could be classified into separate clusters. MetaHeps® from patients with DicloDILI showed distinct changes in protein expression patterns after diclofenac challenge compared to other analyzed groups. Pathway analysis showed significant effects of diclofenac in RedOx-stress associated protein families.

Conclusions: Diclofenac seems to evoke specific changes in protein expression patterns in MetaHeps® from DicloDILI patients. Our study provides evidence that combination of MetaHeps® and MS-based proteomics may present a novel method to identify drug-specific biomarkers for iDILI.