Abstract

   Idiosyncratic drug-induced liver injury (iDILI) is a major cause of
   acute liver failure resulting in liver transplantation or death.
   Prediction and diagnosis of iDILI remain a great challenge, as current
   models provide unsatisfying results in terms of sensitivity,
   specificity, and prognostic value. The absence of appropriate tools for
   iDILI detection also impairs the development of reliable biomarkers.
   Here, we report on a new method for identification of drug-specific
   biomarkers. We combined the advantages of monocyte-derived
   hepatocyte-like (MH) cells, able to mimic individual characteristics,
   with those of a novel mass spectrometry-based proteomics technology to
   assess potential biomarkers for Diclofenac-induced DILI. We found over
   2,700 proteins differentially regulated in MH cells derived from
   individual patients. Herefrom, we identified integrin beta 3 (ITGB3) to
   be specifically upregulated in Diclofenac-treated MH cells from
   Diclofenac-DILI patients compared to control groups. Finally, we
   validated ITGB3 by flow cytometry analysis of whole blood and
   histological staining of liver biopsies derived from patients diagnosed
   with Diclofenac-DILI. In summary, our results show that biomarker
   candidates can be identified by proteomics analysis of MH cells.
   Application of this method to a broader range of drugs in the future
   will exploit its full potential for the development of drug-specific
   biomarkers. Data are available via ProteomeXchange with identifier
   PXD008918.

   Keywords: DILI, biomarker, proteomics, drug-development,
   monocyte-derived hepatocyte-like cells

Introduction

   Drug-induced liver injury is the leading cause for acute liver failure
   in United States and Europe ([33]Kullak-Ublick et al., 2017). Toxic
   reactions due to drug intake can be dose-dependent, as in the case of
   APAP ingestion and are termed “intrinsic DILI.” In contrast, iDILI
   affects only susceptible patients, and even though it is a rare event
   (1:10,000 to 1:100,000 per drug), it often results in death or liver
   transplantation ([34]Bell and Chalasani, 2009). Preclinical models
   usually fail to predict iDILI ([35]Atienzar et al., 2016) and thus,
   iDILI is often being detected only after market approval. Even then,
   iDILI diagnosis remains a diagnosis of exclusion after elimination of
   other etiologies for liver damage (e.g., viral infections such as
   hepatitis or autoimmune diseases). Consequently, iDILI diagnosis is
   incorrect in up to 25% of the cases ([36]Teschke et al., 2014).
   Moreover, current causality assessment methods, such as the RUCAM score
   and expert opinion encounter difficulties in identifying the causative
   drug in polymedicated patients ([37]Andrade et al., 2009; [38]Anderson
   and Borlak, 2011; [39]Teschke and Eickhoff, 2016). While both methods
   have their strengths, they both heavily rely on a comprehensive
   collection of data requiring extensive investigations and documentation
   of the course of disease ([40]Danan and Teschke, 2016). Additionally,
   both methods require an experienced investigator to perform optimally.
   Since DILI is still a diagnosis of exclusion, correct causality
   assessment is time consuming and expensive ([41]Hayashi, 2016).

   The lack of a reliable in vitro diagnostic tool for iDILI also impairs
   the development of safety biomarkers, as false positive or false
   negative diagnosis impair the identification of specific biomarkers
   ([42]Teschke et al., 2017). Thus, several attempts to develop safety
   biomarkers for iDILI provided unsatisfying results because of low
   predictive values ([43]Singer et al., 2010; [44]Spraggs et al., 2011).
   Since iDILI is a rare event, this may in part results from dilution
   caused by false positives in the suspected iDILI groups. Several
   promising biomarker candidates have recently been proposed, e.g.,
   HMGB1, MCSFR1, cytokeratin 18, and micro-RNAs miR-122 and miR-192
   ([45]Robles-Díaz et al., 2016). However, some limitations exist: the
   majority of these markers are not liver specific (HMGB1, cytokeratin
   18, and MCSFR1), and even if they specifically detect liver damage, the
   issue of causality remains the most pressing challenge. Moreover, none
   of these candidates has been shown to be drug-specific, which would be
   an important advantage in improving drug safety ([46]Andrade et al.,
   2009). Thus, there is currently no valid diagnostic or prognostic
   biomarker available for iDILI ([47]Teschke et al., 2017).

   We have previously reported on the development of the first in vitro
   test able to diagnose iDILI using a blood sample from respective
   patients with iDILI suspicion. The test is based on the generation of
   MH cells, which reflect individual donor characteristics, such as toxic
   reactions to drug treatment ([48]Benesic et al., 2012). This test is
   able to distinguish iDILI from other causes of liver damage and
   furthermore, to identify the culprit drug in polymedicated patients
   ([49]Benesic et al., 2016, [50]2018; [51]Benesic and Gerbes, 2018). In
   the current study, we combine the ability of MH cells to simulate
   specific toxic reactions in samples from individual donors with a new
   and improved proteomics technology in order to identify drug-specific
   biomarkers for iDILI ([52]Kulak et al., 2014). Here, we focus on
   Diclofenac-DILI, since Diclofenac is the worldwide most prescribed
   NSAID ([53]Altman et al., 2015) and is the most frequently implicated
   agent in DILI caused by NSAIDs ([54]Fontana et al., 2009;
   [55]Björnsson, 2016; [56]Schmeltzer et al., 2016). Diclofenac-DILI
   displays a well characterized drug signature which allows accurate
   diagnosis by clinical assessment methods such as RUCAM ([57]Boelsterli,
   2003). Moreover, the injury mechanisms of Diclofenac are well
   described, involving both metabolic cell pathways and immunologic
   reactions ([58]Boelsterli, 2003). Thus, our goal was to investigate
   whether Diclofenac-treatment of MH cells generated from patients
   clinically diagnosed with Diclofenac-DILI results in different protein
   expression patterns compared to vehicle control and to other control
   groups, respectively.

Materials and Methods

Patients and Causality Assessment

   Subjects without liver damage and patients presenting with ALI and
   iDILI suspicion were included in this study. DILI was diagnosed by
   clinical causality assessment, according to previously published
   criteria ([59]Benesic et al., 2016; see also Supplementary Table
   [60]S1) as well as RUCAM ([61]Danan and Teschke, 2016). The resulting
   classifications were “definite,” “highly likely,” or “probable” for
   iDILI and “possible” or “unlikely” for other ALI (liver injury due to
   other causes than iDILI). Informations on subjects and patients are
   presented in Supplementary Tables [62]S2, S3.

Cell Culture

   Monocyte-derived hepatocyte-like cells were generated by gradient
   centrifugation and adherence separation of peripheral blood monocytes
   and cultured as described in [63]Benesic et al. (2012). For toxicity
   testing, freshly generated MH cells were treated with 100 μM Diclofenac
   for 48 h (optimal concentration was determined by dose–response curves
   in 25 donors – unpublished data), followed by toxicity measurements, as
   described elsewhere ([64]Benesic et al., 2016). In brief, following
   cell exposure to Diclofenac or vehicle, lactate dehydrogenase (LDH) was
   measured by optical density using the Promega Cytotox non-radioactive
   Assay^® (Promega, Mannheim, Germany) from supernatants and cell lysates
   after lysis with 0.1% TWEEN in MOPS buffered saline. Each condition was
   tested in triplicate. LDH release was calculated from ODsupernatant:
   (ODsupernatant + ODlysate). MH cell test results were considered as
   positive when increase in LDH-release exceeded the fourfold standard
   deviation of vehicle control. For proteomics analysis, cryoconserved MH
   cells were thawed and treated with 100 μM Diclofenac or vehicle (DMSO)
   for 48 h, followed by sample preparation for proteomics analysis.

Proteomics Analysis

   Sample preparation was performed as described previously ([65]Kulak et
   al., 2014). For each condition, three biological replicates were used.
   Briefly, 50,000 MH cells were lysed, denatured, reduced, and alkylated
   in SDC buffer for 10 min at 95°C. Proteins were digested with Trypsin
   and LysC (1:100 w/w) at 37°C for 3 h. Acidified peptides were purified
   using SDB-RPS StageTips. Eluted peptides were completely dried using a
   SpeedVac centrifuge and suspended in LC loading buffer. Peptides were
   separated on a reverse phase column (50 cm length, 75 μm inner
   diameter) packed in-house with ReproSil-Pur C18-AQ 1.9 μm resin (Dr.
   Maisch GmbH, Ammerbuch, Germany). Reverse-phase chromatography was
   performed with an EASY-nLC 1000 ultrahigh pressure system, coupled to a
   Q-Exactive HF Mass Spectrometer (Thermo Scientific). Peptides were
   loaded with buffer A (0.1% (v/v) formic acid) and eluted with a
   nonlinear 100-min gradient of 5–60% buffer B (0.1% (v/v) formic acid,
   80% (v/v) acetonitrile) at a flow rate of 250 nL/min. After each
   gradient, the column was washed with 95% buffer B and re-equilibrated
   with buffer A. Column temperature was kept at 50°C by an in-house
   designed oven with a Peltier element. Operational parameters were
   real-time monitored by the SprayQC software ([66]Scheltema and Mann,
   2012). Raw files were analyzed by MaxQuant software (version 1.5.3.2)
   ([67]Cox and Mann, 2008) and peak lists were searched against the Homo
   sapiens Uniprot FASTA database (Version 2014/4) and a common
   contaminants database (247 entries) by the Andromeda search engine
   ([68]Cox et al., 2011). Label-free quantification was done using the
   MaxLFQ algorithm ([69]Cox et al., 2014) integrated into MaxQuant. Data
   was analyzed by using the Perseus software ([70]Tyanova et al., 2016).
   Positions containing non-valid values were filtered out in order to
   obtain a protein data set present in all samples. Means and standard
   deviations were calculated from the remaining data and groups according
   to the study subjects were annotated. In addition, the annotations
   contained information on vehicle (baseline) or Diclofenac treatment
   (treated) of the respective MH cells. The data were log2-transformed
   and normalized using the Z-score function of the Perseus software. The
   MS proteomics data have been deposited to the ProteomeXchange
   Consortium via the PRIDE ([71]Kullak-Ublick et al., 2017) partner
   repository with the data set identifier PXD008918.

Flow Cytometry Analysis

   Ethylenediaminetetraacetic acid-blood was diluted 1:2 in PBS 0.5% BSA
   and stained with 5 μL of anti-ITGB3 (#564174; BD Biosciences) or
   anti-IgG (#554681; BD Biosciences) for 45 min on ice. Samples were then
   washed twice, followed by erythrocyte lysis (lysis solution: 1.5 M
   NH[4]Cl, 0.1 M KHCO[3], 1 mM EDTA disodium salt, in double distilled
   water) for 7–10 min at room temperature. Samples were then centrifuged,
   the cell pellet was resuspended in PBS 0.5% BSA and analyzed for ITGB3
   expression using a BD Accuri flow cytometer (BD Biosciences).

Immunohistochemistry

   Liver samples for immunohistochemical analyses were selected from the
   tissue archives of the Institute of Pathology, Ludwig Maximilian
   University of Munich, Germany. Liver samples originated from patients
   with clinical indication for liver biopsy. Tissue sections were stained
   with anti-ITGB3 (Ventana) on an automated slide staining platform from
   Ventana, according to manufacturer’s instructions. For analysis,
   representative brightfield images were taken on a Leica DMD 108
   microscope at the indicated magnifications. ITGB3-positive infiltration
   spots at inflammation sites were counted at 40× magnification.

Statistics

   Data analysis and statistics for proteomics data including PCA, Heat
   Map, hierarchical clustering were performed by Perseus software
   ([72]Tyanova et al., 2016). The “proteinGroups.txt” file produced by
   MaxQuant was further analyzed in Perseus (version 1.5.1.6). PCA was
   done on the logarithmized LFQ intensities of all single-shot runs.
   Unsupervised hierarchical clustering of significantly (ANOVA, FDR <
   0.05) regulated proteins (z-scored MaxLFQ values) was performed in
   Perseus. Data from FACS-analysis and immunohistochemistry were analyzed
   using SPSS-software (IBM SPSS Statistics 25.0). After testing for
   normal distribution Student’s t-test or Kruskal–Wallis test were
   applied. Where applicable the Chi-Square test was applied. Results were
   considered significant when p < 0.05.

Results

   We included in our study six healthy subjects and 16 patients
   presenting with liver injury, initially suspected of iDILI. We further
   divided these subjects into two groups: one that had previously
   ingested Diclofenac (Diclo) and one which had never been exposed to
   Diclofenac before (Control), as a negative control (Table [73]1).
   Patients with liver injury were diagnosed based on clinical causality
   assessment (Supplementary Tables [74]S1, S2) including RUCAM. This
   resulted in the following subgroups: patients suffering from iDILI due
   to Diclofenac (DicloDILI), patients suffering from iDILI due to other
   drugs (Diclo_otherDILI and Control_otherDILI) and patients suffering
   from liver injury due to other causes than iDILI (Diclo_otherALI and
   Control_otherALI) (Figure [75]1A and Supplementary Table [76]S2).
   Subjects without liver injury were named Diclo_tolerator if they have
   been exposed to Diclofenac without resulting liver injury and
   Control_healthy if there was no Diclofenac exposure in the history.
   Importantly, all DicloDILI patients had a causality likelihood of at
   least “highly likely” for iDILI due to Diclofenac. In parallel to
   causality assessment, we generated MH cells from blood samples of each
   subject and used them for toxicity testing (MH cell test) and
   proteomics analysis (Figures [77]1B,C). The MH cell test was positive
   for Diclofenac only in DicloDILI patients, whereas co-medications in
   these patients tested negative (Supplementary Table [78]S2). These
   results confirmed the previously reported ability of MH cells to mirror
   individual drug response ([79]Benesic et al., 2016).

Table 1.

   Study subjects.
   Diclofenac exposure Yes No Comment
   DILI due to Diclofenac 4 (DicloDILI) n.a. Patients with DILI due to
   Diclofenac
   Healthy 3 (Diclo_tolerator) 3 (Control_healthy) Subjects without signs
   of liver injury
   DILI due to another drug 2 (Diclo_otherDILI) 4 (Control_otherDILI)
   Patients with DILI due to another drug
   Other ALI 3 (Diclo_otherALI) 3 (Control_otherALI) Patients with acute
   liver injury of non-drug cause
   Total 12 10 Total subjects: 22
   [80]Open in a new tab

   MH cells for proteomics analysis were obtained from 22 subjects: 12
   patients exposed to Diclofenac of whom four had DILI due to Diclofenac
   (DicloDILI), three had tolerated Diclofenac without signs of liver
   injury (Diclo_tolerator), two had DILI due to another drug but were
   concomitantly treated with Diclofenac (Diclo_otherDILI), and three were
   exposed to Diclofenac but had acute liver injury due to a non-drug
   cause (Diclo_otherALI). Another 10 patients were never exposed to
   Diclofenac, of whom three had no signs of liver injury
   (Control_healthy), four had DILI due to a drug other than Diclofenac
   (Control_otherDILI), and three had acute liver injury due to a non-drug
   cause (Control_otherALI).

FIGURE 1.

   [81]FIGURE 1
   [82]Open in a new tab

   Experimental design. (A) Generation of MH cells followed by proteomics
   analysis and toxicity testing in parallel and (B) protocol for MH cell
   treatment prior to proteomics analysis. Brightfield images are shown
   for illustrative purposes only.

   Mass spectrometry-based proteomics analysis of MH cells from the
   individual patients identified more than 2,700 proteins expressed in
   all subject groups. Hierarchical clustering of this data set revealed
   considerable differences in protein expression between sample groups
   (Figures [83]2A,B and Supplementary Figure [84]S1). As shown by the
   heat map diagram, DicloDILI specimens built a separate cluster compared
   to the other groups, independently of Diclofenac-treatment (vertical
   clustering in Figure [85]2A). Thus DicloDILI specimens clustered
   furthest away from samples of healthy/exposed subjects, which had
   received and tolerated Diclofenac. These observations were confirmed by
   PCA, showing that the proteome of untreated MH cells from DicloDILI
   patients (DicloDILI_baseline) display a unique signature compared to
   all other subject groups (termed “other_baseline”) (Figure [86]2B and
   Supplementary Figure [87]S1). Moreover, protein expression in MH cells
   from DicloDILI patients showed a distinct shift after
   Diclofenac-treatment compared to vehicle control (DicloDILI_treated).
   In contrast, Diclofenac-treatment failed to induce substantial changes
   to protein expression in MH cells derived from nonDicloDILI subjects
   (other_treated; Figure [88]2B). Furthermore, hierarchical clustering
   identified two main protein expression clusters (horizontal clustering
   in Figure [89]2A): one that is predominantly downregulated in DicloDILI
   (turquois) and one that is mainly upregulated in DicloDILI (red). This
   distinct regulation pattern was only present in DicloDILI specimens and
   not in the other groups. Together with the results of the PCA analysis,
   this suggests that differences in the proteome of MH cells could
   reflect individual susceptibility to Diclofenac-DILI.

FIGURE 2.

   [90]FIGURE 2
   [91]Open in a new tab

   Proteomics analysis of MH cells identifies differentially regulated
   proteins in the investigated subject groups. (A) Hierarchical
   clustering of the different sample groups, treated with vehicle (–) or
   Diclofenac (+). Vertical clustering displays similarities between
   sample groups, while horizontal clusters reveal differentially
   regulated protein groups. The upregulated protein cluster marked by the
   dotted rectangle was further assessed for pathway enrichment, as
   displayed in (C). (B) Principal component analysis of protein
   expression in MH cells treated with either vehicle (baseline) or 100 μM
   Diclofenac (treated). MH cells of DicloDILI patients were compared to
   all other groups (Diclo_tolerator, Diclo_otherALI, Diclo_otherDILI,
   Control_healthy, Control_otherALI, and Control_otherDILI), grouped
   under the term “other.” (C) Pathway enrichment analysis within the
   protein cluster mainly upregulated in DicloDILI (dotted rectangle from
   A).

   Since Diclofenac caused toxic responses only in MH cells of patients
   with Diclofenac-DILI and downregulation of proteins may be due to
   cytotoxicity, we focused on the protein cluster predominantly
   upregulated in DicloDILI (rectangle in Figure [92]2A) as a source of
   potential drug specific biomarkers. Therefore, we performed pathway
   enrichment analysis to reveal main biological mechanisms underlying
   DILI by Diclofenac (Figure [93]2C). Thus, we identified five signaling
   pathways, which were significantly upregulated in DicloDILI upon
   Diclofenac exposure (Figure [94]2C and Supplementary Table [95]S4).

   In order to identify specific biomarkers for Diclofenac-DILI, we
   searched for proteins that were upregulated upon Diclofenac-treatment
   exclusively in MH cells from DicloDILI patients and not in the other
   groups. Additionally, the potential biomarker should be easy to detect,
   e.g., from a blood sample, to facilitate clinical application. Under
   the top 10 upregulated proteins in MH cells from DicloDILI patients,
   only one membrane protein met these criteria: ITGB3, which was fourfold
   upregulated in the DicloDILI condition upon Diclofenac-treatment, in
   contrast to the other groups (Supplementary Table [96]S5). In line with
   this finding, several cell adhesion molecules were also enriched within
   the protein cluster upregulated in DicloDILI (Figure [97]2C). ITGB3 is
   expressed on peripheral blood cells ([98]Luscinskas and Lawler, 1994;
   [99]Harris et al., 2000), which indicated that it can be measured in
   blood samples.

   Integrin beta 3 expression was determined by flow cytometry analysis of
   erythrocyte depleted whole blood (Supplementary Figure [100]S2). Flow
   cytometry analysis of ITGB3 expression in the different subject groups
   revealed that patients diagnosed with Diclofenac-DILI displayed a
   reduction in the relative amount of ITGB3-positive cells in whole blood
   compared to other groups (Figures [101]3A,B). Based on the results of
   the ROC analysis, we determined a threshold of 60% ITGB3-positive cells
   in order to discriminate DicloDILI patients from other subjects.
   Importantly, all values of DicloDILI patients were below this
   threshold, while those of the other ALI and other DILI subjects were
   above the threshold. We also observed a correlation between the levels
   of ITGB3-positive cells and the course of liver parameters
   (Supplementary Figure [102]S3). Thus, DicloDILI2 displayed the lowest
   relative amount of ITGB3-positive cells, and had the worst outcome,
   requiring liver transplantation. By contrast, DicloDILI3 yielded the
   highest value for ITGB3 among the DicloDILI group and recovered within
   a few weeks after the initial liver injury and stopping of Diclofenac
   administration.

FIGURE 3.

   [103]FIGURE 3
   [104]Open in a new tab

   Diclofenac-DILI patients express lower levels of ITGB3 compared to
   control subjects. (A) Histogram plots of erythrocyte-lysed whole blood
   samples from three DicloDILI patients and one healthy subject, analyzed
   by flow cytometry for ITGB3 expression. In red: percentage of
   ITGB3-positive cells. (B) Distribution of ITGB3 values among subjects
   from each group. n = 3 for healthy, n = 7 for other ALI, n = 13 for
   other DILI, and n = 3 for DicloDILI. Red dotted line: threshold (= 60%)
   determined by receiver operating characteristic (ROC) analysis and used
   to discriminate between ITGB3-positive and ITGB3-negative samples. ^∗p
   < 0.05.

   We further investigated whether ITGB3 expression changes with disease
   progression/remission. DicloDILI1 and DicloDILI2 were included in the
   study during the acute phase of the drug reaction, also confirmed by
   strong elevation of the liver enzymes ALT and AST (Supplementary
   Figures [105]S3A,B). At this time point, ITGB3 expression was below the
   determined threshold of 60% (Supplementary Figures [106]S3A,B). Closer
   monitoring of ITGB3 expression in DicloDILI1 and DicloDILI2 over a time
   period revealed that ITGB3 correlated inversely with liver enzymes (ALT
   and AST) and thus, with patient recovery (Supplementary Figures
   [107]S3A,B). These results suggest that ITGB3 might also be used to
   monitor disease progression and possibly support decision making for
   the need of medical interventions.

   Finally, we searched for an explanation why ITGB3 is upregulated in
   Diclofenac-treated MH cells from DicloDILI patients, but downregulated
   in whole blood of the same subject population. We postulated that
   ITGB3-positive cells would be recruited from the peripheral blood into
   the liver during Diclofenac-induced liver injury. To prove this theory,
   we performed immunohistochemical staining for ITGB3 in liver biopsies
   of DicloDILI2, one other ALI and one other DILI patient. We observed an
   enhanced expression of ITGB3 for DicloDILI2 compared to the other two
   samples, predominantly at inflammation sites (Figure [108]4A).
   Quantification of ITGB3 expression at these sites revealed significant
   differences between DicloDILI2 and other ALI/other DILI (Figure
   [109]4B).

FIGURE 4.

   FIGURE 4
   [110]Open in a new tab

   Immunohistochemical staining of liver biopsies with antibody against
   ITGB3. (A) Representative brightfield images of ITGB3 expression in
   liver biopsies from the different patients (10× and 20×). Arrows
   indicate ITGB3-positive infiltrations. Diffuse brown areas represent
   unspecific staining. Scale bar: 100 μm. (B) Relative quantification of
   ITGB3-positive infiltrations at inflammation sites (n = 18 for
   DicloDILI2, n = 17 for other ALI, and n = 24 for other DILI. ^∗p <
   0.05, ^∗∗p < 0.005, whereas n represents the number of inflammatory
   sites per biopsy). (C) Schematic representation of the role of ITGB3 in
   Diclofenac-DILI. After initial liver injury by Diclofenac,
   ITGB3-positive cells may be specifically recruited into the liver of
   susceptible patients and contribute to inflammation and liver damage.

Discussion

   The need for specific DILI biomarkers is an international acknowledged
   issue targeted by the EMA through the IMI projects SAFE-T^[111]1 and
   MIP-DILI^[112]2. Moreover, the United States FDA encourages these
   programs and has issued a letter of support for the further development
   and use of the biomarkers identified by the SAFE-T Consortium
   ([113]Food Drug Administration, 2016). Nevertheless, up to date, DILI
   biomarker candidates are either not liver specific, or they cannot
   differentiate between the causes for liver damage and ultimately
   between the responsible drugs ([114]Andrade et al., 2009). To our
   knowledge, we are the first to report on a combined technique which
   uses proteomics analysis of MH cells to identify drug-specific
   biomarkers. Through analysis of protein expression profiles of MH cells
   from different donors, we found that patients suffering from
   Diclofenac-DILI cluster away from healthy controls and patients with
   liver injury due to other causes. Thus, differences in baseline protein
   expression might reflect predisposition to develop Diclofenac-DILI,
   which is known to affect only susceptible patients ([115]Boelsterli,
   2003). Moreover, Diclofenac-treatment of MH cells from DicloDILI
   patients lead to upregulation of a significant amount of proteins.
   Pathway enrichment analysis revealed that many of these proteins are
   involved in immune response and metabolism by cytochrome P450 enzymes.
   This is in line with existing data, showing that both mechanisms
   contribute to hepatocellular injury by Diclofenac ([116]Boelsterli,
   2003). The same data set also showed enrichment in cell adhesion
   molecules, such as ITGB3. This suggests that ITGB3 plays an important
   role in inflammatory response and liver regeneration upon injury
   through Diclofenac-treatment. In support of this theory, our
   preliminary results showed ITGB3 accumulation at inflammatory sites in
   the liver of a DicloDILI patient, compared to one other ALI and one
   other DILI subject. This indicates that ITGB3-expressing cells might be
   recruited into the liver upon injury, which would also explain their
   decrease in peripheral blood (Figure [117]4C).

   A major challenge in the search for drug-specific DILI-biomarkers is
   the rare frequency of events. Even in large registries, iDILI cases
   adjudicated to the same drug rarely exceed 10 or 20 individuals. Thus,
   even a few false positives can cause significant dilution and lead to
   disappointing results. In this proof-of-concept study we could show
   that the combination of novel techniques for diagnosis and sample
   analysis can help to overcome major challenges in the iDILI setting:
   Even few test subjects allow the identification of promising novel
   markers. Taken altogether, this study provides evidence for ITGB3 as
   potential biomarker for Diclofenac-induced DILI. Although limited by a
   small sample cohort, ITGB3 meets all of the criteria defined by EMA for
   DILI biomarkers ([118]European Medicines Agency, 2016). Furthermore,
   investigations to support our findings are ongoing: currently, we could
   confirm the ITGB3 decrease in blood in three cases with DicloDILI. EMA
   requires that the DILI biomarker provides an early or earlier diagnosis
   compared to current standards. ITGB3 can be used as an early diagnostic
   marker, which specifically detects Diclofenac-DILI from a small blood
   sample. ITGB3 can be as easily assessed as liver enzymes, and in
   contrast, is able to identify the cause of the liver damage. According
   to EMA, a DILI biomarker should also have the ability to predict DILI
   outcome and monitor disease progression. In this regard, ITGB3
   expression correlated inversely with progression/remission in two
   individual patients. Additionally, the patient displaying the lowest
   ITGB3 values was the one requiring a liver transplant, suggesting that
   ITGB3 might also be used for outcome prediction. The last criterion of
   EMA refers to the ability of the biomarker to differentiate between
   adaptors and patients which incur upon drug treatment. ITGB3 was found
   to be upregulated only in Diclofenac-treated MH cells from DicloDILI
   patients and not in healthy subjects exposed to Diclofenac.
   Nevertheless, future investigations for the analysis of initial liver
   enzyme elevations, followed by ALI or adaption and correlations with
   ITGB3 expression are required. As a current context of use we propose
   measurement of ITGB3 in whole blood derived from patients who present
   with ALI and a history of Diclofenac intake. If ITGB3 is decreased,
   this strongly points toward the diagnosis DILI by Diclofenac. Further
   studies of ITGB3 expression and regulation shall provide valuable
   insights into cellular mechanisms underlying Diclofenac-DILI, thereby
   addressing a pressing challenge in the field of iDILI characterization
   and prediction ([119]Funk and Roth, 2017).

Ethics Statement

   This study was conducted in accordance with the ethical standards of
   the Declaration of Helsinki (1964) and was approved by the local ethics
   committee (Project No. 055-13; [120]ClinicalTrials.gov Identifier
   [121]NCT02353455). All participating subjects have signed the informed
   consent form prior to their inclusion in the study.

Author Contributions

   DD, AB, and AG conceived and designed the experiments. GP and NK
   performed proteomics measurements and analysis. DD performed MH
   testing, FACS experiments, and data analysis with AB. HB performed
   immunohistological stainings and analysis. DD and AB wrote the paper.
   AG critically revised and approved the manuscript.

Conflict of Interest Statement

   AB: stockholder MetaHeps GmbH and owner of IP. DD: employee MetaHeps
   GmbH. AG: stockholder MetaHeps GmbH and owner of IP. GP and NK:
   founders of PreOmics GmbH. HB: nothing to disclose.

Acknowledgments