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Toxicology and Applied Pharmacology xxx (2010) xxx–xxx

YTAAP-11948; No. of pages: 12; 4C: 7, 8

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Toxicology and Applied Pharmacology

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EU Framework 6 Project: Predictive Toxicology (PredTox)—overview and outcome

Laura Suter a,⁎, Susanne Schroeder b, Kirstin Meyer c, Jean-Charles Gautier d, Alexander Amberg d,Maria Wendt e, Hans Gmuender e, Angela Mally f, Eric Boitier d, Heidrun Ellinger-Ziegelbauer c,Katja Matheis g, Friedlieb Pfannkuch a

a Hoffmann-La Roche AG, Basel, Switzerlandb Nycomedc Bayer Schering Pharma AG, Berlin, Germanyd Sanofi aventis R&D Disposition, Safety & Animal Research, Frankfurt, Germanye Genedata AG, Basel, Switzerlandf University of Wuerzburg, Wuerzburg, Germanyg Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany

⁎ Corresponding author. F. Hoffmann-La Roche AG, N310a, 4070 Basel, Switzerland.

E-mail address: [email protected] (L. Sute

0041-008X/$ – see front matter © 2010 Elsevier Inc. Aldoi:10.1016/j.taap.2010.10.008

Please cite this article as: Suter, L., et al., EUPharmacol. (2010), doi:10.1016/j.taap.2010

a b s t r a c t
a r t i c l e i n f o
Article history:Received 9 June 2010Revised 6 October 2010Accepted 9 October 2010Available online xxxx

Keywords:ToxicogenomicsProteomicMetabolomicsPredictive toxicologyFP6Consortium

In this publication, we report the outcome of the integrated EU Framework 6 Project: Predictive Toxicology(PredTox), including methodological aspects and overall conclusions. Specific details including data analysisand interpretation are reported in separate articles in this issue. The project, partly funded by the EU, wascarried out by a consortium of 15 pharmaceutical companies, 2 SMEs, and 3 universities.The effects of 16 test compounds were characterized using conventional toxicological parameters and “omics”technologies. The three major observed toxicities, liver hypertrophy, bile duct necrosis and/or cholestasis, andkidney proximal tubular damage were analyzed in detail.The combined approach of “omics” and conventional toxicology proved a useful tool for mechanisticinvestigations and the identification of putative biomarkers. In our hands and in combination withhistopathological assessment, target organ transcriptomics was the most prolific approach for the generationof mechanistic hypotheses. Proteomics approaches were relatively time-consuming and required carefulstandardization. NMR-based metabolomics detected metabolite changes accompanying histopathologicalfindings, providing limited additional mechanistic information. Conversely, targetedmetabolite profilingwithLC/GC-MS was very useful for the investigation of bile duct necrosis/cholestasis. In general, both proteomicsand metabolomics were supportive of other findings.Thus, the outcome of this program indicates that “omics” technologies can help toxicologists to make betterinformed decisions during exploratory toxicological studies. The data support that hypothesis on mode ofaction and discovery of putative biomarkers are tangible outcomes of integrated “omics” analysis.Qualification of biomarkers remains challenging, in particular in terms of identification, mechanisticanchoring, appropriate specificity, and sensitivity.

on-clinical safety, Building 73/

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l rights reserved.

Framework 6 Project: Predictive Toxicology.10.008

© 2010 Elsevier Inc. All rights reserved.

Introduction

Hepato- and nephrotoxicity are major attrition factors in preclinicaldrugdevelopment. Thus, a strongendeavourofpharmaceutical researchis to reduce the failure rate by improving preclinical safety evaluation.Currently, routine preclinical safety assessment relies on a specific set ofparameters consisting mainly of clinical observations, serum biochem-istry, hematology, andhistopathology.However, several of the currentlyused endpoints and biomarkers are insensitive, or not robust or specificenough for certain organ toxicities (Olson et al., 1998, 2000). As anexample, nephrotoxicity is routinely detected by blood urea nitrogen

(BUN) and serum creatinine, which show significant changes only aftersubstantial kidney injury has occurred (Hewitt et al., 2004; Vaidya et al.,2006). Also, it is widely accepted that no single-test-based biomarker issufficiently effective for the investigation of drug-induced liver injury interms of specificity and sensitivity (Shi et al., 2010). Thus, experienceshows that many liver and kidney markers lack sensitivity and are onlyaltered when substantial tissue damage has taken place. Therefore,there is an urgent need to improve our understanding of thepathophysiological processes associated with toxicity. A means toreach that goal is the integration of molecular profiling (“omics”)technologies, like transcriptomics, proteomics, and metabolomics, intoregular toxicological assessments (Luhe et al., 2005; Ruepp et al., 2005;Lord et al., 2006; Keun and Athersuch, 2007; Gallagher et al., 2009).Within the InnoMed PredTox project, performed under the EUFramework Program 6, conventional toxicological parameters and

(PredTox)—overview and outcome, Toxicol. Appl.

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2 L. Suter et al. / Toxicology and Applied Pharmacology xxx (2010) xxx–xxx

different “omics” technologies were applied together to evaluatetoxicological studies in rats. The goals of this collaborative effort weredefined as follows:

• more informed decision making earlier in preclinical safetyevaluation by combining results from “omics” technologies togetherwith conventional toxicology methods

• development of scientists within Systems Toxicology due tocollaborative approach and intensive networking.

A project of this size and complexity could only be performedwithina collaboration partly publicly funded (EU Framework 6 program) andwith participation of committed parties from the private (Pharma andSMEs) and public (Universities) sectors. Thus, 15 pharmaceuticalcompanies, 2 SMEs, and 3 universities actively participated during the3 years of the project with a total budget of € 8 Mio (Table 1). Thecollaboration started in October 2005 and finished in January 2009.During this period, the consortium set up and refined the scientific plan,completed the operational aspects, carried out the experiments,performed the data analysis, and provided mechanistic reports and asummary of the outcome (Fig. 1). Due to the complexity of the scientifictopic, theworkwasdivided intoworkpackages each including scientistswith relevant expertise. In addition, once the data had been collected,three expert working groups (EWGs) were formed to evaluate in detailthree toxicity phenotypes of interest, namely kidney toxicity (reportedin this issue (Matheis et al., submitted for publication)), bile ductnecrosis (reported in this issue (Ellinger-Ziegelbauer et al., submittedfor publication)), and liver hypertrophy (reported in this issue (Boitieret al., submitted for publication)). These expert groups analyzed all dataavailable for studies displaying pertinent pathologies and discussed theunderlyingmolecularmechanisms. The experimentalplanwasbasedonin vivo animal studies in rats using a number of drug candidates forwhich development was discontinued partly due to liver and/or kidneytoxicities. Hence, the first challenging task in this project was theidentification and selection of an appropriate set of compounds to betested. Also, a pivotal activity was to define standardized and optimizedstudy protocols to perform the most appropriate in vivo animal studieswith the selected compounds. This included considerations on rat strain,gender, compound dose, and endpoints to be evaluated. Besides theperformance of the necessary animal studies, data storage and analysisstrategies needed to be defined and implemented to allow dataexchange among the participants as well as optimal data mining andanalysis. Finally, biological and mechanistic interpretation of the

Table 1Participants of the PredTox Consortium.

Participant name Type

Bayer-Schering Pharma (Bayer) PharmaBayer-Schering Pharma (Schering) PharmaBio-Rad (Ciphergen)* SMEBoehringer Ingelheim PharmaF. Hoffmann-La Roche PharmaGenedata SMEJohnson & Johnson PharmaLilly* PharmaMerck-Serono (Merck) PharmaMerck-Serono (Serono) PharmaNovartis PharmaNovo-Nordisk PharmaNycomed (Altana) PharmaSanofi-aventis (D) PharmaSanofi-aventis (F) PharmaSchering-Plough (Organon) PharmaServier PharmaUniversity College Dublin AcademiaUniversity of Hacettepe AcademiaUniversity of Würzburg Academia

Note: *indicates participant not claiming funding by EU, names in brackets indicatecompany names at the start of the consortium.

Please cite this article as: Suter, L., et al., EU Framework 6 Project: PredPharmacol. (2010), doi:10.1016/j.taap.2010.10.008

findings was addressed by performing confirmatory and complemen-tary experiments based on the results obtained from the in vivo studies.

In this publication, we report the scope of the project and themethodology used and summarize the overall outcome of ourcollaborative effort including a preliminary comparative evaluation ofthe added value of each “omics” technology. Specific details includingdata analysis and interpretation are reported in separate articles(reported in this issue (Boitier et al., submitted for publication;Ellinger-Ziegelbauer et al., submitted for publication; Matheis et al.,submitted for publication)).

Materials and methods

Compound selection and study design. Test compounds for this projectwere selected by the participating pharmaceutical companies basedon pre-defined criteria. Substances were included whose develop-ment had been discontinued at certain stages of preclinical develop-ment due to toxicological findings in liver and/or kidney in 2- to 4-week systemic rat studies. The compounds included 14 proprietarydrug candidates from participating companies and 2 reference toxiccompounds: gentamicin and troglitazone (Table 2). For most of thecompounds, only preclinical information was available, whereas forthe model compounds, clinical data exist.

Each of the compounds was tested in a 2-week systemic rat studyusing male Wistar rats (N=15 per dose group) treated either withvehicle (control), a low dose or a high dose of the test substance(Table 3). Doses were selected based on pre-existing information,with the aim of achieving target organ toxicity after 2 weeks ofexposure with the high dose to provide phenotypic anchoring. Inorder to follow the development of the toxicological process over doseand time, groups of 5 animals were sacrificed at day 2 (singleadministration), day 4 (three daily administrations), and day 15 (14daily administrations). For all animals, clinical observations and bodyweights were recorded. Serum, plasma, blood, as well as liver andkidney tissue were collected at necropsy for further investigations.Portions of the tissues were either snap frozen in liquid nitrogen for“omics” endpoints or fixed in 10% buffered formalin for histopatho-logical evaluation. Serum and plasma (in EDTA-coated tubes) wereprocessed following standard procedures. In addition, for someanimals, urine was collected overnight under refrigerated conditionsat several time points (days−3, 2, 4, and 13). All animal studies wereperformed according to European or national animal welfare regula-tions and authorized by the corresponding local ethical committees.

Collection of data. For all animals, conventional toxicologicalendpoints were collected alongside with transcriptomics (Tx),proteomics (Px), and metabolomics (Mx) profiles. The sample typesutilized to collect this information are schematically represented inFig. 2. Conventional toxicological endpoints included histopathologyof the target organs liver and kidney (including an independent peerreview), serum biochemistry, hematology, and urine analyses.Whenever possible, toxicokinetic parameters were collected at theexpected maximal exposure (Cmax).

Histopathology slides were scanned and linked to a web-basedmultimedia management system, Digital SlideServer (DSS), tofacilitate remote viewing by pathologists and peer review (Mulraneet al., 2008).

Transcriptomics (Tx). Liver, kidney, and blood (in PAXgene tubes)weresubjected to total RNA extraction and sample processing for microarrayanalysis following the provider's recommendations at 11 different sites(Novartis, Merck, Nycomed, Schering, Bayer, Boehringer Ingelheim,Serono, Johnson & Johnson, Eli Lilly, Novo Nordisk, and Organon).Briefly, approximately 100 mg (b 250 mg) of frozen tissue washomogenized for total RNA extraction using RNeasy columns (Qiagen).Purified total RNA was submitted to DNase digestion using RNase-Free

ictive Toxicology (PredTox)—overview and outcome, Toxicol. Appl.


Fig. 1. Project plan and time lines.

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DNase (Qiagen) before 5 μg purified total RNA were used for cDNAsynthesis, in vitro transcription, and fragmentation using the GeneChipExpression 3′ Amplification One-Cycle Target Labelling kit and ControlReagents (Affymetrix). For blood gene expression analysis, 0.5 ml bloodwas collected directly into a 2-ml tube containing 1.38 ml PAXgenestabilizing solution (aliquoted from PAXgene blood RNA tubes,PreAnalytiX). Total RNA extraction was basically performed followingthe PAXgene instructions slightly adapted to the smaller volumes used.Blood total RNA (3 μg) was mixed with 3 μl globin reduction reagents(Genelogic) before cDNA synthesis; otherwise, sample processing wasanalogous to that for tissue RNA. Fragmented in vitro transcripts(cRNAs) were hybridized onto RAE 230_2.0 microarrays (Affymetrix),scanned, and used for further analysis.Microarrayqualitywas evaluatedusing the Refiner Array software (Genedata). Statistical gene expressionanalysis was performed using Expressionist (Genedata), and theresults are reported in the accompanying papers (reported in thisissue by (Boitier et al., submitted for publication; Ellinger-Ziegelbaueret al., submitted for publication; Matheis et al., submitted forpublication)).

Proteomics (Px). Proteomics analysis was performed in target organtissue (liver and/or kidney), plasma and urine of selected studies.Three different proteomics platforms were used depending on thebiological matrix: 2D-PAGEwas used for the analysis of urine samples,SELDI for plasma, and tissue samples and 2D-DIGE for selected tissuesamples.


For surface-enhanced laser desorption ionization (SELDI) analysis,liver kidney and plasma were processed at three different experimentalsites (Merck-Serono, Bio-Rad, University College Dublin) following astandardized protocol. All spectrawere collected using a PCS-4000 SELDI-TOF mass spectrometer (Bio-Rad Laboratories) using cation exchange(CM10 pH 4.0) and anion exchange (Q10 pH 9.0) chromatographicsurfaces for all samples. Spectra were collected using a low mass (2–25 kDa) and highmass (10–200 kDa) range protocol. Raw SELDI-TOFMSspectral data were processed and analyzed using two independentmethods, including Bio-Rad's ProteinChip DataManager software andGenedata's Expressionist® Software with Refiner MS Module (Collinset al., 2010). Statistical analyses, including Mann–Whitney, N-ANOVA,and t-tests were used to screen for protein peaks that exhibitedstatistically significant differences between treatment groups.

Urineproteomics analysis by2D-PAGEwereall performedatone site(F. Hoffmann-La Roche Ltd.). For each selected study, urines collected at4 different time points from 5 controls and 5 high dose-treated animalswere used for analysis. All urine samples were analyzed by two-dimensional gel electrophoresis (nonequilibrium pH gel electrophore-sis, 12% SDS-PAGE), and the proteins were identified bymatrix-assistedlaser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Urine samples were desalted by ultra filtration with asimultaneous buffer exchange to isoelectrofocusing buffer. Each gelwas loaded with 0.75 mg of total protein and stained after separationwith colloidal Coomassie blue. The spotswere automatically detected, inmost cases, supplemented bymanual editing. In average, 300–400 spotsper gel were automatically excised and placed into 384-well microtiter



Table 2Compounds included in the analysis.

FP6 study name Compound sponsor Compound ID(sponsor)

Main toxicitytarget organ

Expected outcome of in-life study Actual outcome of in-life study Inclusion in EWG analysis

FP001RO Roche R2717 Liver Liver hypethrophy, increased bilirubin Liver: increase in bilirubin and minimal tomild hepatocellular hypertrophy

(EWG I)

FP002BI Boehringer Ingelheim BI-2 Kidney Intracytoplasmatic inclusion bodies(but no papillary necrosis) after 2 weeks,renal papillary necrosis after 4 weeks

Kidney: hyaline droplets and increasedrelative kidney weights

FP003SE Serono MS001 Liver Slight to moderate increases in ALP, ALT, AST;phospholipids, changes in cholesterol andtriglycerides, and increased bilirubin

Liver: increase in serum triglycerides, transaminases,and decrease in serum protein

EWG I

FP004BA Bayer Bay16 Liver Liver necrosis, bile duct hypertrophy/hyperplasia Liver: bile duct damage and hepatocyte necrosis;regenerative hyperplasia and inflammatoryresponses, and cholestasis and fibrosis

EWGII

FP005ME Merck LCB 3343 Liver Liver necrosis, cell hyperplasia and fibrosis,bile duct necrosis, and increased bilirubin

Liver: hepatocellular apoptosis, necrosis; alsoperibiliary inflammation and fibrosis, bile ductproliferation and necrosis, and hepatocellularhypertrophy

EWGII

FP006JJ Roche R7199 Liver Liver steatosis, changes in cholesterol and triglycerides Liver: increased fat staining of hepatocellular cytoplasm,consistent with steatosis

FP007SE Boehringer Ingelheim BI-3 Liver and kidney Necrosis in liver and kidney, hypertrophy in liver Liver: increased transminases, ALP, and bilirubin;Pericholangitis with bile duct hyperplasia, cholestasis,inflammation, hepatocellular hypertrophy, and vacuolation.Kidney: necrosis, basophilia, dilation and casts of renaltubules, partly associated with acute inflammation

EWGII, EWGIII

FP008AL Altana BYK001 Liver Increased liver weight and mild centrilobularhypertrophy in liver

Liver: increased liver weights, elevated liver enzymes,and slight liver hyperthrophy.

EWG I

FP009SF Reference compound(Gentamicin)

Gentamicin Kidney Tubular necrosis in kidney Kidney: minimal to mild tubular degeneration/necrosisassociated with mild to moderate tubular regenerationand minimal to moderate mononuclear cell infiltrate

EWGIII

FP010SG Reference compound(Troglitazone)

Troglitazone Liver Peroxisome proliferation, increased liver weights Liver: decrease in triglycerides and liver weight increase,associated with liver hypertrophy

(EWG I)

FP011OR Organon Org 31565 Liver Necrosis in the liver Liver: moderate (midzonal) vacuolation of hepatocytes,and mild liver hypertrophy

EWG I

FP012SV Servier SAI2007 Liver Increased liver weight and hypertrophy, changesin cholesterol and triglycerides

Liver: increase in liver weight

FP013NO Novartis IMM125 Liver & Kidney Liver necrosis and bile duct hyperplasia.Tubular toxicity at after 4 weeks

Kidney: increased tubular basophilia and mononuclearcell foci; minimal to moderate corticomedullary mineralization,tubular swelling, and vacuolation. Liver: absence ofhepatocellular glycogen deposits in animals of groupsincreased fatty change

EWGIII

FP014SC Bayer Schering Pharma(formerly Schering)

ZK226830 Liver Liver necrosis Liver: severe, acute necrotic liver injury and elevated livertransaminases; cholestasis, hepatocellular vacuolation andhypertrophy as well as regeneration and increased mitosis

EWGII

FP015NN Novo Nordisk NN414 Liver Liver weight increase and diffuse hepatocytehypertrophy

Liver: increase in liver weight

FP016LY Lilly LSN2032406 Liver and kidney Hepatocellular vacuolation and hypertrophy inthe liver; cortical tubule regeneration in the kidney

Liver: hepatocellular hypertrophy and hepatocellular vacuolation.Kidney: no histopathological changes but increased kidney weight

EWG I

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Table 3Study design for each of the compounds.

Group Animal ID No. ofanimals

Treatment Applicationvolume

Total Nof doses

A 1–5 5 Vehicle 5 ml/kg 1B 6–10 5 Low dose 5 ml/kg 1C 11–15 5 High dose 5 ml/kg 1

D 16–20 5 Vehicle 5 ml/kg 3E 21–25 5 Low dose 5 ml/kg 3F 26–30 5 High dose 5 ml/kg 3

G 31–35 5 Vehicle 5 ml/kg 14H 36–40 5 Low dose 5 ml/kg 14I 41–45 5 High dose 5 ml/kg 14

Note: low and high doses were defined for each compound based on previousknowledge and personal communications.

Fig. 2. Graphic representation of study design and sample collection. Several body fluids andanalyzed as depicted in this scheme. *Indicates that selected samples (not all animals) wer



plates for in-gel digestion with trypsin. The obtained peptides wereanalyzed byMALDI-TOF-MS, and 10–40proteins per gelwere identifiedby comparison to protein databases using in-house developed software.The detection of specific biomarkers was performed by comparison ofthe MS identifications in the urine samples from the treated incomparison to the control animals.

Tissue 2-dimensional difference gel electrophoresis (2D-DIGE) anal-ysis was performed at three different sites (Sanofi-aventis, Novo Nordiskand Novartis). Proteins were extracted from frozen tissue samples fromhigh-dose and control animals collected on day 15. Extracted proteins(50 μg) from each sample were labeled with two different fluorescentcyanine dyes. Protein extracts from all samples were pooled and labeledwith a third fluorescent cyanine dye in order to serve as an internalstandard for spotmatchingwithinagel andnormalizationacross eachgel.The three dye-labeled samplesweremixed andwere separated onto a 2Dgel which was performed in the pH range of 5.5–6.7 and frommolecular

tissues were collected from animals in the 16 studies, and the indicated endpoints weree submitted to the analysis.




weight of 20 to 150 kDa. For a gel, separate images of all three dyes wereobtained with a CCD camera and analyzed with Decyder version 6.5 (GEHealthcare). Significantly modulated spots were determined byperforming a Student's t-test with following criteria: fold change≥1.3inabsolutevalueandp-value≤0.01.Deregulated spotswereexcised fromthe gels using an automated spot picker and proteins digested withtrypsin. The tryptic peptides were subsequently analyzed by MALDI-TOFMS orMALDI-TOF/TOF or nanoLC-MS/MS (depending on sites) followingstandardizedprotocols. The peptidepeak listswereused toqueryUniprotor NCBI nr mammal's databases using Mascot search program (version2.1.04) with standardized parameters (one missed cleavage, cysteinecarbamidomethylation for global modification, methionine oxidation forvariablemodification, andamass toleranceof75 ppm). Eachmatchabovea 95% confidence interval with a significant probability-based Mowsescore defined by a Mascot probability analysis was carefully manuallyvalidated.

Metabolomics (Mx). Metabolomics analysis was performed in serumand urine samples using both 1HNMR and LC-MS technologies. 1HNMRspectroscopy of serum and urine was performed at 5 sites (BoehringerIngelheim, Lilly, Novo Nordisk, sanofi-aventis and Schering), followingthe agreed standardized protocol. Briefly, 630 μl urine was mixedwith 70 μl of a 1 M phosphate buffer in D2O (pH 7.0) containing 10 mMd4-trimethylsilylpropionate (TSP) as shift lock reagent or 200 μl serumwas mixed with 400 μl of 0.9% saline in water/D2O (09:10). Thensamples were centrifuged and transferred into 5-mm NMR tubes.Samples were measured on BRUKER 500 MHz spectrometer. Watersuppression was achieved for urine and serum with the noesygppr1dpulse sequence, for serum additionally, the cpmgpr pulse sequence wasused. The obtained high-resolution 1H NMR spectra were manuallyphase andbaseline correctedand referenced toTSP (0.00 ppm) forurineor to lactate (1.33 ppm) for serum samples. The spectra were thencalibrated, normalized to the total integral, and finally integrated intobins of 0.04 (urine only), 0.01 and 0.001 ppm. Signals of water and urea(for urine only) were excluded by deleting bins of the spectral regions

Fig. 3. Data bas


for urine between 4.4 and 6.2 ppm and for serum between 4.5 and5.1 ppm. Metabolites were identified by their chemical shifts (ppm)with the help of the Chenomx NMR Suite 5.1 software.

LC-MS-based metabolic profiling of urine and serum samples wereperformed at 6 sites (Boehringer Ingelheim, Novartis, sanofi-aventis,Servier, Organon, and Waters) according to a harmonized protocol. Forsample preparation, 50 μl of urinewasmixedwith 100 μl water and 50 μlserumwasmixedwith100 μl acetonitrile. Sampleswere thencentrifuged(13,000 rpm, 10 min) and transferred into LC-MS vials. LC/MS analysiswas run on a Acquity UPLC System coupled to Micromass LCT Premier(ESI-TOF) spectrometer controlledbyMassLynxversion4.1 software. Theanalytical columnwas an ACQUITY BEH C18 (dimensions: 2.1×100 mm,1.7 μm) kept at 40 °C. An injection volume of 10 μl was used with a flowrate of 0.500 ml min−1. Solvents were water with 0.1% formic acid(solvent A) and acetonitrile with 0.1% formic acid (solvent B) with thefollowing gradient: 0 min 100 % A, 4.00 min 80 % A, 9.00 min 5 % A,10.00 min 5 % A, 12.00 min 100 % A. The following MS parameters wereused: ionization mode: ESI-negative and ESI-positive (2 separate runs),survey scan mass range, 50–1000 Da; nebulization gas, 700 lh−1 at450 °C; cone gas, 15–20 lh−1; source temperature, 120 °C; cone voltage,40 V, capillary voltage, 3000 V (ES+) or 2500–2800 V (ES–); LCT-Woptics mode, 12,000 resolution using dynamic range extension (DRE);data acquisition rate, 0.1 s with a 0.01-s interscan delay; lock spray,leucine/enkephalin (lock mass, 556,2771m/z); data collection, centroidmode averaged over 10 scans.

Construction and implementation of a relational database and dataanalysis. The variety of data types and amount of data collected atseveral sites and using different platforms for the 16 defined studiesrequired a customized, relational database (DB) able to store compound-related information, animal- and sample-related information, as well asthe measured data. The DB was designed by the participant Genedatawith input fromall other participants (defining user requirements basedon specific platform needs) and hosts all the data produced during thisproject, including raw and processed data (Fig. 3). This DB is a pivotal

e content.



Fig. 4. Example of liver hypertrophy (EWG I). Exemplary photomicrographs(magnification 400×) from one representative specimen of (A) control animal withnormal liver morphology and (B) liver hypertrophy in a high-dose animal of the samestudy (FP008AL). Arrows indicate central vein. Depictions kindly provided by SeanCallanan and Laoighse Mulrane (University College, Dublin).


resource from which the scientists participating in the data analysisprocessedandthescientists of theExpertWorkingGroups (EWGs) coulddraw the necessary information. Data analysis strategies were thusdesigned for each data type taking into consideration platform-specificaspects. Genedata provided initial data analysis for each technology andbiological matrix based on agreed quality parameters, normalizationstrategies, and statistical approaches. The vast amount of data wasanalyzed in several ways, depending on each specific scientific question.Each study was analyzed independently or together with other studiespresenting similar phenotypes for mechanistic investigations. Also,overall analysis was performed to some extent for the construction ofpredictive models within and across technology platforms. However,due to the overwhelming amount of data, the analysis could not beexhaustive and additional approaches could be applied to this very richresource. The full data set has been submitted to the EBI (EuropeanBioinformatics Institute:www.ebi.ac.uk/), curated via the ISA infrastruc-ture (Rocca-Serra et al., 2010) andwill be accessible via themulti-omicsBioInvestigation Index database (http://www.ebi.ac.uk/bioinvindex/browse_studies.seam). This will be made publicly available by mid-2010 at EBI Statistical, and biological interpretation of the data wasperformed in close collaboration with the technology experts, the studysponsors (responsible for the assessment of each individual study), andthe EWGs.

Confirmatory and mechanistic investigations. Several approaches werepursued to support the biological interpretation of the data from thedifferent studies. Hypotheses regarding the mechanism of individualcompounds derived from “omics” data were further investigated togenerate additional supportive data. To corroborate interesting findingsobtained usingmetabolomics platforms, specific, targeted LC-MS analysisand additional GC-MS analyseswere performedon annumber of selectedsamples. The performance of putative liver and kidney biomarkersidentified from “omics” results and literature was assessed in tissue andbody fluid samples and compared to the conventional endpoints, i.e.,histopathology and clinical chemistry. To this end, changes in theexpression/excretion of novelmarker genes, proteins, and/or biochemicalbiomarkers indicative of hepatic or renal injury were analyzed using avariety of techniques such as qRT-PCR, immunoblotting, immunohisto-chemistry, ELISA, LC-MS/MS, and their sensitivity and specificity for renaland liver injury relative to clinical chemistry parameters were assessedusing receiver–operator characteristics (ROC) analyses. Outcome of theseinvestigations is reported elsewhere (Adler et al., 2010; Hoffmann et al.,2010). To make best use of the limited amount of tissue available, tissuemicroarrayswere created from formalin-fixed paraffin-embedded (FFPE)of rat liver and kidney tissue specimens (Mulrane et al., 2008), andsubsequently used for immunolocalization of putative biomarkers intarget and non-target tissues.

Results and discussion

Within the InnoMed PredTox project, we addressed the use of“omics” technologies in the field of toxicology with the aims of makingmore informed decisions in preclinical safety evaluation. Moreover, thecollaborative nature of the program enabled us to intensify scientificexchange that promoted the development of scientists within SystemsToxicology. In this publication, we report the overall outcome of theproject and the conclusions we reached, whereas specific results arereported in other publications (Adler et al., 2010; Collins et al., 2010;Hoffmann et al., 2010) and also reported in this issue (Boitier et al.,submitted for publication; Matheis et al., submitted for publication)).

Study outcome

For eachof the studies, the compound sponsormade ananalysis of allcollected data and put it into the context of the previous knowledge onthe compound. It would be beyond the scope of this publication to


report the outcome of each individual study. However, some pivotaldata that were necessary to draw the overall conclusions aresummarized in Table 2. For most of the studies, the expectedtoxicological outcome was achieved. However, and contrarily to ourexpectations, in some cases (e.g., FP014SC), the onset of thedamagewasalready clear at the low dose and at early time points. In other cases, theexpected pathology was not observed after 2 weeks of treatment. Forthe study FP013NO, additional groups of animals treated for 28 daysshowed the expected pathology. Differences in outcome are inherent toanimal experimentation and had to be taken into account for the dataanalysis and interpretation process.

The individual study data were used for further integrated analysiswith the primary aim focusing on the molecular characterization of thethree major phenotypes observed, namely liver hypertrophy (Fig. 4),liver bile duct hyperplasia and hepatocyte necrosis and/or cholestasis(Fig. 5), and kidney proximal tubular damage (Fig. 6). We wereinterested in investigating common changes within each of thesephenotypically anchored groups at the gene, protein, and metabolitelevel. Hence, the studies were assigned into three groups that wereindependently analyzed by the three defined EWGs, summarized belowand published separately in more detail (reported in this issue (Boitieret al., submitted for publication; Ellinger-Ziegelbauer et al., submittedfor publication; Matheis et al., submitted for publication)).

Technology outcome

It was not a main objective of this project to make a systematicassessment of technology platforms, and the study design precludes us


http://www.ebi.ac.uk/

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Fig. 5. Example of bile duct hyperplasia (EWG II). Exemplary photomicrograph(magnification 400×) from one representative liver showing bile duct hyperplasia in theportal region (arrowheads) in a high-dose animal of study FP005ME. Depictions kindlyprovided by Sean Callanan and Laoighse Mulrane (University College, Dublin).


from making a thorough assessment. However, results from conven-tional endpoints and “omics” approaches including several technologyplatforms, were collected and analyzed. Thus, some aspects regardingperformance, outcome, robustness, throughput, and general ease of useand usefulness could be discussed within the project.

Sample collection of blood and urine has inherent constraints interms of form and frequency of collection but is amenable to repeatedsampling and longitudinal studies, while tissue sampling can only bedone at necropsy. Thus, clinical chemistry, proteomics, and metabo-lomics (in serum/plasma and urine) or transcriptomics (in blood) areclearly less invasive than target organ histopathology, transcrip-tomics, or proteomics in tissue.

Conventional toxicology parameters includemainly clinical biochem-istry and histopathology. The former usually complements histopatho-logical evaluation, is fast, is inexpensive, and can rely onwell-establishedexperience but is often insensitive and does not provide muchmechanistic information. Histopathological assessment of target organs,includingpeer-reviewof slides, is the current gold standard in toxicology.It is cost-effective, relatively fast to perform, and delivers assessmentsbased on professional experience regarding biological interpretation.However, it has a subjective component difficult to incorporate intostatistical analysis, and the information on underlying mechanisms islimited. In general and whenever performed, immunolocalization ofselectedmarkers showed good agreementwith gene expression changesand histopathology, providing additional information into the initiation

Fig. 6. Example of kidney necrosis/regeneration (EWG III). Exemplary photomicrograph(magnification 400×) from one representative kidney showing tubular regeneration(arrow) and individual cell necrosis (arrowhead); from a high-dose animal from studyFP009SF. Depictions kindly provided by Sean Callanan and Laoighse Mulrane(University College, Dublin).


andprogressionof toxic injury. For example, Kim-1was overexpressed intissue as one of the earliest responses detected after proximal tubularinjury (Hoffmann et al., 2010).

Regarding proteomics approaches, gel-basedmethods (2D-PAGE and2D-DIGE) were relatively time-consuming and required careful stan-dardization. For 2D-PAGE, harmonizationwas achieved by processing allsamples in one laboratory, while 2D-DIGE was standardized byperforming pilot studies and ensuring identical operating procedures atthree different laboratories. The use of these proteomics methodspresented some advantages and disadvantages. On the one hand, wefaced the issues of relatively low throughput (0.5 month per study forurine 2D-PAGE and3 months per study for one target organ for 2D-DIGE)andhigh costs in termsofmanpower that precludedus fromanalyzing allcollected samples in the context of this collaboration. In addition, and alsodue to the partial coverage of available samples, the sensitivity withrespect to dose could not be evaluated because only a limited number ofsamples were tested (mainly control and high-dose samples). Also,mainly high abundance cytosolic proteins appeared to be detected with2D-DIGE. On the other hand, 2D-DIGEprovideduseful informationon themechanism of toxicity even if a limited portion of the proteome wasinvestigated. For example, cross-study 2D-DIGE proteomics analysis ofproximal tubule injury showed a deregulation of proteins oxidativestress, detoxification, and energy metabolism (reported in this issue,Matheis et al., submitted for publication), and proteins regulated aftertreatment with troglitazone reflected its pharmacology. Some of theprotein changes detected by 2D-DIGE were confirmed by immunoblot(see below). In some cases, the expression of the markers was alsomodulated at the lower dose, giving these markers potential asbiomarkers for early detection. SELDI approaches posed also a challengewith regard to detection of lower abundance proteins and reproducibilitybetween laboratories. The latter could be solved with the correct level ofstandardization, and the throughput was acceptable (0.5 month for allplasma samples in a study, including profiling and data analysis). Here,the rate-limiting process was the identification of candidate markers,which was cost- and resource-intensive. In addition, some identifiedmarkers such as CRP fragment, haptoglobin, β2-microglobulin, ApoA1,and ApoM were known markers of stress and unspecific for a particularphenotype. This specificity issue could potentially be overcome bysample pre-processing to remove high abundance proteins in order tofocus on the most toxicologically relevant proteins.

In our experimental setup, NMR-detectedmetabolite changeswere assensitive as histopathology with respect to time and dose yet providedlittle contribution to mechanistic understanding due to the limitednumber of metabolites covered. In addition, metabolite annotation workwas time-consuming and had to be performed manually using theChenomx software. An automated way of annotation could speed up thisprocess. Urine turned out to be the most informative biofluid in terms ofdiscriminatingNMRmetabolite candidates. Such as an increase in urinaryphenylacetylglycine and an urinary decrease in trigonelline as a potentialmarker for mobilization of oxidative stress signaling or decreasedglutamine and increased glucose in urine as potential markers for PPARα/γ agonism. However, and similarly to some proteomics markers, someof these metabolites were common between the several phenotypes,suggesting a low level of specificity. Finally, although metaboliteidentification is a crucial step, toxicological annotation/interpretation ofthe identified metabolites is in its infancy and therefore much effortwould be required to improve this toxicologically relevant knowledge forfuture applications. It was outside the scope of this collaboration toevaluate the overall potential of LC-MS. However, a targeted approachanalyzing individual bile acids was applied for the bile duct necrosis/cholestasis phenotype evaluated by the EWG II. A targeted bile acidanalysis confirmed hypotheses drawn from classical and transcriptomicsdata and suggested unconjugated vs. conjugated bile acids as potentialmechanism-basedmarkers for hepatocyte vs. bile duct damage (reportedin this issue, Ellinger-Ziegelbauer et al., submitted for publication). Thus,this approach was very effective for confirmation of hypotheses derived




from toxicogenomics, proteomics and metabolomics (NMR). Additionalanalyses by GC-MS (Sieber et al., 2009) revealed common metabolicchanges within all 3 studies showing bile duct effects. In addition, for anindividual study (FP005ME), LC/MS analysis of 4-hydroxy-trans-2-none-nal-derived glutathione conjugates and their corresponding mercapturicacids supported the hypothesis that reduced detoxification of reactivealdehydes through inhibition of aldo-keto-reductase, the pharmacolog-ical target of the drug, may contribute to liver injury. This was consistentwith transcriptomics data showing oxidative stress/Nrf2 responsefollowing exposure to this compound (Unpublished data). In this case,prior knowledge of the pharmacological target was necessary for theinterpretation of the ‘omic-data.

Our results indicate that gene expression in some cases indicatesonset of molecular changes before (with respect to time and dose)conventional endpoints. For example, cholestasis with onset after2 weeks of treatment could be predicted using gene expression profilesobtained after 2 days (reported in this issue, Ellinger-Ziegelbauer et al.,submitted for publication). Similarly, in some cases, amarked inductionof genes involved in fatty acid metabolism/β-oxidation was observedafter 2 days whereas the phenotype liver hypertrophy was diagnosedhistopathologically only 15 days after treatment (reported in this issue,Boitier et al., submitted for publication). Also, in our hands, target organtranscriptomics was used for data analysis and interpretation and wasthe most prolific “omics” approach for the generation of mechanisticmodels. Blood transcriptomics provided only little additional informa-tion, mainly related to general stress and without phenotype-specificinsights.

One important aspect from this collaboration refers to dataanalysis and interpretation. Although there is a plethora of ways ofanalyzing multivariate “omics” data and several different ways ofinterpreting the results, in our experience, the overall outcome wasrelatively independent of the tools applied. This applied to bothinternal analysis tools from each participant member and to differentstatistical approaches performed.

Confirmatory assays

Within the scope of this collaboration, a subset of pathwaysand biomarkers selected based on “omics” results and literaturewere subjected to further analysis using alternative methodologies(qRT-PCR, immunoblotting, IHC, ISH, ELISA, LC-MS/MS). Selectedbiomarkers from the kidney were kidney injury molecule (Kim-1),clusterin, lipocalin-2/NGAL, tissue inhibitor ofmetalloproteinase(Timp-1), osteopontin, heme oxygenase 1 (HO-1), glycine amidino-transferase (AGAT), peroxiredoxin 1, catalase, glutathione peroxidase1, phosphoenol-pyruvate carboxykinase 1, and plasma retinol bindingprotein 4. Candidate liver markers included clusterin, paraoxonase(PON-1), thiostatin, and lipocalin-2.

For nephrotoxicity, three compounds that caused proximal tubuledamage resulted in alterations of candidate renal biomarkers thatwere frequently detected at earlier time points than traditionalclinical parameters. The overexpression of the known kidney injurymarker Kim-1 in tissue was one of the earliest responses detected. Inaddition urinary Kim-1 together with clusterin was increased at earlytime points and thus appeared to be the most sensitive, non-invasivemarker tested with regard to kidney injury. In contrast, Lipocalin-2,which has been proposed as a marker to predict acute kidney injury inthe clinical setting, was found to be not specific for kidney toxicity(Hoffmann et al., 2010).

In the liver, several markers were assessed in connection with theinduction of bile duct damage that resulted in some alterations ofcandidate biomarkers. For example, while serum clusterin and PON-1were not consistently altered in response to liver injury, lipocalin-2/NGAL and thiostatin levels in urine and/or serum generally reflectedthe onset and degree of liver injury (Adler et al., 2010).


EWG outcome

As mentioned above, the analysis and interpretation of the dataacross technology platforms were divided into three groups based onthe phenotypical anchoring provided by the pathological evaluation.These groups were named ExpertWorking Groups I through III and theoutcomeof their investigations are reported separately. Here,we brieflysummarize the overall conclusions of their analyses. EWG I covered allstudies where hypertrophy in the liver was themajor histopathologicalfinding. Based on results from individual analyses, some studies showedhepatocellular hypertrophy mainly associated with induction ofxenobiotic metabolizing enzymes (FP003SE, FP008AL, FP011OR, andFP016LY), while others showed hepatocellular hypertrophy mostlycharacterized by peroxisomal proliferation (FP001RO and FP010SG:troglitazone). EWG II focused on bile duct damage and/or cholestasis,and hepatocyte necrosis, represented in the studies FP004BA, FP005ME,FP007SE, and FP014SC. EWG III addressed nephrotoxicity characterizedby proximal tubular damage (basophilia, degeneration/regeneration,necrosis) and infiltration of mononuclear cells. Studies FP007SE,FP009SF (gentamicin) and FP013NO were included in this group.

EWG 1: hepatocellular hypertrophy. Data from the studies charac-terized either byXME-mediatedor PP-mediated liver hypertrophywereanalyzed collectively (reported in this issue, Boitier et al., submitted forpublication). In this analysis, transcriptomic evaluation of the liver wasthe most fruitful approach for the generation of mechanistic models. Inresponse to a xenobiotic stimulus, a marked increase in the expressionof target genes for nuclear receptors such as AhR, CAR, and PXR wasobserved, consisting of phase I and II metabolizing enzymes, and phaseIII transporters. This resulted in a striking proliferation of the smoothendoplasmic reticulum (SER) accompanied by the marked up-regula-tion of many SER-specific genes under the transcriptional regulation oftheUnfoldedProteinResponse (UPR) sensorXBP-1. TheNRF2-mediatedoxidative stress signalingpathwaywas alsopositively affected, likely viathe up-regulation of the UPR protein kinase PERK. Therefore, thephenotypic expression of the induction of SER proliferation wasconsistent with the hepatocellular hypertrophy observed microscopi-cally in these 4 XME studies. Differently, compounds causing peroxi-someproliferation via PPAR alpha activation showed amildmodulationof XME genes together with a repression of SER-specific genes. Instead,these compounds caused the characteristic up-regulation of genesinvolved in peroxisomal fatty acid beta oxidation associated with anincrease in peroxisome proliferation, as shown by the up-regulation ofthe peroxisomal peroxins PEX11A and PEX19 involved in peroxisomebiogenesis. Therefore, for this subset of compounds (2 PP studies), theincrease in peroxisome abundance was the main mechanism respon-sible for the mild hepatocellular centrilobular hypertrophy. Thismechanism derived from transcriptomics findings was furthersupported by the induction of certain proteins detected by 2D-DIGE,namely proteins involved in fatty acid beta oxidation, ketogenesis,carbohydrate metabolism, and xenobiotic metabolism.

EWG2: liver bile duct necrosis. This group was characterized by bileduct (BD) damage and hyperplasia (except 14SC) and/or increasedbilirubin and cholestasis, hepatocyte damage including enzyme release,hepatocyte regeneration and hypertrophy, and inflammation in the bileduct and/or hepatocyte area (reported in this issue, Ellinger-Ziegelbaueret al., submitted for publication). It consisted of four studies (FP004BA,FP005ME, FP007SE, and FP0014SC). The different “omics” data typescontributed to various extents to mechanistic interpretation of thephenotype. Transcriptomics data revealed common gene expressionchanges in the livers, which allowed to develop a potential sequence ofmolecular events leading to the histopathological observations. Thisincluded acute damage to BD epithelial cells or hepatocytes, character-ized by early stress responses followed by apoptosis/necrosis, regener-ative processes, inflammation with inflammatory cell immigration,




fibrotic processes, and cholestasis. Due to the inherent limitation oftissue transcriptomics, gene expression data did not allow us todistinguish between effects on bile duct epithelium or hepatocytes,which initiated immunolocalization investigations. Studies with anti-bodies against lipocalin-2/NGAL and proliferating cell nuclear antigen(PCNA) co-located with the sites of injury observed histologically.Proteomics analysis (2D-DIGE) of the livers of the 5ME study identifiedhigh abundance proteins indicative of an acute response. Targetedmetabolomics based on LC/MSdata suggested increased conjugated andunconjugated bile acids in serum and urine as biomarker forextrahepatic and intrahepatic cholestasis, respectively. Increased levelsof thiostatin (2D-PAGE, confirmed by ELISA) in urine, as well aslipocalin-2/NGAL levels in urine and/or serum (determined by ELISA),were detected in parallel to BD damage (Adler et al., 2010), yet areprobably not specific but rather general markers for tissue damage.Overall, transcriptomics data were useful to generate mechanistichypotheses that explain classical observations. Metabolomics (LC/MS)provided confirmatory information and suggested potential biomarkersmeasurable in body fluids.

EWG3: kidney proximal tubular damage. Cross study and cross “omics”analysis of Expert working group III focused on three studies (FP007SE,FP009SF: gentamicin and FP013NO) showing nephrotoxicity to proximaltubules (reported in this issue, Matheis et al., submitted for publication).Biological interpretation of the kidney PTD-transcriptomics data revealedderegulation of genes known to be associated to kidney damage,complement system, immune response, cytoskeleton components, celldeath, proliferation, and oxidative stress. For each of these group ofgenes, a comparison was performed with respect to specificity of genederegulation in kidney as compared to the kidney transcriptomics data ofthe 13 remaining PredTox studies. Transcriptomics data werecomplemented by proteomics results, resulting in identification ofputative biomarkers. Some of the identified genes (e.g., ATF3, SPP1,PSMB9, CP, TIMP1, and CALB1) appeared to be prodromalmarkers, as theywere deregulated before (in terms of dose or time point) morphologicaldamage of the proximal tubules was observed histopathologically.

With regard to the biomarkers identified using “omics” technologieshere and described in literature, additional determinations either by RT-PCR, immunohistochemistry, Western blot, and ELISA were performed,among them, Kim-1, Clusterin, Lcn2, Vimentin, Hmox1 (HO-1), Osteo-pontin (Spp1), and Timp1. Changes in gene/protein expression generallycorrelatedwellwith renal histopathological changes andwere frequentlydetected earlier than traditional clinical parameters. Overall, no consis-tent changes between studies were evident, although overexpression ofKim-1 was often one of the earliest responses and might be seen as themost sensitive tissuemarker of kidney injury. In contrast, lipocalin-2wasfound to be less sensitive and not specific for kidney toxicity (Hoffmannet al., 2010).

Overall conclusions

At the beginning of this project, we dedicated a relatively largeamount of time to the selection of appropriate compounds based onwell-defined selection criteria in terms of expected toxicity. It wasrecognized how crucial an appropriate set of test compounds was tothe project, in particular since only a very limited number ofsubstances could be tested. Similarly important for the success ofour endeavour was the appropriate study design, based on availableknowledge from the participant pharmaceutical partners. In order toaddress if “omics” technologies could provide prodromalmarkers thatwould predict a toxic injury at lower doses and/or earlier time pointsthan histopathology, it was mandatory that we obtained tissues fromanimals treated with compounds without displaying the toxicphenotype. This was not always the case, since the reproducibilityof animal studies was not always warranted. However, in most cases,


the outcome of the in vivo studies matched our expectations(Table 2).

One of the centerpieces of the project was the DB, with a highlycustomized design to allow interlinked storage of very different datatypes and mining across these data, within each individual study andacross several studies. Within the scope of the cross-omic dataanalysis, only a limited “omics” data integration was possible, andmuch of the work was essentially performed manually. The mainreasons for this shortcoming were the incomplete data set, since dueto time and resource constraints not all “omics” endpoints could beevaluated for each sample. In addition, the available item annotationdiffered widely between technologies hampering systems biology-type approaches. Furthermore, systems toxicology analysis platformsable to carry out such integrated evaluations are still lacking. Furtheranalysis of this very rich resource should be applied once it is publiclyavailable at EBI (European Bioinformatics Institute: www.ebi.ac.uk/).

Regarding the utilized technologies, metabolomics approachesusing 1H NMR represent a stable, standardized, and well-establishedtechnology with fast data generation, while LC/MS approaches aremore difficult to standardize but more sensitive than NMR resulting ina higher number of detected metabolites. In our setting, targeted LC/MS metabolomics contributed to the mechanistic interpretation andthe identification of potential toxicity biomarker candidates for bileduct necrosis/cholestasis, while NMR approaches had little impact onour mechanistic conclusions. Similarly, most of the proteomics datathat were supportive of the mechanistic hypothesis were based ontissue proteomics (2D-DIGE). This technology, despite offeringrelatively low throughput, provided valuable information that wascomplementary to other data sources. Analysis of proteins in the urinewith 2D-PAGE, provided some putative biomarkers for toxicity buttheir specificity to a given lesion was not warranted. SELDI analysis ofsamples was difficult to interpret, mainly due to inherent restrictionsin detecting lower abundance proteins and the difficulties of peakidentification. Transcriptomics platforms such as Affymetrix and othercommercial providers are stable, standardized and well established inmany laboratories (Guo et al., 2006; Shi et al., 2006). The fast datageneration and the intrinsic availability of biological annotationsfacilitate biological interpretation. However, they are costly and thelink to mechanistic models remains complex and generally requiressubsequent confirmatory experiments. Nevertheless, in our experi-mental setup, transcriptomics, together with conventional endpoints,built the back bone of most of the mechanistic interpretations.

Despite of the enumerated limitations, many valuable conclusionscould be drawn. From the onset of the program, it became clear thatwe could not generate and validate predictive models based only onthree to five compounds displaying similar toxicities, since predictive,robust statistical models would require a much larger number oftested compounds. Therefore, we focused mainly on the mechanisticanalysis accompanied by the discovery of molecular biomarkers basedeither on our own data or on literature.

We concluded that the main changes that led to mechanisticinterpretation of the findings were similarly interpreted using differentstatistical and pathway mapping tools, highlighting the robustness ofthe outcome. Overall, tissue transcriptomics data in combination withhistopathology delivered the best results formechanistic understandingtoxic injury. In addition, 2D-DIGE proteomics and targeted LC/MSmetabolomics results provided supportive evidence. As an example, forthe evaluation of hepatocellular hypertrophy, only invasive “omics”approaches (transcriptomics and 2D-DIGE applied to the target organ)delivered detailedmechanistic information. Moreover, and in particularregarding transcriptomics, compounds with no or little hepatic liabilitycaused very few gene expression changes in the liver, while compoundswithout known or observed nephrotoxicity did not significantly affectthe gene expression profiles in the kidney. Thus, the technology in ourhands was not prone to cause false positive signals, a major source ofconcern in preclinical safety assessment.


http://www.ebi.ac.uk/



For the identification of suitable biomarkers, tissue transcrip-tomics, proteomics, and metabolomics were deemed as useful,whereas little additional information was obtained from the analysisof gene expression in blood. In general, “omics” technologies provideda subset of genes/proteins/metabolites that were associated withtissue damage. However, the specificity of the induction of a givenendpoint and the identification of the discovered metabolites andproteins were challenging and required additional, tailor-madedeterminations. On the one hand, the specificity of markers such aslipocalin-2/NGAL for bile duct necrosis was questionable, since it is aknown stress marker and was also modulated by compounds thatcaused nephrotoxicity but not liver injury. However, considering thesensitivity and good correlation with histopathological findings,lipocalin-2/NGAL and thiostatin determined in urine may serve asminimally invasive diagnostic markers of inflammation and tissuedamage. On the other hand, known markers like Kim-1 (transcriptand protein) specific for kidney tubular damage could be confirmed inour experiments by a variety of technologies. Regarding transcrip-tomics analysis, additional insights could be obtained if regulatoryprocesses such as regulation by miRNA would also be evaluated.

Summarizing, the outcome of this exciting program indicates that“omics” technologies provide additional information that can helptoxicologists to make better informed decisions during exploratorytoxicological studies. Although we did not address predicitivity of“omics” with the limited data set, the data fully support thatconfirmation of mechanistic hypothesis and discovery of putativebiomarkers are very tangible outcomes of integrated “omics” analysis.At least some of the identified, putative markers are regulated beforehistopathological effects are detected and can be considered prodro-mal biomarkers. Further qualification of these putative biomarkerswith larger number of samples, time points, and compounds will givea conclusive answer regarding their predictive power in terms offorecasting tissue damage before it is detectable by other, moreconventional, tools used in toxicology assessment. Thus, we haveinsufficient information to draw a definitive conclusion on predici-tivity and have seen most of the value on “omics” technologies inidentifying putative biomarkers and in generating mechanistichypothesis. The caveat that needs to be kept in mind is thatpreliminary knowledge on the pharmacology and whenever possiblethe toxicological profile of the substance under investigation arenecessary or at least greatly helpful whenmaking an integrated safetyassessment. In addition to the scientific outcome, due to the intensiveexchange from a personal and scientific point of view, we areconvinced that we have educated ourselves and other scientists in thefield of systems toxicology. Those two main goals of the project werethus fulfilled. In the future, the learning drawn from this program interms of technology and data analysis should be taken into account tofurther promote the advancement of this field. Bearing this in mind,active discussion with toxicological societies, regulatory authorities,and scientists in general should be sought.

Conflict of interest statement

The PredTox project was evaluated by independent experts onrequest of the European Commission. There are no conflicts of interestof authors or consortium members since the project was funded bypublic (EU) funds. The PredTox project is dedicated to basic research,and there are no commercial interests.

Acknowledgments

We wish to thank all colleagues who have contributed toperformance of the various studies, measurements, and analyses forthis project, for without their commitment, the performance of thestudies and the data collection would not have been possible.


ThePredToxProject (www.innomed-predtox.com)was supportedbypartial funding by the 6th Research Framework Program of the EuropeanUnion (Innomed PredTox, LSHB-CT-2005-518170). Additional financialand/or scientific support was provided by the consortium members(Bayer-Schering Pharma—former Bayer and Schering, Bio-Rad—formerCiphergen, Boehringer Ingelheim, F. Hoffmann-La Roche, Genedata,Johnson & Johnson, Lilly, Merck-Serono—former Merck Darmstadt andSerono,Novartis,Novo-Nordisk,NycomedGmbH—formerAltanaPharmaAG, sanofi-aventis (D), sanofi-aventis (F), Schering-Plough—formerOrganon, Servier, University College Dublin, University of Hacettepe,University of Wuerzburg). In addition, technology providers such asAffymetrix, Entelos, Waters, and Genelogic (Ocimem) were very helpfulwith technical support.

Wewould also like to thank the scientific advisors for their supportthroughout the project (Timothy Gant, Christopher Gerner, JohnHaselden, Peter Kasper). A special thank goes to Prof. David Tweats forhis thoughtful input and guidance.

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toxicology and applied pharmacology - genedata...eu framework 6 project: predictive toxicology...

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