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Current Molecular Medicine 2005, 5, 53-64 53

The Role of Transcriptome Analysis in Pre-Clinical Toxicology

George H. Searfoss*, Timothy P. Ryan and Robert A. Jolly

Lilly Research Laboratories, Department of Investigative Toxicology, Eli Lilly and Company,Greenfield, IN 46140, USA

Abstract: A major benefit of the genomics revolution in biomedical research has been theestablishment of transcriptome analysis as an enabling technology in the drug development process.

Nowhere in the realm of drug development has the expectation of the impact of transcriptome analysis

been greater than in the area of pre-clinical toxicology. Transcriptome analysis, along with other new

high-content data generating technologies, has the potential to radically improve the drug safety

assessment process by allowing drug development teams to identify potential toxicity liabilities earlier,

and thus proceed only with those molecules that have both efficacy at the target and a low potential

for toxicity in the human population. In this review we will briefly describe the major ways in which

transcriptome analysis is being applied in the pre-clinical safety assessment process, focusing

primarily on four areas where transcriptome analysis has already begun to have impact. These include

using transcriptome analysis to: 1) understand mechanisms of toxicity: 2) predict toxicity: 3), develop

in vivo and in vitro surrogate models and screens; and, 4) develop toxicity biomarkers. We will close

by briefly addressing future trends and needs in the application of transcriptome analysis to drug

safety assessment.

INTRODUCTION trends suggest that this approach may not besustainable, and that more affordable and predictiveapproaches are needed (see:FDA-Critical Path whitepaper http://www.fda.gov/oc/initiatives/criticalpath/)This realization is forcing the industry to reevaluatethe drug development process in an attempt toreduce cycle times and improve efficiency [1].

Assessing the pre-clinical toxicity of candidatedrugs is a critical component of the multi-step, multi-year development process that begins when targetsare first identified, and ends with the release of atherapeutic into the patient population. Safetyscreening in animal models has traditionally beeninitiated during the lead optimization phase of pre-clinical drug development (see Figure 1), although insilico and in vitro cytotoxicity data for a chemicalplatform is now driving earlier engagement at the hit-to-lead phase. As molecules move through lead

optimization and candidate selection into first humandose, more extensive rodent and large animalstudies are required. Assessing the toxicologicalproperties of a molecule in these later phases relieson the traditional approach of detailed clinicalobservations in test animals, and analysis of organsand bodily fluids. This analysis consists of clinicalchemistry and hematology measurements from bloodand urine, gross morphology, and histopathologicalassessment of all organs and tissues. This process iseffective in identifying coincident toxicities, but it islabor intensive, costly, and can sometimes fail topredict longer-term toxicities. The traditionalapproach to drug safety assessment is primarily a

descriptive process, often with minimal mechanisticunderstanding derived from the data, and does notallow scientists to predict outcomes from studies oflonger duration or at higher doses. Although theparadigm described above has served thepharmaceutical industry well for many years, recent

One way to realize improvements in efficiency indrug development is to take advantage of emergingtechnologies, many of which provide high contentdata in a high through-put manner, require lesscompound and fewer animal studies, and better

predict potential liabilities of molecules. Many ofthese emerging technologies were borne out ofsignificant advances in digital electronicsminiaturization, robotics, and computational tools, alof which have been leveraged against the completegenome sequence. One of the earliest genomictechnologies to emerge was transcriptome analysisHerein, transcriptome analysis is defined broadly toinclude any of a number of methodologies thamonitors the transcription state (mRNA population) oa cell, tissue, or organ. Transcriptome analysisranges from the examination of a single transcript tolarge-scale transcript analysis (microarray), where thelevels of thousands of transcripts are measured

simultaneously. It is beyond the scope of this reviewto describe the various transcript profilingapproaches and platforms or the numerous dataacquisition and analysis tools used to deconvolutelarge-scale transcript data. The authors direct thereaders to a number of excellent reviews on thesesubjects [2-6].

The use of transcriptome analysis in addressingissues of toxicity is a major component of therecently emerged discipline known asToxicogenomics. Toxicogenomics can be broadly

*Address correspondence to this author at the Lilly ResearchLaboratories, Department of Investigative Toxicology, Eli Lilly andCompany, Greenfield, IN 46140, USA; Tel: (317) 276-1045; Fax: (317)277-6770; E-mail: [email protected]

1566-5240/05 $50.00+.00 2005 Bentham Science Publishers Ltd.


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54 Current Molecular Medicine, 2005, Vol. 5, No. 1 Searfoss et al

Figure 1. This diagram illustrates where transcriptome analysis can have an impact in the drug development process. Thedrug development process is indicated by a stylized pipeline, with major steps and phases indicated within. The double-

headed arrows indicate broadly the range of where in the process the three major transcriptome analysis applications can

be used. Application color coordinated text boxes above the pipeline briefly describe the nature of the impact of

transcriptome analysis at various places in the drug development process.

defined as applying genomics-based approaches tothe characterization of toxic responses. There hasbeen considerable discussion in the toxicologycommunity concerning the application oftoxicogenomic approaches, and there are a number

of comprehensive reviews on the subject available tothe reader [7-10]. There is a sense that geneexpression data may be more sensitive thentraditional toxicological endpoints with the potential,when implemented earlier in the drug developmentprocess, to provide valuable information about bothdrug safety and efficacy. Ultimately, the appropriateapplication of transcriptome analysis has thepotential to improve safety, shorten the drugdevelopment cycle time, and reduce costs [11]. Inthis review we will discuss broadly the major areas inwhich transcriptome analysis is being employed inpre-clinical safety assessment, including: 1)understanding mechanisms of toxicity; 2) developing

predictive toxicogenomics tools; 3) developingsurrogate models and screens identifying; and, 4)developing toxicity biomarkers. We will also brieflytouch on future directions and challenges for theapplication of transcriptome analysis in pre-clinicalsafety assessment.

development stages depicted in Figure (1), theymust show value in mechanistically determining howtoxicities manifest or how they will progress with doseand time. From its inception, transcriptome analysishas had application for understanding mechanisms

of toxicity. The reason that analysis of thetranscriptome can provide insight into the underlyingmechanisms of toxicity is that cells respond tochanges in their environment, both internally andexternally, by altering gene transcription. Thisresponse can occur rapidly, as the cell has a highlydeveloped sensing and signal transductionmachinery that transmits signals of perturbation viacomplex and directed pathways to control elementsof target genes within the nucleus. With thesequence of relevant genomes now known, globagene expression arrays can be produced and usedto interrogate changes in the mRNA populationswhose steady state has been altered by drug

treatment. The general axiom that changes intranscript levels reflect changes in levels of theproteins they encode, coupled with the knowledge ofthe role of the encoded proteins in the cell, allowsresearchers to develop an understanding of how thesystem is being perturbed by the molecule undeinvestigation. Using global transcriptome analysis founderstanding toxicity mechanisms addresses thefundamental underpinning of the drug safetyassessment process for several reasons: 1) transcripprofiles can be used to develop and verifyhypotheses; 2) the enhanced understanding otoxicity mechanisms provided by transcriptome

USE OF TRANSCRIPTOME ANALYSIS INUNDERSTANDING MECHANISMS OFTOXICITY

Before new toxicology methods and practices canbe applied to any of the pre-clinical drug


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The Role of Transcriptome Analysi s in Pre-Clini cal Toxicology Current Molecular Medicine, 2005, Vol. 5, No. 1 55

analysis can provide additional information to thedevelopment team to help make decisions onmolecular platforms; and, 3) understandingmechanisms of toxicity can aid in developingappropriate surrogate screens and biomarkers, whichin turn can be used to monitor for toxicity throughoutthe pre-clinical and clinical drug developmentprocess.

hepatocytes in the intact organ. These data allowedthe authors to choose appropriate treatment timesfor testing molecules based on which hepatocytesystems were operative temporally.

Transcriptome analysis has also been applied tomechanistic understanding of toxicity in organs othethan liver. Kaminski et al. [20] used gene expressionprofiling to understand the mechanisms of bleomycin

induced pulmonary fibrosis in a mouse model whileVoehringer et al. [21] used microarrays to identifytranscript changes that correlate with resistance toradiation induced injury and apoptosis in mouse B-cell clonal cell lines with differing sensitivities toirradiation induced injury. All of the studiesdemonstrate that understanding mechanisms oftoxicity adds focus to assessing the potentialiabilities of drug targets and the molecules beingused to modulate their activity. In the followingsection we will describe how this basic understandingof the targets, molecules, and the models used toassess toxicity is fundamental to building molecularapproaches to pre-clinical drug development.

There are a number of examples in the literatureof transcriptome analysis being used to derive insightinto mechanisms of toxicity [10,12-14]. As the liver isa common target organ of toxicity, several groupshave used transcriptome analysis to characterizehepatotoxicity [15]. Waring et al. [16] used globalexpression profiling to characterize the mechanism oftoxicity of a candidate drug that elevated serumALTs and induced liver hypertrophy in rat. Whenthey compared the expression pattern from ratstreated with the test compound to a database ofknown hepatotoxins, the authors found that the testcompounds gene expression pattern matched thatof compounds that activate the aryl hydrocarbon

response and concluded that the compound waslikely a ligand for the aryl hydrocarbon receptor.Hamadeh et al. [17] used gene expression data, incombination with clinical chemistry and pathologydata, to evaluate a furan-mediated hepatotoxicity ina rat model. The authors identified gene expressionchanges indicative of the inflammation and fibrosisdescribed in the pathological observations. Bothexamples illustrate the ability of transcriptomeanalysis to classify compounds by mechanism ofaction and to correlate gene expression data toclassical toxicity end-points. Two groups, Yoo etal.[13] and, Fang et al. [18], used transcriptomeanalysis to examine an LPS mediated acute phaseresponse (APR) in liver tissue in mouse and ratmodels, respectively. Both groups observed up-regulation of classic acute phase responsetranscripts and, in addition, Yoo et al. identifiedchanges in transcripts indicative of an adaptiveimmune response alongside the innate immuneresponse characteristic of APR. Fang et al. usedtranscriptional data to explain how nuclear hormonereceptor transcripts, including RXR, CAR, PXR,PPAR alpha, FXR and LXR, were responsible formediating the lipid, sterol, bile acid, and drugmetabolism changes observed clinically upon LPSadministration. These findings shed new light on theacute phase response in the liver and illustrate thepower of transcriptome analysis to verify existinghypotheses as well as provide new understanding ofa well-described phenomenon. Finally, Baker etal.[19] applied gene expression profiling tounderstand the biochemical and signaling changesthat occur in hepatocytes as they transition from theintact liver environment to a culture environment. Theauthors were able to show that gene expressionchanges accurately described alterations in cellularmetabolism as cells acclimated to the cultureenvironment and dedifferentiated with the loss ofactivities (i.e. drug metabolism) characteristic of

TARGET ASSESSMENT

In the Target to Hit stage of development, wherebiologists and chemists are thinking in terms of targetdrugability, the toxicologist can contributesignificantly to target selection by using tools thathave become available via genomics. A significantway in which transcriptome analysis can impact drugsafety assessment at the very earliest stages is inevaluating the potential liabilities inherentlyassociated with a drug target. This is especially trueif an ontarget toxicity is suspected, and thus where

toxicity is expected to track with development of thecompound and quantitative safety margindetermination will be critical for decision making [22]Characterizing toxicity risk for the target is achievedby generating extensive transcript tissue maps usingglobal expression profiling of nave animals and ofnormal human tissue samples [23]. From thesetissue maps, target development team members canreadily determine the tissue distribution of targetproteins and their interacting partners Figure (2).

Evaluating potential target-related toxicologicaliabilities via transcript tissue mapping can also havean impact both on initial target choice decisions, aswell as on developing the chemical platform once a

target choice has been made. Understanding targetrisk is especially important when potential therapeutictargets have multiple functions or act in severaldifferent pathways. On-target toxicities are adverseeffects caused by drug candidates action at thetarget, such as drugs that target inhibition of anenzyme activity which is needed in other criticaareas of cell function, or agonize a receptor whosesignaling may impinge on other areas of the cellsphysiology in addition to the targeted pathway. Ontarget toxicities are exemplified by thethiazolidinedione (TZD) PPAR gamma agonists


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Figure 2. This figure depicts the process of using transcriptome analysis to generate transcript tissue maps and the use ofthe maps to help understand on and off-target effects, as well as to develop tissue specific biomarkers. An on-target effect

is more likely if the effects are seen only in tissues expressing the target gene. An off-target effect is more likely if the

effects are seen in tissues other than, or in addition to, those expressing the target gene.

The transcript tissue distribution chart is:from: GNF SymAtlas v.8.0, Genomics Institute of the Novartis Research Foundation ("GNF")http://symatlas.gnf.org/SymAtlas/.

which improve insulin sensitivity and lower bloodglucose levels in type 2 diabetes [24,25]. At thesame time, the TZD molecules cause weight gaindue to an increase in fat depots as a result ofincreased adipocyte differentiation [26]. Theincrease in fat cell differentiation is a direct result ofagonism of the PPAR gamma receptor in pre-adipocytes where it promotes adipocytedifferentiation. Off-target toxicities are those causedby actions un-related to the target, such as non-specific immune or inflammatory reactions orhepatotoxicities due to drug metabolism effects. On-target toxicities are addressed by obtaining accuratesafety margins using biomarkers, where as off-targettoxicities can be overcome by developing screensthat allow the chemist to choose compounds whichdo not carry the toxic liability.

with target modulation without waiting for a ligand tobe synthesized. However, once a ligand is availablethe Toxicologist must determine the liabilityassociated both with the target modulation and thechemical directed against that target. A number ofcomputational tool are available that optimize thedrug-like properties of a chemical series. Howevertoxicology is currently at a disadvantage, since fewtools are available which accurately predict toxicityafter long term dosing from in vitro or short term invivo studies. Currently, toxicologists lack a battery ofpredictive assays that improve early decisions inoptimizing hit and lead molecules with regard tosafety.

Predictive toxicology is the area wheretoxicogenomics has been anticipated to mostdramatically affect drug safety assessment. Thepremise behind the approach is that geneexpression patterns derived from large-scaledifferential expression analysis in short term animastudies, or from an in vitro cell based system, canpredict a toxicological phenotype in longer term

OPTIMIZING HITS AND LEADS

By engaging in target assessment, toxicologistscan frame potential liabilities that may be associated


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Table 1. Examples of some predictive toxicology products.

Provider Product Location Description of product/approach

Iconix Pharmaceuticalswww.iconixpharm.com

DrugMatrixChemogenomics

Database

Mountain View, CA Global Exprsession profiling in vivo and in vitro. >600 compoundchemogenomic database, and 200 validated gene signature sets.

CuraGen Corp.

www.curagen.com

PTS

(predictive tox screen)

New Haven, CT Directed expression set profiling of primary rat hepatocytes.

Prediction of 10 different liver toxicities.

Gene Logicwww.genelogic.com

ToxExpress Gaithersburg, MD ToxExpress reference database predictive system with in vivoand in vitro models. Extensive liver toxicity database. Multifacetedtools and services.

Exonhitwww.exonhit.com

Safe-Hit Paris - FRANCE Novel DATAS screening technology reveals the presence of novelalternatively spliced mRNAs, and thus protein isoforms linked withtoxicity

animal studies and identify potential safety concernsin the human patient population. Research effortsfocused on the development of gene expressionbased predictive toxicology have been considerable[27-30]. The current state of predictive

toxicogenomics and future directions in the field areably examined in recent reviews by Suter et al. andStorck et al. [1,31]. The hope that the successfulimplementation of a predictive toxicogenomicsapproach could reduce cost and cycle times in thedrug development process, industry wide, hasspurred the development of a number of predictivetoxicology products by biotechnology firms (Table 1).

To date, the use of transcriptome analysis for theprediction of toxicity in drug development hasemphasized two areas. Initial work focused ondemonstrating the ability of gene expression data toclassify and categorize toxic molecules primarily on

the basis of mechanism, and to show that the geneexpression data could be correlated with thephenotypic data associated with the animal. Waringet al. [28,32]. demonstrated that gene expressiondata can be used both in vivo and in vitro to classifyhepatotoxins by mechanism of action, and showedthat there was a strong correlation between geneexpression patterns and histopathology and clinicachemistry induced by the chemical agents. Hamedahet al. in two companion papers [33,34], describe theuse of gene expression analysis in a rathepatotoxicity model, first to develop chemical-specific gene expression profiles, and then to usethose profiles as a training set to correctly place

blinded samples into appropriate categories. deLongueville et al. [35], using rat primary hepatocytesand a low-density array of 59 transcripts chosen tohave relevance for toxicity and metabolism, wereable to correctly classify compounds with similarhepatocellular toxicities. Others have usedtranscriptome analysis to discriminate direct-actingand indirect-acting genotoxins, and identified sets otranscripts that could effectively classify genotoxiccompounds by mechanism of action after only fourhours of treatment [36]. Finally, Gunther et al.[37]elegantly demonstrated the power of using geneexpression data, coupled with supervisedclassification analysis methods to classify

compounds (in this case by therapeutic class) and tocorrectly place unknown compounds in theappropriate class with greater than 80% accuracy.

The majority of predictive toxicogenomicapproaches are based on developing databasesconsisting of xenobiotic induced gene expressiondata combined with non-expression data (clinicalchemistry, hematology, histopathology, binding dataand biochemical assays, etc.) using sets of referencecompounds that embody the biological properties ofinterest. Importantly, developing robust predictivemodels using this approach requires that a largenumber of reference compounds and treatments areincluded to ensure representation of as many toxicphenotypes as necessary to obtain the greatestpredictive value, a substantial and expensiveundertaking. A number of computationally intensivemethods, such as K-nearest neighbors, lineardiscriminate analysis, and support vector machineshave been used to generate gene expressionsignatures or fingerprints which are highly predictivefor specific toxicity end-points (phenotypes)[5].These early studies demonstrate that the geneexpression pattern of a test molecule can beanalyzed for similarity to defined gene expressionsignature sets to identify the toxic liabilities of thetest molecule. The key to this method is thedevelopment of these signature sets based on anextensive training set of reference compounds thatmap out mechanistic space taking into account bothpharmacological and toxicological effects. Theauthors refer the readers to (Table 1) for informationabout commercially available predictivetoxicogenomic databases that have been developedand have application in drug development.

More recently, research efforts in predictivetoxicogenomics have begun to address thechallenging goal of using gene expression data topredict toxicity prior to occurrence of a toxicphenotype. The ability to use gene expression datato predict toxicity at doses and times where there isno phenotypic evidence of toxicity is considered bymany to be the ultimate validation of the value ousing transcriptome analyses in pre-clinica


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toxicology. Kier et al. [29], generated a databasecontaining gene expression, histopathology, andclinical chemistry data from rats treated with 89compounds. In this study, the authors were able touse the database to predict organ toxicities at 72hours using gene expression data from 24 hours.Because single transcripts were not in themselvespredictive, they were also able to develop gene

signature sets whose expression patterns had apredictive accuracy of approximately 90%. Using anacetominophen induced liver toxicity model Heinlothet al. [27] were able to demonstrate that patterns ofgene expression could predict toxicity at dosesconsidered to be nontoxic by traditional measures.Finally, Fielden et al.[38], using a large referencedatabase and validated gene signature sets, wereable to predict chronic renal tubule injury in a ratmodel at an early treatment time when there was noclinical or histopathological evidence for injury. In thisstudy, evidence for injury using traditional measuresof toxicity was not evident until much later in thetreatment regimen. As these last examples from the

recent literature illustrate, the use of predictivetoxicogenomics in pre-clinical drug safetyassessment has begun to mature and todemonstrate the potential to significantly impact thedrug development process.

1. Early predictive toxicogenomic screeningwhere chemical diversity is high, increases thelikelihood of choosing high quality moleculesthat pose the least toxicological risk.

2. Streamlined screening of a large number omolecules in vitro minimizes animal usage.

3. Less compound is required for screening usingin vitro cell models.

One of the most extensively used in vitro modelsin toxicology is primary hepatocytes. Hepatocytes arean appropriate cell type for evaluating compoundassociated toxicities in drug safety assessment foseveral reasons. First, liver toxicity continues to be amajor cause of compound failure in drugdevelopment. Second, hepatocytes, have a centrarole in drug metabolism, detoxification, and effluxLast but not least, hepatocytes have the ability toact as a sentinel cell for generalized cytotoxictieswhich may be relevant to other organ toxicities [40]Primary hepatocytes from rats and humans havebeen well characterized relative to intact liver, liver

slices, and hepatic cell lines. This characterizationhas included the evaluation of their gene expressionprofiles as well as their ability to act as a surrogatefor in vivo effects [19,41-43].

Despite the obvious advantages of in vitro modelsnoted above, their utility is also limited by the factthat cultured cells cannot accurately reflect thecomplex cellular interactions present in the tissueand organ environment. When treating cells withmolecules added directly into the cell culture media,one cannot mirror the many complex cellular andorgan based interactions that are responsible fordrug absorbtion, metabolism and excretion (ADME)In addition, even primary culture systems such asprimary hepatocytes, cardiomyocytes, andendothelial cells, differ significantly from their in-organ counterparts as has been reflected in theirgene expression profiles [19,41]. Despite theseshortcomings, in vitro models, such as primaryhepatocytes still have considerable relevance to thein vivo state, and utility in predictive toxicogenomicapplications. However, researchers need to beaware that, when developing an in vitro cell basedmodel, a thorough characterization of the in vitromodels relevance to the in vivo model should alwaysbe performed [19,44]. In most cases, an in vivoapproach is preferable for transcriptome analysis of

toxicity, as this will provide data most reflective of thereal-world whole-organism environment.

It is important to note that if decisions cannot bemade based on early gene expression data withoutfully characterizing dose and time relationships fortraditional endpoints and transcript signatures, theutility of a predictive database would be diminishedsince it would largely confirm, rather than predict,outcome. To achieve this end, predictive databasesneed to contain gene expression data along withwell defined signature sets that correlate withoutcome for multiple toxic phenotypes that can bepresent in the target organs of interest. For the liver,it has been estimated that there are 1020 distincttoxic phenotypes, including steatosis, multifocalnecrosis, hypertrophy, etc. [39]. The resolution andspecificity of global gene expression profiling dataprovided by current technology offers the potential todefine these phenotypes, but this will depend on thegeneration and validat ion of transcript signature setsthat are accurately predictive for thesepathophysiological endpoints. Despite thesechallenges, it is clear that profiling compounds usingpredictive gene signature sets early is a promisingpath forward to increase efficiency and lower thecost of drug development.

SURROGATE MODELS FOR OPTIMIZINGHITS AND LEADS

In addition to its extensive use with in vitromodels, transcriptome analysis can play a role inleveraging in vivo animal models that are usefusurrogates for evaluating potential safety liabilities inman. The dog and the monkey are the moscommon large animal species used in drug safetyassessment. Each model has particular utilities inmirroring potential human toxicities, with the dogbeing a particularly useful model for evaluatingtoxicities affecting cardiac function [45-48]. Whilemicroarrays containing human sequences can be

The potential for predictive toxicogenomics togenerate the greatest resource savings lies in usingin vitro cell based systems to predict in vivo toxicitiesin the hit and lead optimization phases of pre-clinicaldrug development. The use of predictive in vitromodels upstream in the development process couldprovide considerable savings in both resources andtime because:


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used with success in applying global expressionanalysis to the monkey, the dog has been untilrecently, relatively inaccessible. This inaccessibilityhas been due to the lack of sufficient high qualityDNA sequence information from both canine cDNAand genomic sequences needed to generate a dogmicroarray. Recently a canine microarray containingover 13,000 dog specific probes was developed in

our laboratory and was used to evaluate an LPSmediated acute phase response in a beagle dogmodel [49]. More recently, a commercial caninemicroarray has become available from AffymetrixInc (Sunnyvale, CA). The major shortcoming of thecanine microarrays, up to this point, has been a lackof thorough descriptive annotation for probespresent on the microarray, with many of the probesrepresenting ESTs of undefined function. With therecent completion of the draft sequence of thecanine genome [50] researchers can expect morecomplete annotation for canine array probes to be

just over the horizon, thus enhancing the utility ofthe dog chip for safety assessment in this valuable

large animal model.

development. We finish with a discussion oftoxicology biomarkers, which is an area otoxicological research where global transcriptanalysis is beginning to have significant impact. Abiomarker is a characteristic that can be objectivelymeasured and evaluated as an indicator of a normabiological process, a pathologic process, or apharmacologic response to therapeutic intervention

A biomarker of toxicity measures any suchcharacteristic that is adverse [22]. The ability tosimultaneously profile the gene expression patternsof thousands of different transcripts, under varioustreatment conditions from multiple tissue typesprovides an unprecedented opportunity to identifynovel toxicity biomarkers. These markers can be thetranscripts themselves used either singly or as setsor they can be the encoded proteins identified fromthe transcript profile, which can be monitored non-invasively from accessible tissues, such as blood[56].

The ability to identify candidate biomarkers, fromthe often overwhelming amount of data generated in

even a modest global expression profilingexperiment, requires mining the data in anappropriate manner. After applying quality controand statistical filters, additional filters for secreted ortransmembrane proteins can be applied. Theresearcher should be cautioned to avoid being tooconservative when examining data generated fromcandidate biomarker identification. Potential novemarkers may be overlooked if they appear, on initiaexamination, to be unconnected to a predeterminedmechanism of action. It also should be recognizedthat, although global gene expression profiling maybe successful in rapidly identifying scores of potentiacandidates, the time and effort needed for thenecessary biological validation and development isstill considerable.

Besides the rodent, dog, and monkey modelsused in drug safety assessment there are a numberof non-mammalian (zebrafish) and nonvertebratemodels (C. elegans, D. melanogaster, and yeast)being investigated for potential utility in safetyassessment [51]. Of these non-mammalian models,the zebrafish (Danio rerio) appears to have excellentpotential, having many of the desirable qualities ofan in vitro model including higher throughput, smallersize, lesser compound requirement, and easymaintenance. These qualities, coupled with theadvantage of being a vertebrate in vivo model with ahigh degree of similarity to mammals, at the gene,biochemical, signaling, and physiological levels,make zebrafish especially attractive as an in vivosurrogate for use in evaluating potential humantoxicities. Two additional advantages of the zebrafishmodel are that the embryos are transparent, allowingfor ready visualization of organ systems, and thatembryos are able to absorb many chemicals directlyfrom the surrounding medium [52-55], allowing forease of treatment. There are also considerablegenomics resources now available for zebrafish,including microarray reagents. The promise ofzebrafish as a drug development model continues tostimulate considerable interest, and as a resultseveral biotechnology companies devoted to the useof the zebrafish model in biomedical research havearose, including; Phylonix Pharmaceuticals,Inc.(Cambridge, Mass) Zygogen, Inc.(Atlanta, GA)and Znomics, Inc. (Portland, OR).

An example of using transcriptome analysis toassist in the identification, validation, anddevelopment of a toxicity biomarker is illustrated bythe work done in our laboratory on the identificationof adipsin as a marker for the gastrointestinal toxicityobserved when administering a candidate drugtreatment for Alzheimers Disease [56]. Global geneexpression profiling was used initially to identifyadipsin as a transcript that was regulated in amanner that was consistent with the expectedmechanism of toxicity, as the adipsin expression

pattern matched the patterns of other secretory celgenes. The up-regulation of transcripts for secretorycell gene products were indicative of cell populationchanges associated with the dysregulation of Notchsignaling and cell fate determination which isnecessary for normal development of the epitheliumlining the gastrointestinal tract. The impairment ofNotch function was predicted as a possible off-targeeffect of the gamma secretase inhibitor compoundWe were able to mechanistically link adipsinexpression in the gut to the Notch signaling pathwayand further develop adipsin as a marker of thegamma secretase inhibitor mediated toxicity that

TRANSCRIPTOME ANALYSIS USE INTOXICITY BIOMARKER IDENTIFICATION

Like mechanisms of toxicity, which are afundamental underpinning of the entire preclinicaldevelopment pipeline, the use of biomarkers oftoxicity can impact the process at every phase of


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Figure 3. This figure depicts the process that was used to develop a non-invasive toxicity biomarker for a novelgastrointestinal toxicity, which occurred during the development of a gamma secretase inhibitor molecule as a potential

Alzheimers Disease therapeutic. The right portion of the figure depicts the steps taken to develop the adipsin biomarkerfrom the initial gross pathological observation (1) through global gene expression profiling experiments (2), molecular

validation (3), antibody screening (4), and finally cell localization (5). The left portion of the figure illustrates the linking of

the adipsin biomarker to the proposed toxicity mechanism: the disruption of Notch signaling by the -secretase inhibitor,

with ensuing cell population changes favoring the secretory cell lineage that adipsin expression correlates with.

could be monitored non-invasively in the feces oftreated animals. The development of adipsin as abiomarker exemplifies the considerable effort neededto validate and develop a potential marker that isinitially identified from a large-scale gene expressionscreen Figure (3).

mode are more likely to be implemented in amoderate to high throughput format relying onmultiplex analysis of scores of transcripts from asingle minimally processed sample, such as a tissuehomogenate or cell lysate [60,61]. Efforts applied totranscriptome analysis of candidate molecules in thehit-to-lead phase can yield significant dividends, as

novel toxicity biomarkers identified by transcriptomeanalysis early in the drug development process mayalso have utility throughout the development processand on into the clinic.

There are a number of other recent examples

where transcript profiling has played a significant rolein the identification of potential toxicity biomarkers[56-59]. In order to provide continuing benefit in thedrug safety assessment process, the more difficult,but necessary step, is the development andimplementation of the biomarker as a pre-clinicalsurrogate screen or as a marker for monitoringpatients in a clinical setting [22]. Only byimplementation, through integration as a routinesurrogate screen in all pre-clinical studies, cantranscript based biomarkers prove their value inroutinely monitoring for toxic endpoints Figure (4).The transcript based biomarkers used in screening

New advances in sample preparation, andpreservation, and techniques which allow theresearcher to obtain robust gene expression datafrom ever smaller RNA quantities [62-65] are makingit possible to screen for transcript based biomarkersin various tissues that can be accessed non-invasively. There is compelling data that in manycases gene expression changes monitored inperipheral blood mononuclear cells (PBMC) and


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Figure 4. This diagram illustrates a proposed approach to improve the development and implementation process for toxicitybiomarkers within the pre-clinical safety assessment process. Transcriptome analysis is one of several tools used to

identify and validate a toxicity biomarker. Initial characterization is performed in the more accessible in vitro model.

Implementation of biomarker screening in all lead optimization studies allows for the development of baseline ranges for the

analytes, and improves the sensitivity and utility of the biomarker panels.

other sources (buccal cells, saliva, milk, feces,semen, vaginal secretions, hair follicles, etc.) can beused as surrogate markers for toxicities observed inan inaccessible tissue [66-68]. This capability to usereadily obtainable samples provides moreopportunities for screening for toxicities pre-clinicallyin real time, and for transitioning the desiredexpression biomarker into clinical evaluation. Onecould envision being able to monitor a toxicity for aninaccessible tissue in a large animal species (dog ormonkey) by regularly removing blood and evaluatingthe surrogate PBMC marker transcript changes

across the treatment time course without ever havingto sacrifice the animal. Rapid transcript analysismethodologies that can be applied to crude celllysates without the need to purify RNA andenzymatically prepare hybridization sample such asbranched DNA [bDNA] would be ideal for thisapplication [60]. The use of surrogate tissueanalysis, as described by J. Rockett [66], might alsohave application for predictive toxicogenomics,where global expression profiling could be performedon PBMCs from treated animals using a large libraryof compounds with known toxicological end-points invarious target tissues. Correlations between the

PBMC gene expression patterns and the varioustoxicological phenotypes could be identified and adatabase of the PBMC toxicity signatures could thanbe used to assess the toxicity of new drugcandidates.

NEEDS AND TRENDS

The preceding survey, describing some of theways in which transcript profiling is being used indrug safety assessment, illustrates how fatoxicologists have come in employing the

technology, but at the same time, points to whatneeds to be accomplished before it is fully integratedinto the drug development process. For a newapproach, such as using transcriptome analysis tocharacterize a drugs safety liabilities, to be fullyaccepted it needs to be thoroughly validated foboth data quality and value to the drug safetyassessment process. The practical value of usingtranscriptome analysis for drug safety assessment inboth time and cost savings has to be demonstratedbefore more widespread use of the technology canbe expected within the pharmaceutical industry.


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In order to gain widespread acceptance in thedrug development process, there are still severalareas where improvements in transcriptome profilingtechnology are needed. We will touch on two ofthese areas, the need for improvements theintegration of multiple data types into geneexpression databases, and the need for more rapidand cost effective gene expression sample

preparation.

Clearly, a thorough understanding of toxicmechanism is the initial step to developing bettebiomarkers and surrogate screens for monitoringcompound toxicity. Thus, linking of compounds tomechanistic classes of toxicity based on transcripdata is the first step towards predictivetoxicogenomics. Since then, the early proof-ofconcept experiments in transcript based predictive

toxicology have given way to the current state opredictive toxicogenomics wherein large referencedatabases and predictive gene signature sets arebeginning to be applied to assessing compoundtoxicity liabilities in the pre-clinical setting.

Integration of the many data types (one of whichis transcript data) available to the drug developmentscientist into a large meta-database that is readilyqueried, and has a full complement of analysis toolsis a major priority. Only in concert with other datatypes can transcript data help mold a comprehensiveunderstanding of the interactions of xenoboioticsand the complex living systems they affect. No singledata type can provide the level of insight andconfidence that can be derived from multiplecorrelative data types. This approach, which meldsmultiple data types (histopathology and clinicalchemistry analysis, biochemical assays, genomics,

proteomics, and metabonomics) exemplifies a truesystems biology approach and is already the modelused by predictive toxicogenomics databaseproviders. (Table 1) Extensive databases linkinggene expression and toxicity end-points are alsobeing developed in the public domain by groups atNIEHS, HESI and elsewhere [69-71]. (For anexcellent overview of the major toxicogenomicsdatabase efforts in the public sector see the reviewby Mattes et al. [69]) There also have been anumber of consortiums established to examineparticular issues around toxicogenomics, especiallythrough the International Life Science Institute (ILSI)[http://www.ilsi.org]. This effort, to compile databasesof compound specific gene expression data andmake it freely available to the public, points to thetime when much of drug safety assessmentevaluation may be performed in silico, and potentialadverse effects can be linked to particular chemicalstructures.

Initial expectations for an immediate impact ofgenomics on drug development were overlyoptimistic. Despite this initial over-optimism, theprogress in establishing the technology has beensignificant. No longer is the majority of the discussionabout transcriptome analysis concerned with thequality and rigor of the data. Discussions are nowcentered on how to best position genomictechnologies in the drug development process, in

order to achieve the greatest efficiencies and addthe greatest value. Genomics, and transcriptomeanalysis in particular, is an approach that, much likemolecular biology in the 1970s and 1980s, will taketime to be fully integrated into the drug developmenprocess, but once established, transcriptomeanalysis will be one of the primary tools that drugdevelopment teams will routinely use to evaluate thepotential risks associated with molecules undedevelopment. Transcriptome analysis is but onecomponent of a new synthesis, melding traditionaapproaches to identify, describe, and understandtoxicity with newer high content approaches, such asproteomics, metabonomics, genomics, and in-silicoanalysis.

In addition to the need for more mechanisticinformation, industry fundamentals are also drivingchange in the drug development process. Thecontinued escalation in costs and prolongation ofdrug development time-lines are not sustainablewithout a commensurate increase in productivity. Thecrisis in productivity in the drug developmentprocess, and the need for the establishment of newapproaches such as transcriptome analysis to solvethe pipeline problem was highlighted in a recentFDA white paper entitled Innovation or StagnationChallenge and Opportunity on the Critical Path to

new Medical Products. The FDA proposes a moreproactive approach, working with industry to developthe new technologies that will improve the drugdevelopment process so that more and bettermedicines will more quickly reach the patientpopulation. [FDA-Critical Path white paper can bedownloaded from this website: http://www.fda.govoc/initiatives/criticalpath/] New approaches, such asusing transcriptome analysis in pre-clinical toxicologyare beginning to be more widely adopted and canbe expected to positively impact the drugdevelopment process in the years to come.

The second need, which is essential if transcriptanalysis is to impact earlier in the drug developmentprocess, is the need for increased throughput andlower cost per data point. Sample preparation andmicroarray data generation are still too timeconsuming and costly. Improved sample labelingmethodologies and increased reliance on robotic

liquid handling can provide significant cost and timesavings, which will allow drug development teams toapply transcript profiling earlier in the process (hit-to-lead phase) to evaluate more drug candidates.

CONCLUSIONS

As noted in this review, transcriptome analysis isalready having an impact on pre-clinical toxicology inthe drug development process. The application oftranscriptome analysis is contributing to theunderstanding of mechanisms of drug toxicity.


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ACKNOWLEDGEMENTS [19] Baker, T. K., Carfagna, M. A., Gao, H., Dow, E. R., Li, Q.Searfoss, G. H. and Ryan, T. P. (2001) Chem. Res. Toxicol.,141218-1231.We would like to thank Jim Stevens and Craig

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NIEHS = National Institute of Environmental HealthSciences

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