TECHNOLOGIES

DRUG DISCOVERY

TODAY

Drug Discovery Today: Technologies Vol. 3, No. 3 2006

Editors-in-Chief

Kelvin Lam – Pfizer, Inc., USA

Henk Timmerman – Vrije Universiteit, The Netherlands

Medicinal chemistry

Profiling the kinome for drug discoveryS. Frank Yan1, Frederick J. King2,4, Yingyao Zhou1, Markus Warmuth3,*,

Gang Xia3,*1Cheminformatics, Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121, USA2Lead Discovery, Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121, USA3Kinase Drug Discovery, Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121, USA4Developmental and Molecular Pathways, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA

The human kinome is made up of 518 distinctive serine/

threonine and tyrosine kinases, which are key compo-

nents of virtually every mammalian signal transduction

pathway. Consequently, kinases provide a compelling

target family for the development of small molecule

inhibitors, which could be used as tools to delineate the

mechanism of action for biological processes and

potentially be used as therapeutics to treat human

diseases such as cancer. A myriad of recent technolo-

gical advances have accelerated our understanding of

kinome function, its relationship to tumorigenic devel-

opment, and have contributed to the progression of

small molecule kinase inhibitors into the clinic. Essen-

tial to the continued growth of the field are informatics

tools that can assist in interpreting disparate and volu-

minous data sets and correctly guide decision making

processes. These advances are expected to have a

dramatic impact on kinase drug development and clin-

ical diagnoses and treatment in the near future.

*Corresponding authors: : M. Warmuth (mwarmuth@gnf.org),G. Xia (gxia@gnf.org, gang.xia@gmail.com)

1740-6749/$ � 2006 S. Frank Yan. Published by Elsevier Ltd. All rights reserved. DOI: 10.1

Section Editors:Li-He Zhang – School of Pharmaceutical Science, Universityof Peking, Beijing, ChinaKaixian Chen – Drug Discovery and Design Center, ShanghaiInstitute of Materia Medica, Chinese Academy of Sciences,Shanghai, China

Introduction

The evolution of multi-cellular organisms requires an ever-

increasing demand for more sophisticated cell–cell and cell–

environment communications. The kinome evolved from a

group of structurally distinct histidine specific protein

kinases mostly found in prokaryotes, to the kinome of Schi-

zosaccharomyces pombe that comprises 106 serine/threonine

kinases [1], and to human kinome that includes 518 distinc-

tive serine/threonine and tyrosine kinases [2]. The repertoire

of human kinases creates a complex signal transduction grid

for regulating cell proliferation, differentiation, migration,

and many other cellular processes [2]. Kinase activity is

closely and delicately regulated by multiple levels of cellular

switches; dysregulation of its activity, often arising from

mutations leading to gain-of-function mutant alleles, has

been shown to be pathogenic or, in some cases, tumorigenic

(Table 1).

The development of kinase inhibitors into successful ther-

apeutics has become a mainstay of the biotech and pharma-

ceutical industry; kinases represent the second largest drug

target family currently being targeted by pharmaceutical

companies (the largest being the G-protein coupled receptor

(GPCR) family) [3]. However, among the 518 kinases in the

human genome only a very small fraction have associated

inhibitors that have reached clinical trials or regulatory

approval (Table 1). The interest displayed in the drug devel-

opment community to dramatically expand the list of clini-

cally relevant kinase inhibitors reflects the belief that

untapped opportunities for kinase drug discovery remain.

This has stimulated the creation of technologies that are able

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Drug Discovery Today: Technologies | Medicinal chemistry Vol. 3, No. 3 2006

Table 1. Some molecular mechanisms of disease relevant kinase dysregulation

Functional

effect

Gene Genetic alteration Disease Inhibitor

Amplification GOFa AURKA Many types of cancers [27] VX-680

PDGFR Glioblastomas [28]

Translocation GOF ABL BCR-ABL CMLb [29] Imatinibc, dasatinibd

PDGFRB TEL-PDGFRB CML, MPDe [30]

Activating mutation GOF BRAF V599E Melanoma [31] Sorafenibf

EGFR L858R, G719C, L861Q NSCLCg [7] Gefitinibh, erlotinibi

FGFR3 S249C Bladder and cervical cancers [32]

JAK2 V617F MPD [33]

Deletion GOF EGFR Exons 2–7 deletion, 2240del12 Glioblastomas, NSCLC [7,34] Gefitinib, erlotinib

KIT Multiple cases at juxtamembrane

domain

GISTk [35] Imatinib, sunitinibj

Insertion GOF FLT3 FLT3-ITDl AMLm[36] CEP-701, XL999

Autocrine/paracrine GOF PDGFR Brain tumors, gliomas [37]

Inactivation of negative

regulator

LOFn PTEN Point mutations, frameshift deletion Glioblastomas, prostate cancers [38] RAD001 (indirect)

a Gain-of-function.b Chronic myeloid leukemia.c Gleevec1, Novartis.d Sprycel1, Bristol-Myers Squibb.e Myeloproliferative disease.f Nexavar1, Bayer.g Non-small-cell lung cancer.h Iressa1, AstraZeneca.i Tarceva1, Genentech/OSI Pharmaceuticals.j Sutent1, Pfizer.k Gastrointestinal stromal tumor.l Internal tandem duplication.m Acute myeloid leukemia.n Loss-of-function.

to interrogate biological systems and profile chemical com-

pounds on a ‘kinome-wide’ scale. It is anticipated that the

impact of these technologies to understand kinase biology

and accelerate kinase drug discovery will be realized in the

very near future.

Technologies related to kinome analyzes can be loosely

grouped into three categories: molecular profiling, gene func-

tional profiling, and chemical profiling (Table 2). Although

their individual methodologies are disparate, their resulting

data present an opportunity to apply informatics techniques

that are able to identify synergies and extract hidden infor-

mation. In this review, we discuss how the application of

these technologies for profiling the kinome, either individu-

ally or in combination, contributes to the drug discovery

process.

Molecular profiling of the kinome

Molecular profiling provides a ‘snapshot’ of the current status

of a cell or tissue as characterized by genotyping and gene

expression, proteomic and metabolic measurements, and so

on. [4]. Along with insights to basic kinase biology, these

approaches have the potential to contribute to drug target

identification and validation, assessment and prediction of

efficacy and toxicity of clinical therapeutic candidates, as well

270 www.drugdiscoverytoday.com

as discovery of new biomarkers for disease diagnosis, prog-

nosis, and patient stratification.

Genotyping

Genotyping has been successfully employed to provide link-

age between allelic variation and disease. Analysis of primary

human acute myeloid leukemia (AML) cells identified 16 out

of 46 samples containing known activating mutations in

FLT3 kinase, which was consistent with the frequency of

FLT3 mutations in AML described in other studies [5]. This

report also identified the previously unknown D324N muta-

tion in FLT3. Interestingly, even though the D324N mutation

does not appear to positively regulate FLT3 kinase activity,

the higher occurrence (6.7%) of the D324N allele in myeloid

leukemia samples than in controls (1.5%) indicates that it

might be associated with predisposition of different subtypes

of leukemias [6].

Genotyping also has been used to understand and predict

the clinical response to a kinase inhibitor. The majority of the

non-small-cell lung cancer (NSCLC) tumor samples overex-

press EGFR at the mRNA level, yet only 10% of the NSCLC

patients respond to the EGFR inhibitor gefitinib (Iressa1,

AstraZeneca) [7]. Eight of nine of gefitinib-responsive patients

carried EGFR-activating mutations. This demonstrates the

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Table 2. Aspects in kinome profiling

Molecular profiling Gene functional profiling Chemical profiling

Gene expression

profiling

Genotypic mutation

analysis

Post-translation

protein modification

RNAia (siRNAb, shRNAc) Cell-based profiling Enzyme-based profiling

Technology and

associate source

DNA microarray Sequencing, SNPd chip, human

mapping chip

MSe proteomics,

antibody chip

Dharmacon, Ambion, Qiagen,

Invitrogen, Open Biosystems

ACPf system Invitrogen, Upstate

Application � Clinical sample diagnostics � Target validation � Lead identification

� Biomarker discovery � Target identification � Lead optimization (cross activity)

� Target discovery and validation � Target identification

� Efficacy and toxicity prediction

� Responder identification

Pros � Nonbiased and comprehensive (the entire kinome can be profiled on a single array) � Downregulate specific

gene functions in a

high-throughput fashion

� High-throughput

� Discovery-driven � Compound cross activity for finding

new inhibitor scaffolds

Cons � Large-scale data visualization and analysis � Pathway redundancy � Compound specificity issue

� Alternative splicing complicates signal interpretation � Not applicable for all cell lines � Benefit dependent on compound library diversity

� mRNA expression may not correlated with protein expression � Insufficient downregulation

complicates data interpretation

� Compound cross activity in cellular data interpretation

� May not distinguish active/inactive proteins � Cost

� Not hypothesis-driven and may require additional detailed studies � Off-target cytotoxic effects

Refs [4,8,10,11] [15] [17,19]

a RNA interference.b Small interfering RNA.c Short hairpin RNA.d Single nucleotide polymorphism.e Mass spectrometry.f Automated compound profiling.

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Drug Discovery Today: Technologies | Medicinal chemistry Vol. 3, No. 3 2006

potential for molecular profiling to predict a drug response

for individual patients, which in turn, can lead to more

efficacious clinical trials design based on patient stratifica-

tion. Also, it contrasts the utility of gene array and genotyp-

ing analyses with respect to kinase target identification –

overexpression alone may not fully capture the potential

for a particular kinase to be used as a drug target.

It was inevitable that these early successes with genotyping

analysis would be extended to the development of systems

that would allow numerous kinase loci to be analyzed simul-

taneously. For example, a more global genotyping analysis

focused on identifying tyrosine kinase mutations and their

frequencies in colorectal cancers. Bardelli et al. analyzed 819

exons from all the annotated tyrosine kinase, tyrosine kinase-

like, and receptor guanylate cyclase gene groups [8]. From the

35 colorectal cancer cell lines that were directly sequenced,

somatic mutations in 14 kinase genes were identified; these

findings were extended and the same kinase genes were

determined to harbor a total of 46 mutations in 147 colorectal

tumor samples. Because of the much higher prevalence of

nonsynonymous mutations, the authors concluded that

these mutations are functionally related to colorectal cancer

development.

Although traditional sequencing technology is effective

and widely used today, it is still time consuming, labor

intensive, and difficult to be implemented in routine kinome

genotypic screening for individual patients in a timely fash-

ion. However, the progression in the complexity of the

genotypic analysis described above, along with the emerging

new sequencing technologies [9], suggests that sequencing of

the complete human kinome (2 � 106 bps) will soon not only

be possible but practical in a routine manner. Together with

other molecular profiling technologies, this will revolutio-

nize the way of disease diagnosis, identifying patients who

can most benefit from certain treatment, as well as the drug

discovery process in the pharmaceutical industry.

Proteomics

A different approach to molecular profiling is using proteo-

mics to profile cellular phosphoproteins. This measures the

dynamic behavior of the kinome by characterization of their

cellular substrates. The sensitivity of mass spectrometry tech-

nology to quantitatively determine the levels of phospho-

proteins with minute samples [10] along with providing

information regarding specific phosphorylation sites makes

this an attractive technology for kinome investigation [11].

In one recent study [12], Bose et al. designed a system capable

of simultaneously characterizing phosphoproteins from

three distinctive samples: the control sample; one sample

with increased HER2 kinase activity; and one sample with

HER2 activity inhibited by a selective EGFR/HER2 inhibitor,

PD168393. Several proteins previously unknown to be

involved in the HER2 signaling pathway were identified,

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and major HER2-induced tyrosine phosphorylation was

found to be reversed when treated with PD168393. This study

illustrated the potential of using phosphoprotein profiling to

delineate dysregulated kinase signal transduction pathway

and to test inhibitor activity at the molecular level.

Genetic and chemical profiling of the kinome

Small interfering RNA (siRNA) and related technologies

have become powerful tools to systematically perturb the

expression of individual gene [13]. Because disease-related

kinase dysregulation often results from gain-of-function

mutations (Table 1), the loss-of-function consequence of

RNA interference (RNAi) expression makes this technology

particularly amenable to the interrogation of kinome biology

[14]. This technology has been extensively reviewed else-

where [15].

Biochemical profiling

Another approach for inhibiting kinase activity within a cell

is through the use of specifically designed chemical com-

pounds. Of course, the development of small molecule inhi-

bitors also has clinical implications, as they comprise the

majority of drugs currently on market. The pharmaceutical

industry has embraced high-throughput screening (HTS)

technology, which allows screening of thousands to millions

of compounds in a matter of days, as a main source of lead

compounds for drug discovery projects [16]. Kinases are

popular targets for HTS campaigns, because it is relatively

straightforward to obtain large quantities of purified kinases

and develop assays that are amenable to the miniaturization

that is commonly required in modern HTS.

The utility of a particular kinase inhibitor, both as a drug

and as a research probe, is linked to a determination of its

activity towards a spectrum of kinase targets. Novel metho-

dology for profiling kinase activity has been described

recently [17]. However, in vitro kinase assays remain popular.

Over the past few years, companies such as Upstate Biotech-

nology Inc. (Charlottesville, VA) and Invitrogen (Carlsbad,

CA) have developed in vitro kinase assays for a significant

portion of the human kinome, which can provide a detailed

‘fingerprint’ of a compound’s activity. Not surprisingly,

much of this data are under control of for-profit companies

or licensed knowledgebase (Eidogen-Sertanty, San Diego, CA)

and not accessible to the research community. However, a

limited number of such systematically obtained data sets

have been published [18,19]. For example, Vieth et al. com-

plied in vitro enzymatic IC50 values taken from literature

[18,20]. However, the interpretation of data obtained from

in vitro kinase assays has several important limitations [18].

For example, an in vitro assay is not affected by the ability of

the compound to pass through a cell membrane, which is an

essential prerequisite for a successful kinase inhibitor to be

used as a drug.

Vol. 3, No. 3 2006 Drug Discovery Today: Technologies | Medicinal chemistry

Cellular profiling

Melnick et al. have described a method to perform kinase

inhibition analyses with a cell-based assay [19]. It has been

demonstrated that ectopic expression of an activated allele of

some, if not all, tyrosine kinases in Ba/F3 cells leads to

interleukin-3 (IL-3) independent growth; in turn, the viabi-

lity of the derivative Ba/F3 cell line becomes dependent upon

the activity of the particular kinase (in growth medium that is

not supplemented with IL-3). Therefore, the activity of a

potential kinase inhibitor towards any given tyrosine kinase

can be assessed using an appropriate Ba/F3 derivative by

employing a simple cytotoxicity assay. This approach has

been extended to a large number of human tyrosine kinases

and has generated a large-scale cell-based IC50 data set for

1400 small molecule kinase inhibitors against a panel of 36

tyrosine kinases. Furthermore, a robotic automated com-

pound profiling (ACP) system was used to perform all aspects

of the screen, giving further indication to the use of technol-

ogy in obtaining a data set of such magnitude [19]. Although

conditions where the Ba/F3 system could be adapted to

analyze serine/threonine kinases have not been identified,

it does provide a simple and cost effective method to profile a

compound’s activity against a large number of kinases in a

cellular context.

Mining the data

The data generated from large-scale profiling analyzes have

led to the development of various informatics methods to

assist in the visualization and extraction of meaningful infor-

mation. Clustering of compounds and/or kinases based on

different similarity measures, together with powerful visua-

lization programs like Java TreeView (http://www.jtreeview.

sourceforge.net/), MeV (http://www.tm4.org/mev.html/),

TreeView (http://www.rana.lbl.gov/downloads/TreeView/),

Spotfire (http://www.spotfire.com/), etc., is a powerful tool

to carry out unsupervised learning of compound structure–

activity relationship (SAR) and kinase selectivity.

SAR similarity

Various criteria have been used to group together different

kinases, including similarity among primary sequence, com-

pound activity profile (so-called SAR similarity) [20], struc-

tural interaction with an inhibitor [21], and disease

indication [18]. This has resulted in general ‘rules’ related

to kinase activity that, once confirmed, could assist in selec-

tion of kinase targets and development of kinase inhibitors.

For example, Vieth et al. concluded that if the sequence

similarity between two kinases is above 60%, they are likely

to be inhibited by the same set of compounds [18].

We applied a similar clustering method to study the large-

scale kinase inhibitor activity data generated from the ACP

Ba/F3 screen described above [19]. A comparison was per-

formed of the profile similarity and sequence similarity for

the 36 kinases in the data set based on Manhattan distance

(similar to the SAR similarity proposed by Vieth et al. [20])

using the 935 nontoxic compounds and BLAST sequence

identity. As shown in Fig. 1a, kinase pairs that share >50%

sequence identity largely have a profile similarity value >0.5,

indicating similar inhibition pattern by the 935 probe com-

pounds (e.g. FGFR1–FGFR2 and EphB1–EphB2). However, no

consistent pattern was observed by the inhibition profile of a

second kinase when the sequence identity falls below 50%

(Fig. 1a). For example, FLT3 and TRKB share only 26%

sequence identity but have a profile similarity value of

0.83, while ALK and EphB1 share 36% sequence identity

but only have 0.13 profile similarity. The results ascribed

for FLT3 and TRKB are important for drug discovery, not

only in facilitating the selection of the most relevant, efficient

panel of kinases for compound screening but also in helping

discover drug off-target activities. Drug off-target activity

often is associated with undesirable side effects and a major

incentive for determining the activity of a kinase inhibitor

against as many kinases as possible. However, sometimes it

can lead to new therapeutic, as demonstrated by the activity

of imatinib (Gleevec1, Novartis) against KIT, which led to the

successful clinical evaluation of imatinib as a treatment for

gastrointestinal stromal tumor (GIST), where activating

mutations in KIT have been identified [22].

One caveat for such comparison based on an inhibition

profile is that a sufficient number of active compounds for the

relevant kinases is required; otherwise, a false correlation may

result. For example, JAK3 and RON appear to share a high

profile similarity (0.99, Fig. 1a), while a closer examination of

the data reveal that this is simply due to the lack of selective

active compounds for these two kinases in the probe com-

pound set (see also Fig. 1b). To address this issue, we filtered

out kinases that share similar inhibition profiles with that of

the wild-type kinase. This results in 21 kinases that have a

profile Manhattan distance of at least 0.2 compared with the

wild-type and consequently 210 kinase pairs (red and blue

squares in Fig. 1a). The pairs that involve filtered-out kinase

are nonetheless still shown in Fig. 1a (solid circles).

Cheminformatics

Recently Yan et al. introduced a novel ontology-based pattern

identification (OPI) method for HTS data mining [16,23],

which can effectively identify core compounds that share

both structural similarity and biological profile (e.g. selectiv-

ity pattern against a panel of kinases). We applied this

method to the abovementioned ACP Ba/F3 data set and

the resultant clustered heatmap is shown in Fig. 1b. The

OPI-determined core compounds that belong to the same

chemical scaffold are first grouped together and marked with

alternate magenta and green color bars; they are then subject

to hierarchical clustering according to their kinase inhibition

profile. This method is effective in excluding selectivity

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Drug Discovery Today: Technologies | Medicinal chemistry Vol. 3, No. 3 2006

Figure 1. (a) Relationship between kinase sequence identity and

compound inhibition profile similarity. Protein sequences of the 36

kinases were downloaded from Sugen website (http://

www.kinase.com/) and the Blastall program (http://

www.genome.nhgri.nih.gov/blastall/) was used to calculate all the pair-

wise sequence identity scores. The profile similarity is computed using

a Manhattan distance metric based on the inhibition activities of the

935 compounds in the probe set. The Manhattan distance metric is

defined as: dab ¼ ð1=NÞPN

k¼1 jpICa;k50 � pICb;k

50 j, where

274 www.drugdiscoverytoday.com

outliers within a scaffold and is particularly useful in identi-

fying chemical scaffolds that satisfy a particular kinase selec-

tivity profile of interest for lead discovery [16].

Moreover, it should be mentioned that a large number of

kinase crystal structures with inhibitors has been published,

which presents a great opportunity to study the physical

nature of how inhibitor binds to a kinase [21,24]. This is

pivotal for understanding the nature of kinase selectivity and

consequently successful medicinal chemistry effort in lead

optimization.

Synergistic study of the kinome

All the technologies described above that currently are being

applied to kinome research can be utilized by similar infor-

matics systems and can result in synergistic target identifica-

tion and drug discovery processes. This is exemplified by the

integration of both gene expression and compound screening

data for the 60 cancer cell lines from National Cancer Insti-

tute (NCI60) [25]. Methods such as clustered image map

(CIM) [25] and self-organizing map (SOM) [26] have been

applied to derive relationships between anticancer com-

pounds and potential biological targets. Given the advance

in screening libraries, both biological and chemical, such as

siRNA, complementary DNA (cDNA), diverse compound sets,

etc., and the availability of versatile cell-based screening

systems, it is expected that chemogenomics will play an

increasingly important role in drug discovery, not only in

terms of identifying novel drug targets but also discovering

new chemical leads. Fig. 2 illustrates some ideas along this

pIC50 = �log10IC50, and is normalized in such way that zero distance

corresponds to a similarity score of 1 and the largest distance

corresponds to a similarity score of 0: sab = 1 � dab/max dab. For the 36

kinases, a total of 630 pairs is plotted; specifically, 210 kinase pairs (21

kinases) are in solid red and blue squares and the rest is in solid circles.

The 21 kinases were obtained by filtering out kinases that have a profile

Manhattan distance less than or equal to 0.2 compared with the parental

Ba/F3 cell line. In particular, kinase pairs including FGFR1–FGFR2,

EphB1–EphB2, FLT3–TRKB, and ALK–EphB1 are shown in blue

squares, while artificially correlated kinase pair, JAK3–RON, is shown in

solid grey circle. (b) Clustered heatmap of kinase compound profiling

data. The 935 probe compounds were first clustered into scaffolds

based on chemical structure using Pipeline Pilot program (http://

www.scitegic.com/). The OPI algorithm was then applied to the

scaffolds to identify core compounds that share both structural

similarity and inhibition activity profile against the panel of 36 kinases;

this resulted in 578 core compounds belonging to 40 scaffolds.

Compounds that belong to the same chemical scaffold are marked with

alternate magenta and green color bars. Both compounds and kinases

were hierarchically clustered based on the 36-kinase selectivity profile

and the 578-compound selectivity profile, respectively, using an in-

house program. The distance matrices required for hierarchical

clustering were computed based on a Manhattan distance metric

detailed above (normalized to a number between 0 and 1: dab/max dab).

In particular, the distance between two compounds that belong to

the same scaffold was assigned a zero value for visualization purpose.

OPI: ontology-based pattern identification.

Vol. 3, No. 3 2006 Drug Discovery Today: Technologies | Medicinal chemistry

Figure 2. Ideas for synergistic target identification and drug discovery using chemogenomics approaches. (a) Well-characterized Ba/F3 cell lines

overexpressing a panel of diverse kinases and a collection of cancer cell lines with unknown targets are screened against the same set of kinase inhibitors.

After clustering analysis, if a cancer cell line shares similar inhibition profile with a known Ba/F3 cell line, potential target for this cell line can be suggested. (b)

Kinase inhibitors and siRNA/cDNA libraries are screened against a panel of diversified cellular assays. Statistically significant correlation/anti-correlation

between compound and gene/protein may help identify novel molecular targets when the mechanism of action of the compound is known, or facilitate

discovering new lead compounds if the protein target is well studied. cDNA: complementary DNA, siRNA: small interfering RNA.

line based on both biological and chemical profiling. The

dataset from the Ba/F3 profiling is used in combination with

other types of analyses to identify synergies. For example, the

same compounds are assayed for their cytotoxic activity

against a collection of cancer cell lines. Correlation between

the two analyses could lead to the identification of kinases that

are essential for the viability of the respective tumor cell lines,

which has obvious implications for chemotherapeutic devel-

opment (Fig. 2a). Also, kinase inhibitors and siRNA/cDNA

libraries are screened against a panel of diversified cellular

assays (Fig. 2b). Statistically significant correlation/anti-corre-

lation between compound and gene/protein may help us

identify novel molecular targets when the mechanism of

action of the compound is known, or facilitate discovering

new lead compounds if the protein target is well studied.

Conclusions

Various profiling technologies designed to probe the kinome

from different perspectives have been made dramatic

advancement to our understanding of the relationship

between kinases and disease, and this trend is expected to

accelerate over the next few years. Molecular, gene func-

tional, and chemical profiling all are making significant

inroads to the development of therapeutically relevant kinase

inhibitors and the identification of patients that will be the

beneficiaries from their use. Great challenges still remain to

integrate data from different profiling results and to further

extract meaningful information based on data mining tech-

niques. Synergistic approaches, both for profiling technology

and data analysis, will play an increasingly important role in

studying the kinome as a whole; this will further our under-

standing on the complex, dynamic nature of the kinome and

its part in various human diseases, which may ultimately help

us design more safe and effective drugs.

Acknowledgements

We thank Dr Nathanael Gray for critical reading of the

manuscript and members of the Kinase Drug Discovery

Department at the Genomics Institute of the Novartis

Research Foundation.

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