Jeffrey N. Peterson, CEO

UBS Global Life Sciences Conference9/22/2003

… … EOTrol™ Dynamic Coatings Open New Horizons for Electrophoretic SeparationsEOTrol™ Dynamic Coatings Open New Horizons for Electrophoretic Separations… … EOTrol™ Dynamic Coatings Open New Horizons for Electrophoretic SeparationsEOTrol™ Dynamic Coatings Open New Horizons for Electrophoretic Separations

9/22/03 - Press Release: Target Discovery Introduces First Product Line …9/22/03 - Press Release: Target Discovery Introduces First Product Line …9/22/03 - Press Release: Target Discovery Introduces First Product Line …9/22/03 - Press Release: Target Discovery Introduces First Product Line …9/22/03 - Press Release: Target Discovery Introduces First Product Line …9/22/03 - Press Release: Target Discovery Introduces First Product Line …9/22/03 - Press Release: Target Discovery Introduces First Product Line …9/22/03 - Press Release: Target Discovery Introduces First Product Line …

… … EOTrol™ Dynamic Coatings Open New Horizons for Electrophoretic SeparationsEOTrol™ Dynamic Coatings Open New Horizons for Electrophoretic Separations… … EOTrol™ Dynamic Coatings Open New Horizons for Electrophoretic SeparationsEOTrol™ Dynamic Coatings Open New Horizons for Electrophoretic Separations

Target Discovery, Inc.

Strategic targets … critical leverage points

Multidisciplinary team … innovative breakthroughs

Pragmatic, clear plan of attack … tiered objectives

Laser-like focus on priorities and execution

Target Leads Pre-clinicals

Clinicals Approval Get onFormulary

$500-800M 11-15 years

RescueClinical

OptimizeLead

Pharma R&D Productivity Squeeze

COST COST

TIME TIME

TESTING TESTING

COST COST

RECOVERY

RECOVERY UNDER

UNDER PATENTPATENT

FORMULARYFORMULARYREIMBURSEMENT

REIMBURSEMENT

??

EQUITYEQUITYMARKETS:MARKETS:

3 X 3 X PIPELINE !! PIPELINE !!

PERSONALIZEDPERSONALIZEDMEDICINEMEDICINE

““OMICS”OMICS”

DISAPPOINTMENTS

DISAPPOINTMENTS

Cannot Play the Same Old Game …Cannot Play the Same Old Game …

From “Omics” … to “Knowmics” ™

BioInformation exploding … Current technologies are bogged down Much data is of low quality / utility … and too costly Not converting to “BioKnowledge”

Key: Confirmed Biochemical Pathway Pathway confidence directs efficient development

Lower costs Expedited approval

From “Omics” … to “Knowmics” ™

New Breakthrough Technologies RequiredNew Breakthrough Technologies Required

Key: Confirmed Biochemical Pathway

Need High Quality Data Complete – full horizon proteome and other “omics” Precise – enable meaningful comparisons Cost effective – affordable utility

Need efficient Systems Biology tools Pathway Model generation – flexible alternatives Computational Strategy – speed & complexity Discover Optimized Correlation to high quality data

From “Omics” … to “Knowmics” ™

BioInformation exploding … Current technologies are bogged down Much data is of low quality / utility … and too costly Not converting to “BioKnowledge”

Target Leads Pre-clinicals

Clinicals Approval Get onFormulary

$500-800M 11-15 years

OptimizeLead

OptimizeClinical

TD

I T

ech

no

log

ies

Discovery Biology Reveals the PathwayD

isco

very

Bio

logy

TargetIdentification

TargetValidation

TargetSelection

SystemsBiology

PathEvolve™

Metabolomics MetaSIRMS™

InteractionalProteomics

IGEMS™IDBEST™

ExpressionalProteomics

MDCE™ EOTrol™IMLS™

Intellectual Property Development

US 09/551937 Label ChemistryUS 6379971 IMLS (issued 4/30/02) US 6537432 MDCE (issued 3/24/03)US 09/513907 DatabasesUS 09/553424 Metabolomics (allowed)

US 10/035349 Mass Defect Tags

US 09/033303 IMLS Algorithm

TDI0003 MS Sensitivity (provisional)TDI0005 Systems Biology / A.I. (provisional)Trade Secret Dynamic Coatings

WO 00/63683(pub. 10/01)National Phase

WO 01/49951(pub. 8/02)WO 01/49491(pub. 8/02)

Foreign EquivalentUS Patents and Applications

All current drugs based on 600 known targets 3,000 to 15,000 undiscovered new targets

The Value of “Targets”

Value Proposition

Gene Patent $1-2M 0-2%

Putative Target (set) $1-3M ($5-15M)1-5%

Validated Target $10-12M 3-7%

“Proven” Optimal Target $20-200M 5-15%

Deal Value Royalties

Source: BioCentury, Bank of America, Price Waterhouse Coopers.

““Undiscovered Targets” Market: $10B+Undiscovered Targets” Market: $10B+

TDI Business/Revenue Model

Discovery Biology-Based Pharmaceuticals (Long-Term) Forward integration & partnering of complementary technologies Selected diseases & targets reserved for internal development

Pharmaceutical & Diagnostic Partnerships (Mid-Term) “Target” licenses - by tissue and disease Time-limited exclusivity then, shared access models Value-added extension into validation, modeling, selection Narrow band platform out-licensing to “target’ license clients

Early Commercialization Out-Licensing (Near-Term) EOTrol™ Dynamic capillary coatings IDBEST™ Differential display kits (possible protein chip/MS partner) IMLS™ Sequencing kits (possible MS instrument partner) IGEMS™ MS sensitivity enhancement (MS instrument partner)

TDI Discovery Biology Platform

Target Identification (Expressional Proteomics) Complete proteome separation and quantitation

Multidimensional zero-EOF capillary electrophoresis (MDCE™) Dynamic coatings for capillary EOF control (EOTrol™)

High speed protein identification Inverted mass ladder sequencing (IMLS™)

Proprietary MS sensitivity breakthrough (IGEMS™)

Target Validation (Interactional Proteomics / Metabolomics) Population screening using differential display on protein chips

Isotope differentiated binding energy shift tags (IDBEST™)

Confirmation by metabolomic flux determination in vivo Metabolic flux stable isotope ratio mass spectrometry (MetaSIRMS™)

Target Selection (Systems Biology) Artificial intelligence for physiological model optimization and

in silico target selection (PathEvolve™)

Protein Expression: 2-D Gels vs. MDCE™Wheat Germ (CIEF/CGE)E. coli

Breadth of Proteome 30% 100%Resolution Capacity (theory) 7,000 >30,000Sensitivity (copies per cell) ≈105 <10Quantitative Precision >20% <1%Dynamic Range 102-3 105-7

Automated Analysis No Yes

Performance Measure 2-D Gel MDCE™

Key: Elimination of “EOF”Key: Elimination of “EOF”

US Patent Issued: MDCE™ at Low EOFUS Patent Issued: MDCE™ at Low EOF

11stst Product: EOTrol™ Dynamic Capillary Coatings Product: EOTrol™ Dynamic Capillary Coatings

EOF Causes Resolution Loss in CE

Herr, A. E. et al., Anal. Chem., 72:1053-1057 (2000).

EOF

EOF Must Be EliminatedEOF Must Be EliminatedTo AchieveTo Achieve

High Resolution SeparationsHigh Resolution Separations

EOTrol™ Dynamic Coatings Introduced

Electrophoretic Separations Capillary Electrophoresis Microchannels

Performance Breakthrough or Optimizer:

Resolution Throughput Reproducibility

User-Selectable EOF Control Normal or REVERSE Direction High or Low - and Stable

pH and Buffer Independent New Separations Enabled - Cations! Multiple Applications in 1 Capillary

Quick Strip for EOTrol™ Switches

No Capillary Change-Out!

FREE DEMO PACKFREE DEMO PACKFREE DEMO PACKFREE DEMO PACK

Mass Defect (amu) = Monoisotopic Mass - (#Protons + #Neutrons)

The “Mass Defect”Elements in Proteins

IdealMass DefectLabels

Isotope-Differentiated Binding Energy Shift Tags (IDBEST™)Isotope-Differentiated Binding Energy Shift Tags (IDBEST™)

IDBEST™ Myoglobin Fragmentation

[79Br]-b1-ion[81Br]-b1-ion

b1-ion

Spectral Deconvolution of IMLS Labels

Relative Abundance of50:50 Isotope PairsPreserved

Allows Qualification ofIMLS Sequence Peaks

Label Label-G Label-GL Label-GLS Label-GLSD

Benefit: 100X Speed & Cost ImprovementBenefit: 100X Speed & Cost Improvement

Applicable to Any BiomoleculeApplicable to Any Biomolecule

Key IP: Mass Defect Tags (Method & Composition)Key IP: Mass Defect Tags (Method & Composition) Algorithms (Deconvolution)Algorithms (Deconvolution)

33rdrd Product: IMLS™ Reagents & Software Product: IMLS™ Reagents & Software

Electropherogram

pI 1

FractionCollection

Expressional Proteomics - TDI

Key Advantages:• Speed 100 – 1000 X• Precision 100 X• Sensitivity 100 X• Resolution 4-5 X• Breadth of Proteome 3 X

LabeledProteinSample

LIFDetector pI 2

pI 3MW

ESI-TOFMass Spec

Sequence Tag Algorithm

MSGGFTA

Terminal Sequence

“N”thDimension

(Molecular Weight)

(each stage)

CAPILLARY ELECTROPHORESIS

FirstDimension

(Isoelectric Point)

Mass Spec Sensitivity Breakthrough

10-3

10-2

10-1

100

101

102

103

104

10-4 10-3 10-2 10-1 100

dete

ctio

n (

ppm

)

PEO Weight Concentration (g/L)

Run 1

Run 2

1 in 500 ionsreach detector

IGEMS™

Typical MSsensitivity

TDI Discovery Biology Platform

Target Identification (Expressional Proteomics) Complete proteome separation and quantitation

Multidimensional zero-EOF capillary electrophoresis (MDCE™) Dynamic coatings for capillary EOF control (EOTrol™)

High speed protein identification Inverted mass ladder sequencing (IMLS™)

Proprietary MS sensitivity breakthrough (IGEMS™)

Target Validation (Interactional Proteomics / Metabolomics) Population screening using differential display on protein chips

Isotope differentiated binding energy shift tags (IDBEST™)

Confirmation by metabolomic flux determination in vivo Metabolic flux stable isotope ratio mass spectrometry (MetaSIRMS™)

Current: Protein Chip & MS

• Comparison of 2 spots

• Precision: > 50% std. dev.

Digest and MSPeptides

IDBEST™ Fast / Precise Differential Display

HealthyTissue

Proteins

DiseasedTissue

Proteins

[12C]-Mass Defect

Tag

[13C]-Mass Defect

TagMix

Bind toCapture Surface

Identityby Tandem MS

0

2

4

6

8

10

12

14

0 500 1000 1500 2000 2500

Tandem MSFragmentation Spectrum(Counts)

m/z (amu)

ParentIon

b4

b5

b6

b7

Benefit: 5X Precision ImprovementBenefit: 5X Precision Improvement Eliminates ICAT™ cleanup & false pos/negEliminates ICAT™ cleanup & false pos/neg

Key IP: Mass Defect Tags (Method & Composition)Key IP: Mass Defect Tags (Method & Composition) Algorithms (Deconvolution)Algorithms (Deconvolution)

22ndnd Product: IDBEST™ Reagents & Software Product: IDBEST™ Reagents & Software

Deduce kinetics

Metabolic Flux Confirmation with SIRMS

Stable Isotope Ratio MS of metabolites (MetaSIRMS™) Direct metabolic flux measure

in vivo Track ratios & kinetics down

branch points Estimate pool sizes

Validate protein involvement in pathway of interest

HTS for ADME and Efficacy Studies

Metabolite stoichiometric identification using FTICR-MS

Extract to FTICR-MS

12C

13C

FEED:50% [13C or 15N]-metabolite50% [12C or 14N]-metabolite

Metabolomics in Early EmergenceMetabolomics in Early Emergence

TDI’s Patent Has Been AllowedTDI’s Patent Has Been Allowed

TDI Discovery Biology Platform

Target Identification (Expressional Proteomics) Complete proteome separation and quantitation

Multidimensional zero-EOF capillary electrophoresis (MDCE™) Dynamic coatings for capillary EOF control (EOTrol™)

High speed protein identification Inverted mass ladder sequencing (IMLS™)

Proprietary MS sensitivity breakthrough (IGEMS™)

Target Validation (Interactional Proteomics / Metabolomics) Population screening using differential display on protein chips

Isotope differentiated binding energy shift tags (IDBEST™)

Confirmation by metabolomic flux determination in vivo Metabolic flux stable isotope ratio mass spectrometry (MetaSIRMS™)

Target Selection (Systems Biology) Artificial intelligence for physiological model optimization and

in silico target selection (PathEvolve™)

ComparePredictions toExperimental

Data

SYSTEMMODELIntegrator

Kernel

PK

PK

T

⎛

⎜

⎝

⎞

⎟

⎠

= 1 + Keq

1

S

1

1 +

RR

T

Km

3

⎛

⎜

⎝

⎞

⎟

⎠

RR − P

RR

T

⎛

⎜

⎝

⎞

⎟

⎠

⎡

⎢

⎢

⎣

⎤

⎥

⎥

⎦

+

RR

T

Km

2

⎛

⎜

⎝

⎞

⎟

⎠

RR

RR

T

⎛

⎜

⎝

⎞

⎟

⎠

⎧⎪

⎨

⎪⎩

⎫⎪

⎬

⎪⎭

− 1

RR

RR

T

⎛

⎜

⎝

⎞

⎟

⎠

=

1 − 1 + Keq

1

S

1

PK

T

Km

3

⎛

⎜

⎝

⎞

⎟

⎠

PK

PK

T

⎛

⎜

⎝

⎞

⎟

⎠

⎡

⎢

⎣

⎤

⎥

⎦

RR − P

RR

T

⎛

⎜

⎝

⎞

⎟

⎠

1 +

PK

T

Km

2

⎛

⎜

⎝

⎞

⎟

⎠

PK

PK

T

⎛

⎜

⎝

⎞

⎟

⎠

⎡

⎢

⎢

⎣

⎤

⎥

⎥

⎦

Stiff DifferentialEquations requiring

Numerical Integration

Systems Biology Paradigm & Problems

PhysioTool ® 2000 Target Discovery, Inc./All Rights Reserved

Keq1S1 + PK <=======> PK*

k1PK + ATP <=====> PK•ATP --------> PK-P + ADP

k'2RR + PK-P <=====> PK•P•RR --------> RR-P + PK

k3RR-P + PK* <======> RR•P•PK* --------> RR + PK*

k4RR-P --------> RR

LinearizedEquilibria

Biologist’sConsensus

PhoE

PR-

PR-

Omp

C/F

Pi

R-OH

PhoA

PhoS

Pst

A

Pst

B

PstC

Pi

PhoR

PhoSP

ADP ATP

P

Promotes Pho-gene

Transcription

PhoB PhoB P

Pi

Pi

PhoR

Repression

Signal ?

+

Activation

Signal ?

R

PhoUPhoU

D

Outer Membrane

Inner Membrane

PhoE

PR-

PR-

Omp

C/F

Pi

R-OH

PhoA

PhoS

Pst

A

Pst

B

PstC

Pi

PhoR

PhoSP

ADP ATP

P

Promotes Pho-gene

Transcription

PhoB PhoB P

Pi

Pi

PhoR

Repression

Signal ?

+

Activation

Signal ?

R

PhoUPhoU

D

Outer Membrane

Inner Membrane

Mine forRates and

Concentrations

Iterate?…

PHYSIOLOGY MODULES(subroutines of

dimensionless algebraic & differential equations)

Equilibria-Binding-Phase-Reactional Enzymes

-Michaelis- Menton-Ping-Pong

Transport-Passive-Facilitated-Active Transcription

-Constitutive-Repression/ Activation

IntertissueTransport-Convection-Diffusion

Accumulation-Absorption-Fluid Pools

PHYSIOLOGYDATABASES

(rates andconcentrations)

GeneExpression

ProteinExpression

Metabolism

DataQuality

DifferenceData

ModelFlexibility

ComputationalWorkload

Human MindComplexity

Limits

TDI Systems Biology: PathEvolve™

Unit Operations strategy –math models for biological process elements

Artificial intelligence algorithms … from VLSI IC design process Accelerates exploration of alternative pathway models Accelerates convergence on optimal model … by correlation to data Accommodates all designated data and design constraints

Unique computational strategy … from process control industry Works directly with differential display data formats

GeneChip™, ICAT™, IDBEST™, MetaSIRMS™ and SIR-NMR

Up to 1012 acceleration potential for convergence on optimum models

Optimized models provide confidence and direction Allow sensitivity analysis for target selection Enables in silico experimentation for target / lead / clinical optimization Comprehensive approach eliminates subjective biases and assumptions

TDI Team

Senior Management / Founders

CEO: Jeffrey N. Peterson Abbott Laboratories (CEO/GM Abbott South Africa)General Electric (Engineered Materials & Plastics Groups)MIT (MSChemE, BSChemE)

CSO: Dr. Luke V. Schneider SRI (Stanford Research Institute) International

Dir. Technology Development Dir. Combinatorial Methods Center Dir. Upconverting Phosphor Diagnostics Winner, Monsanto Million Dollar Challenge

DuPont (Central R&D, Coatings)Princeton (PhDChemE, MAChemE)USF (MSEChemE, BSESChemE, BABiology)

TDI Team

Scientific Advisory Board Dr. Juan Santiago (Stanford) Dr. Jack Shively (City of Hope) Dr. Alan Smith (Stanford PAN Facility) Dr. Evan Williams (UC Berkeley) Dr. Leon Yengoyan (San Jose State)

Board of Directors Jeffrey N. Peterson, CEO Dr. Luke V. Schneider, CSO Clayton A. Struve (CEO CSS, ex-MD SwissBank, O’Connor) Steven M. Rauscher (CEO Genome Therapeutics,

AmericasDoctor.com, Affiliated Research Centers, Abbott)

TDI Growth Trajectory

2 0 0 52 0 0 42 0 0 3

$3-7 M$3-7 MB RoundB Round

CloseClose

$7 M$7 MA RoundA Round(’99–’03)(’99–’03)

CloseClose

IDBEST™IDBEST™IntroIntro

IMLS™IMLS™IntroIntro

MDCE™MDCE™IntroIntro

11stst Full FullDiscoveryDiscoveryBiologyBiology

PartnershipPartnership

11stst TDI TDICommercialCommercialRevenuesRevenues

IGEMS™IGEMS™Demon-Demon-strationstration

IGEMS™IGEMS™Out-Out-

LicensedLicensed

OptionalOptionalC RoundC RoundGrowthGrowthAcceler-Acceler-

ationationDecisionDecision

EOTrol™EOTrol™IntroducedIntroduced

11stst NIH NIHGrantGrantCloseClose

Target Discovery, Inc.

Strategic targets … critical leverage points

Multidisciplinary, innovative breakthrough technologies

Pragmatic, clear plan of attack … tiered objectives

Laser-like focus on priorities and execution

From Omics to Knowmics™

www.targetdiscovery.com