9/22/03 - Press Release: Target Discovery Introduces First Product Line …
Jeffrey N. Peterson, CEO
UBS Global Life Sciences Conference
9/22/2003
… EOTrol™ 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
Pharma R&D Productivity Squeeze
PERSONALIZED
MEDICINE
Optimize
Lead
Rescue
Clinical
Cannot Play the Same Old Game …
Target
Leads
Preclinicals
$500-800M
Clinicals
Approval
11-15 years
Get on
Formulary
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” ™
 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
New Breakthrough Technologies Required
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”
Discovery Biology Reveals the Pathway
Discovery Biology
Target
Identification
Interactional
Proteomics
Target
Validation
Metabolomics
Target
Selection
Systems
Biology
Optimize
Lead
Target
TDI Technologies
Expressional
Proteomics
Leads
Preclinicals
$500-800M
MDCE™ EOTrol™
IMLS™
IGEMS™
IDBEST™
MetaSIRMS™
PathEvolve™
Optimize
Clinical
Clinicals
Approval
11-15 years
Get on
Formulary
Intellectual Property Development
US Patents and Applications
US 09/551937
US 6379971
US 6537432
US 09/513907
US 09/553424
Label Chemistry
IMLS (issued 4/30/02)
MDCE (issued 3/24/03)
Databases
Metabolomics (allowed)
US 10/035349 Mass Defect Tags
US 09/033303 IMLS Algorithm
TDI0003
TDI0005
Trade Secret
Foreign Equivalent
WO 00/63683
(pub. 10/01)
National Phase
WO 01/49951
(pub. 8/02)
WO 01/49491
(pub. 8/02)
MS Sensitivity (provisional)
Systems Biology / A.I. (provisional)
Dynamic Coatings
The Value of “Targets”
 Value Proposition
Deal Value
Royalties
 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%
 All current drugs based on 600 known targets
“Undiscovered Targets” Market: $10B+
 3,000 to 15,000 undiscovered new targets
Source: BioCentury, Bank of America, Price Waterhouse Coopers.
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™
E. coli
Wheat Germ (CIEF/CGE)
Key: Elimination of “EOF”
US Patent Issued: MDCE™ at Low EOF
Performance Measure
2-D Gel
MDCE™
1stBreadth
Product:
EOTrol™ Dynamic Capillary
Coatings
of Proteome
30%
100%
Resolution Capacity (theory)
7,000
>30,000
Sensitivity (copies per cell)
≈105
<10
Quantitative Precision
>20%
<1%
Dynamic Range
102-3
105-7
Automated Analysis
No
Yes
EOF Causes Resolution Loss in CE
EOF
EOF Must Be Eliminated
To Achieve
High Resolution Separations
Herr, A. E. et al., Anal. Chem., 72:1053-1057 (2000).
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 PACK
The “Mass Defect”
Elements in Proteins
Ideal
Mass Defect
Labels
Mass
Defect (amu) = Monoisotopic
Mass - Shift
(#Protons
#Neutrons)
Isotope-Differentiated
Binding Energy
Tags+(IDBEST™)
IDBEST™ Myoglobin Fragmentation
b1-ion
[81Br]-b1-ion
[79Br]-b1-ion
Spectral Deconvolution of IMLS Labels
Benefit: 100X Speed & Cost Improvement
Applicable to Any Biomolecule
Key IP: Mass Defect Tags (Method & Composition)
Algorithms (Deconvolution)
Relative Abundance of
50:50 Isotope Pairs
Preserved
3rd Product:
IMLS™ Reagents & Software
Label
Allows Qualification of
IMLS Sequence Peaks
Label-G
Label-GL
Label-GLS
Label-GLSD
Expressional Proteomics - TDI
CAPILLARY ELECTROPHORESIS
First
Dimension
(Isoelectric Point)
“N”th
Dimension
(Molecular Weight)
Electropherogram
pI 1
LIF
Detector
Labeled
Protein
Sample
Fraction
Collection
(each stage)
Key Advantages:
• Speed
100 – 1000 X
• Precision
100 X
• Sensitivity
100 X
• Resolution
4-5 X
• Breadth of Proteome
3X
pI 2
MW
ESI-TOF
Mass
Spec
pI 3
Terminal Sequence
Sequence Tag Algorithm
MSGGFTA
Mass Spec Sensitivity Breakthrough
104
103
1 in 500 ions
reach detector
h
detection
(ppm)
102
101
100
IGEMS™
Run 2
10-1
10-2
10-3 -4
10
Typical MS
sensitivity
Run 1
10-3
10-2
PEO Weight Concentration (g/L)
10-1
100
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™)
IDBEST™ Fast / Precise Differential Display
[12C]Benefit:
Mass Defect
Tag
Bind to
CaptureKey
Surface
IP:
2nd
 Current: Protein Chip & MS
Diseased
Tissue
Proteins
• Comparison of 2 spots
[ C]- • Precision: > 50% std. dev.
5X Precision Improvement
13
Mass Defect
Eliminates
TagICAT™
Mix
cleanup & false pos/neg
Mass Defect Tags (Method & Composition)
Algorithms (Deconvolution)
Product:
Digest and MS
IDBEST™
Peptides
Reagents & Software
14
b
b
12
Tandem MS
Fragmentation Spectrum
(Counts)
Healthy
Tissue
Proteins
5
4
Identity
by
Tandem
MS
b
6
10
b
8
Parent
Ion
7
6
4
2
0
0
500
1000
1500
m/z (amu)
2000
2500
Metabolic Flux Confirmation with SIRMS
FEED:
50% [13C or 15N]-metabolite
50% [12C or 14N]-metabolite
 Stable Isotope Ratio MS of
metabolites (MetaSIRMS™)
 Direct metabolic flux measure
in vivo
 Track ratios & kinetics down
branch points
 Estimate pool sizes
Metabolomics in Early Emergence
Extract to FTICR-MS
TDI’s Patent Has Been Allowed
 Validate protein involvement in
pathway of interest
 HTS for ADME and Efficacy
Studies
 Metabolite stoichiometric
identification using FTICR-MS
12C
13C
Deduce kinetics
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™)
Systems Biology Paradigm & Problems
PHYSIOLOGY MODULES
(subroutines of
dimensionless algebraic
& differential equations)


 RR T  RR  P   RR T  RR
 PK   1  K S 1  
  
 
 
 PK 
eq 1 



1 
 Km 3   RR T    Km 2   RR T
T 
Computational
Workload
Transcription
-Constitutive
-Repression/
Activation
Difference
Data
Human Mind
Complexity
Limits
PhoE
Outer Membrane
Omp
C/F
R-OH
Pi
PhoA
Activation
Signal ?
Biologist’s
Accumulation Model
Consensus
-Absorption
Flexibility
-Fluid Pools
PhoS
P
PhoS
PhoR
PhoR
ADP
PstC
PhoU
D
PhoU
R
ATP
Pi
PhoB
PhoB
Repression
Signal ?
P
+
Pi
Promotes Pho-gene
Transcription
PhysioTool ® 2000 Target Discovery, Inc./All Rights Reserved
Compare
Predictions to
Experimental
Data
Inner Membrane
Pst Pst
A
B
P
Protein
Expression
Pi
R- P
(rates and
concentrations)
Gene
Expression
k1
PK + ATP <=====> PK•ATP --------> PK-P + ADP
k'2
RR + PK-P <=====> PK•P•RR --------> RR-P + PK
k3
RR-P + PK* <======> RR•P•PK* --------> RR + PK*
k4
RR-P --------> RR
Linearized
Equilibria
PHYSIOLOGY
DATABASES
Mine for
Rates and
Concentrations
Keq1
S1 + PK <=======> PK*
R- P
-Convection
-Diffusion
1
Enzymes
Transport
Intertissue
Transport

 

 

 PK T  PK  RR  P
 


1  1  Keq S 

1  Km   PK   RR
1

3
T 
T 
 RR  
 RR 
PK



T 
T   PK  

1  


 Km 2   PK T 

-MichaelisMenton
-Ping-Pong
-Passive
-Facilitated
-Active
Integrator
Kernel
Stiff Differential
Equations requiring
Numerical Integration
Equilibria
-Binding
-Phase
-Reactional
SYSTEM
MODEL
Iterate?…
Data
Quality
Metabolism
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
$7 M
A Round
(’99–’03)
Close
2
1st NIH
Grant
Close
0
0
EOTrol™
Introduced
1st TDI
Commercial
Revenues
IGEMS™
Demonstration
$3-7 M
B Round
Close
3
2
0
IMLS™
Intro
IDBEST™
Intro
0
4
IGEMS™
OutLicensed
2
MDCE™
Intro
1st Full
Discovery
Biology
Partnership
0
0
Optional
C Round
Growth
Acceleration
Decision
5
Target Discovery, Inc.
From Omics to Knowmics™
 Strategic targets … critical leverage points
 Multidisciplinary, innovative breakthrough technologies
 Pragmatic, clear plan of attack … tiered objectives
 Laser-like focus on priorities and execution
www.targetdiscovery.com