CONFIDENTIAL
MetaTox: leveraging the power of systems biology for
improving drug safety
Andrej Bugrim
GeneGo, Inc.
Copyright GeneGo 2000-2006
Agenda
CONFIDENTIAL
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Understand what is missing today and why more drugs are failing human tox
than ever before
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How we plan to address this problems
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Run through case studies to validate our ideas
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Summary
Copyright GeneGo 2000-2006
What’s missing in the marketplace currently?
Despite applying pre-clinical toxicogenomics as a
tool for safety evaluation for more than 5
years, the percentage of drugs failing in
clinical trials due to human toxicity has
grown.
One problem is the lack of analytical tools for
interpretation of toxicogenomics omics data,
as statistics-generated gene signatures for
drug response are an insufficient instrument
for comprehensive analysis.
GeneGo plans to provide new
tools and content from a
SYSTEMS perspective
14
candidates per approved drug
Toxicology’s Holy Grail remains finding
translational and safety biomarkers that can
predict or anticipate toxic manifestation and
detect damage earlier in human trials.
CONFIDENTIAL
12
10
8
6
4
2
0
1995-2000
2000-present
- Candidates entering Phase II or Phase III clinical trials per
one approved drug*
- Toxicity in human accounted for 31% withdrawals in 2000**
*Source:” Merck's Recall of Rofecoxib — A Strategic Perspective”. New
England Journal of Medicine, 2006, Volume 351:2147-2149
**Source: CMR International
Copyright GeneGo 2000-2006
CONFIDENTIAL
Toxicogenomics: traditional approach
Problems:
-Poor reproducibility between platforms and experiments
-Can’t explain biology/mechanistic tox
Drug gene signatures,
Descriptors:
-<100 genes
- consistent between
repeats
- Statistical methods
Recognize pattern,
Know tox
Tox. associated with known drug
New compounds, unknown tox
Copyright GeneGo 2000-2006
CONFIDENTIAL
Functional analysis must run in parallel to statistical signatures. Why?
Differentially expressed genes (normalized according to MAQC guidelines)
•Statistical descriptors
•Gene signatures
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Functional analysis:
-Pathways,
-Networks
-Process ontology
Functional analysis:
• Networks, pathways,
• Enrichment in ontology's
• Prioritization stats
• AI methods for data interpretation
• Functional descriptors
• Predictive models
• Interconnected gene modules
Copyright GeneGo 2000-2006
GeneGo Functional Descriptors™ explained
CONFIDENTIAL
Mapping on descriptors
Enrichment by category
Pathways maps
Toxicity, process maps
Sub-networks, modules, nodes
Predictive models
Indexing & scoring by tox. category
Copyright GeneGo 2000-2006
Gene signatures usually don’t make biological functional sense. No conciseCONFIDENTIAL
networks
70-genes metastases signature t’Veer: DI network
van 'T Veer L. J., Dai H, van de Vijer, M.J., , He Y.D., et al, Gene expression profiling
predicts clinical outcome of breast cancer. Nature, 2002, 415, 530-36
Wang Y. et al. Gene-expression profiles to predict distant metastasis
of lymph-node-negative primary breast cancer. Lancet, 2005, 365, 671-79
Copyright GeneGo 2000-2006
CONFIDENTIAL
Gene signatures usually don’t make biological functional sense. No concise
networks
76-genes metastases signature Wang: DI network
van 'T Veer L. J., Dai H, van de Vijer, M.J., , He Y.D., et al, Gene expression profiling
predicts clinical outcome of breast cancer. Nature, 2002, 415, 530-36
Wang Y. et al. Gene-expression profiles to predict distant metastasis
of lymph-node-negative primary breast cancer. Lancet, 2005, 365, 671-79
Copyright GeneGo 2000-2006
In contrast network signatures have mechanistic interpretation CONFIDENTIAL
Network signature differentiating tamoxifen and phenobarbital treatments
GeneGo – FDA collaborative study
Copyright GeneGo 2000-2006
Let’s take a closer look: nephrotoxicity case study 1
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CONFIDENTIAL
Training set:
– 15 nephrotoxicants
– 49 non-nephrotoxicants
Test set:
– 9 nephrotoxicants
– 12 non-nephrotxicants
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Microarray data profiling
– Treatment: male Sprague-Dawley rats, 7-8 weeks of age
– Animal selection: 3 rats at day 5 for each treatment for microarray profiling
– 10 rats at day 28 for histopathology profiling
– Expression profiling: Amersham Codelink Uniset Rat 1 Bioarray
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Nephrotoxicity is assessed by histopathology
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Published models accuracy:
– Training set error 16%
– Test set error 25%
Same lab, same platform, same animal group - why loss of accuracy?
How would perform across platforms/labs/animal groups?
1Source:
Fielden MR, Eynon BP, Natsoulis G, Jarnagin K, Banas D, Kolaja KL. A gene expression signature that predicts
the future onset of drug-induced renal tubular toxicity. Toxicol Pathol. 2005;33(6):675-83.
Copyright GeneGo 2000-2006
Toxicity is functionally complex
CONFIDENTIAL
Compounds identified as nephrotoxic by histopathology have in fact very
different “functional profiles”
Copyright GeneGo 2000-2006
Why gene signatures are not robust?
Training set – in fact is a
“functional mixture”
CONFIDENTIAL
Test set – DIFFERENT
“functional mixture”
Same organotoxicity
Fitting this specific
composition
Good prediction
within training set
Poor prediction outside
training set
Gene signature –
uniquely represents
training set
Functional analysis must
accompany modeling
Copyright GeneGo 2000-2006
Regulation of transport by nephrotoxicants
CONFIDENTIAL
GeneGo network for Transport_Sodium transport. Nephrotoxicants
Many ion channels are down regulated!
Copyright GeneGo 2000-2006
Regulation of transport by non-nephrotoxicants
CONFIDENTIAL
GeneGo network for Transport_Sodium transport. Non-nephrotoxic
Regulation of transport shows striking differences between two groups!
Copyright GeneGo 2000-2006
Lessons learned:
CONFIDENTIAL
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Different mechanisms = same histopathology (nephrotoxicity)
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Similar mechanisms could be shared by toxicants and non-toxicants alike
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Dataset is too small to build a robust model
What we need:
Functional and mechanistic analysis must guide building predictive
models
Larger datasets are needed to address functional diversity
Copyright GeneGo 2000-2006
CONFIDENTIAL
Network modularization approach for drug response biomarkers
new files or
HT datasets:
~40-50K overlapping
modules
CEBS,
EDGE
Iconix
GeneLogic
Visualization
on networks
Copyright GeneGo 2000-2006
CONFIDENTIAL
GeneGo Functional Descriptors
Sample 1
Sample 2
Sample 3
Sample 4
Gene 1
1
4
3
2
Gene 2
4
2
7
6
Gene 3
2
9
3
8
Gene 4
2
5
4
2
Copyright GeneGo 2000-2006
Distances between samples in pathways expression space
S1 / S2
S1 / S3
S1 / S4
S2 / S3
S2 / S4
CONFIDENTIAL
S3 / S4
Pathway 1
…
Pathway n
Copyright GeneGo 2000-2006
Example: GeneGo – FDA collaborative study
Mestranol
CONFIDENTIAL
Phenobarbital
Copyright GeneGo 2000-2006
CONFIDENTIAL
Functional analysis makes use of full data sets that can be lost using conventional
methods
Square blocks - genes with fold change > 1.4 and p < 0.1. Their number is very
small on the map (five) – this pathway map would have been missed by
conventional methods.
Copyright GeneGo 2000-2006
Biological networks in toxicity predictions
CONFIDENTIAL
Biological networks as functional descriptors
Robust
Multi-dimensional
Mechanistic interpretation
Platform/study
independence
Fine-tuning of
comparison/selection
Understand human/rat
differences
Copyright GeneGo 2000-2006
Case study#2. Networks analysis of J&J toxicogenomics dataset CONFIDENTIAL
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Data*
– CEBS J&J set of 137 compounds, 600 profiles
– 1,471 genes cDNA array
– Pros
• The only large scale dataset available
• High quality
• Internally consistent
– Cons
• Few compounds in each tox category
• Small array
• Non-standard array
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Classifications
– Toxic vs. drugs with no severe toxicity
– By molecular mechanism of toxicity
– By system/organ affected
– By carcinogenicity
– By type of hepatopatology
– By basic therapeutic effect
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The Analysis
– Collection of pre-built networks: general and toxicity-related biological processes
– Whole array mapping (no filtering)
– P-value on functional categories: GeneGo process networks, canonical pathways maps, GO, tox. networks
– Linear Discriminant Analysis (LDA) models are built to evaluate performance of networks as toxicity predictors.
A.Y. Nie et al. Predictive toxicogenomics approaches reveal underlying toxicogenomics mechanisms of nongenotoxic cardiogenicity. Mol. Carcinogenesis, 2006
Copyright GeneGo 2000-2006
CONFIDENTIAL
Drug classification by cancerogenecity
Carcinogenic
Non-cancerogenic
DNA damagers (genotoxic)
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Acetamidofluorene
Amsacrine
Busulfan
Carbon Tetrachloride
Carmustine
Chlorambucil
Cisplatin
Cyclophosphamide
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Felbamate
Dacarbazine
Dimethylnitrosamine
Doxorubicin
Etoposide
Hydrazine Hydrate
Streptozocin
Thioacetamide
Non-genotoxic
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Aniline
Atenolol
Bromocryptine
Butyl OH toluene
Chlorpromazine
Dantrolene
Dapsone
Dieldrin
Gabapentin
Isoniazid
Felbamate
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Furosemide
Lansoprazole
Methapyrilene
Monocrotaline
Phenobarbital
Piperonyl butoxide
Raloxifene
Rifampin
Sulfamethoxazole
Valproic Acid
Carbamazepine
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Benzafibrate
Benzbromarone
Clofibrate
Dichloroacetate
DiEH phthalate
PPAR activators
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Diflunisal
Fenbufen
Perfluoro decanoate
Perfluoro octanoate
WY14643
Macrophage activators
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Allyl alcohol
Carbon Tetrachloride
Concanavalin A
Coumarin
Dimethylnitrosamine
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Buspirone
Aspirin
Captopril
Clozapine
Dexamethasone
Dimethylmaleate
Acetamidophenol
Dipyridamole
Disulfiram
Enalapril
ErythroMC Estolate
Famotidine
Fenbufen
Flufenamic Acid
Fluoxetine
Gentamycin
Glucosamine
Glybenclamide
Hexachlorocyclohexane
Ibuprofen
Isoproterenol
Ketoconazole
Mebendazole
Metformin
Methotrexate
Methyldopa
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Metoprolol
Mifepristone
Mycophenolic Acid
Naltrexone
Niacin
Nifedipine
Nimesulide
Methotrexate
Nizatidine
Perhexilene
Phenylephrine
Phorone
Puromycin
Quercetin
Ranitidine
Vitamin A
Rosiglitazone
Rotenone
Tannic Acid
Tetracycline
Trans Anethole
Troglitazone
Verapamil
Gadolinium
Galactosamine
LPS
Zymosan A
Copyright GeneGo 2000-2006
CONFIDENTIAL
Drug classification by type of hepatopathology
Fibrosis
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Carbon Tetrachloride
Concanavalin A
Dimethylnitrosamine
Galactosamine
LPS
Methotrexate
Streptozocin
Thioacetamide
Zymosan A
Cholestasis
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Phospholipidosis
Captopril
Clofibrate
ErythroMC Estolate
Ethinyl Estradiol
Glibenclamide
LPS
Niacin
Perfluoro decanoate
Perfluoro octanoate
Phalloidin
Rifampin
Sulindac
WY14643
Zymosan A
Troglitazone
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Amiodarone
Benzbromarone
ErythroMC Estolate
Fluoxetine
Gentamycin
Paraquat
Perhexiline
Steatosis
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Amiodarone
Aspirin
Bromobenzene
Carbon Tetrachloride
Cerium Chloride
DiEH phthalate
Dieldrin
Flufenamic Acid
Dimethylnitrosamine
ErythroMC Estolate
Ethinyl Estradiol
Galactosamine
Hexachlorocyclohexane
Hydrazine Hydrate
Methotrexate
Puromycin
Tetracycline
Valproic Acid
Disulfiram
Necrosis
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Acetamidofluorene
Anline
Bromobenzene
Butyl OH toluene
Cadmium Chloride
Carbon Tetrachloride
Nimesulide
Indomethacin
Hexachlorocyclohexane
Concanavalin A
Coumarin
Methapyrilene
Cyclophosphamide
Ibuprofen
Diclofenac
Methylthiazole
Flurbiprofen
LPS
Fenbufen
Menadione
Dieldrin
Diflunisal
Dimethylmaleate
Dimethylnitrosamine
Disulfiram
Flutamide
Ethinyl Estradiol
Furosemide
Gadolinium
Galactosamine
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Niacin
Piperonyl butoxide
Precocene I
Simvastatin
Tacrine
Tannic Acid
Thioacetamide
Trans Anethole
Valproic Acid
Vitamin A
Zymosan A
Venoocclusion
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Antimycin A3
Chlorambucil
Cyclophosphamide
Dimethylnitrosamin
Monocrotaline
Phenobarbital
Phenylephrine
Rotenone
Tacrine
Bile duct
specific Tox
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ANIT
Anline
Methylenedianiline
Copyright GeneGo 2000-2006
Computational analysis for descriptor networks
CONFIDENTIAL
Pre-built expert-curated networks
Tox networks
GeneGo
Ontologies
Toxicogenomic
datasets
Network-specific
expression data
LDA
Best scoring networks
Predictive models
Mechanistic Tox
Copyright GeneGo 2000-2006
Multidimensionality helps: toxicity is not always obvious!CONFIDENTIAL
Set of oxidative stress-inducers for which primary network (oxidative stress) fails to
predict toxicity, but secondary networks classify them correctly
Networks
Drugs
Oxidative
stress
Xenobiotic
methabolism
Cox1 cox2
Cholesterol
biosynthesis
Sodium
transport
Precocene I
0.336427
0.353022
0.676502
0.213828
0.176312
Dexamethasone
0.236131
0.375851
0.766899
0.521262
0.885393
Chlorpromazine
0.179996
0.174931
0.894265
0.461668
0.697081
Isoproterenol
0.151302
0.303179
0.299783
0.837951
0.449224
Tacrine
0.142234
0.402957
0.390129
0.637611
0.792162
Consider potential toxicities in the context of drug’s
indication, it may hit the target but have bad side effects
Likely toxic
May be toxic
Sodium transport bad for cardiovascular diseases
Likely nontoxic
Cholesterol or inflammation bad for Atherosclerosis
Copyright GeneGo 2000-2006
CONFIDENTIAL
GG processes related to overall toxicity
GeneGo preocess
# of genes
overall error
Development_Ossification and bone remodeling
21
0.049295775
Inflammation_Neutrophil activation
30
0.049295775
Muscle contraction
22
0.056338028
Cell adhesion_Platelet aggregation
32
0.063380282
Immune_Phagosome in antigen presentation
61
0.063380282
Immune_Phagocytosis
Our predictions are up to
95% accurate if sufficient
training dataset is
available!
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0.063380282
20
0.063380282
30
0.070422535
46
0.077464789
22
0.077464789
26
0.077464789
23
0.077464789
44
0.077464789
Cytoskeleton_Regulation of cytoskeleton rearrangement
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0.084507042
Signal transduction_Leptin signalig
25
0.084507042
Inflammation_Jak-STAT Pathway
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0.084507042
Translation_Elongation-Termination
28
0.084507042
Reproduction_FSH-beta signaling pathway
29
0.091549296
Proliferation_Positive regulation cell proliferation
21
0.098591549
Inflammation_IL-2 signaling
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0.098591549
Response to hypoxia and oxidative stress
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0.098591549
Proliferation_Negative regulation of cell proliferation
24
0.098591549
Signal transduction_TGF-beta, GDF and Activin signaling
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0.098591549
Cell cycle_Mitosis
Cell adhesion_Amyloid proteins
Inflammation_MIF
Cytoskeleton_Actin filaments
Development_Angiogenesis
Cell cycle_G2-M
Immune_Antigen presentation
Copyright GeneGo 2000-2006
Visualization of networks as toxicity predictors
CONFIDENTIAL
severe toxicity likely
severe toxicity possible
severe toxicity un-likely
Copyright GeneGo 2000-2006
Conclusions for case study #2
CONFIDENTIAL
• Drugs with subtle manifestations of toxicity could be detected
by multidimensional network signatures
• Network descriptors allow to evaluate toxicity in the context of
drug’s indications
• For datasets of sufficient size, networks descriptors feature
very high prediction accuracy (4% error)
Copyright GeneGo 2000-2006
Annotation projects for MetaTox
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Toxicity ontology and processes
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Toxicity networks and maps
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Human/mouse/rat differences – differential drug toxicity
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Drug action/drug targets maps
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Drug response from structure/ligand-receptor interactions
CONFIDENTIAL
Copyright GeneGo 2000-2006
CONFIDENTIAL
Drug-induced pathological processes (GeneGo annotations)
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Drug-induced pathological processes
Drug-induced pathology classification has
a hierarchical structure and is based on literature data
Genes associated with drug-induced pathologies
Compounds associated with drug-induced pathologies
Cited articles (unique)
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600
30
250
(!) Public domain DOES NOT have structured information (which can be queried
for miming and download) about drug-induced pathological processes
connections and genes.
Copyright GeneGo 2000-2006
CONFIDENTIAL
Toxicity annotation
Drug-induced toxicity
Organotoxicity
Cellular processes
38
16
Organospecify
pathological processes
main processes
Toxicity tree
Generic toxic processes
Copyright GeneGo 2000-2006
Drug toxicity tree
CONFIDENTIAL
38 Drug-induced pathological
processes
Folders from MeSH
Folders created at GeneGo
based on reviews
Copyright GeneGo 2000-2006
Generic tox-response processes
CONFIDENTIAL
Process
Cell cycle
Apoptosis
DNA damage and repair
Adhesion, cytoskeleton, ECM
Proliferation
Inflammation
Immune response
Blood coagulation
Oxidative stress
Gene expression (transcription, translation)
Chemotaxis
Development
Signal Transduction
Transport
Metabolism
Protein folding
Copyright GeneGo 2000-2006
Process of gene/compound – drug-induced toxicity annotation CONFIDENTIAL
GeneGo Toxicity
Tree
In-house software for
scanning public databases
like Gene and pulling out
disease-related information
Gene-pathology links
Gene-pathologies
links in Database
Compound-pathology
links in database
Extended annotation
Information based on
articles and reviews
First-pass manual
curation, removing
incorrect links
Drug - pathology
links in Database
Copyright GeneGo 2000-2006
Auto-upload and info parsing from public databases
CONFIDENTIAL
Copyright GeneGo 2000-2006
Types of “gene/ compound – drug-induced pathology” links
CONFIDENTIAL
Gene/compound ->drug-induced pathologe
Level of change
Type of change
DNA
mutation
SNP
RNA
alternative transcript
splice-variant
alteration quantity of RNA
Protein
splice-variant
Causative
risk
hypothesis
manifistation
protect
no relation
isoform
posttranlation modification
alteration of interaction
alteration of localization
alteration quantity of protein
Compound (endogenic)
alteration quantity of compaund
Compound (exodogenic, drugs)
couse
Copyright GeneGo 2000-2006
Gene – toxicity annotation. Example 1
CONFIDENTIAL
Copyright GeneGo 2000-2006
Gene – toxicity annotation. Example 1
CONFIDENTIAL
Copyright GeneGo 2000-2006
Difference between human and rat toxicity. Case study
CONFIDENTIAL
Pyrasinamide may cause accumulation of excess of a uric acid in L-Triptophan and Purine metabolic pathways
in human but not rat
Copyright GeneGo 2000-2006
Androstenedione and testosterone biosynthesis and metabolism
CONFIDENTIAL
Mouse, Rat
Mouse
Copyright GeneGo 2000-2006
Leucune, isoleucine and valine metabolism
CONFIDENTIAL
Mouse
Copyright GeneGo 2000-2006
Fine metabolic differences between rodents, human
CONFIDENTIAL
Unique genes
Human
Mouse, Rat
Unique genes and orthologs catalyse one reaction
141 mouse genes
74 rat genes
There is no human orthologs
for Protein A
Unique genes catalyze unique reactions
9 mouse genes
2 rat genes
Orthologs catalyse different reactions
1 mouse gene
1 rat gene
Copyright GeneGo 2000-2006
Annotation of protein complexes common, different for three organisms
CONFIDENTIAL
Object contains protein-complex for three organisms
Copyright GeneGo 2000-2006
MetaTox Consortium Business Model
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CONFIDENTIAL
Potential members: Boehringer Ingelheim, JNJ, Merck KGaA, Pfizer, Novartis, GSK,
Wyeth, Roche, Vertex, Serono, Altana, BMS, Eli Lilly….
Charter member: FDA
Director: Richard Brennan
3 years: annual meetings including training
Early access to content, workflows and analysis tools
– Includes unlimited access to all MetaTox products for the term of the consortium and one
perpetual named user license thereafter
Immediate deliverables
– Two additional MetaDrug seats, One named user MapEditor and One named user
MetaLink ($144k value)
– 400 Pre built Tox networks as part of MetaDrug
– Two-ways connectivity with ArrayTrack (FDA)
– An uploaded normalized set of 137 compound-response signatures from CEBS JNJ
• GeneGo experts have made the data ready for analysis and comparison
• Compound profile grouped by type of toxicity and mode of action
Consortium member option:
– Each member may provide 100 compounds with expression data using whole genome arrays under
CDA. This information will not be shared with other members. The data will be used to develop tox
signature networks that will be available to all members. The original data will be returned to the
member
Copyright GeneGo 2000-2006