Primary Human Cell Systems Analysis of Drug Mechanisms, presented by Ellen Berg at the SBS 15th Annual Conference, Lille, France, April 2009

1. Primary Human Cell Systems
Analysis of Drug Mechanisms
Ellen L. Berg, PhD
BioSeek, Inc.
SBS 15th Annual Conference
Lille, France
28 April 2009
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2. Presentation Overview
• BioMAP Human Cell Systems Platform
 Primary human cell-based disease models
• Analysis of PPAR agonists
 Discriminate clinical-stage compounds
• Class and compound-specific activities
 Explore alternative clinical indications
• Prioritize compounds for indications and/or safety related activities
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3. Goals for Human Cell Systems Biology Platform
• Covers a lot of biology
 Targets, pathways, therapeutic areas, diseases
• Covers the right biology
 Human disease biology
• Is quantitative, reproducible, robust, high throughput
 Standardized, amenable to database generation
• Is useful to broad range of stakeholders
 Project leaders, biologists, chemists, preclinical scientists, clinicians
• Is predictive
 Biomarkers
 Clinical indications, efficacy, toxicity
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4. BioMAP® Technology Platform
Assays Profile Database Informatics
LPS
BF4T
SM3C
Human primary cells Biological responses to Specialized informatics tools
Disease-like culture drugs and stored in the are used to mine and analyze
conditions database biological data
Complementary to biochemical target and phenotypic screening
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5. BioMAP® Technology Platform
Assays
• Assay endpoints are cell-based clinical
BioMAP Systems include human assays
biomarkers and risk complex human
engineered to modelfactors (proteins)
LPS
disease biology
 Cytokines, chemokines
• Human primary cells receptors
 Adhesion and growth
BF4T
• Co-cultures, multiple (prostaglandins, etc.)
 Biological mediators stimulation factors, activated cells
SM3C • Quantitative protein readouts plasminogen activators)
 Proteases, enzymes (MMPs, - biomarkers
• Pharmacologically relevance - validated with known
 Others (hemostatic factors, matrix components)
Human primary cells
Disease-like culture  drugs
Clinically relevant
conditions
>25 BioMAP Systems
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6. BioMAP® Technology Platform
Assays Profile Database
• > 2000 agents
• Approved drugs
LPS
• Clinical stage &
BF4T failed drugs
• Experimental
SM3C
compounds
Human primary cells Biological responses to drugs • Biologics
Disease-like culture and stored in the database
conditions • Toxicants
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7. Assays are Robust and Highly Reproducible
High Correlation of Experimental Replicates
Pearson Correlation Coefficient
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12
R1 1
R2 0.95 1
R3 0.96 0.94 1
R4 0.98 0.98 0.96 1
R5 0.93 0.94 0.91 0.94 1
R6 0.96 0.96 0.93 0.97 0.98 1
R7 0.94 0.91 0.9 0.93 0.89 0.9 1
R8 0.95 0.98 0.94 0.98 0.94 0.98 0.92 1
R9 0.91 0.92 0.88 0.92 0.89 0.91 0.93 0.93 1
R10 0.88 0.9 0.81 0.89 0.93 0.93 0.85 0.91 0.83 1
R11 0.94 0.97 0.9 0.94 0.91 0.93 0.94 0.96 0.91 0.89 1
R12 0.92 0.9 0.84 0.89 0.96 0.96 0.89 0.91 0.87 0.92 0.91 1
5 µM dose
Consistent data experiment-to-experiment
Pearson correlation >0.8 (perfect match = 1)
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8. Classification of Drugs By Mechanism
Pairwise Correlation of BioMAP Reveals Functional Similarities
Protein Estrogen R
synthesis
Microtubule
PKC Activation Destabilizers
Transcription
PI-3K
JNK
NFκB
mTOR
Hsp90
DNA
Calcineurin synthesis Retinoids
CDK
HMG-CoA
reductase
Ca++ Mitochondrial
Mobilization ET chain
p38 MAPK
Microtubule
Stabilizers
MEK
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9. BioMAP Systems are Validated
Corticosteroids (Prednisolone) Are Active in Inflammation Systems
BioMAP Systems
Log expression ratio
(Drug/DMSO control)
99% significance
envelope
Control (no drug) Dose
Response
Cytotoxicity Readouts
Readout Parameters (Biomarkers)
Profiles retain shape over multiple concentrations
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10. BioMAP Systems are Validated
Activities of Corticosteroids Match Clinical Effects
Log expression ratio
SAA
(Drug/DMSO control)
PAI-1 PAI-1
MMP-1
IL-8
MCP-1 IL-8
PGE2 E-selectin
Collagen I & III
TNF-α
MCP-1, IL-8, E-sel. decrease PGE2 decrease Collagen I, III decrease PAI-1, SAA increase
Leukocyte recruitment Skin atrophy CV complications
Pain, swelling Sartori et al., 1999
Many, e.g. Jilma et al., 2000 Sebaldt et al., 1990 Autio et al., 1994
Fyfe et al., 1997
Readouts in BioMAP show the same pattern as has been
reported for patients receiving steroid therapy
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11. Project Goal
• Characterize PPAR agonists by BioMAP profiling
 Compare and contrast PPARγ agonists (anti-inflammatory
activities)
Rosiglitazone (Avandia)
PPARγ Troglitazone (Resulin) PPARα Fenofibrate (Tricor)
Pioglitazone (Actos)
 Identify shared and unique pathway effects
 Identify potential new indications
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12. BioMAP Profile of Rosiglitazone
BioMAP Systems
Eot3 IP-10 E-sel
IP-10 MCP-1
I-TAC MCSF IL-8
I-TAC
CD40 TNFα
VCAM
Macrophage
activation Monocyte
activation T cell
activation
• Rosiglitazone has strong anti-inflammatory activities
 Inhibition of monocyte and T cell activation (T cell proliferation ) & recruitment
 Inhibition of inflammatory chemokines (Eotaxin3, IP-10, ITAC, IL-8)
 Consistent with inhibition of NFκB pathway by rosiglitazone
• Consistent with efficacy in vivo
 Mouse models of colitis (Shah, Y.M., et al., Am. J. Physiol. Gastrointest. Liver Physiol. 2007,
292:G657; Saubermann, L.J., Inflamm. Bowel Dis., 2002, 8:330).
 Animal model of exposure-induced asthma (Lee, J. Immunol, 2006 117:5248).
 MCP-1 and TNFα are clinical biomarkers
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13. BioMAP Profile of Rosiglitazone
BioMAP Systems
Col IV
Eot3 MMP9 E-sel
IP-10 uPAR Col III MCP-1
PAI-1 MCSF IL-8
I-TAC Col III
CD40 TNFα
VCAM
Macrophage
activation Monocyte
activation T cell
activation
• Rosiglitazone has strong effects on tissue remodeling parameters
 Inhibition of MMP9, PAI-1, uPAR, Collagen III; upregulation of Collagen IV; Strong inhibition
of myofibroblast activation
 Consistent with modulation of TGFβ pathway by rosiglitazone
• Consistent with results from in vivo studies
 Rosigitazone is effective in models of neointimal hyperplasia (MMP9 is a biomarker in vivo)
 Rosiglitazone protects in scleroderma model (myofibroblast accumulation and Collagen III)
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14. BioMAP Profile of Rosiglitazone
BioMAP Systems
PGD2 PGE2
PGJ2 PGF2a PGJ2 PGD2 PGJ2 PGD2
PGF1a PGF2a PGF2a
PGF1a PG1a PGF2a
Bronchial epithelial cell-containing systems Leukocyte-containing systems
• Rosiglitazone upregulates prostaglandins
 In both bronchial epithelial and leukocyte-containing systems
 Potent activity
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15. Upregulation of Prostaglandins by Rosiglitazone
• Are prostaglandin effects PPARγ-dependent?
 Not reversed by PPARγ antagonists
 Reversed by COX1/2 inhibitors
 Non-TZD PPARγ agonists do not upregulate prostaglandins
• Consistent with secondary activity / activities
 Rosiglitazone has been reported to inhibit 15-hydroxy-
prostaglandin dehydrogenase and CYP450 2C8
 Q: What about other TZDs, PPAR ligands?
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16. Rosiglitazone Upregulation of PGE2 is not a Class Effect
Search of BioMAP Database for Compounds that Increase PGE2
PPARα
TXA2 inhibitor
Compound
Specific Effect
PPARγ
Retinoids
JNK Inhibitor
RNA Synthesis
Inhibitor AMPK Mechanism
activator
Class Effect
CYP450
Inhibitor
Microtubule
mTOR Destabilizers
Inhibitor
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17. BioMAP Profile of Pioglitazone
PGD2 PGD2
PGD2 PGD2 IL-8 PGE2 PGF2a
PGJ2 PGF2a PGJ2 PGF2a PGJ2 PGF2aPGJ2 MCP-1
CD40 MMP9 VCAM CD38
ITAC PGF1a MCSF PGF1a
CD40
Monocyte T cell
activation activation
• Pioglitazone shows few anti-inflammatory activities
 Modest inhibition of VCAM, ITAC
 Pioglitazone may be a weaker inhibitor of NFκB than rosiglitazone or have reduced
cell uptake
• Pioglitazone has modest effects on tissue remodeling parameters
 Inhibition of MMP9
 Pioglitazone has no effect on myofibroblast activation (in contrast to rosiglitazone)
• Pioglitazone has differential effects on prostaglandins
 Prostaglandins are inhibited in leukocyte/endothelial cell systems; unaffected in
bronchial epithelial cells
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18. BioMAP Profile of Troglitazone
PGD2 PGD2
PGF2a PGF2a TM Col IV
PGF1a PGF1a MMP1
MCP-1
Eot3 MMP9 TF
TNFα uPAR MCP-1
IP-10 E-sel
I-TAC Col III
Macrophage Monocyte T cell
activation activation activation
• Troglitazone shows modest anti-inflammatory activities
 Activities are similar to those of rosiglitazone
 Inhibition of inflammatory chemokines (Eotaxin3, IP-10, ITAC, IL-8)
 Troglitazone is cytotoxic at higher concentrations
• Troglitazone also affects tissue remodeling parameters
 Inhibition of MMP9, PAI-1, Collagen III, some inhibition of myofibroblast activation
 Upregulation of thrombomodulin in CASM3C system
• Troglitazone affects prostaglandin pathways
 Upregulation of PGF1a, PGF2a, and PGD2 in bronchial epithelial cells
 No effect in leukocyte-containing systems (/LPS and /SAg)
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19. BioMAP Profile of Fenofibrate - PPARα
TM
PGD2 PGD2 MMP1
PGJ2 PGF2a PGJ2 IL-8 PGJ2 TM
PGF2a
Eot3 IL-8 MMP9 VCAM PGF1a IL1α MCP-1
ITAC MCSF
PGD2 PGD2 uPAR
Col III
CD69
PGE2
PGF1a
PGF2a IL-8
VCAM Mig
PGF2a HLA-DR
Monocyte T cell
activation activation
• Fenofibrate shows modest anti-inflammatory activities
 Some inhibition of monocyte and T cell activation
 Inhibition of inflammatory chemokines (Eot3, IL-8, ITAC)
• Modest effects on tissue remodeling parameters
 Inhibition of MMP9, Collagen III; upregulation of MMP1
• Differential modulation of prostaglandins QuickTimeª and a
Inhibition of prostaglandins in leukocyte-containing systems (/LPS and /SAg)
decompressor
 are needed to see this picture.
 No effect on prostaglandins in epithelial cell-containing systems
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20. Summary of PPAR Agonists
• BioMAP profiling can discriminate PPAR agonists
 Compound-and class-specific effects
• PPAR agonists exhibit anti-inflammatory activities consistent with
inhibition of NFkappaB pathway
 Rosiglitazone, Fenofibrate > Troglitazone > Pioglitazone
• Some PPAR agonists inhibit myofibroblast activation (TGFβ signaling)
 Rosiglitazone, Troglitazone, but not Pioglitazone
• PPAR agonists have diverse effects on prostaglandins
 Rosiglitazone upregulates prostaglandins in both leukocyte-containing systems and
bronchial epithelial cells
 Troglitazone upregulates prostaglandins in bronchial epithelial cells
 Pioglitazone and Fenofibrate inhibit prostaglandins in leukocyte-containing systems
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21. Summary
• Differential activities can suggest prioritization for therapeutic utility
 Anti-inflammatory activities ( inhibition of T cell, monocyte activation)
• Autoimmune disease, vascular inflammation, atherosclerosis
 Inhibition of myofibroblast activation / TGFβ signaling
• Fibrotic diseases (IPF, scleroderma)
 Upregulation of prostaglandins
• Bronchodilation, potential utility in respiratory disease
• Differential effects may also be associated with potential for side effects
 Differential clinical effects of pioglitazone and rosiglitazone with respect to
cardiovascular outcomes (Winkelmeyer, W., 2008, Comparison of cardiovascular
outcomes in elderly patients with diabetes who initiated rosiglitazone vs
pioglitazone therapy. Arch Intern Med 168:2368)
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22. Acknowledgements
• BioSeek • Stanford
 Eric Kunkel  Eugene Butcher
 Jennifer Melrose  Rob Tibshirani
 Dat Nguyen  Trevor Hastie
 Elen Rosler
 Stephanie Tong
 Jian Yang
 Antal Berenyi
 David Patterson
 Jonathan Bingham
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