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Primary Human Cell Systems Analysis of Drug Mechanisms

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Presentation at the SBS 15th Annual Conference in Lille, France, 28 April 2009

Presentation at the SBS 15th Annual Conference in Lille, France, 28 April 2009


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  • 2 1
  • BioMAP systems are complex primary human cell based disease models that can be used directly for phenotypic screening. The most attractive feature of this platform, however, is it’s ability of the platform to provide target and pathway mechanisms of action. This enables reverse pharmacology of bioactive agents and drugs as well as in depth characterization of leads for identifying on versus off-target biology, which in turn impact safety and also clinical indication selection.
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    • 1. Primary Human Cell Systems Analysis of Drug Mechanisms Ellen L. Berg, PhD BioSeek, Inc. SBS 15th Annual Conference Lille, France 28 April 2009 Bio Seek
    • 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
    • 3.
      • 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
      Goals for Human Cell Systems Biology Platform
    • 4. BioMAP ® Technology Platform Complementary to biochemical target and phenotypic screening Assays Human primary cells Disease-like culture conditions LPS BF4T SM3C Profile Database Informatics Biological responses to drugs and stored in the database Specialized informatics tools are used to mine and analyze biological data
    • 5.
      • BioMAP Systems are cell-based assays engineered to model complex human disease biology
        • Human primary cells
        • Co-cultures, multiple stimulation factors, activated cells
        • Quantitative protein readouts - biomarkers
        • Pharmacologically relevance - validated with known drugs
      BioMAP ® Technology Platform Assays Human primary cells Disease-like culture conditions >25 BioMAP Systems
      • Assay endpoints include human clinical biomarkers and risk factors (proteins)
        • Cytokines, chemokines
        • Adhesion and growth receptors
        • Biological mediators (prostaglandins, etc.)
        • Proteases, enzymes (MMPs, plasminogen activators)
        • Others (hemostatic factors, matrix components)
        • Clinically relevant
      LPS BF4T SM3C
    • 6. Assays Human primary cells Disease-like culture conditions Profile Database Biological responses to drugs and stored in the database BioMAP ® Technology Platform
      • > 2000 agents
        • Approved drugs
        • Clinical stage & failed drugs
        • Experimental compounds
        • Biologics
        • Toxicants
      LPS BF4T SM3C
    • 7. Assays are Robust and Highly Reproducible High Correlation of Experimental Replicates 5  M dose Pearson Correlation Coefficient Consistent data experiment-to-experiment Pearson correlation >0.8 (perfect match = 1)
    • 8. Microtubule Stabilizers Mitochondrial ET chain Retinoids Hsp90 CDK NF  B MEK DNA synthesis JNK Protein synthesis Microtubule Destabilizers Estrogen R PI-3K Ca ++ Mobilization Classification of Drugs By Mechanism Pairwise Correlation of BioMAP Reveals Functional Similarities mTOR PKC Activation p38 MAPK HMG-CoA reductase Calcineurin Transcription
    • 9. BioMAP Systems are Validated Corticosteroids (Prednisolone) Are Active in Inflammation Systems Profiles retain shape over multiple concentrations BioMAP Systems Readout Parameters (Biomarkers) Cytotoxicity Readouts Log expression ratio (Drug/DMSO control) Control (no drug) 99% significance envelope Dose Response
    • 10. E-selectin TNF-  IL-8 BioMAP Systems are Validated Activities of Corticosteroids Match Clinical Effects PGE 2 IL-8 MCP-1 Readouts in BioMAP show the same pattern as has been reported for patients receiving steroid therapy Log expression ratio (Drug/DMSO control) MCP-1, IL-8, E-sel. decrease Leukocyte recruitment Many, e.g. Jilma et al., 2000 PGE 2 decrease Pain, swelling Sebaldt et al., 1990 Collagen I & III Collagen I, III decrease Skin atrophy Autio et al., 1994 MMP-1 PAI-1 SAA PAI-1, SAA increase CV complications Sartori et al., 1999 Fyfe et al., 1997 PAI-1
    • 11. Project Goal
      • Characterize PPAR agonists by BioMAP profiling
        • Compare and contrast PPAR  agonists (anti-inflammatory activities)
        • Identify shared and unique pathway effects
        • Identify potential new indications
      PPAR  PPAR  Rosiglitazone (Avandia) Troglitazone (Resulin) Pioglitazone (Actos) Fenofibrate (Tricor)
    • 12. BioMAP Profile of Rosiglitazone Eot3 IP-10 I-TAC VCAM E-sel Monocyte activation Macrophage activation T cell activation MCP-1 TNF  MCSF CD40 IL-8 IP-10 I-TAC
      • 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 NF  B 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 TNF  are clinical biomarkers
      BioMAP Systems
    • 13. BioMAP Profile of Rosiglitazone Eot3 IP-10 I-TAC MMP9 VCAM E-sel Monocyte activation Macrophage activation T cell activation MCP-1 TNF  MCSF CD40 IL-8 uPAR Col III Col IV PAI-1
      • 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 TGF  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)
      Col III BioMAP Systems
    • 14. BioMAP Profile of Rosiglitazone
      • Rosiglitazone upregulates prostaglandins
        • In both bronchial epithelial and leukocyte-containing systems
        • Potent activity
      PGJ2 PGF1a PGD2 PGF2a PGD2 PGF2a PGJ2 PGF1a PGJ2 PG1a PGE2 PGD2 PGF2a PGF2a Leukocyte-containing systems Bronchial epithelial cell-containing systems BioMAP Systems
    • 15. Upregulation of Prostaglandins by Rosiglitazone
      • Are prostaglandin effects PPAR  -dependent?
        • Not reversed by PPAR  antagonists
        • Reversed by COX1/2 inhibitors
        • Non-TZD PPAR  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?
    • 16. Rosiglitazone Upregulation of PGE2 is not a Class Effect Search of BioMAP Database for Compounds that Increase PGE2 Retinoids Microtubule Destabilizers TXA2 inhibitor PPAR  PPAR  RNA Synthesis Inhibitor mTOR Inhibitor AMPK activator JNK Inhibitor CYP450 Inhibitor Compound Specific Effect Mechanism Class Effect
    • 17. BioMAP Profile of Pioglitazone MMP9 IL-8 Monocyte activation MCSF CD40 ITAC MCP-1 CD38 T cell activation PGJ2 PGD2 PGF2a PGJ2 PGD2 PGE2 PGF2a PGJ2 PGD2 PGF2a PGF1a PGJ2 PGD2 PGF2a PGF1a VCAM CD40
      • Pioglitazone shows few anti-inflammatory activities
        • Modest inhibition of VCAM, ITAC
        • Pioglitazone may be a weaker inhibitor of NF  B 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
    • 18. BioMAP Profile of Troglitazone Eot3 IP-10 I-TAC MMP9 E-sel Monocyte activation Macrophage activation T cell activation TNF  uPAR MCP-1 TF Col III Col IV MMP1 TM MCP-1
      • 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)
      PGF1a PGD2 PGF2a PGF1a PGD2 PGF2a
    • 19. BioMAP Profile of Fenofibrate - PPAR 
      • 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
        • Inhibition of prostaglandins in leukocyte-containing systems (/LPS and /SAg)
        • No effect on prostaglandins in epithelial cell-containing systems
      Eot3 IL-8 MMP9 Monocyte activation T cell activation MCSF CD69 uPAR HLA-DR TM Col III MMP1 IL-8 MCP-1 PGJ2 PGD2 PGF2a PGJ2 PGD2 PGF2a PGD2 PGE2 PGF2a PGF1a PGJ2 PGD2 PGF2a PGF1a Mig VCAM ITAC VCAM IL1  IL-8 TM
    • 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 (TGF  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
    • 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 / TGF  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)
    • 22. Acknowledgements
      • BioSeek
        • Eric Kunkel
        • Jennifer Melrose
        • Dat Nguyen
        • Elen Rosler
        • Stephanie Tong
        • Jian Yang
        • Antal Berenyi
        • David Patterson
        • Jonathan Bingham
      • Stanford
        • Eugene Butcher
        • Rob Tibshirani
        • Trevor Hastie
    • 23.