Berg ellen 7th braz medchem 12nov2014
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Human Cell Systems Biology for Drug Discovery and Chemical Safety. Presentation at the 7th Brazilian Symposium on Medicinal Chemistry, November 12, 2014, Campos do Jordao-SP, Brazil. Ellen Berg.
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Berg ellen 7th braz medchem 12nov2014
- 1. Human Cell Systems for Drug Discovery and Chemical Safety Ellen L. Berg, Scientific Director The 7th Brazilian Symposium on Medicinal Chemistry Campos do Jordão-SP, Brazil, November 9-12, 2014
- 2. Agenda • Challenges in pharmaceutical research • Primary human cell systems – BioMAP platform • Case studies - Understanding ADRs - thrombosis-related side effects - Drug combinations 2
- 3. • Problem: - Pharmaceutical productivity is at an all time low - We are swimming in oceans of data • A need for new approaches - Better physiological relevance - More predictive of clinical effects Challenges in Drug Discovery We need to do something different: A Turning Point 3
- 4. Complexity of Biology Scale (meters) molecules pathways cells tissues humans 10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M Human exposureMolecular targets 4 • Human biology is complex - Modular, redundant, highly networked, & full of feedback loops
- 5. Complexity of Biology Scale (meters) molecules pathways cells tissues humans 10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M Human exposureMolecular targets 5 • Human biology is complex - Modular, redundant, highly networked, & full of feedback loops • Prediction (and understanding!) is difficult - Emergent properties Primary human cell systems
- 6. Solution: Primary Human Cell Systems • BioMAP® Profiling: - In Vitro testing in primary human cell based tissue and disease models • Data driven chemical biology approach - Data-driven research methodology - Leverages the analysis of a large chemical biology dataset • Applications in drug discovery - Compound characterization across a broad range of biology - Drug mechanisms of action – anchored on clinical outcomes - Guidance for translational studies, indications & biomarkers Confidential6
- 7. Data Driven Research OLD or 7
- 9. Data Driven Research Issues Many hypotheses are generated Each hypothesis requires validation Validation requires both computational and “domain” expertise Solution Incorporate “domain” expertise upfront
- 10. BioMAP® Technology Platform BioMAP® Assay Systems Reference Profile Database Predictive Informatics Tools Human primary cells Disease-models 30+ systems Biomarker responses to drugs are stored in the database >3000 drugs Custom informatics tools are used to predict clinical outcomes High Throughput Human Biology 10
- 11. BioMAP® Systems – Key Features 11 Primary human cell types Physiologically relevant “context” Complex activation settings Co-cultures Translational biomarker endpoints
- 12. Feature Mice Man Lifespan 2 Years 70 Years Size 60 g 60 kg Environment Animal facility, cage-mates Outside world, people, animals, etc. Why Human? Key differences: DNA repair mechanisms Control of blood flow, hemostasis Immune system status 12
- 13. Closer to the disease process Downstream of multiple pathways and integrate information “Decision-making” Used by clinicians to guide therapy - Provide clinical “line of site” Predictive Why Translational Biomarkers? mRNA, epigenome Phospho-sites, intracellular proteins, metabolome Cell surface, secreted molecules 13
- 14. Panel of BioMAP® Systems 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT Endothelial Cells Endothelial Cells PBMC + Endothelial Cells PBMC + Endothelial Cells Bronchial epithelial cells Coronary artery SMC Fibroblasts Keratinocytes + Fibroblasts Th1 Th2 TLR4 TCR Th1 Th1 Th1 + GF Th1 + TGF Acute Inflammation E-selectin, IL-8 E-selectin, IL- 1a, IL-8, TNF- a, PGE2 IL-8 IL-1a IL-8, IL-6, SAA IL-8 IL-1α Chronic Inflammation VCAM-1, ICAM- 1, MCP-1, MIG VCAM-1, Eotaxin-3, MCP-1 VCAM-1, MCP-1 MCP-1, E- selectin, MIG IP-10, MIG, HLA-DR MCP-1, VCAM- 1,MIG, HLA- DR VCAM-1, IP-10, MIG MCP-1, ICAM- 1, IP-10 Immune Response HLA-DR CD40, M-CSF CD38, CD40, CD69, T cell Prolif., Cytotox. HLA-DR M-CSF M-CSF Tissue Remodeling uPAR, MMP-1, PAI-1, TGFb1, SRB, tPA, uPA uPAR, Collagen III, EGFR, MMP-1, PAI-1, Fibroblast Prolif., SRB, TIMP-1 MMP-9, SRB, TIMP-2, uPA, TGFβ1 Vascular Biology TM, TF, uPAR, EC Proliferation, SRB, Vis VEGFRII, uPAR, P- selectin, SRB Tissue Factor, SRB SRB TM, TF, LDLR, SMC Proliferation, SRB Vascular Biology, Cardiovascular Disease, Chronic Inflammation Asthma, Allergy, Oncology, Vascular Biology Cardiovascular Disease, Chronic Inflammation, Infectious Disease Autoimmune Disease, Chronic Inflammation, Immune Biology COPD, Respiratory, Epithelial Biology Vascular Biology, Cardiovascular Inflammation, Restenosis Tissue Remodeling, Fibrosis, Wound Healing Skin Biology,Psoriasis, Dermatitis EndpointTypes Disease / Tissue Relevance BioMAP System Primary Human Cell Types Stimuli ! ! ! ! ! Endothelial Cells Bronchial Epithelial Cells Keratinocytes Smooth Muscle Cells Dermal Fibroblasts Peripheral Blood Mononuclear Cells Profile compounds across a panel of assays 14
- 15. Panel of BioMAP® Systems 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT Endothelial Cells Endothelial Cells PBMC + Endothelial Cells PBMC + Endothelial Cells Bronchial epithelial cells Coronary artery SMC Fibroblasts Keratinocytes + Fibroblasts Th1 Th2 TLR4 TCR Th1 Th1 Th1 + GF Th1 + TGF Acute Inflammation E-selectin, IL-8 E-selectin, IL- 1a, IL-8, TNF- a, PGE2 IL-8 IL-1a IL-8, IL-6, SAA IL-8 IL-1α Chronic Inflammation VCAM-1, ICAM- 1, MCP-1, MIG VCAM-1, Eotaxin-3, MCP-1 VCAM-1, MCP-1 MCP-1, E- selectin, MIG IP-10, MIG, HLA-DR MCP-1, VCAM- 1,MIG, HLA- DR VCAM-1, IP-10, MIG MCP-1, ICAM- 1, IP-10 Immune Response HLA-DR CD40, M-CSF CD38, CD40, CD69, T cell Prolif., Cytotox. HLA-DR M-CSF M-CSF Tissue Remodeling uPAR, MMP-1, PAI-1, TGFb1, SRB, tPA, uPA uPAR, Collagen III, EGFR, MMP-1, PAI-1, Fibroblast Prolif., SRB, TIMP-1 MMP-9, SRB, TIMP-2, uPA, TGFβ1 Vascular Biology TM, TF, uPAR, EC Proliferation, SRB, Vis VEGFRII, uPAR, P- selectin, SRB Tissue Factor, SRB SRB TM, TF, LDLR, SMC Proliferation, SRB Vascular Biology, Cardiovascular Disease, Chronic Inflammation Asthma, Allergy, Oncology, Vascular Biology Cardiovascular Disease, Chronic Inflammation, Infectious Disease Autoimmune Disease, Chronic Inflammation, Immune Biology COPD, Respiratory, Epithelial Biology Vascular Biology, Cardiovascular Inflammation, Restenosis Tissue Remodeling, Fibrosis, Wound Healing Skin Biology,Psoriasis, Dermatitis EndpointTypes Disease / Tissue Relevance BioMAP System Primary Human Cell Types Stimuli ! ! ! ! ! 15
- 16. • Challenges - Cells and assays are expensive - Primary cells (all cell-based assays!) are variable - Very large number of assay components / choices • Cell types, media, additives, time points, endpoints Experimental Design 16
- 17. • Solutions - Automation • Microwell plate-based - Standardized methods • Quality management system (SOPs) • Strict assay acceptance criteria - Incorporate methods to reduce variability • Cells from pooled donors, prequalified • Normalize data within plate (Log10 ratio of compound/vehicle) • 6+ vehicle replicates, two positive controls per plate Experimental Design 17
- 18. • Compromises: - Single well per endpoint, but: • Multiple concentrations (4+) per compound • Multiple assay systems per compound • Multiple endpoints per assay system - Single timepoint • Suboptimal for some endpoints, but optimal for most endpoints (24 hr – 6 days) - Pauciparameter (7-22 endpoints per assay system), but: • Highly informative disease biomarker endpoints Experimental Design 18
- 19. BioMAP Profile of Positive Control • Colchicine is an inhibitor of microtubules - It is active in every system and used as a positive control on every plate • Colchicine profile has a distinctive pattern of activities or “shape” BioMAP Systems Readout Parameters (Biomarkers) Cytotoxicity Readouts Colchicine 1.1 μM Logexpressionratio (Drug/DMSOcontrol) Vehicle Control (no drug) 95% significance envelope 19
- 20. Reproducibility of Profiles • 16 Experiments over many months • Pairwise correlation of profiles (Pearson’s) were > 0.8 BioMAP Systems Readout Parameters (Biomarkers) Houck, K.A., J. Biomolecular Screening, 2009, 14:1054-66.20 Logexpressionratio (Drug/DMSOcontrol) Vehicle Control (no drug) 95% significance envelope
- 21. • Assess cytotoxicity in primary human cells - Cytotoxicity mechanisms are cell type and activation dependent - Note: cytotoxicity is a confounder • Flag compounds (concentrations) that are overtly cytotoxic • Analyze overall activity profiles - Profile characteristics - Unsupervised and supervised approaches to compare profiles • Focus on individual endpoints - Correlate to external data - Build an understanding of clinical mechanisms What Can We Do With BioMAP Profile Data? 21
- 22. Types of BioMAP Profiles InactiveActive – Sharp dose-response Active – Dose resistantActive – Selectively 22
- 23. Rapamycin (mTOR) Genistein (multi-target) Dose Resistance A Profile “Characteristic” • “Dose resistant” compounds have similar activity profiles over a wide range of concentrations - No sharp activity jumps; Rapamycin > Genistein • Characteristic of approved drugs & target-selective compounds - Rapamycin is highly selective for mTOR; Genistein has multiple targets - The dose resistance index of Rapamycin is > 60,000x 23
- 24. BioMAP Profiling: Example Profile Reference p38 MAPK Inhibitor Logexpressionratio (Drug/DMSOcontrol) Control (no drug) 99% significance envelope BioMAP Systems Readout Parameters (Biomarkers) Dose Response Cytotoxicity Readouts 24 This profile shows dose-resistance – similar over a range of concentrations
- 25. BioMAP Profiling: Example Profile Reference p38 MAPK Inhibitor Logexpressionratio (Drug/DMSOcontrol) 25 Activities relevant to the role of p38 in monocyte / Th1-type inflammation p38 kinase is important for Th1-dependent inflammatory responses Takanami-Ohnishi Y, et al., Essential role of p38 mitogen-activated protein kinase in contact hypersensitivity. J Biol Chem. 2002, 277:37896-903. IL-8 HLA-DR Monocyte activation IL-6IL-1aCD38 HLA-DR TNF-a
- 26. BioMAP Profiling: Example Profile Reference p38 MAPK Inhibitor Logexpressionratio (Drug/DMSOcontrol) 26 Activities relevant to anti-thrombotic effects of p38 inhibitors Tissue factor is the primary cellular initiator of coagulation p38α deficiency impairs thrombus formation Sakurai K, et al. Role of p38 mitogen-activated protein kinase in thrombus formation. J Recept Signal Transduct Res. 2004;24(4):283-96. Tissue Factor
- 27. BioMAP Profiling: Example Profile Reference p38 MAPK Inhibitor Logexpressionratio (Drug/DMSOcontrol) 27 Activities relevant to side effects – clinical finding: skin rash Upregulation of VCAM and ITAC are characteristic of skin hyperreactivity Melikoglu M, et al., Characterization of the divergent wound-healing responses occurring in the pathergy reaction and normal healthy volunteers. J Immunol. 2006, 177:6415-21. ITAC VCAM MMP1 VCAM
- 28. 28 BioMAP® Data Analysis Predictive Informatics Tools Custom informatics tools are used to predict clinical outcomes • Unsupervised Analyses - Similarity Search of our reference database - Clustering • Supervised Analyses - Computational models (classifiers) for mechanism of action
- 29. 29 BioMAP® Reference Database BioMAP® Reference Database Biomarker responses to drugs are stored in the database >3000 drugs • More than 3000 agents - Drugs – Clinical stage, approved, and failed - Experimental Chemicals - Research tool compounds, environmental chemicals, nanomaterials - Biologics – Antibodies, cytokines, factors, peptides, soluble receptors • Availability of reference data - EPA ToxCast and selected reference data are published and have been made available (Houck, 2009; Berg, 2010; Berg, 2013; Kleinstreuer, 2014)
- 30. Similarity Analysis of Profiles Highly correlated Similar Pearson’s correlation of r > 0.7 Low correlation Not similar Pearson’s correlation of r < 0.7 30
- 31. Microtubule Stabilizers Mitochondrial ET chain Retinoids Hsp90 CDK NFkB MEK DNA synthesis JNK Protein synthesis Microtubule Destabilizers Estrogen R PI-3K Ca++ Mobilization Clustering of Compound Profile Data Compounds Cluster According to Mechanisms of Action mTOR PKC Activation p38 MAPK HMG-CoA reductase Calcineurin Transcription 31 Each circle represents a compound tested at a single dose Lines are drawn between compounds whose profiles are similar (r > 0.7) Figure adopted from Berg, JPTox Meth. 2010
- 32. Microtubule Stabilizers Mitochondrial ET chain Retinoids Hsp90 CDK NFkB MEK DNA synthesis JNK Protein synthesis Microtubule Destabilizers Estrogen R PI-3K Ca++ Mobilization BioMAP Data Can Cluster Compounds According to Mechanisms of Action mTOR PKC Activation p38 MAPK HMG-CoA reductase Calcineurin Transcription p38 MAPK Calcineurin mTOR Mitochondrial ATPase 32 Each circle represents a compound tested at a single dose Lines are drawn between compounds whose profiles are similar (r > 0.7) Figure adopted from Berg, JPTox Meth. 2010
- 33. Microtubule Stabilizers Mitochondrial ET chain Retinoids Hsp90 CDK NFkB MEK DNA synthesis JNK Protein synthesis Microtubule Destabilizers Estrogen R PI-3K Ca++ Mobilization BioMAP Data Can Cluster Compounds According to Mechanisms of Action mTOR PKC Activation p38 MAPK HMG-CoA reductase Calcineurin Transcription Mechanism of Action (On-Target) Pathway Relationships 33
- 34. Consensus Profiles for Mechanism Classes p38 MAPK inhibitor 1 p38 MAPK inhibitor 2 p38 MAPK inhibitor 3 Consensus profile reflects target-specific biology Can define mechanism class 34 1 1 1 1 1 1 1 1 1
- 35. Mechanism Class Consensus Profiles AhR Agonist Calcineurin Inhibitor EGFR Inhibitor EP Agonist ER Agonist GR Agonist (Full) H1 Antagonist HDAC Inhibitor HMG-CoA Reductase Inhibitor Hsp90 Inhibitor IKK2 Inhibitor IL-17A Agonist JAK Inhibitor MEK Inhibitor Microtubule Disruptor Microtubule Stabilizer Mitochondrial Inhibitor mTOR Inhibitor p38 MAPK Inhibitor PDE IV Inhibitor PI3K Inhibitor PKC (c+n) Inhibitor Proteasome Inhibitor RAR/RXR Agonist SR Ca++ ATPase Inhibitor Src Family Inhibitor TNF-alpha Antagonist Vitamin D Receptor Agonist Patterns reflect “mechanism class” or target biology Reproducible patterns permit building of classifiers for automated mechanism assignment MechanismClasses BioMAP Assay / Endpoints CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF K CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF K CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3C CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3C CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI TIMP−2 −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI TIMP−2 −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI TIMP−2 uPA −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI TIMP−2 uPA −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI TIMP−2 uPA −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI TIMP−2 uPA −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI TIMP−2 uPA −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD54/ICAM−1 CD62E/E−Selectin CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR Proliferation SRB Visual CCL2/MCP−1 CCL26/Eotaxin−3 CD106/VCAM−1 CD62P/P−selectin CD87/uPAR SRB VEGFR2 CCL2/MCP−1 CD106/VCAM−1 CD142/TissueFactor CD40 CD62E/E−Selectin CXCL8/IL−8 IL−1alpha M−CSF sPGE2 SRB sTNF−alpha CCL2/MCP−1 CD38 CD40 CD62E/E−Selectin CD69 CXCL8/IL−8 CXCL9/MIG PBMCCytotoxicity Proliferation SRB CD87/uPAR CXCL10/IP−10 CXCL9/MIG HLA−DR IL−1alpha MMP−1 PAI−I SRB TGF−betaI tPA uPA CCL2/MCP−1 CD106/VCAM−1 CD141/Thrombomodulin CD142/TissueFactor CD87/uPAR CXCL8/IL−8 CXCL9/MIG HLA−DR IL−6 LDLR M−CSF Proliferation SerumAmyloidA SRB CD106/VCAM−1 CollagenIII CXCL10/IP−10 CXCL8/IL−8 CXCL9/MIG EGFR M−CSF MMP−1 PAI−I Proliferation_72hr SRB TIMP−1 CCL2/MCP−1 CD54/ICAM−1 CXCL10/IP−10 IL−1alpha MMP−9 SRB TGF−betaI TIMP−2 uPA −1.5 −1.4 −1.3 −1.2 −1.1 −1.0 −0.9 −0.8 −0.7 −0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 LogRatio Profiles Cyclopamine 40 uM Cyclopamine 13.333 u... Cyclopamine 4.444 uM Cyclopamine 1.482 uM 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT 35 TF VCAM
- 36. Building Support Vector Machine Classifiers • 88 Compounds • 28 Target/Pathway mechanisms • 1-8 concentrations • 327 Profiles • 84 endpoints (8 BioMAP Systems) • Support Vector Machine • 2-class models • Mechanism class versus “Null” set • Result = Decision Value (DV) • PPV – positive predictive value (fraction of profiles that are correctly classified) • PPV = TP / (TP + FP)) • Sensitivity (fraction of profiles that are assigned to the class) • Sensitivity = TP / (TP + FN)) Mitochondrial Inhibitor Microtubule Stabilizer Hsp90 Inhibitor Classifier Performance: Examples PDE IV Inhibitor Generate Data Set Build Classifiers Test Performance of Classifiers Berg, Yang & Polokoff, 2013, J. Biomol Screen. 18:1260.36
- 37. • AhR agonist (Aryl Hydrocarbon) • Calcineurin • EGFR (Epidermal Growth Factor R) • SERCA (SR Ca++ ATPase) • EP agonist • Estrogen R agonist • Glucocorticoid R agonist • H1R Antagonist (Histamine) • HDAC • HMG-CoA-Reductase • Hsp90 Inhibitor • IKK2 • IL-17 R agonist • JAK Confidential37 List of Classifiers (SVM Mechanism Models) • MEK • Microtubule Disruptor • Microtubule Stabilizer • Mitochondrial Inhibitor • mTOR • p38 MAPK • PDE IV (Phosphodiesterase • PI3K • PKC (c+n) • Proteasome • RAR-RXR agonist • Src family • TNF (Tumor Necrosis Factor) • VDR agonist (Vitamin D R) Berg, Yang & Polokoff, 2013, J. Biomol Screen. 18:1260.
- 38. • Compound characterization - Broad biological fingerprint - Cell types, pathways, possible clinical indications • Mechanism of action - Triage hits from phenotypic drug discovery programs - Unexpected off-targets (toxicity) • Support therapeutic hypotheses - Compare to competitor molecules, clinical standards of care - Identify translational biomarkers Applications 38
- 39. Case Study: Elucidating Mechanisms Underlying Adverse Effects
- 40. EPA ToxCastTM Program – BioSeek Goal is identification of in vitro assays that can help forecast in vivo toxicity of environmental and other agents (including pharmaceuticals) 40 Task Order Compound Number Compound Type TO1 320 Environmental compounds TO2 500 Environmental compounds TO3 200 Environmental and Failed Pharma Compounds TO4 39 Nanomaterials TO5 31 Nanomaterials TO6 100 Failed Pharma Compounds, etc. TO7 39 Nanomaterials Total 1229 40 Houck, K.A., J. Biomolecular Screening, 2009, 14:1054-66; Kleinstreuer, 2014, Nature Biotechnology, 32:583-91.
- 41. Chemical Groups & Classes in ToxCast Most active Least active 41 Overall: 73% Active (33 – 83%) Kleinstreuer, 2014, Nature Biotechnology, 32:583-91.
- 42. Results of Supervised Analysis Performance of SVM Mechanism Classifiers Mechanism Class1 Number of Compounds Correctly Assigned % Comment p38 MAPK Inhibitor 2 2 100% Estrogen R Agonist 10 6 60% Not classified: meso-Hexestrol, 4- nonylphenol and diethystilbestrol HMG-CoA Reductase Inhibitor 3 3 100% Histamine R1 Antagonist 1 1 100% Microtubule Inhibitor 2 1 50% Herbicides GR Agonist 3 3 100% Mitochondrial Inhibitor 2 2 100% Fungicides PDE IV Inhibitor 8 6 75% RAR/RXR Agonist 2 2 100% Total 33 26 79% • 1Mechanisms for which classifiers were available and mechanisms were known - Dataset: Kleinstreuer, Nature Biotechnology, 2014, 32:583 - Classifiers: Berg, Yang and Polokoff, JBS, 2013, 18:1260 42
- 43. Unsupervised Analysis (Self Organizing Maps) AhR Phenotypic Signature • Phenotypic signature of compounds in SOM cluster #57 - Box and whisker plot for cluster 57 representing a signature for AhR activation • Confirmation of AhR activity - 85% of members of clusters 57, 67 (adjacent in the 10X10 SOM) were active in an AhR reporter gene assay (examples shown here). Tissue Factor Kleinstreuer, 2014, Nature Biotechnology, 32:583-91.43
- 44. Unsupervised Analysis (Self Organizing Maps) Estrogen R Actives: Phenotypic Signatures • Two clusters of chemicals defined by their BioMAP signatures - Blue = Estradiol, Estrogen Receptor Agonists - Red = Estrogen Receptor Antagonists, “Selective Estrogen R Modulators” • Increased levels of Tissue Factor by SERMs and ER antagonists Kleinstreuer, 2014, Nature Biotechnology, 32:583-91. Estrogen Receptor Antagonists Estrogen Receptor Agonists Tissue Factor 44
- 45. Tissue Factor Primary Cellular Initiator of Blood Coagulation RW Colman 2006 J. Exp. Med Blood Coagulation 45
- 46. • Pathologic setting – aberrant coagulation thrombosis - The formation of a blood clot (coagulation) within a vein - Deep vein thrombosis (DVT), stroke - Pulmonary embolism thrombi break off and get lodged in the lung Thrombosis SMC Endothelial cells Vessel Lumenplatelets in fibrin clot 46
- 47. Thrombosis is Required for Normal Wound Healing 47
- 48. • Associated with: - Exposure to Smoking & Pollution • Polycyclic aromatic hydrocarbons (“Aryl Hydrocarbons”) - Contraceptives, hormonal replacement therapy - Various other drugs • mTOR inhibitors (everolimus) • 2nd generation anti-psychotics Thrombosis-Related Side Effects 48
- 49. • Aryl Hydrocarbon receptor agonists - PAHs, Benz(a)anthracene - Smoking (Cigarette smoke extract) • mTOR inhibitors - Everolimus (Baas, 2013, Thromb Res 132:307) • Anti-Estrogens / SERMS, oral contraceptives - Tamoxifen, Clomiphene, Cyproterone • Second generation anti-psychotics - Clozapine • Others - Crizotinib Mechanisms / Drugs Associated with Thrombosis-Related Side Effects All show increased Tissue Factor levels in 3C and LPS Systems 49
- 50. • Search our reference database for all compounds / test agents that increase TF in the 3C system - What are the mechanisms represented? - Do they share any common biology? • Issues - Large chemical-biology datasets will have errors • Inactive concentrations, toxic concentrations, variability - How do we increase our confidence? • Require compound effects at more than one concentration • Effect size >20% (4 SD) • Multiple compounds with same target mechanism Is There A Connection? 50
- 51. Reference Compounds that Increase TF Compound Name Mechanism Compound Name Mechanism 2-Mercaptobenzothiazole AhR agonist 3,5,3-Triiodothyronine Thyroid H R agonist 3-Hydroxyfluorene AhR agonist Concanamycin A Vacuolar ATPase Inhibitor Benzo(b)fluoranthene AhR agonist MK-2206 AKT Inhibitor C.I Solvent yellow 14 AhR agonist Crizotinib ALK, c-met Inhibitor FICZ AhR agonist N-Ethylmaleimide Alkylating agent Abiraterone CYP17A Inhibitor Terconazole Anti-fungal Ketoconazole CYP17A Inhibitor GDC-0879 B-Raf Inhibitor Clomiphene citrate Estrogen R Antagonist KN93 CAMKII Inhibitor Histamine H1R agonist 8-Hydroxyquinoline Chelating agent Histamine Phosphate H1R agonist Linoleic Acid Ethyl Ester Fatty Acid Cobalt(II) Chloride Hexahydrate HIF-1α Inducer Tris(1,3-dichloro-2-propyl) phosphate Flame retardant Tin(II) Chloride HIF-1α Inducer Fenaminosulf Fungicide Chloroquine Phosphate Lysosome Inhibitor Mancozeb Fungicide Primaquine Diphosphate Lysosome Inhibitor Primidone GABA R agonist Temsirolimus mTOR Inhibitor Mometasone furoate GR agonist Torin-1 mTOR Inhibitor Desloratadine H1R antagonist Torin-2 mTOR Inhibitor A 205804 ICAM, E-selectin inhibitor Bryolog PKC activator Dodecylbenzene Industrial chemical Bryostatin 1 PKC activator UO126 MEK Inhibitor Bryostatin 2 PKC activator Imatinib PDGFR, c-Kit, Bcr-Abl Inhibitor Phorbol 12-myristate 13-acetate PKC activator ZK-108 PI-3K Inhibitor (βγ-selective) Phorbol 12,13-didecanoate PKC activator GW9662 PPARγ agonist Picolog PKC activator PP3 SRC Kinase Inhibitor Z-FA-FMK Cysteine protease Inhibitor TX006146 Unknown Mifamurtide NOD2 agonist TX006237 Unknown Ethanol Organic Solvent TX011661 Unknown Oncostatin M OSM R agonist U-73343 Unknown PAz-PC Oxidized phospholipid 55/3187 = 1.7% 51
- 52. Mechanisms that Increase TF Test Agents Mechanism Confidence in Mechanism 2-Mercaptobenzothiazole AhR agonist High 3-Hydroxyfluorene AhR agonist High Benzo(b)fluoranthene AhR agonist High C.I Solvent yellow 14 AhR agonist High FICZ AhR agonist High Abiraterone CYP17A Inhibitor High Ketoconazole CYP17A Inhibitor High Clomiphene citrate Estrogen R Antagonist High Histamine H1R agonist High Histamine Phosphate H1R agonist High Cobalt(II) Chloride Hexahydrate HIF-1α Inducer High Tin(II) Chloride HIF-1α Inducer High Chloroquine Phosphate Lysosome Inhibitor High Primaquine Diphosphate Lysosome Inhibitor High Temsirolimus mTOR Inhibitor High Torin-1 mTOR Inhibitor High Torin-2 mTOR Inhibitor High Bryolog PKC activator High Bryostatin PKC activator High Bryostatin 1 PKC activator High Phorbol 12-myristate 13-acetate PKC activator High Phorbol 12,13-didecanoate PKC activator High Picolog PKC activator High 3,5,3-Triiodothyronine Thyroid H R agonist Good Concanamycin A Vacuolar ATPase Inhibitor Good Mifamurtide NOD2 agonist Good Oncostatin M OSM R agonist Good Ethanol Organic Solvent Good PAz-PC Oxidized phospholipid Good Z-FA-FMK Cysteine protease Inhibitor Good 8-Hydroxyquinoline Chelating agent Unknown A 205804 ICAM, E-selectin inhibitor Unknown AZD-4547 FGFR Inhibitor Unknown Crizotinib ALK, c-met Inhibitor Unknown Desloratadine H1R antagonist Unknown Dodecylbenzene Industrial chemical Unknown Fenaminosulf Fungicide Unknown GDC-0879 B-Raf Inhibitor Unknown GW9662 PPARγ agonist Unknown Imatinib PDGFR, c-Kit, Bcr-Abl Inhibitor Unknown KN93 CaMKII Inhibitor Unknown Linoleic Acid Ethyl Ester Fatty Acid Unknown Mancozeb Fungicide Unknown MK-2206 AKT Inhibitor Unknown Mometasone furoate GR agonist Unknown N-Ethylmaleimide Alkylating agent Unknown PP3 SRC Kinase Inhibitor Unknown Primidone GABA R agonist Unknown Sulindac Sulfide NSAID Unknown Terconazole Anti-fungal Unknown Tris(1,3-dichloro-2-propyl) phosphate Flame retardant Unknown TX006146 Unknown Unknown TX006237 Unknown Unknown TX011661 Unknown Unknown U-73343 Unknown Unknown UO126 MEK Inhibitor Unknown ZK-108 PI-3K Inhibitor (βγ-selective) Unknown Mechanisms that Increase TF AhR Agonist CYP17A Inhibitor Estrogen R Antagonist H1R Agonist HIF-1α Inducer Lysosomal Inhibitor mTOR Inhibitor PKC Activator Thyroid H R Agonist Vacuolar ATPase Inhibitor NOD2 Agonist OSM R Agonist 52
- 53. Mechanisms that Increase TF Mechanisms that Increase TF AhR Agonist CYP17A Inhibitor Estrogen R Antagonist H1R Agonist HIF-1α Inducer Lysosomal Inhibitor mTOR Inhibitor PKC Activator Thyroid H R Agonist Vacuolar ATPase Inhibitor NOD2 Agonist OSM R Agonist Implicate Autophagy 53
- 54. Autophagy • Cellular response to nutrient deprivation • Also contributes to recycling of dysfunctional organelles, handling of protein aggregates, bacteria and viruses54
- 55. mTOR Concanamycin AVATPase Chloroquine Caspase Z-FA-FMK Increased Tissue Factor The Autophagy Connection Autophagy Lysosomal Function 55
- 56. mTOR Temsirolimus PI3Kβ AKT ZK-108 MK-2206 Concanamycin AVATPase Chloroquine Caspase Z-FA-FMK PAz-PC Nutrient Sensing Increased Tissue Factor The Autophagy Connection Autophagy 56
- 57. mTOR Temsirolimus Oxygen Sensing CoCl2 TnCl2 HIF-1α PI3Kβ AKT ZK-108 MK-2206 Concanamycin AVATPase Chloroquine Caspase Z-FA-FMK PAz-PC REDD1 Ethanol Nutrient Sensing Increased Tissue Factor The Autophagy Connection Autophagy 57
- 58. mTOR Temsirolimus Oxygen Sensing CoCl2 TnCl2 HIF-1α PI3Kβ AKT ZK-108 MK-2206 Concanamycin AVATPase Chloroquine Benzo(b)fluoranthene ER Clomiphene Caspase Z-FA-FMK NPC1 AhR PAz-PC Estrogen AbirateroneCYP17A1 REDD1 Ethanol Lipid Sensing Nutrient Sensing Increased Tissue Factor The Autophagy Connection Autophagy 58
- 59. mTOR Temsirolimus Oxygen Sensing NOD2Mifamurtide CoCl2 TnCl2 HIF-1α PI3Kβ AKT ZK-108 MK-2206 PKC PMA Concanamycin AVATPase Chloroquine Benzo(b)fluoranthene ER Clomiphene Caspase Z-FA-FMK NPC1 AhR PAz-PC Estrogen AbirateroneCYP17A1 REDD1 Ethanol Lipid Sensing Nutrient Sensing Bacterial Sensing Increased Tissue Factor The Autophagy Connection Autophagy Berg, Polokoff, O’Mahony, Nguyen & Li, submitted, 201459
- 60. • Summary - Compounds that increase TF are associated with thrombosis related side effects - Compounds that increase TF also increase autophagic vacuoles (increase formation or decrease breakdown) - Mechanistic Hypothesis: thrombosis-related side effects are associated with alterations in the process of autophagy that increase TF cell surface levels • Take home message: - This case study illustrates how chemical biology datasets, combined with external knowledge, can give rise to higher level mechanistic understanding of toxicity mechanisms Tissue Factor, Autophagy & Thrombosis 60
- 61. Adverse Outcome Pathway Framework MIE Key Event Adverse Outcome Key Event Key Event Molecular Initiating Event Clinical Effect • Framework for integrating mode of action hypotheses to outcomes for chemical risk assessment (OECD) - http://www.oecd.org/chemicalsafety/testing/adverse-outcome-pathways- molecular-screening-and-toxicogenomics.htm • Focused on the clinical outcome - Anchored at both ends 61
- 62. AOP for DVT MIE Key Event Adverse Outcome Inhibition of mTOR Upregulation of Tissue Factor Deep Vein Thrombosis Initiation of Coagulation Key Event Key Event Molecular Initiating Event Clinical Effect Increase in Autophagic Vacuolization 62
- 63. AOP for DVT MIE Key Event Adverse Outcome Inhibition of mTOR Upregulation of Tissue Factor Deep Vein Thrombosis Initiation of Coagulation Key Event Key Event Molecular Initiating Event Clinical Effect MIE Activation of AhR Increase in Autophagic Vacuolization Key Event Inhibition of NPC1 Key Event HDF3CGF In vitro disease model 3C 3C 4H LPS Endothelial Cells Endothelial Cells PBMC + Endothelial Cells P En Th1 Th2 TLR4 BioMAP System Primary Human Cell Types Stimuli ! ! ! 63
- 64. Case Study: Drug Combinations
- 65. • Challenges for studying drug combinations: - System must include both targets - Physiologically relevant setting (ideally all human) - Suitably robust to capture combination effects • Case Example - BioMAP Oncology systems that model tumor-host microenvironments - Trametinib (MEK kinase inhibitor) + Dabrafenib (Braf inhibitor) • Combination approved for treatment of melanoma Drug Combinations 65
- 67. BioMAP Oncology Systems System Primary Human Cell Types Disease / Tissue Relevance Biomarker Readouts StroHT29 HT-29 colon adenocarcinoma cell line + Primary Human Fibroblasts + PBMC Oncology: Host Tumor- Stromal Microenvironment sVEGF, MMP9, TIMP2, tPA, uPA, uPAR, collagen I, collagen III, PAI-1, SRB, sIL-2, pCyt, sIL-6, sIL-10, sIFNγ, sTNFα, sIL-17A, sGranzyme B, Keratin 20, CEACAM5, IP-10, VCAM-1 VascHT29 HT-29 colon adenocarcinoma cell line + Primary Human Endothelial cells + PBMC Oncology: Host Tumor- Vascular Microenvironment CD40, CD69, uPAR, collagen IV, MCP-1, VCAM-1, pCyt, SRB, sIL-2, sIL-6, sIL-10, sIFNγ, sTNFα, sIL- 17A, sGranzyme B, CEACAM5, Keratin 20, IP-10, MIG • Biomarker Endpoints: • Immunomodulation: IL-2, IL-6, IL-10, IL-4, IFNγ, CD40, CD69, IL-17, Granzyme B • Inflammation: TNFα, MCP-1, VCAM, CXCL9/MIG, • Metastasis / Remodeling: MMP9, TIMP2, Collagens I, III, IV, uPA, uPAR, PAI-1 • Angiogenesis / Fibrinolysis: uPA, uPAR, PAI-1, VEGF • Tumor specific markers: CEACAM5, CK20 67
- 68. Dabrafenib (B-raf) Trametinib (MEK) Dabrafenib +Trametinib 68 Combination Study Example: B-Raf + MEK Inhibitor
- 69. Dabrafenib (B-raf) Trametinib (MEK) Dabrafenib +Trametinib • Combination effects of Dabrafenib (B-raf) and Trametinib (MEK) - Tumor cell marker (CEACAM5) is reduced only in the combination (green arrow) - Consistent with the combination being more efficacious against tumors in vivo 69 Combination Study Example: B-Raf + MEK Inhibitor
- 70. Dabrafenib (B-raf) Trametinib (MEK) Dabrafenib +Trametinib • Combination effects of Dabrafenib (B-raf) and Trametinib (MEK) - Tumor cell marker (CEACAM5) is reduced only in the combination - Consistent with the combination being more efficacious against tumors in vivo - Reduced levels of Inflammatory endpoints; collagen III (grey arrows) - Consistent with reduced Trametinib-related skin side effects (Flaherty, 2012, NEJM 367:1694). 70 Combination Study Example: B-Raf + MEK Inhibitor
- 71. • Chemical profiling in human cell systems generates activity profiles that can be used to: - Group chemicals into bioactivity classes - Generate MoA hypotheses - Identify activities that may correlate with in vivo outcomes • High throughput in vitro data is most informative when combined with external information - Known targets - In vivo bioactivities Summary Confidential71
- 72. • Applications for predicting in vivo effects must also consider: - Exposure - level and route - Distribution - Metabolism - Human variability Challenges and Considerations Confidential72
- 73. • BioSeek - Mark A. Polokoff - Dat Nguyen - Xitong Li - Antal Berenyi - Alison O’Mahony - Jian Yang (Oracle) • UCSF - Kevan Shokat Acknowledgements • EPA - Keith Houck - Nicole Kleinstreuer - Richard Judson • Support - NIH/NIAID (SBIR) - EPA (EP-D-12-047, EP- W-07-039) 73
- 74. Contact: Ellen L. Berg, PhD Scientific Director BioSeek, a division of DiscoveRx eberg@bioseekinc.com
Editor's Notes
- The complexity of biological systems makes it difficult to predict outcomes from both target-based as well as phenotypic drug discovery efforts. The BioMAP platform of cell-based assay platform designed to include more of the biological complexity of human disease, but yet in a practical format with sufficient throughput to be used in early discovery. So what do we mean by biological complexity?
- The complexity of biological systems makes it difficult to predict outcomes from both target-based as well as phenotypic drug discovery efforts. The BioMAP platform of cell-based assay platform designed to include more of the biological complexity of human disease, but yet in a practical format with sufficient throughput to be used in early discovery. So what do we mean by biological complexity?
- So what does a BioMAP profile look like?
- So what does a BioMAP profile look like?
- So what does a BioMAP profile look like?
- So what does a BioMAP profile look like?