Berg ellen slas 2015 11_february2015
•Download as PPTX, PDF•
2 likes•897 views
A Chemical Biology Approach Using Primary Human Cell Systems and Co-Cultures for Understanding Target Biology. Presentation at SLAS 2015 4th Annual Conference, February 11, 2015, Washington DC. Ellen Berg.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (19)
Similar to Berg ellen slas 2015 11_february2015
Similar to Berg ellen slas 2015 11_february2015 (20)
Recently uploaded
Recently uploaded (20)
Berg ellen slas 2015 11_february2015
- 1. A Chemical Biology Approach Using Primary Human Cell Systems and Co-cultures for Understanding Target Biology Ellen L. Berg, PhD Scientific Director, BioSeek, a division of DiscoveRx Physiologically Relevant Target Strategies SLAS 2015, Washington DC 11 February 2015
- 2. • Problem: - Pharmaceutical productivity is too 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 better data, not more data 2
- 3. Target-Based Drug Discovery - Challenges • Target validation - Biology has a modular architecture - Function depends on “context” • Target selectivity (poly-pharmacy) - Most drugs interact with more than one target - Targets interact with one another 3
- 4. Solution: Primary Human Cell Systems • BioMAP® Profiling Platform: - In Vitro testing in primary human cell- based tissue and disease models • Chemical biology approach - Data-driven research methodology - Large scale chemical biology datasets • Applications in drug discovery - Compound /target validation - Translational biology - Drug mechanisms of action – in context of disease 4
- 5. BioMAP® Technology Platform BioMAP® Assay Systems Reference Profile Database Predictive Informatics Tools Human primary cells Disease-models > 50 systems Biomarker responses to drugs are stored in the database > 3000 drugs and agents Custom informatics tools are used to predict clinical outcomes High Throughput Human Biology 5
- 6. BioMAP® Systems – Key Features Primary human cell types Physiologically relevant “context” Complex activation settings Co-cultures Translational biomarker endpoints 6
- 7. Feature Mouse 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 7
- 8. Closer to the disease process Downstream of multiple pathways and integrate information “Decision-making” Used by clinicians to guide therapy - Provide clinical “line of sight” Why Translational Biomarkers? mRNA, epigenome Phospho-sites, intracellular proteins, metabolome Cell surface, secreted molecules 8
- 9. Primary Human Cell Systems Panels 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 9
- 10. Panel of Primary Human Cell Systems BioMAP® Predictive Tox Panel 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 ! ! ! ! ! 10
- 11. • Challenges: - Cells and assays are expensive - Primary cells are variable • Solutions: - Standardized methods, automated and run at scale – strict QA - Methods to manage variation • Donor qualification, donor pools • Plate based normalization - Singlicate endpoint measurements, but • Multiple concentrations (4+) per compound • Multiple endpoints per assay system • Multiple assay systems per compound Experimental Design 11
- 12. 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 12
- 13. 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, 14:1054-66.13 Logexpressionratio (Drug/DMSOcontrol) Vehicle Control (no drug) 95% significance envelope
- 14. Diversity of BioMAP Profile Patterns Profile Shapes & Concentration-Response Patterns InactiveActive – Sharp dose-response Active – Dose resistantActive – Selectively 14
- 15. Rapamycin (mTOR) Genistein (multi-target) Dose Resistance A Compound “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 15
- 16. 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 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 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 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 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 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 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 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 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT BioMAP Profiling: Example Profile Trametinib (MekinistTM) MEK Kinase Inhibitor Logexpressionratio (Drug/Vehiclecontrol) Control (no drug) 95% significance envelope BioMAP Systems Biomarker Endpoints Concentration Response Cytotoxicity Readouts 16 • Trametinib is approved for the treatment of metastatic melanoma • Common side effects are rash, diarrhea, peripheral edema, fatigue, and dermatitis • Skin toxicity requiring hospitalization occurred in 6% of patients treated with trametinib, most commonly for secondary infections of the skin or severe skin toxicity (METRIC Phase III study)
- 17. 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 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 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 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 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 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 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 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 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT BioMAP Profiling: Example Profile Trametinib (MekinistTM) MEK Kinase Inhibitor Logexpressionratio (Drug/Vehiclecontrol) BioMAP Systems 17 • Activities relevant to the role of MEK kinase in the cell cycle - MEK kinase (MAP2K) is a member of the MAPK signaling cascade - Key activities include: inhibition of endothelial cell, T cell, smooth muscle cell and fibroblast proliferation EC Prolif. T cell Prolif. SMC Prolif. Fibroblast Prolif.
- 18. 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 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 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 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 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 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 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 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 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT BioMAP Profiling: Example Profile Trametinib (MekinistTM) MEK Kinase Inhibitor Logexpressionratio (Drug/Vehiclecontrol) BioMAP Systems 18 • Activities relevant to the role of MEK kinase in cancer - Angiogenesis (PMID: 10209122) • Decreased EC proliferation; increased cell surface KDR (receptor for VEGF) - Tissue remodeling and metastasis • Decreased proteolysis (uPAR, tPA, PAI-1), matrix metalloprotease (MMP1, TIMPs) uPAR uPAR MMP1 PAI-1 tPA EGFR MMP1 PAI-1 TIMP-1 TIMP-2 EC Prolif. VEGFR2
- 19. 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 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 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 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 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 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 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 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 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT BioMAP Profiling: Example Profile Trametinib (MekinistTM) MEK Kinase Inhibitor Logexpressionratio (Drug/Vehiclecontrol) BioMAP Systems 19 • Activities relevant to side effects – risk of infections - Immunosuppression • Inhibition of T cell proliferation and T cell activation T Cell Proliferation T cell activation
- 20. 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 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 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 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 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 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 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 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 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT BioMAP Profiling: Example Profile Trametinib (MekinistTM) MEK Kinase Inhibitor Logexpressionratio (Drug/Vehiclecontrol) BioMAP Systems 20 • Activities relevant to side effects – risk of infections - Immunosuppression • Inhibition of T cell proliferation and T cell activation - Inhibition of acute inflammatory markers IL-1α, IL-8 and TNFα T Cell Proliferation T cell activation IL-8 IL-1a TNF-a IL-8 IL-1a IL-8 IL-8 IL-1a
- 21. 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 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 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 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 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 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 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 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 3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT BioMAP Profiling: Example Profile Trametinib (MekinistTM) MEK Kinase Inhibitor Logexpressionratio (Drug/Vehiclecontrol) BioMAP Systems 21 • Activities relevant to side effects – skin rash - Upregulation of VCAM and IP-10 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. IP-10 VCAMVCAM
- 22. • Confirm expected target activities • Identify unexpected activities - Target or off-target • Evaluate suitability for in vivo preclinical studies - Safety window - Dose-resistance • Guide in vivo preclinical studies - Dose selection - Identify new biomarkers or pathways to track How Can These Data Help in TDD? 22 Alert!! Prioritize Predict Confirm
- 23. • Testing Drug Combinations - Preclinical testing of drug combinations to preview in vivo effects • Elucidating Mechanisms of Toxicity - Data mining chemical biology datasets - Connect target biology in knowledge frameworks built around clinical outcomes Case Studies 23
- 24. Case Study: Drug Combinations 24
- 25. • 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 25
- 26. Trametinib + Dabrafenib Combination Dabrafenib Trametinib • Combination approved by the US FDA June 2014 for the treatment of BRAF V600E mutation-positive unresectable or metastatic melanoma • Improved patient responses • Reduced incidence of cutaneous squamous cell carcinoma
- 28. Human In Vitro Models of Tumor Microenvironment BioMAP CRC 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 28
- 29. Dabrafenib (B-raf) Trametinib (MEK) Dabrafenib +Trametinib 29 Combination Study Example: Dabrafenib (B-Raf) + Trametinib (MEK Inhibitor)
- 30. 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 30 Combination Study Example: Dabrafenib (B-Raf) + Trametinib (MEK Inhibitor)
- 31. 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) and reduced skin proliferative disorders 31 Combination Study Example: Dabrafenib (B-Raf) + Trametinib (MEK Inhibitor)
- 32. Case Study: Elucidating Mechanisms of Toxicity 32
- 33. • GOAL: To develop a cost-effective approach for efficiently prioritizing the toxicity testing of thousands of chemicals • Profiling in BioMAP Systems since 2007: • > 1100 Chemicals Profiled • > 300,000 Datapoint Chemical Biology Dataset for ToxCast EPA ToxCastTM Program • Patterns in the data Insights
- 34. Cluster of Chemicals Identified by SOM Key Feature: Increased Tissue Factor • Cluster of chemicals defined by their BioMAP signature - Key feature: Increased Tissue Factor (TF) in BioMAP 3C system Nicole Kleinstreuer, et al., NBT, 2014 Tissue Factor
- 35. • Phenotypic signature of compounds in SOM cluster #57 - Box and whisker plot for cluster 57 representing a signature for AhR activation • Compounds: AhR Agonists - 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, 32:583-91.35 Cluster of Chemicals Identified by SOM Aryl Hydrocarbon Receptor Agonists
- 36. Tissue Factor (TF) Primary Cellular Initiator of Blood Coagulation RW Colman 2006 J. Exp. Med Blood Coagulation 36 Thrombosis
- 37. Thrombosis is Required for Normal Wound Healing 37
- 38. • Pathologic setting – aberrant coagulation thrombosis - The formation of a blood clot (coagulation) within a vein - Clinical manifestations • Deep vein thrombosis (DVT), stroke, and pulmonary embolism thrombi break off and get lodged in the lung • Ebola – consumptive coagulopathy Thrombosis Can Also Be Pathologic Smooth muscle cells Endothelial cells Vessel Lumenplatelets in fibrin clot 38
- 39. • 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 (oncology drug) Mechanisms / Drugs Associated with Thrombosis-Related Side Effects All show increased Tissue Factor levels in the BioMAP 3C System 39
- 40. • Leverage our large chemical biology database of >3800 compounds • Search the database for all compounds / test agents that increase TF in the 3C system - How common is this activity? - What are the mechanisms represented? - Is there a connection that helps us better understand the regulation of thrombosis? Are These Mechanisms Connected? 40
- 41. Analysis of Reference Compounds 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 41 • Increased TF is an uncommon activity • 55/3187 compounds (1.7%) Implicate Autophagy Berg, et al., IJMS, 2015
- 42. Autophagy • Intracellular self-degradation system • Cellular response to nutrient deprivation • Also contributes to recycling of dysfunctional organelles, handling of protein aggregates, bacteria and 42
- 48. The Autophagy Connection Lysosomal Function Berg, et al., IJMS, 2015
- 49. • Summary - Mechanistic Hypothesis: thrombosis-related side effects are associated with alterations in the process of autophagy that increase TF cell surface levels - In moderation, during nutrient deprivation, an increase in TF leading to the recruitment of nutrient-rich platelets to a tissue sites would be a beneficial response • Next Step: - Incorporation of these data in a knowledge framework Tissue Factor, Autophagy & Thrombosis 49
- 50. Adverse Outcome Pathway (AOP) Knowledge 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/chemical safety/testing/adverse-outcome-pathways- molecular-screening-and-toxicogenomics.htm • Focused on the clinical outcome - Anchored at both ends 50
- 51. An AOP for DVT MIE Key Event Adverse Outcome Activation of AhR Upregulation of Tissue Factor Deep Vein Thrombosis Initiation of Coagulation Key Event Key Event Molecular Initiating Event Clinical Effect Increase in Autophagic Vacuolization HDF3CGF In vitro disease model 3C 3C 4H LPS SAg Endothelial Cells Endothelial Cells PBMC + Endothelial Cells PBMC + Endothelial Cells BioMAP System Primary Human Cell Types ! ! ! ! 51 MIE Inhibition of Estrogen R
- 52. • Profiling across primary human cell systems can be applied in target-based drug discovery for: - Defining characteristics of good drugs • Confirming expected target activities • Identifying unexpected activities (target or off-target) • Concentration-response characteristics (dose resistance) - Guide in vivo preclinical and clinical studies • Dose selection, safety window • Identify new biomarkers or pathways to track • Preview drug combination effects • Applications at the organizational level: - Connect target biology across programs and therapeutic areas - Improve safety prediction early in discovery - Facilitate opportunities for new indication discovery In Conclusion 52
- 53. • BioSeek - Mark A. Polokoff - Dat Nguyen - Xitong Li - Antal Berenyi - Alison O’Mahony • NIEHS (ILS) - Nicole Kleinstreuer Acknowledgements • EPA - Keith Houck - Richard Judson - David Dix - Bob Kavlock - David Reif - Matt Martin - Ann Richard - Tom Knudsen 53
- 54. Contact: Ellen L. Berg, PhD, Scientific Director BioSeek, a division of DiscoveRx 310 Utah Avenue, Suite 100 South San Francisco, CA 94080 +1-650-416-7621 eberg@bioseekinc.com 54