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Mike Romanos Vice President Discovery Technology  GlaxoSmithKline   [email_address]
Integrating Disruptive Technologies into Translational Research
Drug Discovery Challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Disruptive Technologies ,[object Object],[object Object],[object Object],[object Object]
A Strategy for Improving Translational Science Classical disease Target Leads Animal models of efficacy Candidates Broad PoC outcome studies Mechanistic disease Pathway Targets Leads Human 1 o  cell models PD animal models Disease tissue Stratified PoC studies Candidates Mechanistically linked models with common PD biomarkers
siRNA screen of TNF-driven IL6 secretion in human RA synovial fibroblasts Normal knee joint Arthritic knee joint Synovial fibroblast culture Response to TNF   established and intervention with siRNA validated Screen druggable genome siRNA library Validate hits in independent synovial fibroblast culture Assess robustness of screen and identify hits Example 1: Target selection in human disease cell siRNA screen
Example 2a: Target validation in human primary cells siRNA studies in stimulated primary CD4+ cells Target A  (TCR response)   Profound inhibition of key cytokines in Rheumatoid Arthritis (TNF  ) and Asthma (IL13) 0 1000 2000 3000 4000 5000 6000 7000 TNFa IL13 pg/ml Control T Target A
Example 2b: Target validation in disease tissue Allergen-specific T cell responses in asthma Effect of drug on secretion of Cytokines and other Mediators Supernatant allergic asthmatic biopsy material Allergen  ± Drug p=0.02 Unchallenged  allergen challenged  allergen challenged +drug   Effect of tool compound to Target A on IL-5 release
Example 3: RNA PD markers from cell to clinic PDE4i Placebo Validation of gene signature in human primary cells dosed with cAMP activating drugs Gene expression signature in nasal epithelial from rodents dosed with drug  POC study in allergen challenge chamber with nasal drug dosing  Engagement of mechanism confirmed by gene expression signature in nasal scrapes
Disruptive Technologies ,[object Object],[object Object],[object Object],[object Object],[object Object]
Disruptive Technologies: RNAi Translation/Delivery ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Disruptive Technologies: Human Stem Cells in Translation Science ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Human Pluripotent Stem Cells iPS ,[object Object],[object Object],[object Object]
Human Neurons from Stem Cells (Wu H. PNAS 104:13821; Johnson MA. J Neurosci. 27:3069) ,[object Object],[object Object],[object Object],[object Object],Neural circuit Day 7 Day 14 Day 21 Spontaneous EPSC 5s X 10Hz EPSC Short-term plasticity Frequency (Hz) Action potential 10Wi 10W 7W 6W 4W 30pA 0pA
Differentiated Cell Types Being developed, DA neurons most advanced More relevant models Specific neurons Being developed Diabetes/metabolic research Pancreatic    cells Ready for initial deployment Drug metabolism Hepatocytes Ready for initial deployment Cardiotoxicity testing including long QT, target validation, efficacy testing  Cardiomyocytes Ready for initial deployment Biological models for target validation, efficacy testing Neurons Status  Major uses in drug development Cell type
Integration of Stem Cell Technology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object]

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Integrating Disruptive Technologies Into Translational Research Hinxton Hall May2008

  • 1. Mike Romanos Vice President Discovery Technology GlaxoSmithKline [email_address]
  • 2. Integrating Disruptive Technologies into Translational Research
  • 3.
  • 4.
  • 5. A Strategy for Improving Translational Science Classical disease Target Leads Animal models of efficacy Candidates Broad PoC outcome studies Mechanistic disease Pathway Targets Leads Human 1 o cell models PD animal models Disease tissue Stratified PoC studies Candidates Mechanistically linked models with common PD biomarkers
  • 6. siRNA screen of TNF-driven IL6 secretion in human RA synovial fibroblasts Normal knee joint Arthritic knee joint Synovial fibroblast culture Response to TNF  established and intervention with siRNA validated Screen druggable genome siRNA library Validate hits in independent synovial fibroblast culture Assess robustness of screen and identify hits Example 1: Target selection in human disease cell siRNA screen
  • 7. Example 2a: Target validation in human primary cells siRNA studies in stimulated primary CD4+ cells Target A (TCR response) Profound inhibition of key cytokines in Rheumatoid Arthritis (TNF  ) and Asthma (IL13) 0 1000 2000 3000 4000 5000 6000 7000 TNFa IL13 pg/ml Control T Target A
  • 8. Example 2b: Target validation in disease tissue Allergen-specific T cell responses in asthma Effect of drug on secretion of Cytokines and other Mediators Supernatant allergic asthmatic biopsy material Allergen ± Drug p=0.02 Unchallenged allergen challenged allergen challenged +drug Effect of tool compound to Target A on IL-5 release
  • 9. Example 3: RNA PD markers from cell to clinic PDE4i Placebo Validation of gene signature in human primary cells dosed with cAMP activating drugs Gene expression signature in nasal epithelial from rodents dosed with drug POC study in allergen challenge chamber with nasal drug dosing Engagement of mechanism confirmed by gene expression signature in nasal scrapes
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. Differentiated Cell Types Being developed, DA neurons most advanced More relevant models Specific neurons Being developed Diabetes/metabolic research Pancreatic  cells Ready for initial deployment Drug metabolism Hepatocytes Ready for initial deployment Cardiotoxicity testing including long QT, target validation, efficacy testing Cardiomyocytes Ready for initial deployment Biological models for target validation, efficacy testing Neurons Status Major uses in drug development Cell type
  • 16.
  • 17.