• Save
Integrating Disruptive Technologies Into Translational Research   Hinxton Hall May2008
Upcoming SlideShare
Loading in...5
×
 

Integrating Disruptive Technologies Into Translational Research Hinxton Hall May2008

on

  • 877 views

Introduction to a session with the same title. Addresses the challenges in drug discovery and how disruptive technologies can help. Focusses on use of RNAi and stem cells in translational studies

Introduction to a session with the same title. Addresses the challenges in drug discovery and how disruptive technologies can help. Focusses on use of RNAi and stem cells in translational studies

Statistics

Views

Total Views
877
Views on SlideShare
877
Embed Views
0

Actions

Likes
0
Downloads
0
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Integrating Disruptive Technologies Into Translational Research   Hinxton Hall May2008 Integrating Disruptive Technologies Into Translational Research Hinxton Hall May2008 Presentation Transcript

  • Mike Romanos Vice President Discovery Technology GlaxoSmithKline [email_address]
  • Integrating Disruptive Technologies into Translational Research
  • Drug Discovery Challenges
    • Drug discovery has uniquely long cycle times (>15 years)
    • Attrition of unprecedented targets unacceptably high (>95% of targets)
      • Targets not validated until Ph2/3 – major cost in the industry impacting innovation
    • In basic terms the process consists of
      • Identifying the right target
      • Getting the right compound
      • Testing it in the clinic in the best way
  • Disruptive Technologies
    • Can new technologies alter these realities?
    • Many have had a slower and lesser impact than promised
    • Judging where and when to invest in technology is difficult
      • Technology investment has been both essential and an unproductive diversion for the industry
  • 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
    • Delivery of gene modulating medicines
      • Potential to develop RNAi as a generic product platform to address large segment of undruggable targets
      • Potential of related approaches, e.g. miRNA and other NA drugs
      • If the common issue of Delivery is not addressed this will remain a niche area
    • Stem cell technology
  • Disruptive Technologies: RNAi Translation/Delivery
    • Current biotech/industry clinical focus:
      • Acute indications
      • Local/topical delivery e.g. eye/nose/lung, or passive delivery for the liver
    • Technologies for systemic/targeted delivery
      • Liposomal, nanoparticle, conjugates, viral delivery
      • All will be needed to increase target organ scope
      • Some promise but this will be challenging, empirical and long-term
    • GSK investment: collaboration with SIRNA (Respiratory), Santaris (Antivirals), Regulus (miRNA/Inflammation)
  • Disruptive Technologies: Human Stem Cells in Translation Science
    • Potential to revolutionise biology and drug discovery
    • Three major areas of application
      • Regenerative cell therapy
      • As therapeutic targets (regeneration or cancer)
      • Cell models for drug discovery
    • Fills gap for most human cell types – big leap forward in translational science
      • Example: limitations of current  -amyloid studies
      • In future a significant proportion of stem screening in human stem cell-derived cells
      • Target identification/validation, efficacy screening, drug metabolism and safety screening
  • Human Pluripotent Stem Cells iPS
    • Potential for availability of human cells in any amount on demand
    • Scale should enable early discovery including HTS – cell enrichment and screening facilitated by genetic manipulation
    • iPS technology allows testing of individuals with different genetic traits and generation of autologous cells
  • Human Neurons from Stem Cells (Wu H. PNAS 104:13821; Johnson MA. J Neurosci. 27:3069)
    • Neurons at >80% purity with mixed phenotype
    • Functional maturation achieved
    • Synapse and network maturation accelerated by astrocyte co-culture
    • Induced plasticity
    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
    • Technical hurdles:
      • Protocols for reproducible differentiation into required cell type, esp for iPS cells
      • Assuring authentic fully differentiated phenotype
      • Potential cell heterogeneity
      • Scaling up
    • Not a panacea - limitations:
      • Not able to reproduce native 3D cellular systems, organs or in vivo physiology – mitigate through co-culture, matrix, or generating chimaeric animals
      • Not disease cells
    • This is a long-term activity. GSK is investing in this area in R&D China (Shanghai) and through academic collaborations
  • Conclusion
    • Truly disruptive technologies may not be easily integrated - problems arise that need to be solved
    • Need to balance vision with understanding of the challenges and commitment in the long term