Transcript Discovery of Therapeutics to Improve Quality of Life

Discovery of Therapeutics to Improve Quality of Life
Ram Samudrala
University of Washington
Overall research areas
PROTEIN STRUCTURE PREDICTION PROTEIN FUNCTION PREDICTION PROTEIN INTERACTION PREDICTION
CASP6 prediction for T0281
4.3 Å Cα RMSD for all 70 residues
PROTEOME APPLICATION
INTEGRATED SYSTEMS BIOLOGY
CASP6 prediction for T0271
2.4 Å Cα RMSD for all 142 residues (46% ID)
http://protinfo.compbio.washington.edu
http://bioverse.compbio.washington.edu
Drug discovery – current approach
Pink et al, September 2005
Drug discovery – our approach
Computational protein docking with
molecular dynamics protocol enables in
silico discovery of compounds that inhibit
multiple targets and diseases
Several papers published; first ones in 2003
Pink et al, September 2005
Prediction of HIV-1 protease-inhibitor binding energies
Jenwitheesuk E, Samudrala R. Antiviral Therapy 10: 157-166, 2005.
Jenwitheesuk E, Samudrala R. BMC Structural Biology 3: 2, 2003.
Ekachai Jenwitheesuk
Prediction of HIV inhibitor resistance/susceptibility
http://protinfo.compbio.washington.edu/pirspred/
Jenwitheesuk E, Wang K, Mittler J, Samudrala R. AIDS 18: 1858-1859, 2004.
Jenwitheesuk E, Wang K, Mittler J, Samudrala R. Trends in Microbiology 13: 150-151, 2005.
Ekachai Jenwitheesuk/
Kai Wang/John Mittler
Set of FDA approved, experimental and
naturally occurring compounds
Inhibitor X
Disease A
Protein A…
Protein A2
Protein A1
1…
2…
3…
4 Inhibitor X
5…
6…
Disease B
Protein B…
Protein B2
Protein B1
1…
2 Inhibitor X
3…
4…
5…
6…
Disease C
Protein C…
Protein C2
Protein C1
1…
2…
3…
4…
5 Inhibitor X
6…
Disease XXX
...........
Protein XXX…
Protein XXX2
Protein XXX1
1…
2…
3 Inhibitor X
4…
5…
6…
Binding affinity calculation using docking with dynamics protocol
Multi-target multi-disease therapeutic discovery
Disease A
•Protein A1
•Protein A2
•Protein A3
•…
•…
Disease B
•Protein B1
•Protein B2
•Protein B3
•…
•…
Disease C
•Protein C1
•Protein C2
•Protein C3
•…
•…
Disease XXX
•Protein XXX1
•Protein XXX2
•Protein XXX3
•…
•…
Ekachai Jenwitheesuk
Identification of multi-target inhibitors against malaria
Ekachai Jenwitheesuk
Jenwitheesuk E, Samudrala R. Journal of the American Medical Association 294: 1490-1491, 2005.
Identification of multi-strain herpesvirus inhibitors
Ekachai Jenwitheesuk/Michael Lagunoff
Summary of our multi-target multi-disease drug discovery efforts
Dengue – 3 targets
HIV – 5 targets
Influenza – protease inhibitors for 4 strains
Leishmania – 4 targets
M. tuberculosis
Plasmodium – 14 targets
SARS – protease inhibitor published
T. cruz – 15 targets
T. brucei – 14 targets
Herpesviruses – protease inhibitors for 5 strains
Cancer – 31 targets
Ekachai Jenwitheesuk
Summary of our multi-target multi-disease drug discovery efforts
Ekachai Jenwitheesuk
What do we want to do: Ideal world scenario
Focus on discovery of inhibitors for third world diseases
Foster an “open drug” development approach where discoveries
are rapidly published
Build on infrastructure created by drug companies to validate
and deliver therapeutics to the people who need it
What do we want to do: Specifics
Package HIV drug resistance prediction server into a
standalone tool for use in a clinical setting
Screen drugs computationally for more targets and diseases
most relevant to global health
Perform in vitro assays of drugs being predicted with
collaborators (SBRI, UW, UCSF)
Perform in vivo studies
Conduct clinical trials to ensure follow through of leads
Limitations
Exhausting the limits of academic research
Academic funding not adequate for translating these predictions
into a clinical setting
Possible funding models
For-profit: focus on a disease of importance to industrialised
nations; form a startup; obtain VC funding; build up a pipeline
that includes drugs against third world diseases
– misplaced focus, control issues
Not-for-profit: obtain funding from granting agencies and
foundations
- slow, not generalisable
Hybrid: focus on third world diseases; have a NFP mechanism
in place to push through our leads
– infrastructure can be generally used to generate revenue
Funding specifics
Costs are reduced due to:
Computational discovery
Use of preapproved drugs
Lower number of failed drugs
Probabily of success is higher due to:
Multi-target inhibition
Mechanism of action is understood
Use of preapproved drugs
Side effects may be predicted