Transcript Drug development

Samudrala group - 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
INTEGRATIVE 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
Samudrala group – specific applications
PROTEIN INHIBITOR PREDICTION
PROTEIN DESIGN/BIONANOTECHNOLOGY
Technology for the discovery of inhibitors
• Identify protein targets where the three-dimensional structure can be elucidated.
• Prioritise targets based on Bioverse networks and functional annotation.
• Computationally predict inhibitors against the targets:
- small molecule inhibitors are predicted using our docking with dynamics protocol
to screen a library of FDA approved and experimental compounds used for other
indications.
- peptide inhibitors are predicted using our all-atom energy function to identify and
design peptide sequences that have a strong binding energy.
• Lowered risk for drug development since extensive toxicology profiles are
available for most of these compounds.
• Other collections of compounds could also be screened to fit specific collaborative
programs.
• Make derivatives of the best inhibitors and computationally determine binding
affinities.
• Screen other targets using the best inhibitors to determine potential side effects and
cross-reactivity.
Outcome of technologies
• HIV protease and RT drug resistance prediction
- most accurate drug resistance prediction method when combined with knowledgebased methods (published)
• HIV gp41 peptide inhibitor prediction (published)
• HIV integrase inhibitor predictions (in progress)
• Malaria multi-target inhibitor prediction
- found 5-6 known antimalarials in a screen of 14 targets (published)
• Herpesvirus broad spectrum inhibitor prediction
- found one inhibitor experimentally validated in cell culture against CMV, HSV,
and KHSV (in progress)
• Inhibitor discovery and analysis for various other diseases
- SARS, CMV (published)
-Trypanosomal infection, Leishmania, avian and dog influenza
• HIV opportunistic pathogens, cancer (in progress)
Ekachai Jenwitheesuk
Predicted inhibitor against CMV, HSV, and KHSV proteases
Ekachai Jenwitheesuk
Predicted inhibitor against CMV protease
On to experimental validation studies by Michael Lagunoff
What we have
• A robust technology for predicting protein structures.
• A generalisable technology for predicting potential protein inhibitors.
• Putative inhibitors of tens of disease targets.
• A demonstration of the value of the technology for herpesvirus infection.
• A potential drug development opportunity.
Businesses that could be created
• Herpes therapeutic development.
• Therapeutic discovery in collaboration with drug development companies.
• Second option requires validation made possible by the first.
What’s required in the short term
• Need to show that our inhibitor works in mouse models of herpes.
• Need to measure dissociation constants (Kd) between our inhibitor and target
proteases.
What’s required in the longer term
Drug discovery:
• Need lots of computers to do screening for specific targets, especially if we
partner with drug development companies.
Drug development:
• Need resources for in vitro validation of predicted inhibitors.
• Need resources for in vivo validation.
• Need resources for clinical development.
Screen library of FDA approved or experimental
compounds using docking with dynamics protocol
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…
More than a dozen publications.
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 – small molecules
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
Multi-target multi-disease therapeutic discovery – peptides
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
...........
Jenwitheesuk E, Samudrala R. Antiviral Therapy 10: 893-900, 2005.
Protein XXX…
Protein XXX2
Protein XXX1
1…
2…
3 Inhibitor X
4…
5…
6…
Stability calculation using all-atom scoring function
Find high stability regions on surface of a protein structure;
design high stability variants using all-atom function
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
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
Identification of multi-target inhibitors against malaria
Ekachai Jenwitheesuk
Jenwitheesuk E, Samudrala R. Journal of the American Medical Association 294: 1490-1491, 2005.
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
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 – 30 targets
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
Future (long-term) applications of our research
Screen host (human) proteins for side effects
Drug target discovery using the Bioverse framework
Personalised drugs based on SNP discovery
Integrate with high-resolution structure elucidation
Use protein design/nanotechnology for targeted delivery
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