1. NOVEL PARADIGMS FOR DRUG DISCOVERY
SHOTGUN COMPUTATIONAL MULTITARGET SCREENING
RAM SAMUDRALA
ASSOCIATE PROFESSOR
UNIVERSITY OF WASHINGTON
NIH DIRECTOR’S PIONEER AWARD 2010
How does the genome of an organism specify
its behaviour and characteristics?
How can we use this information to improve
human health and quality of life?

2. GENOME SEQUENCE TO PROTEIN AND PROTEOME…
STRUCTURE
FUNCTION
INTERACTION
COMPOUND
DNA/RNA
PROTEIN
SYSTEMS
INFRASTRUCTURE
APPLICATIONS
EVOLUTION
THERAPEUTICS
NANOTECHNOLOGY
RICE
DESIGN

3. DISSOCIATION CONSTANTS (KD) INITIAL CLINICAL TRIALS
SHOTGUN MULTITARGET DOCKING WITH DYNAMICS
ALL KNOWN DRUGS
(~5,000 FROM FDA)
ALL TARGETS WITH KNOWN
STRUCTURE (~5,000-10,000) +
FRAGMENT BASED
DOCKING WITH DYNAMICS
(~50,000,000)
PRIORITISED
HITS
DISSOCIATION CONSTANTS (KD)
(~ )
MACHINE
LEARNING
INITIAL CLINICAL TRIALS
IN VITRO
STUDIES
IN VIVO STUDIES
herpes, malaria, dengue
hepatitis C, dental caries
HIV, HBRV, XMRV
CLINICAL STUDIES/APPLICATION
M Lagunoff (UW), W Van Woorhis (UW),
S Michael (FCGU), J Mittler/J Mullins (UW),
G Wong/A Mason/L Tyrell (U Alberta),
W Chantratita/P Palittapongarnpim (Thailand)

4. INHIBITION OF ALL REPRESENTATIVE HERPES PROTEASES
Predicted:
Observed:
Function is inactivated.
protease ligand KD < μM
protease dimer KD < μM
Jenwitheesuk/Myszka

5. INHIBITION OF ALL HERPESVIRUSES
HSV
KSHV
CMV
Computationally predicted broad spectrum human herpesvirus protease inhibitors is effective in vitro
against members from all three classes and is comparable or better than antiherpes drugs
Viral load
Fold inhibition
HSV
Our protease inhibitor acts synergistically with acyclovir (a nucleoside analogue that inhibits replication)
and it is less likely to lead to resistant strains compared to acyclovir
Viral load
Experiment 1
Experiment 2
Experiment 3
Lagunoff

6. MALARIA INHIBITOR DISCOVERY
10-13
10-12
10-11
10-10
10-9
10-8
10-7
None
Predicted
inhibitory
constant
Jenwitheesuk/
Van Voorhis/Rivas/Chong/Weismann
Trends in Pharmacological Sciences, 2010.

7. MALARIA INHIBITOR DISCOVERY
COMPARISON OF APPROACHES $
Multitarget computational protocol
2,344 compounds
simulation
16 top predictions
experiment
6 ED50 ≤ 1 μM
High throughput protocol 1
2,687 compounds
high
throughput
screen
19 ED50 ≤ 1 μM
High throughput protocol 2
2,160 compounds
high
throughput
screen
36 ED50 ≤ 1 μM
$$$$$
Computational protocol 1
241,000 compounds
simulation
84 top predictions
experiment
4 ED50 ≤ 10 μM
In comparison to other approaches, including experimental high throughput screens, our multitarget docking with dynamics protocol combining theory and experiment is more efficient and accurate.
Computational protocol 1
355,000 compounds
simulation
100 top predictions
experiment
1 ED50 ≤ 10 μM
$$$
Jenwitheesuk/Van Voorhis/Rivas
Trends in Pharmacological Sciences, 2010.

8. DENGUE INHIBITOR DISCOVERY
Prediction #1
Prediction #2
Jenwitheesuk/Michael
PLoS Neglected Tropical Diseases, 2010.

9. DISSOCIATION CONSTANTS (KD)
SHOTGUN MULTITARGET DOCKING WITH DYNAMICS
ALL KNOWN DRUGS
(~5,000 FROM FDA)
FRAGMENT BASED
DOCKING WITH DYNAMICS
(~50,000,000)
PRIORITISED
HITS
DISSOCIATION CONSTANTS (KD)
(~ ) +
ALL TARGETS WITH KNOWN
STRUCTURE (~5,000-10,000)
MACHINE
LEARNING
herpes, malaria, dengue
HIV, HBRV, XMRV
hepatitis C, dental caries
Docking with dynamics
Fragment based
Multitargeting
Use of existing drugs
Drug/target maching learning matrix
PK/ADME/bioavailability/toxicity/etc.
Biophysics + knowledge iteration
Fast track to clinic (paradigm shift)
Cocktails/NCEs/optimisation
Translative: atomic → clinic
CLINICAL STUDIES/APPLICATION
M Lagunoff (UW), W Van Woorhis (UW),
S Michael (FCGU), J Mittler/J Mullins (UW),
G Wong/A Mason/L Tyrell (U Alberta),
W Chantratita/P Palittapongarnpim (Thailand)
DISCOVER NOVEL OFFLABEL USES OF MAJOR THERAPEUTIC VALUE

10. CONCLUSION
High risk endeavour is successful if one or more diseases currently without an effective treatment can be treated completely.

11. Current group members: Past group members:
ACKNOWLEDGEMENTS
Adrian Laurenzi
Brian Buttrick
Chuck Mader
Dominic Fisher
Emilia Gan
Ersin Emre Oren
Gaurav Chopra
George White
Hernan Zamalloa
Jason North
Jeremy Horst
Ling-Hong Hung
Matthew Clark
Manish Manish
Michael Shannon
Michael Zhou
Omid Zarei
Raymond Zhang
Stewart Moughon
Thomas Wood
Weerayuth Kittichotirat
Current group members:
Aaron Chang
Aaron Goldman
Brady Bernard
Cyrus Hui
David Nickle
Duangdao Wichadukul
Duncan Milburn
Ekachai Jenwitheesuk
Gong Cheng
Imran Rashid
Jason McDermott
Juni Lee
Kai Wang
Marissa LaMadrid
Michael Inouye
Michal Guerquin
Nipa Jongkon
Past group members:
Rob Braiser
Renee Ireton
Shu Feng
Sarunya Suebtragoon
Shing-Chung Ngan
Shyamala Iyer
Siriphan Manocheewa
Somsak Phattarasukol
Tianyun Liu
Vanessa Steinhilb
Vania Wang
Yi-Ling Cheng
Zach Frazier

12. ACKNOWLEDGEMENTS Collaborators: Funding agencies: Budget: BGI
Gane Wong
Jun Yu
Jun Wang
et al.
BIOTEC/KMUTT
MSE
Mehmet Sarikaya
Candan Tamerler
UW Microbiology
James Staley
John Mittler
Michael Lagunoff
Roger Bumgarner
Wesley Van Voorhis
Collaborators:
Funding agencies:
National Institutes of Health
National Science Foundation
-DBI
-IIS
Searle Scholars Program
Puget Sound Partners in Global Health
Washington Research Foundation UW
-Advanced Technology Initiative
-TGIF
Budget:
~US$1 million/year total costs

14. PROSPECTIVE PRELIMINARY VERIFICATION
Observed:
Function is inactivated.
KD protease ligand ≤ μM
KD protease dimer ≤ μM
Experiment 1
Experiment 2
HERPES
(HSV, CMV, KSHV)
Predicted
protease (dimer) +
inhibitor:
Herpes viral load
DENGUE
PLoS Neglected Tropical Diseases, 2010.
2/4 ≤ µM ED50
against dengue virus
Prediction #1
Prediction #2
Viral E protein
Multitarget protocol: 2,344 → 16 → 6 ≤ 1 µM ED50
HTS protocol: , → ≤ 1 µM ED50
HTS protocol: , → ≤ 1 µM ED50
Docking protocol: 355,000 → 100 → 1 ≤ 10 µM ED50
Docking protocol: 241,000 → 84 → 4 ≤ 10 µM ED50
14 targets
MALARIA
Trends in Pharmacological Sciences, 2010.

15. HERPESVIRUS PROTEASE DRUG OPPORTUNITY
All these three viruses cause life-threatening diseases in immunocompromised patients.
HSV drugs alone represent a > $2 billion dollar yearly market and growing at a 10% rate. Nearly 90 million people worldwide are infected with the genital herpes virus, and about 25 million of them suffer frequent outbreaks of painful blisters and sores.
CMV is a major cause of mortality in transplant patients, and drugs against it represent a $300 million dollar yearly market.
Acylovir and related drugs are all nucleoside analogues/inhibitors whose patents will soon expire. Our protease inhibitor is a novel type of anti-herpes agent that may be used in combination therapy.
The inhibitor has been evaluated in mouse models of cancer and found to very nontoxic. Inhibitor can be modified.
Topical applications are therefore possible with a high likelihood of success.

16. PLATFORM OPPORTUNITY
Partner with Biotech, Pharma to work on their libraries of compounds, targets, diseases (be a hired gun, share revenue).
Apply platform a set of first world diseases with potential for large revenue, patent findings, and license the findings out. Platform may be applied as a separate company or as a SRA with UW (similar to Pioneer Award budget). Keep drug/target interaction matrix a trade secret. License new uses OR license modifications of those drugs OR both.
Update above list as new drugs and new targets are identified, so a constant set of hits and leads will be available for patenting and licensing.
???

17. BUSINESS ACTIVITIES
Have WA corporation: 3D Therapeutics, Inc. Nominal CEO: Jason North.
Board currently includes Perry Fell (cofounder of Seattle Genetics) and Sonya Erickson (Cooley).
Scientists include Michael Lagunoff, Wesley van Voorhis, Roger Bumgarner, and Ram Samudrala.
License for first generation platform and hits/leads somewhat negotiated with the UW.
Patents:
Michael SF, Isern S, Garry R, Costin J, Jenwithesuk E, Samudrala R. Optimized dengue virus entry inhibitory peptide (DN81). Priority/filing date: July 13, 2007.
Jenwitheesuk E, Lagunoff M, Van Voorhis W, Samudrala R. Compositions and methods for predicting inhibitors of protein targets. Priority/filing date: July 6, 2007.

18. ADVANTAGES OF OUR APPROACHES
Probabily of success is higher:
Multitarget inhibition
Mechanism of action is known
Use of preapproved drugs
Side effects may be predicted
Costs are reduced:
Computational discovery
Use of preapproved drugs
Lower number of failed drugs

19. PROTEIN INHIBITOR DOCKING WITH DYNAMICS
HIV protease
MD simulation time
Correlation coefficient ps
1.0
0.5
with MD
without MD
Jenwitheesuk

20. ACCURACY COMPARISON
Bernard & Samudrala. Proteins (2009).

21. BACKGROUND AND MOTIVATION
My research on protein and proteome structure, function, and interaction is directed to understanding how genomes specify phenotype and behaviour; my goal is to use this information to improve human health and quality of life.
Protein functions and interactions are mediated by atomic three dimensional structure. We are applying all our structure prediction technologies to the area of small molecule therapeutic discovery.
The goal is to create a comprehensive in silico drug discovery pipeline to increase the odds of initial preclinical hits and leads leading to significantly better outcomes downstream in the clinic.
The knowledge-based drug discovery pipeline will adopt a shotgun approach that screens all known FDA approved drug and drug-like compounds against all known target proteins of known structure, simultaneously examining how a small molecule therapeutic interacts with targets, antitargets, metabolic pathways, to obtain a holistic picture of drug efficacy and side effects.
Find new uses for existing drugs that can be used in the clinic, with a focus on third world and neglected diseases with poor or nonexisting treatments.

22. Disease & target identification
MULTITARGET DOCKING WITH DYNAMICS
Disease & target identification
NOVEL FRAGMENT BASED TRADITIONAL SINGLE
MULTITARGET SCREENING TARGET SCREENING
Compound library
High throughput screen
Experimental verification Success rate + Time Cost $$$$$
Computational docking with dynamics
Multiple disease related proteins
Initial candidates
Experimental verification Success rate Time . Cost $
DRUG-LIKE (~5000 from FDA)
Computational docking
Initial candidates
Experimental verification Success rate ++ Time Cost $$$
COMPOUND SELECTION
Compound database (~300,000)
Single disease related protein

23. WHY WILL IT WORK
Fragment based docking with dynamics: dynamics improves accuracy; fragmentation exploits redundancy in existing drugs; most accurate docking protocol out there.
Use of existing drugs: exploits all the knowledge from Pharma.
Multitargeting: multiple low Kd can work synergistically; screening for targets and antitargets simultaneously.
Knowledge based: potential from known structures, will have a big matrix relating drugs, targets, PK, ADME, solubility, bioavailability, toxicity, etc.; rich dataset for combining our biophysics based methods with machine learning tools in an iterative manner.
Targets with known structure
docking score, Kd, PK, ADME,
absorption, bioavailability, toxicity
Known drugs

24. BROADER IMPACT
Multiple drugs can be combined to produce therapeutic effect and overcome disease resistance.
Good for any condition where one or more viable targets exist.
Harnesses the power of all the drug discovery done thusfar; new paradigm for fast track FDA approval
Translational approach goes from providing atomic mechanistic detail to measuring clinical efficacy in one shot.
Protocol can be used to design novel drugs also.

25. SUITABILITY FOR THE PIONEER AWARD
Not good for Pharma because of reuse of existing drugs (most profit in novel compounds)
Not good for Pharma because of focus on third world/neglected diseases.
Not good for Pharma because of nonfocus on single target model they love.
Marked departure from my protein structure prediction work, but now applied research from basic protein folding to producing therapeutics in a clinic.
Funding will help focus work on drug discovery which until now has been done on a shoestring.