Transcript How can we use this information to improve

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?
GENOME SEQUENCE TO PROTEIN AND PROTEOME…
STRUCTURE
FUNCTION
INTERACTION
PROTEIN
DNA/RNA
COMPOUND
SYSTEMS
INFRASTRUCTURE
APPLICATIONS
RICE
THERAPEUTICS
NANOTECHNOLOGY
DESIGN
EVOLUTION
SHOTGUN MULTITARGET DOCKING WITH DYNAMICS
ALL KNOWN DRUGS
FRAGMENT BASED
(~5,000 FROM FDA) DOCKING WITH DYNAMICS
(~50,000,000)
PRIORITISED
HITS
DISSOCIATION CONSTANTS (KD)
(~300-500)
+
ALL TARGETS WITH KNOWN
STRUCTURE (~5,000-10,000)
MACHINE
LEARNING
IN VITRO
STUDIES
herpes, malaria, dengue
hepatitis C, dental caries
HIV, HBRV, XMRV, rabies,
encephalitis, cholera,
Tuberculosis, various cancers
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)
CLINICAL STUDIES/APPLICATION
INITIAL CLINICAL TRIALS
IN VIVO STUDIES
PROTEIN INHIBITOR DOCKING WITH DYNAMICS
HIV protease
Correlation coefficient
1.0
with MD
0.5
without MD
ps
0
0.2
0.4
0.6
0.8
1.0
MD simulation time
Jenwitheesuk
KNOWLEDGE BASED FUNCTION
Bernard & Samudrala. Proteins (2009).
Bernard
FRAGMENT BASED DOCKING
Bernard
FRAGMENT BASED DOCKING RECONSTRUCTION
Bernard
INHIBITION OF ALL REPRESENTATIVE HERPES PROTEASES
Predicted:
Observed:
Function is inactivated.
protease ligand KD < μM
protease dimer KD < μM
Jenwitheesuk/Myszka
INHIBITION OF ALL HERPESVIRUSES
Viral load
HSV
KSHV
CMV
Fold inhibition
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
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
HSV
Viral load
HSV
Experiment 1
Experiment 2
Experiment 3
Lagunoff
MALARIA INHIBITOR DISCOVERY
Predicted
inhibitory
constant
10-13
10-12
10-11
10-10
10-9
10-8
10-7
None
Trends in Pharmacological Sciences, 2010.
Jenwitheesuk/
Van Voorhis/Rivas/Chong/Weismann
MALARIA INHIBITOR DISCOVERY
+++++
COMPARISON OF APPROACHES
$
Multitarget computational protocol
2,344 compounds
High throughput protocol 1
2,687 compounds
simulation
16 top predictions
experiment
6 ED50 ≤ 1 μM
High throughput protocol 2
2,160 compounds
high
high
throughput
throughput
screen
screen
19 ED50 ≤ 1 μM
36 ED50 ≤ 1 μM
++
$$$$$
Computational protocol 1
241,000 compounds
simulation
84 top predictions
In comparison to other approaches, including
experimental high throughput screens, our
multitarget docking with dynamics protocol
combining theory and experiment is more
efficient and accurate.
experiment
4 ED50 ≤ 10 μM
Computational protocol 1
355,000 compounds
simulation
100 top predictions
experiment
1 ED50 ≤ 10 μM
Trends in Pharmacological Sciences, 2010.
+++
$$$
Jenwitheesuk/Van Voorhis/Rivas
DENGUE INHIBITOR DISCOVERY
Prediction #1
Prediction #2
PLoS Neglected Tropical Diseases, 2010.
Jenwitheesuk/Michael
SHOTGUN MULTITARGET DOCKING WITH DYNAMICS
ALL KNOWN DRUGS
FRAGMENT BASED
(~5,000 FROM FDA) DOCKING WITH DYNAMICS
(~50,000,000)
PRIORITISED
HITS
DISSOCIATION CONSTANTS (KD)
(~300-500)
+
ALL TARGETS WITH KNOWN
STRUCTURE (~5,000-10,000)
MACHINE
LEARNING
herpes, malaria, dengue
hepatitis C, dental caries
HIV, HBRV, XMRV, rabies,
encephalitis, cholera,
Tuberculosis, various cancers
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)
CLINICAL STUDIES/APPLICATION
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
DISCOVER NOVEL OFFLABEL USES OF MAJOR THERAPEUTIC VALUE
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.
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.
???
CONCLUSION
High risk endeavour is successful if one or more diseases
currently without an effective treatment can be treated completely.
Particular diseases of interest are neglected tropical ones isolated
to single populations without an effective treatment.
Will be applied to several diseases of commercial interest also.
ACKNOWLEDGEMENTS
Current group members: Past group members:
•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
•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
•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
ACKNOWLEDGEMENTS
Collaborators:
Funding agencies:
•BGI/U Alberta
-Gane Wong
-Jun Yu
-Jun Wang
-Andrew Mason
-Lorne Tyrell
•BIOTEC/KMUTT
•Mahidol University
- Prasit Palittapongarrnpim
- Wasun Chantratita
•MSE
-Mehmet Sarikaya
-Candan Tamerler
-et al.
•UW Microbiology
-James Staley
-John Mittler
-Michael Lagunoff
-Roger Bumgarner
-Wesley Van Voorhis
-et al.
•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
PROSPECTIVE PRELIMINARY VERIFICATION
Predicted
protease (dimer) +
inhibitor:
HERPES
DENGUE
(HSV, CMV, KSHV)
Viral E protein
Observed:
Function is inactivated.
Prediction #1
Prediction #2
KD protease ligand ≤ μM
KD protease dimer ≤ μM
Herpes viral load
Experiment 1
Experiment 2
2/4 ≤ µM ED50
against dengue virus
PLoS Neglected Tropical Diseases, 2010.
14 targets
MALARIA
Multitarget protocol: 2,344 → 16 → 6 ≤ 1 µM ED50
HTS protocol:
2,687
→ 19 ≤ 1 µM ED50
HTS protocol:
2,160
→ 36 ≤ 1 µM ED50
Docking protocol: 355,000 → 100 → 1 ≤ 10 µM ED50
Docking protocol: 241,000 → 84 → 4 ≤ 10 µM ED50
Trends in Pharmacological Sciences, 2010.
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.
ADVANTAGES OF OUR APPROACHES
Costs are reduced:
Computational discovery
Use of preapproved drugs
Lower number of failed drugs
Probabily of success is higher:
Multitarget inhibition
Mechanism of action is known
Use of preapproved drugs
Side effects may be predicted
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.
MULTITARGET DOCKING WITH DYNAMICS
NOVEL FRAGMENT BASED
MULTITARGET SCREENING
Disease &
target identification
TRADITIONAL SINGLE
TARGET SCREENING
COMPOUND SELECTION
Multiple disease related proteins
Compound
database (~300,000)
Single disease related protein
Compound library
DRUG-LIKE
(~5000 from FDA)
Computational docking with dynamics
Initial candidates
Experimental verification
Success rate +++++
Time .
Cost $
Computational docking
Initial candidates
Experimental verification
Success rate ++
Time
.
Cost $$$
High throughput screen
Experimental verification
Success rate +
Time
.
Cost $$$$$
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.
Known drugs
Targets with known structure
docking score, Kd, PK, ADME,
absorption, bioavailability, toxicity
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.
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.