NOVEL PARADIGMS FOR DRUG DISCOVERY

SHOTGUN COMPUTATIONAL MULTITARGET SCREENING

RAM SAMUDRALAASSOCIATE PROFESSOR

UNIVERSITY OF WASHINGTON

NIH DIRECTOR’S PIONEER AWARD 2010

How does the genome of an organism specifyits behaviour and characteristics?

How can we use this information to improvehuman health and quality of life?

GENOME SEQUENCE TO PROTEIN AND PROTEOME…STRUCTURE FUNCTION

SYSTEMS

INFRASTRUCTURE APPLICATIONS

EVOLUTION

THERAPEUTICS

NANOTECHNOLOGY

RICE

DESIGN

INTERACTION

COMPOUND

DNA/RNAPROTEIN

SHOTGUN MULTITARGET DOCKING WITH DYNAMICSALL KNOWN DRUGS (~5,000 FROM FDA)

ALL TARGETS WITH KNOWN STRUCTURE (~5,000-10,000)

+

FRAGMENT BASEDDOCKING WITH DYNAMICS

(~50,000,000)

PRIORITISEDHITS

MACHINE LEARNING

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)

herpes, malaria, dengue hepatitis C, dental cariesHIV, HBRV, XMRV, rabies,encephalitis, cholera, Tuberculosis, various cancers

CLINICAL STUDIES/APPLICATION

INITIAL CLINICAL TRIALS

IN VITROSTUDIES

IN VIVO STUDIES

DISSOCIATION CONSTANTS (KD)(~300-500)

MD simulation time

Co

rrel

atio

n c

oef

fici

ent

ps0 0.2 0.4 0.6 0.8 1.0

1.0

0.5

with MD

without MD

HIV protease

PROTEIN INHIBITOR DOCKING WITH DYNAMICS

Jenwitheesuk

Bernard & Samudrala. Proteins (2009).

KNOWLEDGE BASED FUNCTION

Bernard

FRAGMENT BASED DOCKING

Bernard

FRAGMENT BASED DOCKING RECONSTRUCTION

Bernard

INHIBITION OF ALL REPRESENTATIVE HERPES PROTEASES

Jenwitheesuk/Myszka

Observed:Function is inactivated.

protease ligand KD < μMprotease dimer KD < μM

Predicted:

INHIBITION OF ALL HERPESVIRUSES

HSV KSHVCMV

Computationally predicted broad spectrum human herpesvirus protease inhibitors is effective in vitroagainst members from all three classes and is comparable or better than antiherpes drugs

Lagunoff

Vira

l lo

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Fol

d in

hibi

tion

HSVHSV

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

Vira

l lo

ad

Experiment 1Experiment 2Experiment 3

10-13

10-12

10-11

10-10

10-9

10-8

10-7

None

Predictedinhibitoryconstant

Jenwitheesuk/Van Voorhis/Rivas/Chong/Weismann

MALARIA INHIBITOR DISCOVERY

Trends in Pharmacological Sciences, 2010.

Multitarget computational protocol

2,344 compounds

simulation

16 top predictions

experiment

6 ED50 ≤ 1 μM

COMPARISON OF APPROACHES

MALARIA INHIBITOR DISCOVERY

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

Computational protocol 1

355,000 compounds

simulation

100 top predictions

experiment

1 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.

+++++ $

++ $$$$$

+++ $$$Jenwitheesuk/Van Voorhis/RivasTrends in Pharmacological Sciences, 2010.

DENGUE INHIBITOR DISCOVERY

Jenwitheesuk/Michael

Prediction #1Prediction #2

PLoS Neglected Tropical Diseases, 2010.

MACHINE LEARNING

FRAGMENT BASEDDOCKING WITH DYNAMICS

(~50,000,000)

PRIORITISEDHITS

SHOTGUN MULTITARGET DOCKING WITH DYNAMICSALL KNOWN DRUGS (~5,000 FROM FDA)

ALL TARGETS WITH KNOWN STRUCTURE (~5,000-10,000)

+

CLINICAL STUDIES/APPLICATION

DISCOVER NOVEL OFFLABEL USES OF MAJOR THERAPEUTIC VALUE

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)

herpes, malaria, dengue hepatitis C, dental cariesHIV, HBRV, XMRV, rabies,encephalitis, cholera, Tuberculosis, various cancers

DISSOCIATION CONSTANTS (KD)(~300-500)

Docking with dynamicsFragment basedMultitargetingUse of existing drugsDrug/target maching learning matrixPK/ADME/bioavailability/toxicity/etc. Biophysics + knowledge iterationFast track to clinic (paradigm shift)Cocktails/NCEs/optimisationTranslative: atomic → clinic

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

•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

ACKNOWLEDGEMENTS

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

•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.

Collaborators:

Budget:• ~US$1 million/year total costs

Multitarget protocol: 2,344 → 16 → 6 ≤ 1 µM ED50HTS protocol: 2,687 → 19 ≤ 1 µM ED50HTS protocol: 2,160 → 36 ≤ 1 µM ED50Docking protocol: 355,000 → 100 → 1 ≤ 10 µM ED50Docking protocol: 241,000 → 84 → 4 ≤ 10 µM ED50

14 targets MALARIA

Trends in Pharmacological Sciences, 2010.

DENGUE

PLoS Neglected Tropical Diseases, 2010.

2/4 ≤ µM ED50against dengue virus

Prediction #1Prediction #2

Viral E protein

PROSPECTIVE PRELIMINARY VERIFICATION

Observed:Function is inactivated.

KD protease ligand ≤ μMKD protease dimer ≤ μM

Experiment 1Experiment 2

HERPES(HSV, CMV, KSHV)

Predicted protease (dimer) + inhibitor:

He

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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 discoveryUse of preapproved drugsLower number of failed drugs

Probabily of success is higher:

Multitarget inhibitionMechanism of action is knownUse of preapproved drugsSide 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 TRADITIONAL SINGLEMULTITARGET SCREENING TARGET SCREENING

Disease &target identification

Single disease related protein

Compound library

High throughput screen

Experimental verificationSuccess rate +

Time .Cost $$$$$

Computational docking

Initial candidates

Experimental verificationSuccess rate ++

Time .Cost $$$

COMPOUND SELECTION

Compound database (~300,000)

Computational docking with dynamics

Multiple disease related proteins

Initial candidates

Experimental verificationSuccess rate +++++

Time .Cost $

DRUG-LIKE (~5000 from FDA)

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.

Kn

ow

n d

rug

s

docking score, Kd, PK, ADME, absorption, bioavailability, toxicity

Targets with known structure

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.