Jack TuszynskiCross Cancer InstituteDepartment of PhysicsUniversity of AlbertaEdmonton, Canada

http://www.phys.ualberta.ca/~jtus

“Accelerating Chemotherapy Drug Discovery with High

Performance Computing and Analytics”

“Modern” Pharmacy: Rx

Modern Drug DevelopmentModern Drug DevelopmentSuccess Rate 1:100,000 !Success Rate 1:100,000 !

00 22 44 66 88 1010 1212 1414 1616

DiscoveryDiscovery

Preclinical testingPreclinical testing

Phase IPhase I

Phase IIPhase II

Phase IIIPhase III

ApprovalApproval

Post marketPost market

100,000100,000

100100

55

11

Time in years Cost $1B

Identify disease

Isolate protein

Find drug

Preclinical testing

GENOMICS, PROTEOMICS & BIOPHARM.

HIGH THROUGHPUT SCREENING

MOLECULAR MODELING

VIRTUAL SCREENING

COMBINATORIAL CHEMISTRY

IN VITRO & IN SILICO ADME MODELS

Potentially producing many more targetsand “personalized” targets

Screening up to 100,000 compounds aday for activity against a target protein

Using a computer topredict activity

Rapidly producing vast numbersof compounds

Computer graphics & models help improve activity

Tissue and computer models begin to replace animal testing

VIRTUAL SCREENING

MOLECULAR MODELING

The Evolution in Drug Design and Development

5

Integration of Integration of biological data biological data impacts drug impacts drug developmentdevelopmentinformation stored in the genetic code (DNA)information stored in the genetic code (DNA)

protein sequencesprotein sequences

3D structures of biomolecules3D structures of biomolecules

experimental results from various sources experimental results from various sources (kd, IC50, expression) (kd, IC50, expression)

clinical dataclinical data

patient statisticspatient statistics

scientific literaturescientific literature

6

……and leads to and leads to computational computational

explosionexplosionAn avalanche of An avalanche of data:data:

SequencesSequences

Functional Functional relationsrelations

StructuresStructures

This requires This requires computational computational approachesapproaches

• 100’s of completed genomes• 1000’s of known reactions• 10,000’s of known 3D structures• 100,000’s of protein-ligand interactions• 1,000,000’s of known proteins & enzymes• Decades of biological/chemical know-how• Computational & Mathematical resourcesThe Push to Systems Biology

77

Key areas of Key areas of bioinformaticsbioinformatics

organisation of knowledge (sequences, structures, functional data)

e.g. homology searches

Specifically for drug discovery:

PDB : 50,000 proteins + homologs

1500 targets (human proteins)Approx. 400 (80 in cancer) utilized

Orange Book: 1800 medicinal drugsDrug Bank: 4900 drugsCancer chemotherapy drugs: 103

Protein-drug interactions but alsoProtein-protein interactions

Molecular Targets:Cancer Cell Molecular Targets:Cancer Cell NetworkNetwork

A very complex but algorithmic systemBased on a lock-and-key principle

We will find keys to all these locks by 2061

CANCER CHEMOTHERAPY DRUGSApproximately 100 standard chemotherapeutic drugs:

1)Alkylating agents: Genotoxic (20-25)

2) Plant alkaloids: Inhibition of mitosis (10-15)

3) Antimetabolites: Inhibition of base synthesis (15-20)

4) Antibiotics: Derived from Streptomyces (10-15)

5) Targeted antibodies: Bind cell surface receptors (5-10)

6) Hormones: Inhibit or stimulate hormone signaling (15-20)

7) Directly targeting small molecules

8)Other indirect effects: Angiogenesis or immune modulators (10-15)

Number of current chemotherapy targets: 101

Number of chemotherapy drugs: 102

Potential Targets (Pharmacogenomics): 103

PaclitaxelCisplatin

Methotrexate

Trastuzumab

ImatinibTamoxifen

Doxorubicin

Bevacizumab

G2

M

G1

S

G0

tyrosine kinases

DNA synthesis

topoisomerase I

CDK2

tubulin polymerisatio

n/depolymerisat

ion

Vinca alkaloids*taxol/taxoterehalichondrin*spongistatin*rhizoxin*cryptophycinsarcodictyin eleutherobinepothilonesdiscodermolideD-24851 ?dolastatin*combretastatin*

camptothecin

CDK4

flavopiridol

(R)-roscovitine (CYC202)paullones, indirubins

gleeveciressaOSI774

hydroxyureacytarabineantifolates

5-fluorouracil6-mercaptopurine

nitrogen mustardsnitrosoureasmitomycin C

CDK1

Chk1Chk2

UCN-01, SB-218078debromohymenialdisineisogranulatimide

AhR

actin

kinesin Eg5

monastrol

ecteinascidin 743

podophyllotoxin,doxorubicinetoposide, mitoxantrone

topoisomerase II

ATM/ATR

R115777SCH66336

ROCK

Y-27632

CDC25

DF203

FK317 HMGA

Plk1

Aurora

wortmannincaffeine

ODC/SAMDC

Pin1

GSK-3

Cdc7

nucleotide excision repair

Raf

cytochalasinslatrunculin Ascytophycinsdolastatin 11jasplakinolide

paullones, indirubins

(R)-roscovitine (CYC202)paullones, indirubins

BAY-43-9006

fumagillin,TNP-470

PRIMA-1, pifithrin a

rapamycin

mTOR/FRAP

PS-341

proteasome

bryostatin, PKC412

PKC

histone deacetylase

trichostatin, FK228

HSP90

geldanamycin, 17-AAGATK, MAFP cytosolic phospholipase A2

hexadecylphosphocholine

phospholipase D

CT-2584

choline kinase

MEK1/Erk-1/2

PD98059, U0126

menadione (K3)

farnesyl transferase

phosphatases

okadaic acid, fostreicin, calyculin A

Wee1

PD0166285

polyamine analoguesPin1

p53/MDM2

Source: Cell cycle laboratory, L. Meijer, Roscoff, France

~80 drugs and drug candidates

Cancer chemotherapy is based on cell cycle arrest

CAUSES OF FAILURE IN DRUG DEVELOPMENT

ADME

ANIMAL TOXICITY

LACK OF EFFICACY

ADVERSE EFFECTS IN HUMANS

More than 50% of this failure can be predicted computationally in 2011In 2061: six sigma will be achieved in silico

WET LAB: High-throughput screening WET LAB: High-throughput screening (HTS)(HTS)

Experimental techniqueExperimental technique384-well microplates, florescence-based 384-well microplates, florescence-based detection & desktop robotsdetection & desktop robotsUp to 1M compounds per targetUp to 1M compounds per target

DRY LAB: Virtual screening (VS)DRY LAB: Virtual screening (VS)Ligand-based methodsLigand-based methods

2D structures, substructures, fingerprints2D structures, substructures, fingerprintsVolume/surface matchingVolume/surface matching3D pharmacophores, fingerprints3D pharmacophores, fingerprints

Receptor-based methodsReceptor-based methodsDockingDockingEven 100B compounds per target triedEven 100B compounds per target tried

Receptor flexibility

OUR 1024-PROCESSOR HPC CLUSTER

WE ALSO USE 500 PROCESSORS FROMWEST-GRID AND SHARCNET

Target-Protein StructureMRECISIHVGQAGVQIGNACWELYCLEHGIQPDGQMPSDKTIGGGDDSFNTFFSETGAGKHVPRAVFVDLEPTVIDEVRTGTYRQLFHPEQLITGKEDAANNYARGHYTIGKEIIDLVLDRIRKLADQCTGLQGFSVFHSFGGGTGSGFTSLLMERLSVDYGKKSKLEFSIYPAPQVSTAVVEPYNSILTTHTTLEHSDCAFMVDNEAIYDICRRNLDIERPTYTNLNRLIGQIVSSITASLRFDGALNVDLTEFQTNLVPYPRGHFPLATYAPVISAEKAYHEQLSVAEITNACFEPANQMVKCDPRHGKYMACCLLYRGDVVPKDVNAAIATIKTKRTIQFVDWCPTGFKVGINYEPPTVVPGGDLAKVQRAVCMLSNTTAIAEAWARLDHKFDLMYAKRAFVHWYVGEGMEEGEFSEAREDMAALEKDYEEVGVDSVEGEGEEEGEEY

Primary: amino acid sequence

Secondary: α-helix and β-sheet

Tertiary: 3D-folding

Quaternary: multimeric

arrangement

Molecular Dynamics

• Treats molecules classically:– Point charges and

masses – Spring-like bonds– Numerical integration of

equations of motion

Drug binding sites in tubulin

Of the more than Of the more than 100100 approved approved cancer chemotherapy drugs on cancer chemotherapy drugs on the market, approximately 15% the market, approximately 15% target tubulin directly.target tubulin directly.

None are specific for cancer None are specific for cancer cells, hence associated side cells, hence associated side effectseffects

Drug / Ligand

Protein

Drug ActionDrug Action: Inhibition of Protein-: Inhibition of Protein-Protein InteractionsProtein Interactions

Cavity

Cavity

Cavity

The computational toolboxThe computational toolbox

The three-fold way:The three-fold way:

rational design and rational design and in silicoin silico testing of derivatives of known testing of derivatives of known agents agents

brute-force computational search using existing libraries brute-force computational search using existing libraries (pharma-matrix)(pharma-matrix)

De novo design from common pharmacophores for best De novo design from common pharmacophores for best space filling propertiesspace filling properties

a pocketome data banka pocketome data bank

Reverse docking allows to predict side effectsReverse docking allows to predict side effects

How Do We Solve Our Puzzles?

ContentsContentsCompound data Compound data sources sources (PubChem, Zinc, NCI, (PubChem, Zinc, NCI, SciFinder ~65M compounds)SciFinder ~65M compounds)

Drug data Drug data sources sources (DrugBank, Orange Book, (DrugBank, Orange Book, CMC, WDI, MDDR ~ 250 k drugs)CMC, WDI, MDDR ~ 250 k drugs)

Molecular data Molecular data toolkitstoolkits (OpenEye, Open Babel)(OpenEye, Open Babel)

Computational Methods Computational Methods (MM, MD, QMMM)(MM, MD, QMMM)

Molecule file formats Molecule file formats (PDB, Smilies )(PDB, Smilies )

DockingDocking (Autodock, Dock) (Autodock, Dock) ParallelParallel (Dovis) (Dovis)

Pharma-matrix apps: Pharma-matrix apps: eRxeRx

100 million targets (100,000 proteins x 100 pockets 100 million targets (100,000 proteins x 100 pockets

x 10 mutants): x 10 mutants): pocketomepocketome 100 billion chemical compounds 100 billion chemical compounds 101019 19 potential interactions (filtering)potential interactions (filtering) Hand-in-glove match by brute computational Hand-in-glove match by brute computational

screeningscreening

pharmagooglepharmagoogle

Pocketome generation(pocket clustering)

104 clusters 104 pockets in a cluster

Docking(1012 calculations within blocks)

Docking(1012 calculations within blocks)

Personalized eDx and eRx

in a few decades a personal genome will cost $10 and

will be our ID at birth included in our eRx app

The Virtual Human:The Virtual Human:Multi-Scale ModelingMulti-Scale Modeling

lobule

liver

whole body

hepatocyte

Drug molecules Interaction matrix