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Jack TuszynskiCross Cancer InstituteDepartment of PhysicsUniversity of AlbertaEdmonton, Canadahttp://www.phys.ualberta.ca/...
“Modern” Pharmacy: Rx
Modern Drug DevelopmentModern Drug DevelopmentSuccess Rate 1:100,000 !Success Rate 1:100,000 !00 22 44 66 88 1010 1212 141...
Identify diseaseIsolate proteinFind drugPreclinical testingGENOMICS, PROTEOMICS & BIOPHARM.HIGH THROUGHPUT SCREENINGMOLECU...
5Integration of biological dataIntegration of biological dataimpacts drug developmentimpacts drug developmentinformation s...
6……and leads toand leads tocomputational explosioncomputational explosionAn avalanche of data:An avalanche of data:Sequenc...
77Key areas ofKey areas ofbioinformaticsbioinformaticsorganisation of knowledge(sequences, structures,functional data)e.g....
Specifically for drug discovery:PDB : 50,000 proteins + homologs1500 targets (human proteins)Approx. 400 (80 in cancer) ut...
Molecular Targets:Cancer Cell NetworkMolecular Targets:Cancer Cell NetworkA very complex but algorithmic systemBased on a ...
CANCER CHEMOTHERAPY DRUGSApproximately 100 standard chemotherapeutic drugs:1)Alkylating agents: Genotoxic (20-25)2) Plant ...
G2MG1SG0tyrosine kinasesDNA synthesistopoisomerase ICDK2tubulinpolymerisation/depolymerisationVinca alkaloids*taxol/taxote...
CAUSES OF FAILURE IN DRUGDEVELOPMENTADMEANIMAL TOXICITYLACK OF EFFICACYADVERSE EFFECTSIN HUMANSMore than 50% of this failu...
WET LAB: High-throughput screening (HTS)WET LAB: High-throughput screening (HTS)Experimental techniqueExperimental techniq...
OUR 1024-PROCESSOR HPC CLUSTERWE ALSO USE 500 PROCESSORS FROMWEST-GRID AND SHARCNET
Target-Protein StructureMRECISIHVGQAGVQIGNACWELYCLEHGIQPDGQMPSDKTIGGGDDSFNTFFSETGAGKHVPRAVFVDLEPTVIDEVRTGTYRQLFHPEQLITGKED...
Molecular Dynamics• Treats moleculesclassically:– Point charges andmasses– Spring-like bonds– Numerical integration ofequa...
Drug binding sites in tubulin Of the more thanOf the more than 100100 approvedapprovedcancer chemotherapy drugs oncancer ...
Drug / LigandProteinDrug ActionDrug Action: Inhibition of Protein-: Inhibition of Protein-Protein InteractionsProtein Inte...
The computational toolboxThe computational toolboxThe three-fold way:The three-fold way:rational design andrational design...
How Do We Solve Our Puzzles?
ContentsContentsCompound dataCompound data sourcessources (PubChem, Zinc, NCI, SciFinder(PubChem, Zinc, NCI, SciFinder~65M...
Pharma-matrix apps:Pharma-matrix apps: eRxeRx100 million targets (100,000 proteins x 100 pockets x 10 mutants):100 million...
Pocketome generation(pocket clustering)104clusters 104pocketsin a clusterDocking(1012calculations within blocks)Docking(10...
Personalized eDx and eRxin a few decades a personal genome will cost $10 andwill be our ID at birth included in our eRx app
The Virtual Human:The Virtual Human:Multi-Scale ModelingMulti-Scale Modelinglobuleliverwhole bodyhepatocyteDrug molecules ...
Jack Tuszynski Accelerating Chemotherapy Drug Discovery with Analytics and High Performance Computing
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Jack Tuszynski Accelerating Chemotherapy Drug Discovery with Analytics and High Performance Computing

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Jack Tuszynski's Best of Analytics presentation May 14, 2013 "Accelerating Chemotherapy Drug Discovery with Analytics and High Performance Computing."

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Jack Tuszynski Accelerating Chemotherapy Drug Discovery with Analytics and High Performance Computing

  1. 1. Jack TuszynskiCross Cancer InstituteDepartment of PhysicsUniversity of AlbertaEdmonton, Canadahttp://www.phys.ualberta.ca/~jtus“Accelerating ChemotherapyDrug Discovery with HighPerformance Computing andAnalytics”
  2. 2. “Modern” Pharmacy: Rx
  3. 3. Modern Drug DevelopmentModern Drug DevelopmentSuccess Rate 1:100,000 !Success Rate 1:100,000 !00 22 44 66 88 1010 1212 1414 1616DiscoveryDiscoveryPreclinical testingPreclinical testingPhase IPhase IPhase IIPhase IIPhase IIIPhase IIIApprovalApprovalPost marketPost market100,000100,0001001005511Time in years Cost $1B
  4. 4. Identify diseaseIsolate proteinFind drugPreclinical testingGENOMICS, PROTEOMICS & BIOPHARM.HIGH THROUGHPUT SCREENINGMOLECULAR MODELINGVIRTUAL SCREENINGCOMBINATORIAL CHEMISTRYIN VITRO & IN SILICO ADME MODELSPotentially producing many more targetsand “personalized” targetsScreening up to 100,000 compounds aday for activity against a target proteinUsing a computer topredict activityRapidly producing vast numbersof compoundsComputer graphics & models help improve activityTissue and computer models begin to replace animal testingVIRTUAL SCREENINGMOLECULAR MODELINGThe Evolution in Drug Design and Development
  5. 5. 5Integration of biological dataIntegration of biological dataimpacts drug developmentimpacts drug developmentinformation stored in the genetic code (DNA)information stored in the genetic code (DNA)protein sequencesprotein sequences3D structures of biomolecules3D structures of biomoleculesexperimental results from various sources (kd, IC50,experimental results from various sources (kd, IC50,expression)expression)clinical dataclinical datapatient statisticspatient statisticsscientific literaturescientific literature
  6. 6. 6……and leads toand leads tocomputational explosioncomputational explosionAn avalanche of data:An avalanche of data:SequencesSequencesFunctional relationsFunctional relationsStructuresStructuresThis requiresThis requirescomputationalcomputationalapproachesapproaches• 100’s of completed genomes• 1000’s of known reactions• 10,000’s of known 3D structures• 100,000’s of protein-ligandinteractions• 1,000,000’s of known proteins &enzymes• Decades of biological/chemicalknow-how• Computational & MathematicalresourcesThe Push to Systems Biology
  7. 7. 77Key areas ofKey areas ofbioinformaticsbioinformaticsorganisation of knowledge(sequences, structures,functional data)e.g. homologysearches
  8. 8. Specifically for drug discovery:PDB : 50,000 proteins + homologs1500 targets (human proteins)Approx. 400 (80 in cancer) utilizedOrange Book: 1800 medicinal drugsDrug Bank: 4900 drugsCancer chemotherapy drugs: 103Protein-drug interactions but alsoProtein-protein interactions
  9. 9. Molecular Targets:Cancer Cell NetworkMolecular Targets:Cancer Cell NetworkA very complex but algorithmic systemBased on a lock-and-key principleWe will find keys to all these locks by 2061
  10. 10. 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 molecules8)Other indirect effects: Angiogenesis or immune modulators (10-15)Number of current chemotherapy targets: 101Number of chemotherapy drugs: 102Potential Targets (Pharmacogenomics): 103PaclitaxelCisplatinMethotrexateTrastuzumabImatinibTamoxifenDoxorubicinBevacizumab
  11. 11. G2MG1SG0tyrosine kinasesDNA synthesistopoisomerase ICDK2tubulinpolymerisation/depolymerisationVinca alkaloids*taxol/taxoterehalichondrin*spongistatin*rhizoxin*cryptophycinsarcodictyineleutherobinepothilonesdiscodermolideD-24851 ?dolastatin*combretastatin*camptothecinCDK4flavopiridol(R)-roscovitine (CYC202)paullones, indirubinsgleeveciressaOSI774hydroxyureacytarabineantifolates5-fluorouracil6-mercaptopurinenitrogen mustardsnitrosoureasmitomycin CCDK1Chk1Chk2UCN-01, SB-218078debromohymenialdisineisogranulatimideAhRactinkinesin Eg5monastrolecteinascidin 743podophyllotoxin,doxorubicinetoposide, mitoxantronetopoisomerase IIATM/ATRR115777SCH66336ROCKY-27632CDC25DF203FK317 HMGAPlk1AurorawortmannincaffeineODC/SAMDCPin1GSK-3Cdc7nucleotide excisionrepairRaf cytochalasinslatrunculin Ascytophycinsdolastatin 11jasplakinolidepaullones, indirubins(R)-roscovitine (CYC202)paullones, indirubinsBAY-43-9006fumagillin,TNP-470PRIMA-1, pifithrin arapamycin mTOR/FRAPPS-341 proteasomebryostatin,PKC412PKChistone deacetylasetrichostatin,FK228HSP90geldanamycin, 17-AAGATK, MAFP cytosolic phospholipase A2hexadecylphosphocholinephospholipase DCT-2584 cholinekinaseMEK1/Erk-1/2PD98059, U0126menadione(K3)farnesyl transferasephosphatasesokadaic acid, fostreicin, calyculin AWee1PD0166285polyamine analoguesPin1p53/MDM2Source: Cell cycle laboratory, L. Meijer, Roscoff, France~80 drugs and drug candidatesCancer chemotherapy is based on cell cycle arrest
  12. 12. CAUSES OF FAILURE IN DRUGDEVELOPMENTADMEANIMAL TOXICITYLACK OF EFFICACYADVERSE EFFECTSIN HUMANSMore than 50% of this failure can be predicted computationally in 2011In 2061: six sigma will be achieved in silico
  13. 13. WET LAB: High-throughput screening (HTS)WET LAB: High-throughput screening (HTS)Experimental techniqueExperimental technique384-well microplates, florescence-based detection &384-well microplates, florescence-based detection &desktop robotsdesktop robotsUp to 1M compounds per targetUp to 1M compounds per targetDRY LAB: Virtual screening (VS)DRY LAB: Virtual screening (VS)Ligand-based methodsLigand-based methods2D structures, substructures, fingerprints2D structures, substructures, fingerprintsVolume/surface matchingVolume/surface matching3D pharmacophores, fingerprints3D pharmacophores, fingerprintsReceptor-based methodsReceptor-based methodsDockingDockingEven 100B compounds per target triedEven 100B compounds per target triedReceptor flexibility
  14. 14. OUR 1024-PROCESSOR HPC CLUSTERWE ALSO USE 500 PROCESSORS FROMWEST-GRID AND SHARCNET
  15. 15. Target-Protein StructureMRECISIHVGQAGVQIGNACWELYCLEHGIQPDGQMPSDKTIGGGDDSFNTFFSETGAGKHVPRAVFVDLEPTVIDEVRTGTYRQLFHPEQLITGKEDAANNYARGHYTIGKEIIDLVLDRIRKLADQCTGLQGFSVFHSFGGGTGSGFTSLLMERLSVDYGKKSKLEFSIYPAPQVSTAVVEPYNSILTTHTTLEHSDCAFMVDNEAIYDICRRNLDIERPTYTNLNRLIGQIVSSITASLRFDGALNVDLTEFQTNLVPYPRGHFPLATYAPVISAEKAYHEQLSVAEITNACFEPANQMVKCDPRHGKYMACCLLYRGDVVPKDVNAAIATIKTKRTIQFVDWCPTGFKVGINYEPPTVVPGGDLAKVQRAVCMLSNTTAIAEAWARLDHKFDLMYAKRAFVHWYVGEGMEEGEFSEAREDMAALEKDYEEVGVDSVEGEGEEEGEEYPrimary: amino acidsequenceSecondary: -helix and -α βsheetTertiary: 3D-foldingQuaternary:multimericarrangement
  16. 16. Molecular Dynamics• Treats moleculesclassically:– Point charges andmasses– Spring-like bonds– Numerical integration ofequations of motion
  17. 17. Drug binding sites in tubulin Of the more thanOf the more than 100100 approvedapprovedcancer chemotherapy drugs oncancer chemotherapy drugs onthe market, approximately 15%the market, approximately 15%target tubulin directly.target tubulin directly. None are specific for cancerNone are specific for cancercells, hence associated sidecells, hence associated sideeffectseffects
  18. 18. Drug / LigandProteinDrug ActionDrug Action: Inhibition of Protein-: Inhibition of Protein-Protein InteractionsProtein InteractionsCavityCavityCavity
  19. 19. The computational toolboxThe computational toolboxThe three-fold way:The three-fold way:rational design andrational design and in silicoin silico testing of derivatives of knowntesting of derivatives of knownagentsagentsbrute-force computational search using existing librariesbrute-force computational search using existing libraries(pharma-matrix)(pharma-matrix)De novo design from common pharmacophores for bestDe novo design from common pharmacophores for bestspace filling propertiesspace filling propertiesa pocketome data banka pocketome data bankReverse docking allows to predict side effectsReverse docking allows to predict side effects
  20. 20. How Do We Solve Our Puzzles?
  21. 21. ContentsContentsCompound dataCompound data sourcessources (PubChem, Zinc, NCI, SciFinder(PubChem, Zinc, NCI, SciFinder~65M compounds)~65M compounds)Drug dataDrug data sourcessources (DrugBank, Orange Book, CMC, WDI,(DrugBank, Orange Book, CMC, WDI,MDDR ~ 250 k drugs)MDDR ~ 250 k drugs)Molecular dataMolecular data toolkitstoolkits (OpenEye, Open Babel)(OpenEye, Open Babel)Computational MethodsComputational Methods (MM, MD, QMMM)(MM, MD, QMMM)Molecule file formatsMolecule file formats (PDB, Smilies )(PDB, Smilies )DockingDocking (Autodock, Dock)(Autodock, Dock) ParallelParallel (Dovis)(Dovis)
  22. 22. Pharma-matrix apps:Pharma-matrix apps: eRxeRx100 million targets (100,000 proteins x 100 pockets x 10 mutants):100 million targets (100,000 proteins x 100 pockets x 10 mutants):pocketomepocketome100 billion chemical compounds100 billion chemical compounds10101919potential interactions (filtering)potential interactions (filtering)Hand-in-glove match by brute computational screeningHand-in-glove match by brute computational screeningpharmagooglepharmagoogle
  23. 23. Pocketome generation(pocket clustering)104clusters 104pocketsin a clusterDocking(1012calculations within blocks)Docking(1012calculations within blocks)
  24. 24. Personalized eDx and eRxin a few decades a personal genome will cost $10 andwill be our ID at birth included in our eRx app
  25. 25. The Virtual Human:The Virtual Human:Multi-Scale ModelingMulti-Scale Modelinglobuleliverwhole bodyhepatocyteDrug molecules Interaction matrix

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