Drug Discovery: Proteomics, Genomics

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Drug Discovery: Proteomics, Genomics. Philip E. Bourne Professor of Pharmacology UCSD pbourne@ucsd.edu 858-534-8301. Agenda. Where my perspective comes from The interplay between omics, IT and drug discovery The omics revolution Changes in IT and open science and software licensing - PowerPoint PPT Presentation

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  • Drug Discovery: Proteomics, GenomicsPhilip E. BourneProfessor of Pharmacology UCSDpbourne@ucsd.edu 858-534-8301*SPPS273

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  • AgendaWhere my perspective comes fromThe interplay between omics, IT and drug discoveryThe omics revolutionChanges in IT and open science and software licensingApplying the new biology to drug discoveryExample 1 Drug repositioningExample 2 - Determining side-effects Words of cautionSPPS273*

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  • Some BackgroundWe work in the area of structural bioinformaticsWe distribute the equivalent to the Library of Congress to approx. 250,000 scientists each monthWe are interested in improving the drug discovery process through computationally driven hypotheses on the complete biological system

    Personally:Open science advocateStarted 4 companies Spent whole life in the ivory towerSPPS273*The Source of My Perspective

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  • Observations Glass Empty: drug discovery in the traditional sense is in a woeful stateGlass Full:We have an explosion of data and hence a new emerging understanding of complex biological systemsInformation technology is advancing rapidlyLet optimism rule let traditional computational chemistry and cheminfomatics meet bioinformatics, systems biology and information science to discover drugs in new waysSPPS273*The Take Home Message

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  • Biological Experiment Data Information Knowledge DiscoveryCollect Characterize Compare Model Infer SequenceStructureAssemblySub-cellularCellularOrganHigher-lifeYear9005Computing PowerSequencingData110 10010001059500Human Genome ProjectE.ColiGenomeC.ElegansGenome1 Small Genome/Mo.ESTsYeastGenomeGene ChipsVirus StructureRibosomeModel Metaboloic Pathway of E.coliComplexityTechnologyBrain MappingGenetic CircuitsNeuronal ModelingCardiac ModelingHuman Genome# People/Web Site1061021VirtualCommunitiesThe Drivers of Change Data & IT106BlogsFacebook1000sGWASThe Omics Revolution

  • Number of released entriesYearIts Not Just About Numbers its About ComplexityThe Omics RevolutionCourtesy of the RCSB Protein Data Bank

  • *Metagenomics - 2007New type of genomicsNew data (and lots of it) and new types of data17M new (predicted proteins!) 4-5 x growth in just few months and much more comingNew challenges and exacerbation of old challengesThe Omics Revolution

  • *Metagenomics: Early ResultsMore then 99.5% of DNA in very environment studied represent unknown organismsCulturable organisms are exceptions, not the ruleMost genes represent distant homologs of known genes, but there are thousands of new families

    Everything we touch turns out to be a gold mineEnvironments studied:Water (ocean, lakes)SoilHuman body (gut, oral cavity, human microbiome)The Omics Revolution

  • *Metagenomics New DiscoveriesEnvironmental (red) vs. Currently Known PTPases (blue)Higher eukaryotes1234The Omics Revolution

  • *The Good News and the Bad NewsGood newsData pointing towards function are growing at near exponential ratesIT can handle it on a per dollar basisBad newsData are growing at near exponential ratesQuality is highly variableAccurate functional annotation is sparse The Omics Revolution

  • *Example of the Interplay Between Bioinformatics & Proteomics - The Structural Genomics Pipeline

    The Omics RevolutionStructural biology moves from being functionally driven to genomically drivenFill inprotein fold spaceRobotics-ve dataSoftware engineeringFunctional predictionNotnecessarily

  • Towards Open ScienceOpen access publishingOpen source softwareGeneration of scientists weaned on social networksBlogs, wikis, social bookmarking etc. are becoming a valid form of scientific discourse SPPS273*http://www.osdd.net/

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  • University Tech Transfer Offices are Slow to Embrace this ChangeOvervalue disclosuresInability to market disclosures appropriatelyProtracted negotiations in a fast moving marketDisable rather than enable startups

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  • So Why is All of This So Important to Drug Discovery?We are beginning to piece together a complex living system and we need to understand that to do betterSPPS273*

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  • A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690Why Dont we Do Better?A Couple of ObservationsGene knockouts only effect phenotype in 10-20% of cases , why? redundant functions alternative network routes robustness of interaction networks

    35% of biologically active compounds bind to more than one target

    Paolini et al. Nat. Biotechnol. 2006 24:805815

  • Why Dont we Do Better?A Couple of Observations Tykerb Breast cancer

    Gleevac Leukemia, GI cancers

    Nexavar Kidney and liver cancer

    Staurosporine natural product alkaloid uses many e.g., antifungal antihypertensive

    Collins and Workman 2006 Nature Chemical Biology 2 689-700

  • ImplicationsEhrlichs philosophy of magic bullets targeting individual chemoreceptors has not been realized

    Stated another way The notion of one drug, one target, one disease is a little nave in a complex system

  • So How Can We Exploit All The New Data We are Collecting on This Complex System?Lets Work Through a Couple of ExamplesSPPS273*

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  • What ifWe can characterize a protein-ligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale?

    We could perhaps find alternative binding sites (off-targets) for existing pharmaceuticals and NCEs?

    Exploiting the Structural Proteome

  • What Do These Off-targets Tell Us?Potentially many things:NothingHow to optimize a NCEA possible explanation for a side-effect of a drug already on the marketA possible repositioning of a drug to treat a completely different conditionThe reason a drug failed A multi-target strategy to attack a pathogen

    Exploiting the Structural Proteome

  • Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many ExamplesExploiting the Structural Proteome

    Generic NameOther NameTreatmentPDBidLipitorAtorvastatinHigh cholesterol1HWK, 1HW8TestosteroneTestosteroneOsteoporosis1AFS, 1I9J ..TaxolPaclitaxelCancer1JFF, 2HXF, 2HXHViagraSildenafil citrateED, pulmonary arterial hypertension1TBF, 1UDT, 1XOS..DigoxinLanoxinCongestive heart failure1IGJ

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  • A Reverse Engineering Approach to Drug Discovery Across Gene FamiliesCharacterize ligand binding site of primary target (Geometric Potential)Identify off-targets by ligand binding site similarity(Sequence order independent profile-profile alignment)

    Extract known drugs or inhibitors of the primary and/or off-targetsSearch for similar small moleculesDock molecules to both primary and off-targetsStatistics analysis of docking score correlationsExploiting the Structural Proteome

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  • The Problem with TuberculosisOne third of global population infected1.7 million deaths per year95% of deaths in developing countriesAnti-TB drugs hardly changed in 40 yearsMDR-TB and XDR-TB pose a threat to human health worldwideDevelopment of novel, effective, and inexpensive drugs is an urgent priorityExample 1 Repositioning The TB Story

  • Found..Evolutionary linkage between: NAD-binding Rossmann foldS-adenosylmethionine (SAM)-binding domain of SAM-dependent methyltransferasesCatechol-O-methyl transferase (COMT) is SAM-dependent methyltransferaseEntacapone and tolcapone are used as COMT inhibitors in Parkinsons disease treatmentHypothesis:Further investigation of NAD-binding proteins may uncover a potential new drug target for entacapone and tolcaponeKinnings et al. 2009 PLoS Comp Biol 5(7) e1000423Example 1 Repositioning The TB Story

  • Functional Site Similarity between COMT and InhAEntacapone and tolcapone docked onto 215 NAD-binding proteins from different speciesM.tuberculosis Enoyl-acyl carrier protein reductase ENR (InhA) discovered as potential new drug targetInhA is the primary target of many existing anti-TB drugs but all are very toxicInhA catalyses the final, rate-determining step in the fatty acid elongation cycleAlignment of the COMT and InhA binding sites revealed similarities ...

    Repositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

  • Binding Site Similarity between COMT and InhAKinnings et al. 2009 PLoS Comp Biol 5(7) e1000423Example 1 Repositioning The TB Story

  • Summary of the TB StoryEntacapone and tolcapone shown to have potential for repositioningDirect mechanism of action avoids M. tuberculosis resistance mechanismsPossess excellent safety profiles with few side effects already on the marketIn vivo supportAssay of direct binding of entacapone and tolcapone to InhA reveals a possible lead with no chemical relationship to existing drugsKinnings et al. 2009 PLoS Comp Biol 5(7) e1000423Example 1 Repositioning The TB Story

  • Summary from the TB Alliance Medicinal ChemistryThe minimal inhibitory concentration (MIC) of 260 uM is higher than usually consideredMIC is 65x the estimated plasma concentrationHave other InhA inhibitors in the pipelineKinnings et al. 2009 PLoS Comp Biol 5(7) e1000423Example 1 Repositioning The TB Story

  • Predicted protein-ligand interaction network of M.tuberculosis. Proteins that are predicted to have similar binding sites are connected. Squares represent the top 18 most connected proteins.

    The TB DruggomeBioinformatics 2009 25(12) 305-312

  • The TB DruggomeBioinformatics 2009 25(12) 305-312

  • SMAP p-value < 1e-5drugs

    TB proteinsp < 1e-7p < 1e-6p < 1e-5

    The TB Druggome

  • New Ways of Thinking

    Polypharmacology One or multiple drugs binding to multiple targets for a collective effect aka Dirty Drugs

    Network Pharmacology Measuring that effect on the whole biological networkSPPS273*

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  • Example 2 - The Torcetrapib StoryPLoS Comp Biol 2009 5(5) e1000387

  • Cholesteryl Ester Transfer Protein (CETP) collects triglycerides from very low density or low density lipoproteins (VLDL or LDL) and exchanges them for cholesteryl esters from high density lipoproteins (and vice versa)A long tunnel with two major binding sites. Docking studies suggest that it possible that torcetrapib binds to both of them.The torcetrapib binding site is unknown. Docking studies show that both sites can bind to torcetrapib with the docking score around -8.0.HDLLDLCETPCETP inhibitorXBad CholesterolGood CholesterolPLoS Comp Biol 2009 5(5) e1000387Example 2 - The Torcetrapib Story

  • Docking Scores eHits/AutodockPLoS Comp Biol 2009 5(5) e1000387Example 2 - The Torcetrapib Story

    Off-targetPDB IdsTorcetrapibAnacetrapibJTT705Complex ligandCETP2OBD-11.675 / -5.72-11.375 / -8.15-7.563 / -6.65-8.324 (PCW)Retinoid X receptor1YOW1ZDT-11.420 / -6.600 -6.74-8.696 / -7.68 -7.35-6.276 / -7.28 -6.95-9.113 (POE)PPAR delta1Y0S-10.203 / -8.22-10.595 / -7.91-7.581 / -8.36-10.691(331)PPAR alpha2P54-11.036 / -6.67-0.835 / -7.27 -9.599 / -7.78-11.404(735)PPAR gamma1ZEO-9.515 / -7.31 > 0.0 / -8.25-7.204 / -8.11-8.075 (C01)Vitamin D receptor1IE8>0.0/ -4.73>0.0 / -6.25-6.628 / -9.70-8.354 (KH1) -7.35Glucocorticoid Receptor1NHZ1P93 /-4.43 /-5.63 /-7.08 /-0.58 /-7.09 /-9.42Fatty acid binding protein2F732PY12NNQ>0.0/ -4.33>0.0/-6.13 /-6.40>0.0/ -7.81>0.0/ -6.98 /-7.64-7.191 / -8.49 /-6.33 /6.35???T-Cell CD1B1GZP-8.815 / -7.02-13.515 / -7.15-7.590 / -8.02 -6.519 (GM2)IL-10 receptor1LQS / -4.59 / -6.77 / -5.95???GM-2 activator2AG9-9.345 / -6.26-9.674 / -6.98-8.617 / -6.17??? (MYR) -4.16(3CA2+) CARDIAC TROPONIN C1DTL /-5.83 /-6.71 /-5.79cytochrome bc1 complex1PP9 (PEG) /-6.97 /-9.07 /-6.641PP9 (HEM) /-7.21 /8.79 /-8.94human cytoglobin1V5H /-4.89 /-7.00 /-4.94

  • RASPPARRXRVDR+High blood pressureFABPFA+Anti-inflammatory function?Torcetrapib Anacetrapib JTT705JNK/IKK pathwayJNK/NF-KB pathway?Immune response to infection JTT705PPARPPAR?PLoS Comp Biol 2009 5(5) e1000387Example 2 - The Torcetrapib Story

  • Chang et al. 2009 Mol Sys Biol SubmittedThe Future?

  • Modifications to Early Stage Drug DiscoverySPPS273*http://www.celgene.com/images/celgene_drug_arrow.gif

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  • Some Known LimitationsStructural coverage of the given proteomeFalse hits / poor docking scoresLiterature searchingIts a hypothesis need experimental validationMoney Known Limitations

  • Perceived LimitationsMistrust of computational approaches

    Bioinformatics was previously oversold

    Omics was previously oversold

    Still too cutting edge

    No interest in drug resistance

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  • Questions?pbourne@ucsd.edu

    *****2D hyperbolic view of the phylogenetic tree, colored based on the origin of sequences (red, ocean data set from CVI; blue, NCBI NR)Alignment performed by MUSCLE from sequences identified in a joined ocean80_nr80 database by PDB-BLAST search.Visualization by HyperTree program from Sugen***Absorption, distribution, metabolism and excretion*Tuberculosis, which is caused by the bacterial pathogen Mycobacterium tuberculosis, is a leading cause of mortality among the infectious diseases. It has been estimated by the World Health Organization (WHO) that almost one-third of the world's population, around 2 billion people, is infected with the disease. Every year, more than 8 million people develop an active form of the disease, which claims the lives of nearly 2 million. This translates to over 4,900 deaths per day, and more than 95% of these are in developing countries. Despite the current global situation, antitubercular drugs have remained largely unchanged over the last four decades. The widespread use of these agents has provided a strong selective pressure for M.tuberculosis, thus encouraging the emergence of resistant strains. Multidrug resistant (MDR) tuberculosis is defined as resistance to the first-line drugs isoniazid and rifampin. The effective treatment of MDR tuberculosis necessitates long-term use of second-line drug combinations, an unfortunate consequence of which is the emergence of further drug resistance. Enter extensively drug resistant (XDR) tuberculosis - M.tuberculosis strains that are resistant to both isoniazid plus rifampin, as well as key second-line drugs. Since the only remaining drug classes exhibit such low potency and high toxicity, XDR tuberculosis is extremely difficult to treat. The rise of XDR tuberculosis around the world imposes a great threat on human health, therefore reinforcing the development of new antitubercular agents as an urgent priority.

    Very few Mtb proteins explored as drug targets***Superimposition of the binding sites of COMT and ENRCOMT is show in green, its SAM co-factor is shown in yellow, and its BIE substrate is shown in purple. ENR is shown in blue, its NAD co-factor is shown in orange, and its 641 substrate is shown in red. Protein sequences were aligned according to the NAD and SAM co-factors.

    Similarities in electrostatic potential were also observed in the substrate binding pockets of COMT and ENR. *Purple circular nodes are TB proteins and green rectangular nodes are drugsBinding site similarity is indicated by connecting lines (edges) between the TB...