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Drug Discovery: Proteomics, Genomics

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

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Drug Discovery: Proteomics, Genomics

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  1. Drug Discovery: Proteomics, Genomics Philip E. Bourne Professor of Pharmacology UCSD pbourne@ucsd.edu 858-534-8301 SPPS273

  2. 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 • Applying the new biology to drug discovery • Example 1 – Drug repositioning • Example 2 - Determining side-effects • Words of caution SPPS273

  3. Some Background • We work in the area of structural bioinformatics • We distribute the equivalent to ¼ the Library of Congress to approx. 250,000 scientists each month • We are interested in improving the drug discovery process through computationally driven hypotheses on the complete biological system • Personally: • Open science advocate • Started 4 companies • Spent whole life in the ivory tower SPPS273 The Source of My Perspective

  4. Observations • Glass ½ Empty: drug discovery in the traditional sense is in a woeful state • Glass ½ Full: • We have an explosion of data and hence a new emerging understanding of complex biological systems • Information technology is advancing rapidly • Let optimism rule – let traditional computational chemistry and cheminfomatics meet bioinformatics, systems biology and information science to discover drugs in new ways SPPS273 The Take Home Message

  5. The Drivers of Change – Data & IT Biological Experiment Data Information KnowledgeDiscovery Collect Characterize Compare Model Infer Complexity Technology Data 106 Higher-life 1 10 100 1000 105 Computing Power Organ Brain Mapping Cardiac Modeling Virtual Communities Blogs Facebook Cellular Model Metaboloic Pathway of E.coli Sub-cellular # People /Web Site 102 106 1 Neuronal Modeling Ribosome Assembly Virus Structure Genetic Circuits 1000’s GWAS Structure Human Genome Project Yeast Genome E.Coli Genome C.Elegans Genome 1 Small Genome/Mo. Sequencing ESTs Gene Chips Human Genome Sequence 90 95 00 05 Year The Omics Revolution

  6. Its Not Just About Numbers its About Complexity Number of released entries Year Courtesy of the RCSB Protein Data Bank The Omics Revolution

  7. New type of genomics New data (and lots of it) and new types of data 17M new (predicted proteins!) 4-5 x growth in just few months and much more coming New challenges and exacerbation of old challenges Metagenomics - 2007 The Omics Revolution

  8. More then 99.5% of DNA in very environment studied represent unknown organisms Culturable organisms are exceptions, not the rule Most genes represent distant homologs of known genes, but there are thousands of new families Everything we touch turns out to be a gold mine Environments studied: Water (ocean, lakes) Soil Human body (gut, oral cavity, human microbiome) Metagenomics: Early Results The Omics Revolution

  9. Metagenomics New DiscoveriesEnvironmental (red) vs. Currently Known PTPases (blue) 1 2 3 4 Higher eukaryotes The Omics Revolution

  10. The Good News and the Bad News • Good news • Data pointing towards function are growing at near exponential rates • IT can handle it on a per dollar basis • Bad news • Data are growing at near exponential rates • Quality is highly variable • Accurate functional annotation is sparse The Omics Revolution

  11. Example of the Interplay Between Bioinformatics & Proteomics - The Structural Genomics Pipeline Structural biology moves from being functionally driven to genomically driven Basic Steps • Crystallomics • Isolation, • Expression, • Purification, • Crystallization Target Selection Data Collection Structure Solution Structure Refinement Functional Annotation Publish Fill in protein fold space Robotics -ve data Software engineering Functional prediction Not necessarily The Omics Revolution

  12. Towards Open Science • Open access publishing • Open source software • Generation of scientists weaned on social networks • Blogs, wikis, social bookmarking etc. are becoming a valid form of scientific discourse http://www.osdd.net/ SPPS273

  13. University Tech Transfer Offices are Slow to Embrace this Change • Overvalue disclosures • Inability to market disclosures appropriately • Protracted negotiations in a fast moving market • Disable rather than enable startups SPPS273

  14. 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 better SPPS273

  15. Why Don’t we Do Better?A Couple of Observations • Gene 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 A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690 Paolini et al. Nat. Biotechnol. 2006 24:805–815

  16. Why Don’t 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

  17. Implications • Ehrlich’s 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 naïve in a complex system

  18. So How Can We Exploit All The New Data We are Collecting on This Complex System? Lets Work Through a Couple of Examples SPPS273

  19. What if… • We 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

  20. What Do These Off-targets Tell Us? Potentially many things: Nothing How to optimize a NCE A possible explanation for a side-effect of a drug already on the market A possible repositioning of a drug to treat a completely different condition The reason a drug failed A multi-target strategy to attack a pathogen Exploiting the Structural Proteome

  21. Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Exploiting the Structural Proteome

  22. A Reverse Engineering Approach to Drug Discovery Across Gene Families Characterize 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-targets Search for similar small molecules … Dock molecules to both primary and off-targets Statistics analysis of docking score correlations Xie and Bourne 2009 Bioinformatics 25(12) 305-312 Exploiting the Structural Proteome

  23. The Problem with Tuberculosis • One third of global population infected • 1.7 million deaths per year • 95% of deaths in developing countries • Anti-TB drugs hardly changed in 40 years • MDR-TB and XDR-TB pose a threat to human health worldwide • Development of novel, effective, and inexpensive drugs is an urgent priority Example 1 – Repositioning The TB Story

  24. Found.. Evolutionary linkage between: NAD-binding Rossmann fold S-adenosylmethionine (SAM)-binding domain of SAM-dependent methyltransferases Catechol-O-methyl transferase (COMT) is SAM-dependent methyltransferase Entacapone and tolcapone are used as COMT inhibitors in Parkinson’s disease treatment Hypothesis: Further investigation of NAD-binding proteins may uncover a potential new drug target for entacapone and tolcapone Example 1 – Repositioning The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

  25. Functional Site Similarity between COMT and InhA Entacapone and tolcapone docked onto 215 NAD-binding proteins from different species M.tuberculosisEnoyl-acyl carrier protein reductaseENR (InhA) discovered as potential new drug target InhA is the primary target of many existing anti-TB drugs but all are very toxic InhA catalyses the final, rate-determining step in the fatty acid elongation cycle Alignment of the COMT and InhA binding sites revealed similarities ... Repositioning- The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

  26. Binding Site Similarity between COMT and InhA COMT SAM (cofactor) BIE (inhibitor) InhA NAD (cofactor) 641 (inhibitor) Example 1 – Repositioning The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

  27. Summary of the TB Story Entacapone and tolcapone shown to have potential for repositioning Direct mechanism of action avoids M. tuberculosis resistance mechanisms Possess excellent safety profiles with few side effects – already on the market In vivo support Assay of direct binding of entacapone and tolcapone to InhA reveals a possible lead with no chemical relationship to existing drugs Example 1 – Repositioning The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

  28. Summary from the TB Alliance – Medicinal Chemistry • The minimal inhibitory concentration (MIC) of 260 uM is higher than usually considered • MIC is 65x the estimated plasma concentration • Have other InhA inhibitors in the pipeline Example 1 – Repositioning The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

  29. 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. Bioinformatics 2009 25(12) 305-312 The TB Druggome

  30. Bioinformatics 2009 25(12) 305-312 The TB Druggome

  31. SMAP p-value < 1e-5 drugs TB proteins p < 1e-7 p < 1e-6 p < 1e-5 The TB Druggome

  32. 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 network SPPS273

  33. Example 2 - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

  34. 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. CETP inhibitor X CETP LDL HDL Bad Cholesterol Good Cholesterol Example 2 - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

  35. Docking Scores eHits/Autodock Example 2 - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

  36. JTT705 Torcetrapib Anacetrapib JTT705 VDR – RXR FA + RAS FABP ? PPARα PPARδ ? ? PPARγ High blood pressure + JNK/IKK pathway JNK/NF-KB pathway Anti-inflammatory function Immune response to infection Example 2 - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

  37. The Future? Chang et al. 2009 Mol Sys Biol Submitted

  38. Modifications to Early Stage Drug Discovery Off-targets Systems Biology SPPS273 http://www.celgene.com/images/celgene_drug_arrow.gif

  39. Some Known Limitations • Structural coverage of the given proteome • False hits / poor docking scores • Literature searching • It’s a hypothesis – need experimental validation • Money  Known Limitations

  40. Perceived Limitations • Mistrust of computational approaches • Bioinformatics was previously oversold • Omics was previously oversold • Still too cutting edge • No interest in drug resistance SPPS273

  41. pbourne@ucsd.edu Questions?

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