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Bioinformatics: Impact on Health and Drug Development

Bioinformatics: Impact on Health and Drug Development

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Bioinformatics: Impact on Health and Drug Development

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  1. Bioinformatics: Impact on Health and Drug Development Symposium 6: Ballroom B 7th International ISSX Meeting Vancouver, BC Aug. 31, 2004

  2. Bioinformatics: Impact on Health & Drug Development • 7:40 am – Bioinformatics in Drug Discovery and Development – D.S. Wishart • 8:20 am – PharmGKB: The Pharmacogenetics and Pharmacogenomics Knowledge Base – R. Altman • 9:00 am – Bioinformatics and Visual Genomics: Seeing Genes, Proteins and Metabolism – C. Sensen

  3. Bioinformatics: Impact on Health & Drug Development • 9:40 am – Coffee Break • 10:20 am – Automated Docking and MD Simulations of Substrate Binding in Cytochrome P450 – N. Vermeulen • 11:00 am – Metabolic Profiling Using an LC/MS & NMR Based Approach – J. Shockcor • 11:40 am – Posters and Refreshments

  4. Bioinformatics in Drug Discovery and Development David Wishart, University of Alberta 7th International ISSX Meeting Vancouver, BC Aug. 29-Sept. 2, 2004

  5. The Pyramid of Life Metabolomics Proteomics Genomics 1400 Chemicals B I O I N F O R M A T I C S 10,000 Proteins 30,000 Genes

  6. Drug Discovery & Development $80 $40 $50 $200 $50 million 3.5 yrs 1 yr 2 yrs 3 yrs 2.5 yrs Discovery Phase I Phase II Phase III FDA Approval Drug Development Pipeline Chemistry Genomics Proteomics Metabolomics B I O I N F O R M A T I C S

  7. Bioinformatics (or Computational Biology) • Not just the study of DNA or protein sequence data • Inclusive definition – concerns the storage, display, reduction, management, analysis, extraction, simulation, modelling, fitting or prediction of biological, medical or pharmaceutical data

  8. Key Informatics Challenges in Drug Development • Using genomic, proteomic, metabolomic & structural data to ID drug targets or drug leads • Using genomic, metabolomic and structural data to predict drug metabolism, xenobiotic toxicity and characterize adverse drug reactions

  9. Drugs from Genomes Gene Therapies Protein Drugs Drug Targets

  10. Two Types of Diseases • Diseases that arise from in-born sequence errors in germ cells or spontaneous (or age-related) mutations in somatic cells • Diseases that arise from an infectious vector (virus, bacterium or parasite) that has its origins outside Endogenous Disease Exogenous Disease

  11. Endogenous Diseases • Select cohort with disease or condition • Isolate gene region showing distinct features • Sequence whole region of interest • Compare to Human UniGene Map • ID location of common mutations • Predict function & cell location of gene prdct • Predict/Determine structure of gene product • Design antagonists, agonists or replacement

  12. Exogenous Diseases • Sequence pathogen or pathogens • Identify critical genes • metabolic enzymes • toxins or pseudo-toxins • targeting receptors or coat proteins • Select unique (low homology) genes • Use prior knowledge to ID lead compds • Develop vaccine candidates

  13. Bioinformatics… • Both exogenous and endogenous diseases require methods for rapid and comprehensive genomic, proteomic and metabolomic annotation • Identifying drug targets or drug candidates requires linking metabolomic or chemical compound data with sequence and pathway data

  14. Genome Annotation - Magpie C. Sensen

  15. Metabolomes (KEGG) • Number of pathways 17,263 • Number of organisms 213 • Number of genes 754,236 • Number of compounds 11,165 • Number of glycans 10,895 • Number of chemical reactions 6,140 http://www.genome.jp/kegg/kegg1.html

  16. Therapeutic Target DB (C.Y. Zong) http://xin.cz3.nus.edu.sg/group/cjttd/TTD_ns.asp

  17. Database Integration KEGG Magpie DrugBank TTD

  18. The DrugBank Home Page http://redpoll.pharmacy.ualberta.ca

  19. DrugBank • A freely accessible, web-enabled, fully queryable database that links drug structure/activity data with protein structure/function/sequence data • Contains nomenclature, synthesis, structure, activity, chemistry info on FDA drugs • Contains nomenclature, structure, sequence, pharmacology, drug metabolism info on corresponding biomolecule targets • Extensive querying & search tools

  20. DrugBank Browser http://redpoll.pharmacy.ualberta.ca

  21. DrugBank DrugCard

  22. DrugBank DrugCard • Common names, alternate names, brand names, IUPAC names, CAS #, mixtures, source, manufacturer, MSDS link, PIN, DIN • Structure, formula, solubility, toxicity, state, LogP, melting/boiling point, synthesis, 3D structure, SMILES, MOL-file, PDB file, NMR & MS spectra, l max • Drug class, indication, pharmacology, mechanism, drug target, prescription information, metabolites & metabolism, metabolism SNPs • Target sequence, GenBank link, target structure (2o, 3o or model), PDB file, target MW, target #AA, cellular location, chromosome, chromosome position, SNPs

  23. DrugBank Querying • Sorting (by MW, indication, category) • Text query (boolean query, AND, OR, NOT, *) using GLIMPSE • Sequence query (BLAST search) • Structure query (draw structure, search for similar structures) • Relational data extraction (columns of numbers or text for graphing)

  24. DrugBank Applications • Newly sequenced proteomes can be analyzed automatically for similarities to existing drug targets, giving researchers quick lead ideas • Newly determined protein structures can be “Autodocked” to a large database of known, well-behaved compounds to suggest lead ideas

  25. DrugBank Applications • Newly synthesized or identified lead compounds can be compared to existing structures to assess/predict possible efficacy, cross reactivity, metabolism or physical properties • Existing drugs can be compared or analyzed for key trends, properties or features to help in drug design synthesis efforts

  26. Key Informatics Challenges in Drug Development • Using genomic, metabolomic & structural data to ID drug targets or drug leads • Using genomic, metabolomic & structural data to predict or characterize drug metabolism, xenobiotic toxicity and adverse drug reactions

  27. Predicting Drug Metabolism Through CyP450 Docking N. Vermeulen

  28. Predicting Gene-Drug Interactions via Curated Community Knowledge R. Altman

  29. Seeking Gene-Drug Relations through PolySearch http://redpoll.pharmacy.ualberta.ca

  30. PolySearch • Supports PubMed text searching for gene, drug & disease associations (user provides disease/gene/drug name) • Automatically scores & ID’s genes and searches for known SNPs or mutations against std. SNP databases • Grabs gene sequences and generates primers around SNPs • Archives (MySQL database) or sends results as HTML page to user

  31. PolySearch • Searches over 14 million PubMed records, >3400 diseases (and synonyms), 14,000 human genes (43,000 synonyms), >1000 compounds or drugs (>3000 compound synonyms) • Assesses quality using SCI list of impact factors for 8600+ journals • Example of growing use of text mining in bioinformatics

  32. Characterizing ADR & Drug Metabolism via Spectroscopy • Not all ADRs can be predicted in vitro or in silico • Identifying drug metabolites and characterizing metabolic changes in blood or urine requires advanced computational/bioinformatics methods • Represents an emerging application of bioinformatics & computational biology

  33. Metabonomics Efficacy Primary Molecules Filtration Toxicity Secondary Molecules Dilution Concentration Resorption Chemical Fingerprint

  34. Characterizing ADR & Drug Metabolism via Spectroscopy Sample Injection

  35. 25 PC2 20 15 ANIT 10 5 0 -5 Control -10 -15 PAP -20 PC1 -25 -30 -20 -10 0 10 PAP ANIT Control Classifying ADR via PCA J. Shockcor

  36. Chemical Shift Chromatography Mixture separation by HPLC (followed by ID via Mass Spec) Mixture separation by NMR (simultaneous separation & ID) Chemical Shift Chromatography

  37. Mixture Compound A Compound B Compound C Spectral Fitting (Principles) Constrained Least Squares Fitting

  38. NMR Analysis of Urine Chenomx Inc. – Eclipse 2.0

  39. (+)-(-)-Methylsuccinic Acid 2,5-Dihydroxyphenylacetic Acid 2-hydroxy-3-methylbutyric acid 2-Oxoglutaric acid 3-Hydroxy-3-methylglutaric acid 3-Indoxyl Sulfate 5-Hydroxyindole-3-acetic Acid Acetamide Acetic Acid Acetoacetic Acid Acetone Acetyl-L-carnitine Alpha-Glucose Alpha-ketoisocaproic acid Benzoic Acid Betaine Beta-Lactose Citric Acid Creatine Creatinine D(-)Fructose D-(+)-Glyceric Acid D(+)-Xylose Dimethylamine DL-B-Aminoisobutyric Acid Current Compound List • L-Isoleucine • L-Lactic Acid • L-Lysine • L-Methionine • L-phenylalanine • L-Serine • L-Threonine • L-Valine • Malonic Acid • Methylamine • Mono-methylmalonate • N,N-dimethylglycine • N-Butyric Acid • Pimelic Acid • Propionic Acid • Pyruvic Acid • Salicylic acid • Sarcosine • Succinic Acid • Sucrose • Taurine • trans-4-hydroxy-L-Proline • Trimethylamine • Trimethylamine-N-Oxide • Urea • DL-Carnitine • DL-Citrulline • DL-Malic Acid • Ethanol • Formic Acid • Fumaric Acid • Gamma-Amino-N-Butyric Acid • Gamma-Hydroxybutyric Acid • Gentisic Acid • Glutaric acid • Glycerol • Glycine • Glycolic Acid • Hippuric acid • Homovanillic acid • Hypoxanthine • Imidazole • Inositol • isovaleric acid • L(-) Fucose • L-alanine • L-asparagine • L-aspartic acid • L-Histidine • L-homocitrulline

  40. Acetic Acid Betaine Carnitine Citric Acid Creatinine Dimethylglycine Dimethylamine Hippulric Acid Lactic Acid Succinic Acid Trimethylamine Trimethlyamin-N-Oxide Urea Lactose Suberic Acid Sebacic Acid Homovanillic Acid Threonine Alanine Glycine Glucose Metabolic Microarray Normal Below Normal Above Normal Absent Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8 Patient 9 Patient 10 Patient 11 Patient 12 Patient 13 Patient 14 Patient 15

  41. The Human Metabolome Project • $7.2 million Genome Canada project starting Sept. 1, 2004 (10 PI’s in analytical & clinical chemistry & bioinformatics) • Expect to ID and archive >1400 metabolites and metabolite ranges using NMR, MS, HPLC & informatics • Establishment of the Human Metabolome Databank (HMD)

  42. The HMD • Web-accessible, freely available & continuously updated compilation of base-line metabolites in urine and plasma • Similar content to DrugBank, including pathway prediction and metabolic modeling • Compound ordering

  43. Conclusions • Bioinformatics is being used to integrate genomic, metabolomic & structural data to help ID drug targets or drug leads • Bioinformatics combines genomic, metabolomic & structural data to help predict or characterize drug metabolism, xenobiotic toxicity and adverse drug reactions

  44. Conclusions • Unlike genomics/proteomics data, most drug, drug metabolism, ADR and ADME data is still in books or journals – not in electronic form • This limits development of tools, databases and predictive software • As more data is made electronic, look to increased use of simulation and modelling software to predict ADME, ADR and toxicology

  45. The Future… • Greater integration • More freeware and greater web-accessibility • Greater use of text mining and machine learning methods • Focus on predictions Meta- bolomics B I O I N F O R M A T I C S Proteomics Genomics

  46. Acknowledgements • Anchi Guo (PDF) • Murtaza Hassanali (student) • Nelson Young (RA/Programmer) • Haiyan Zhang (Programmer/Analyst) • Bahram Habibi-Nazhad (PDF) • Jennifer Woolsey (student) • Chenomx Inc. (Edmonton) • Genome Canada, NSERC