Bioinformatics: Impact on Health and Drug Development

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

• 740 am Bioinformatics in Drug Discovery and
  Development D.S. Wishart
• 820 am PharmGKB The Pharmacogenetics and
  Pharmacogenomics Knowledge Base R. Altman
• 900 am Bioinformatics and Visual Genomics
  Seeing Genes, Proteins and Metabolism C. Sensen

3
Bioinformatics Impact on Health Drug
Development

• 940 am Coffee Break
• 1020 am Automated Docking and MD Simulations
  of Substrate Binding in Cytochrome P450 N.
  Vermeulen
• 1100 am Metabolic Profiling Using an LC/MS
  NMR Based Approach J. Shockcor
• 1140 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
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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 IDs 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, gt3400
  diseases (and synonyms), 14,000 human genes
  (43,000 synonyms), gt1000 compounds or drugs
  (gt3000 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

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Metabonomics
Efficacy
Primary Molecules
Filtration
Toxicity
Secondary Molecules
Dilution
Concentration
Resorption
Chemical Fingerprint
34
Characterizing ADR Drug Metabolism via
Spectroscopy
Sample Injection
35
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
Spectral Fitting (Principles)
Constrained Least Squares Fitting
38

NMR Analysis of Urine
Chenomx Inc. Eclipse 2.0
39
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

• ()-(-)-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

• 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

40
Metabolic Microarray
Acetic Acid Betaine Carnitine Citric
Acid Creatinine Dimethylglycine Dimethylamine Hipp
ulric Acid Lactic Acid Succinic
Acid Trimethylamine Trimethlyamin-N-Oxide Urea Lac
tose Suberic Acid Sebacic Acid Homovanillic
Acid Threonine Alanine Glycine Glucose
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
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The Human Metabolome Project

• 7.2 million Genome Canada project starting
  Sept. 1, 2004 (10 PIs in analytical clinical
  chemistry bioinformatics)
• Expect to ID and archive gt1400 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