An integrated dataset for  in silico  drug discovery Simon J Cockell
Drug Repositioning <ul><li>Novel drugs </li></ul><ul><ul><li>10 – 15 years </li></ul></ul><ul><ul><li>$500 million - $2 bi...
Serendipity or Design? <ul><li>High profile repositioning resulting from chance discoveries: </li></ul><ul><ul><li>Viagra ...
Ondex <ul><li>Data integration & visualisation platform </li></ul><ul><li>Everything as a network </li></ul><ul><li>Nodes ...
Ondex Input Integration Analysis Parse Parse Parse Map Map Map Visualise Filter Annotate
Ondex & Repositioning <ul><li>Integrate Drug and Target information </li></ul><ul><li>Add further info: </li></ul><ul><ul>...
Data <ul><li>DrugBank </li></ul><ul><li>UniProt </li></ul><ul><li>HPRD </li></ul><ul><li>KEGG </li></ul><ul><li>BLAST </li...
Workflow Parse Map http://bsu.ncl.ac.uk/ondex/ib2010_data.xml.gz 2D-Tanimoto G-Sesame DrugBank KEGG HPRD UniProt Dataset v...
Chlorpromazine http://en.wikipedia.org/wiki/File:Chlorpromazine-3D-balls.png
Chlorpromazine <ul><li>Known interactions with 3 targets </li></ul><ul><ul><li>Serum Albumin </li></ul></ul><ul><ul><li>D(...
Chlorpromazine Serum Albumin D(2) Dopamine Receptor <ul><ul><li>5-Hydroxytryptamine 2A Receptor </li></ul></ul>- Drug - Ta...
Chlorpromazine
Semantic Motifs Potential Interaction Trimeprazine Chlorpromazine Histamine H1  Receptor drug (2) drug (1) target binds to...
Semantic motifs Protein 1 Protein 2 Disease 1 Disease 2 Drug A Drug B
Conclusions <ul><li>Integrated dataset can be used to find repositioning examples </li></ul><ul><li>Added semantic richnes...
Acknowledgements <ul><li>Newcastle </li></ul><ul><ul><li>Phillip Lord </li></ul></ul><ul><ul><li>Jochen Weile </li></ul></...
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An integrated dataset for in silico drug discovery

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Talk given at the 6th Annual International Symposium on Integrative Bioinformatics, 22nd March.

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  • Viagra – was pulmonary arterial hypertension – now erectile dysfunction Zyban – was antidepressant – now smoking cessation aid Thallidomide – was anti-emetic – now used for treating cancers &amp; leprosy
  • 26,693 instances
  • An integrated dataset for in silico drug discovery

    1. 1. An integrated dataset for in silico drug discovery Simon J Cockell
    2. 2. Drug Repositioning <ul><li>Novel drugs </li></ul><ul><ul><li>10 – 15 years </li></ul></ul><ul><ul><li>$500 million - $2 billion </li></ul></ul><ul><li>Most candidates fail in or before clinic </li></ul><ul><li>Repositioning </li></ul><ul><ul><li>Bypasses many pre-approval tests </li></ul></ul><ul><ul><li>Faster </li></ul></ul><ul><ul><li>Cheaper </li></ul></ul>http://www.recyclenow.com
    3. 3. Serendipity or Design? <ul><li>High profile repositioning resulting from chance discoveries: </li></ul><ul><ul><li>Viagra </li></ul></ul><ul><ul><li>Zyban </li></ul></ul><ul><ul><li>Thallidomide </li></ul></ul><ul><li>A more rational approach is desirable </li></ul><ul><li>Can we exploit data to find new uses for old drugs? </li></ul>http://commons.wikimedia.org/wiki/File:Viagra_in_Pack.jpg
    4. 4. Ondex <ul><li>Data integration & visualisation platform </li></ul><ul><li>Everything as a network </li></ul><ul><li>Nodes & edges annotated with metadata </li></ul><ul><li>Backed by data model & ontology </li></ul>http://www.flickr.com/photos/sjcockell/4405616339/
    5. 5. Ondex Input Integration Analysis Parse Parse Parse Map Map Map Visualise Filter Annotate
    6. 6. Ondex & Repositioning <ul><li>Integrate Drug and Target information </li></ul><ul><li>Add further info: </li></ul><ul><ul><li>Protein similarity </li></ul></ul><ul><ul><li>Small molecule similarity </li></ul></ul><ul><ul><li>Protein families </li></ul></ul><ul><ul><li>Metabolic pathways </li></ul></ul><ul><li>Look for known examples </li></ul>http://www.ondex.org/
    7. 7. Data <ul><li>DrugBank </li></ul><ul><li>UniProt </li></ul><ul><li>HPRD </li></ul><ul><li>KEGG </li></ul><ul><li>BLAST </li></ul><ul><li>2D-Tanimoto </li></ul><ul><li>PFam </li></ul><ul><li>G-Sesame </li></ul><ul><li>SymAtlas </li></ul>
    8. 8. Workflow Parse Map http://bsu.ncl.ac.uk/ondex/ib2010_data.xml.gz 2D-Tanimoto G-Sesame DrugBank KEGG HPRD UniProt Dataset v1 Dataset v2 SymAtlas
    9. 9. Chlorpromazine http://en.wikipedia.org/wiki/File:Chlorpromazine-3D-balls.png
    10. 10. Chlorpromazine <ul><li>Known interactions with 3 targets </li></ul><ul><ul><li>Serum Albumin </li></ul></ul><ul><ul><li>D(2) Dopamine Receptor </li></ul></ul><ul><ul><li>5-Hydroxytryptamine 2A Receptor </li></ul></ul><ul><li>Structurally similar to 4 other drugs </li></ul><ul><ul><li>Trimeprazine </li></ul></ul><ul><ul><li>Prochlorperazine </li></ul></ul><ul><ul><li>Perphenazine </li></ul></ul><ul><ul><li>Promazine </li></ul></ul>
    11. 11. Chlorpromazine Serum Albumin D(2) Dopamine Receptor <ul><ul><li>5-Hydroxytryptamine 2A Receptor </li></ul></ul>- Drug - Target - Protein - has similar structure - has similar sequence - is a
    12. 12. Chlorpromazine
    13. 13. Semantic Motifs Potential Interaction Trimeprazine Chlorpromazine Histamine H1 Receptor drug (2) drug (1) target binds to similar to similar to binds to
    14. 14. Semantic motifs Protein 1 Protein 2 Disease 1 Disease 2 Drug A Drug B
    15. 15. Conclusions <ul><li>Integrated dataset can be used to find repositioning examples </li></ul><ul><li>Added semantic richness allows semantic motifs to be defined </li></ul><ul><li>Semantic motif search + scoring algorithm for automated repositioning search </li></ul><ul><li>New targets? </li></ul>
    16. 16. Acknowledgements <ul><li>Newcastle </li></ul><ul><ul><li>Phillip Lord </li></ul></ul><ul><ul><li>Jochen Weile </li></ul></ul><ul><ul><li>Matthew Pocock </li></ul></ul><ul><ul><li>Darren Wilkinson </li></ul></ul><ul><ul><li>Jennifer Hallinan </li></ul></ul><ul><ul><li>Anil Wipat </li></ul></ul><ul><li>e-Therapeutics </li></ul><ul><ul><li>Dmytro Andrychenko </li></ul></ul><ul><ul><li>Claire Wipat </li></ul></ul><ul><ul><li>Malcolm Young </li></ul></ul><ul><li>BBSRC </li></ul><ul><ul><li>Grant # BB/F006039/1 </li></ul></ul><ul><li>Everyone involved in the Ondex project </li></ul><ul><ul><li>See here: http://ondex.org/people.html </li></ul></ul>http://twitter.com/sjcockell

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