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GRIDs in Drug Discovery and Knowledge Management

GRIDs in Drug Discovery and Knowledge Management. Manuel C. Peitsch Novartis. Dr. Olivier Schwartz / Science Photo Library. The Challenges in Drug Discovery. “Drug Discovery suffers from a high attrition rate as many candidates

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GRIDs in Drug Discovery and Knowledge Management

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  1. GRIDs in Drug Discoveryand Knowledge Management Manuel C. PeitschNovartis Dr. Olivier Schwartz / Science Photo Library

  2. The Challenges in Drug Discovery “Drug Discovery suffers from a high attrition rate as many candidates prove ineffective or toxic in the clinic, owing to a poor understanding of the diseases, and thus the biological systems, they target” • Mechanism-based Drug Discovery • Understanding Disease • Pathways elucidation • Target validation • Clinical PoC • New candidate drug with maximised therapeutic window. } Systems Biology: Combination of *Omics & Mathematical Modelling

  3. Macromolecular Structure & Function Lab Computational Chemistry Lab Bioinformatics Lab Fully Leverage in silico sciences Target finding Target validation Lead finding Lead optim. In Silico Drug Discovery Pipeline Text Informatics In silico Profiling Protein Modeling In silico Combichem Comparative Genomics HT Docking

  4. SETI@Home recognised as a leading new concept (ComputerWorld Award) GeneCrunch recognised as a leading new concept (ComputerWorld Award) SWISS-MODEL and 3D-Crunch recognised as a leading new concept (ComputerWorld Award) UD recognised for visionary use of information technology in the category of Medicine (ComputerWorld Award) In Silico Drug Discovery Pipeline: Can it be done? 1990 1995 2000 2005 First PC-GRID at Novartis GeneCrunch First semi-automated In Silico Drug Discovery Pipeline ? Productive Automated Protein modelling email server SETI@Home Docking in production at Novartis 3D-Crunch Productive Automated Protein modelling Web server Full Transcriptome Modelling at Novartis Genome scale Automated Protein modelling Protein Model Structure database Automated ToxCheck and other CIx tools

  5. 5 ... 0 - 5 - 10 - 15 - 20 - 25 - 30 0 50 100 150 200 Systems Biology Study and Understand Biological Networks / “GRIDs ;-)” “Omics” Experiments Mathematical Models

  6. Target Target Drug Ligand ACTIVE Influencing Biomolecular Processes INACTIVE Target = enzyme, receptor, nucleic acid, … Ligand = substrate, hormone, other messenger, ...

  7. Our 1st PC Grid Success Story: Protein Kinase CK2 Inhibition • Target finding: • Protein Kinase CK2 has roles in cell growth, proliferation and survival. • Protein Kinase CK2 has a possible role cancer and its over expression has been associated with lymphoma. Steve Dschmeissner / Science Photo Library Target validation: To elucidate the different functions and roles of CK2 and confirm it as a drug target for oncology, one needs a potent and selective inhibitor. Approach: The problem was addressed by in silico screening (docking).

  8. Virtual Screening by in silico Docking > 400,000 Compounds DockingProcessandSelectionofpossiblehits < 10 Compounds

  9. Important results Conclusion We have identified a 7-substitued Indoloquinazoline compound as a novel inhibitor of protein kinase CK2 by virtual screening of 400 000 compounds, of which a dozen were selected for actual testing in a biochemical assay. The compound inhibits the enzymatic activity of CK2 with an IC50 value of 80 nM, making it the mostpotent inhibitor of this enzyme ever reported. Its high potency, associated with high selectivity, provides a valuable tool for the study of the biological function of CK2. “The reported work clearly shows that large database docking in conjunction with appropriate scoring and filtering processes can be useful in medicinal chemistry. This approach has reached a maturation stage where it can start contributing to the lead finding process. At the time of this study, nearly one month was necessary to complete such a docking experiment in our laboratory settings. The Grid computing architecture recently developed by United Devices allows us to now perform the same task in less than five working days using the power of hundreds of desktop PC’s. High-throughput docking has therefore acquired the status of a routine screening technique.”

  10. Peru In silico DD for Dengue ( Talk by M. Posdvinec)

  11. + + + [KR]|{P} Relative Intensität Isolate m/z Proteome Informatics (Talk by P. Hernandez) Trypsin Extract Slide from M. Podvinec Digest AcCN AcOH LC column HPLC Separate

  12. Knowledge GRIDsData and Information complexity Literature Molecular Structure Anatomy & Clinical Pathways Raw data from instruments Genomics and Proteomics

  13. Connecting the Knowledge Bodies (requirements) • Intelligent integration of heterogeneous data to enable “Seamless Navigation”: • One-stop shop. • Re-useable, in any Web and Office application. • Intelligent, i.e. knows about biology, medicine, chemistry, diseases, business, people, etc… • On demand and easy to use. • Configurable.

  14. Connecting the Knowledge Bodies (Components) • Indexing of large heterogeneous data collections (databases, full texts) to enable semantic expansion. • Information Retrieval and Extraction, entity recognition, semantic enrichment. • Knowledge Map (navigating the conceptual network). • Terminology Hub (thesauri and ontologies). • Ontology-associated business rules.

  15. What entities constitute our Terminology? • Chemical entities – IUPAC names, trivial names, trade names, INNs, compound codes, ligands. • Biological entities – targets, genes/protein, modes of actions… • Diseases, Indications, Side Effects, Contraindications • Institutions, Affiliations, People • Geographic locations • …

  16. The Ultralink: Contextual Hyperlinking • The Ultralink is an “intelligent” context-sensitive Hyperlink created at run time. • The Ultralink is a menu of links instead of a single link. • This menu will only offers sensible actions/options based on a set of rules attached to an ontology. • The UltraLink allows the dynamic inter-connection of any piece of text or information with any database, search engine and application in the Knowledge Space. • The UltraLink enables seamless information Navigation

  17. GPS Lexical Analysis Server Tools Terminology Zoning DocStructures Lexical Extraction Tagging Meta-Rules The Ultralink can be called from many applications:e.g. Internet Explorer Internet Explorer Integration GPS Add-in 1 User requests for analysis 4 Injection of specific HTML tags Web Page Tagged Document 3 Gets back tagged parts Web Service (WSDL) 2 Sends the document for analysis

  18. MouseOver Click Color coding according to concept type. In this example: Yellow = Gene Name; Red = Institution

  19. BLAST Interface

  20. Acknowledgements • University of Basel: • Torsten Schwede • Michael Podvinec • Jürgen Kopp • Rainer Pöhlmann • Konstantin Arnold • Dominique Zosso • Novartis • Thérèse Vachon • Martin Romacker • Olivier Kreim • Uwe Plikat • Pierre Parisot • Nicolas Grandjean • Brigitte Charpiot • Jean-Marc von Allmen • Daniel Cronenberger • Eric Vangrevelinghe • Pascal Afflard, Armin Widmer • Christian Bartels & Said Karfane • Jan van Oostrum & Team • Carolyn Cho & Team • CSCS • Marie-Christine Sawley • Peter Kunszt • Sergio Maffioletti • Arthur Thomas • Vital-IT: • Victor Jongeneel • Bruno Nyffeler • Heinz Stockinger

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