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CCS Staff Meeting

This presentation, led by Robin Smith at the CCS Staff Meeting on February 11, 2010, explores the evolution of high-throughput screening (HTS) in drug discovery, highlighting methodologies from history to present. It discusses the diverse assays, challenges in data management, and the role of bioassay ontology in enhancing data usability. Insights into the PubChem repository and the BioAssay Ontology Project reveal how data can be annotated and standardized for easier access. The session emphasizes collaborative efforts and technological tools aimed at revolutionizing pharmacological research.

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CCS Staff Meeting

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  1. CCS Staff Meeting February 11th, 2010 Robin Smith

  2. Experimenting in parallel 24 wells 12 wells 6 wells 96-, 384- and 1536-well plates http://thumbs.dreamstime.com http://www.genome.gov/

  3. High throughput http://www.piramallifesciences.com/

  4. Lemmon Lab: Medium Throughput

  5. Evolution of “drug discovery” ~2000’s Systems chemical biology / network (poly)-pharmacology, chemically tractable target combinations, phenotypic screening 1980’s Validated biological targets; selective modulators (molecular biology, HTS, combinatorial chemistry etc.) Synthetic compounds’ phenotypic effect (animal testing) 1950’s Identification and first synthesis of active (natural) components ~1900’s ~800BC Natural extracts’ observed effect (traditional human experience)

  6. High Throughput Screening Campaigns

  7. What does the data look like?

  8. Where does all of that data go? • Within Pharmaceutical Companies: • Internal data warehouses (relational databases) • Other private informatics companies that specialize in analysis, e.g. GeneGo, NextBio, CDD • Within Universities • Excel spreadsheets • University IT provided storage systems • Public data stores, e.g. ChEMBL, PDSP, PubChem

  9. PubChem: repository for HTS data

  10. Pubchem bioassays by date @Time of grant proposal: 1500 @Time of Robin start: 1885 @Last Month: 2029

  11. The problems with Pubchem • Very generic data repository: Flexible to put data in, difficult to get data out • Few annotations of assay technologies and the underlying biology • No standardization of data • Difficult to search

  12. Example: Pubchem Protocols

  13. Example: Pubchem Endpoints

  14. The BioAssay Ontology Project http://www.bioassayontology.org

  15. BAO Objectives • Develop an ontology in OWL (web ontology language) to describe high-throughput (and high content) bioassays • Annotate data from Pubchem and other sources using BAO concepts and store in a repository • Develop software tools to allow users to browse and visualize annotations in the repository

  16. What can ask with an ontology? • Which compounds affect members of the STAT3 signaling pathway? • Which compounds have similar profiles to my compound but are less toxic? • What cell lines has my kinase been targeted in? • Which compounds are active in a set of related bioassays? • What technologies could be used to test whether my hit compound is acting non-specifically?

  17. Breaking down a BioAssay BioAssay Perturbagen Purpose • e.g.Primary screening, confirmatory • e.g. Counter assay, Alternate Technology e.g. Compound e.g. RNAi Measure Group Endpoint e.g. Activity linked measure For high content assays! Target 3 5 1 2 P 4 Technology e.g. Viability e.g. Binding Format e.g. Primary Cells e.g. Purified Proteins e.g. Activity at 10mM e.g. IC50

  18. Ontology Engineering: Protégé

  19. Text-Mining for Technology Concepts Protocol Enriched Description Enriched

  20. Ontology Visualization

  21. Annotation of assay data

  22. Ontology Team • Stephan Schürer • Vance Lemmon • Ubbo Visser • Robin Smith • Saminda Abeyruwan • Mitsunori Ogihara • Dusica Vidovic • Software Team • Chris Mader • Felimon Gayalino • Nakul Datar

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