2011-11-07 Open PHACTS Poster


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A summary of the first 6 months of the Open PHACTS IMI project, presented by Richard Kidd to the Healthcare Innovation Seminar 2011.

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2011-11-07 Open PHACTS Poster

  1. 1. An infrastructure project Major work streams Build: OPS service layer and resource integration “commons”Develop / apply a set of robust standards… Drive: Development of exemplars & applications Sustain: Community engagement and long-term sustainabilityImplementing the standards in a semantic integration Work Stream 2: Exemplar Drug Discovery Informatics toolsplatform (“Open Pharmacological Space”)… Develop exemplar services to test OPS Service Layer Target Dossier (Data Integration) Pharmacological Network Navigator (Data Visualisation) Target Pharmacological „Consumer‟ Dossier Compound Dossier Networks Compound Dossier (Data Analysis)Delivering services to support on-going drug discovery Firewall OPS Service Layer Std Publicprograms in pharma and public domain Assertion & Meta Data Mgmt Transform / Translate Vocabularies Business Work Stream 1: Open Pharmacological Space (OPS) Service Layer Integrator Rules Standardised software layer to allow public Supplier DD resource integrationMix ideal with the pragmatic. Build open that can Firewall − Define standards and construct OPS service layer Db 2 − Develop interface (API) for data access, integrationaccommodate non-open components in the real world. Db 4 − and analysis Develop secure access models Corpus 1 Db 3 Corpus 5 Existing Drug Discovery (DD) Resource Integration Guiding principle is open access, open usage, open source - Key to standards adoption - Open flavours OPS Open - open access to all OPS Services OPS Consortia - data sets licensed just to the consortia •Integrate data on target expression, biological pathways OPS Academia - fully open to academia. and pharmacology to identify the most productive points for “My OPS” therapeutic intervention •Investigate the in vitro pharmacology and mode-of-action Open Source of novel targets to help develop screening assays for drug Open Access Infrastructure. GUI and back-end platform, discovery online at openphacts.org or download both + data for local •Compare molecular interaction profiles to assess potential setup. off-target effects and safety pharmacology Open Services: for example, RSC services. •Analyse chemical motifs against biological effects to Open Data + Private Data: licensing fun for all the family. deconvolute high content biology assays Commercial providers: abstract service interface to swap in commercial and open source platforms A use case driven approach Polypharmacology browser A use case driven approach Fusion/aggregation of map coverage of the chemo-biological space for data from different polypharmacological profiling of small molecules domains to improve predictions of drug- transporter interactions Prioritised Prioritised Exemplars research data sources Developers Prioritised Prioritised BenchmarkDevelopers research data sources pilots questions (Builders) (Builders) questions Combination of physicochemical Chem/bio space navigator data & data from of sets of pharmacologically annotated small transporter molecules, by chemical substructures, pharmacophores, interaction for Target dossiers biological activities prediction of blood- about targets, incorporating related End users brain barrier End users information on sequences, structures, OMe (Drivers) permeation and pathways, diseases and small tissue distribution (Drivers) molecules MeO N NH2 N Target validation MeO work-bench: in silico NH2 target validation studies
  2. 2. Prioritised research questions analysisExample research questions Prevalent Concepts Compound Required cheminformatics Bioassay functionality•Give all compounds with IC50 < xxx for target Y in species Target Chemical substructureW and Z plus assay data searching Pathway•What substructures are associated with readout X (target, Chemical similarity Diseasepathway, disease, …) Prevalent data relationships searching•Give all experimental and clinical data for compound X Compound – target Required bioinformatics•Give all targets for compound X or a compound with a Compound – bioassay functionalitysimilarity > y% Bioassay – target Sequence and Compound – target – mode of action similarity searching73 questions identified across consortium Target – target classification Bioprofile similarity Target – pathway searching Target – disease Pathway – disease Selection of prioritised data Agile development: 6 month “lash up” sources • Produce a working “lash up” system Biology Chemistry • Constrained to technologies in consortium + a few ChEMBL EntrezGene HGNC data sources DrugBank • Focused on 2 prioritized research questions (Q15 and ChEBI Uniprot Interpro Q30) PubChem ChemSpider SCOP • Q 15: All oxidoreductase inhibitors active Human Metabolome DB Wikipathways <100nM in both human and mouse Wombat (commercial) OMIM • Q 30: For a given compound [clozapine], give Ontologies IUPHAR me the interaction profile with [human or AmiGo (The Gene Ontology) mouse] targets KEGG (Kyoto Encyclopedia of Genes • Minimum requirements: two data sources (one and Genomes) targets, one compounds) and able to produce OBI (The Ontology for Biomedical answers in “manual time”. Investigations) • Brenda, KEGG, PDSP, ChEMBL, ChEBI, ENZYME Bioassay Ontology EFO (Experimental Factor Ontology) DB, Chem2Bio2RDF Outcomes of exercise: Build a lash up • Team building interface • Performance / scalability Identity chemical Resolution User analysis Utopia Path Interface resolution Concept WikiUI Service Docs Physio software • Does it provide an adequate triple store Term mapping answer to the Concept SPARQL id mapping Wiki Bridge questions 15 DB IRS to disambiguate LarKC LarKC in the and 30? Other core mappings Uri mapping • Demo for users SPARQL or LarKC plugin (drive group) to concept mapping Data sources recalibrate build rdf mapping Linked tasks in order to Chem2Bio2RDF Chem ScaiView Distributed system Concept Linked Wiki Open DrugData ChEMBL Spider Index Wiki LifeData Pathways better respond to user data sources requirements text mining
  3. 3. Demo: www.youtube.com/openPHACTS Pharmacological data Exact and structure search “Lash Up” Sanity CheckLSP4All (Lundbeck) Navigate from compounds to targets Q15: All oxidoreductase inhibitors activeGeneric Interface search by enzyme familyQ15: All oxidoreductase inhibitors active <100nMolars in both <100nM in both human and mousehuman & mouse •IC50 values and compounds fully coincident between the automatic and manual search. •“Lash up” identified a compound lost in the manual search (Raloxifene) which value after doing a new manual search was correct. •Manual search took 3 days (Mabel Loza’s team @USC) •Automated search took milliseconds (OWLlm). PathVisio (Maastricht U) Biological dataUTOPIA Documents (U Manchester) Genes suggestion for selected protein Onwards and Upwards Connection between developers and users Solidify interfaces for exemplar developers Review lash up for technology, content and exemplars Prototype Architecture Architecture Services: e.g. entity identification and resolution and representing similarity, ORCID, DataCite Models: RDF / Nanopublication model spec and guidelines Tender documents for commercial storage providers Prototype March 2012: Internal Prototype Delivery September 2012: Release 1st Prototype
  4. 4. OPS Community Workshops The Open PHACTS project is funded by the IMI Programme. The Innovative Medicines Initiative (IMI) is a unique Focus on different aspects of drug discovery, the public-private partnership designed by the European technology used, data sharing, sustainability, Commission and European Federation of Pharmaceutical Industries and Associations (EFPIA). It is a pan- licensing and practical applications. European collaboration that brings together large biopharmaceutical companies, small- and medium sized 1st Volendam (near Amsterdam) September 19-20, 2011 enterprises (SMEs), patient organisations, Joint with GEN2PHEN academia, hospitals and public authorities. Solving Bottlenecks in Data Sharing in the Life Sciences 2nd Location TBD April 16-17, 2012 Starting date: 01/03/2011 Duration: 36 months IMI funding: € 9.988.867 Other contributions: € 2.265.938 EFPIA in kind contribution: € 4.142.649 Total project cost: € 16.397.454 CONTACTS Project Coordinator: Bryn Williams-Jones Pfizer / Connected Discovery Email: bryn@connecteddiscovery.com Managing entity of IMI beneficiaries Prof Gerhard Ecker Academia-Commercial Venture Professor of Pharmacoinformatics Department of Medicinal Chemistry Focus University of Wien, Austria Email: gerhard.f.ecker@univie.ac.at One area - pharmacology “Production Level” software EFPIA MEMBER COMPANIES Currency/Updates & Licensing key • AstraZeneca AB, Sweden Semantic Pragmatics: everyday use by Developers • Eli Lilly and Company Ltd, UK scientists not informaticians (Builders) • GlaxoSmithKline Research & Development Ltd, UK • H. Lundbeck A/S, Denmark • Laboratorios del Dr. Esteve S.A, Spain Future • Merck, Germany An infrastructure that can be built upon, to • Novartis Pharma, AG, Switzerland provide a stable foundation for further pre- • Pfizer Ltd, UK competitive informatics collaboration UNIVERSITIES, RESEARCH ORGANISATIONS, PUBLIC BODIES & NON- Sustainability End users PROFIT (Drivers) • Barcelona Mar Parc Health Consortium, Spain • Christian Association for Higher Education, Research and Patient Care, NetherlandsSummary • Leiden University Medical Centre(LUMC), Netherlands • Maastricht University, NetherlandsRobust standards and techniques • National Centre for Cancer Research (CNIO), Spain Solid integration between data sources via semantic • Rheinische Friedrich-Wilhelms-Universität Bonn, Germany technologies • Royal Society of Chemistry, UK • Technical University of Denmark, Denmark Development of high quality assertions • University of Hamburg, Germany Workflows and analysis pipelines across resources • University of Manchester, UK • University of Santiago de Compostela, Spain • University of Wien, AustriaA semantic integration hub (“Open Pharmacological Space”) Open, public domain infrastructure for drug discovery data SMEs integration • Academic Concept Knowledge Limited, UK • BioSolveIT GmbH, Germany Open web-services for drug discovery Secure access model to enable queries with proprietary data (pharma, SME, NGO and PPP) Project communication pmu@openphacts.orgDeliver services To support on-going drug discovery programs in pharma and public domain www.openphacts.org Align development of standards, vocabs and data integration to selected drug discovery issues @open_phacts