Open PHACTS Data integration for all. Andrew Leach

Transcription

1 Open PHACTS Data integration for all Andrew Leach

2 Task, workflow and results Task: create a focussed set to identify leads against voltagegated potassium channels AUREUS search targets: voltage-gated potassium channels Apply filters (MW, clogp, Lipinski + remove undesirable target) ~1000 molecules Series for lead optimisation Similarity searches (RG, TP, Daylight) Cluster analysis ~10000 molecules selected IonWorks single shot screening 5 full curve actives (in at least one test occasion) 240 single shot hits progressed into full curve assay Stefan Senger, ca. 2004

3 We (may) know where the data is, but integrating is a pain, bespoke, and often only for experts Q: Identify all oxidoreductase inhibitors with an activity <100nM in both mouse and human Q: The current Factor Xa lead series is characterised by substructure X. Retrieve all bioactivity data in serine protease assays for molecules that contain substructure X. Q: For a given interaction profile, give me compounds similar to it. ChEMBL DrugBank Gene Ontology Wikipathways ChEBI Uniprot UMLS ConceptWiki ChemSpider Internal etc.

4 The Innovative Medicines Initiative Biggest public-private partnership in area of medicine Collaboration between European Commission and European Federation of Pharmaceutical Industries and Associations (EFPIA) Promotion of medical innovation in Europe Tackle key bottlenecks Recognises in kind contributions Focus on key problems Efficacy, Safety, Education & Training, Knowledge Management

5 Public Domain Drug Discovery Data Pharma are accessing, processing, storing & re-processing GSK Literature PatentsLiterature PubChem Genbank PatentsLiterature PubChem Genbank PatentsLiterature PubChem Genbank Patents PubChem Genbank Databases Databases Databases Databases Downloads Downloads Downloads Downloads AZ Pfizer Merck Firewalled Databases Data Integration Data Analysis Firewalled Databases Data Integration Data Analysis Firewalled Databases Data Integration Data Analysis Firewalled Databases Data Integration Data Analysis Why repeat at each company?

6 Information Tombs Built for primary use-case Tailored indexes Tailored GUIs Unique language & metadata Poor interoperability/integration In vivo Portfolio Literature HR Synthesis SAR Docs Safety Etc

7 Project Partners Pfizer Limited Coordinator Universität Wien Managing entity Technical University of Denmark University of Hamburg, Center for Bioinformatics BioSolveIT GmBH Consorci Mar Parc de Salut de Barcelona Leiden University Medical Centre Royal Society of Chemistry Vrije Universiteit Amsterdam Spanish National Cancer Research Centre University of Manchester Maastricht University Aqnowledge University of Santiago de Compostela Rheinische Friedrich-Wilhelms-Universität Bonn AstraZeneca GlaxoSmithKline Esteve Novartis Merck Serono H. Lundbeck A/S Eli Lilly Netherlands Bioinformatics Centre Swiss Institute of Bioinformatics ConnectedDiscovery EMBL-European Bioinformatics Institute Janssen OpenLink

8 A use-case driven approach, focussed on delivery for the real world Main architecture, technical implementation and primary capabilities driven by a set of prioritised research questions Based on the main research questions define prioritised data sources Develop three Exemplars to demonstrate the capabilites of the Open PHACTS System and to define interfaces and input/output standards

9 Work Streams Build: Service layer and resource integration Drive: Development of exemplar work packages & Applications Sustain: Community engagement and long-term sustainability Consumer Firewall Supplier Firewall OPS Service Layer Assertion & Meta Data Mgmt Transform / Translate Integrator Corpus 1 Target Dossier Db 2 Compound Dossier Db 3 Db 4 Pharmacological Networks Std Public Vocabularies Business Rules Corpus 5 Work Stream 2: Exemplar Drug Discovery Informatics tools Develop exemplar services to test OPS Service Layer Target Dossier (Data Integration) Pharmacological Network Navigator (Data Visualisation) Compound Dossier (Data Analysis) Work Stream 1: Open Pharmacological Space (OPS) Service Layer Standardised software layer to allow public DD resource integration Define standards and construct OPS service layer Develop interface (API) for data access, integration and analysis Develop secure access models Existing Drug Discovery (DD) Resource Integration

10 Platform Explorer Apps API Standards

11 Prioritised research questions Number sum Nr of 1 Question All oxido,reductase inhibitors active <100nM in both human and mouse Given compound X, what is its predicted secondary pharmacology? What are the on and off,target safety concerns for a compound? What is the evidence and how reliable is that evidence (journal impact factor, KOL) for findings associated with a compound? Given a target find me all actives against that target. Find/predict polypharmacology of actives. Determine ADMET profile of actives For a given interaction profile, give me compounds similar to it The current Factor Xa lead series is characterised by substructure X. Retrieve all bioactivity data in serine protease assays for molecules that contain substructure X. Retrieve all experimental and clinical data for a given list of compounds defined by their chemical structure (with options to match stereochemistry or not) A project is considering Protein Kinase C Alpha (PRKCA) as a target. What are all the compounds known to modulate the target directly? What are the compounds that may modulate the target directly? i.e. return all cmpds active in assays where the resolution is at least at the level of the target family (i.e. PKC) both from structured assay databases and the literature Give me all active compounds on a given target with the relevant assay data Give me the compound(s) which hit most specifically the multiple targets in a given pathway (disease) Identify all known protein-protein interaction inhibitors Kamal Azzaoui et al, DDT in press 2013

12 Pathways Interactions Proteins Genes Transcripts ` Pharmacological Activities Clinical Drug Applications Biological Processes Pathological Processes Drugs Compounds Chemicals Diseases Indications

13 Open PHACTS will be built upon semantic technologies and standards, providing an opportunity to: Demonstrate that semantic technologies can perform to the same degree as existing systems Provide an open platform to address common drug discovery questions; expose pharma s use-cases and knowledge Create a pre-competitive infrastructure that can be sustained and expanded into new areas; providing the platform for future collaboration Why Semantic Technologies? Rapidly developing technology, powerful algorithms for integration and querying of data schema free Open standards facilitating sharing public, private, commercial A community of developers, leverage work going on elsewhere

14 User Interfaces & Applications Linked Data API Linked Data Cache Identity Mapping Service Identity Resolution Service Domain Specific Services Data Key architecture components

15 Core Platform Open PHACTS Explorer 1 st Gen Apps Partner Apps Oct App Framework Identity Resolution Service (ConceptWiki) Identifier Management Service (BridgeDb+) Adenosine receptor 2a P12374 EC CS4532 Linked Data API (RDF/XML, TTL, JSON) Semantic Workflow Engine (LARKC) Data Cache (Triple Store) Chemistry Normalisation & Q/C ChemSpider Domain Specific Services VoID VoID VoID VoID VoID Data Import Nanopub Nanopub Nanopub Public Ontologies Db Db Public Content Db Commercial Db User Annotations

16 Building Quality High quality chemical names and synonyms. Leverage ChemSpider and Concept wiki curation, Q/C and mapping ChemSpider Validation and Standardization Platform (CVSP) for flagging chemical representation issues Basic curation interface for editing concept terms available through Concept Wiki Data quality issues detected in data sources reported back to depositors for their evaluation

17 STANDARD_TYPE STANDARD_UNITS COUNT(*) Quantitative Data Challenges IC50 nm STANDARD_TYPE UNIT_COUNT IC50 ug.ml IC AC50 7 IC50 ug/ml 2038 Activity 421 IC50 ug ml EC50 39 IC50 mg kg IC50 46 IC50 molar ratio 178 ID50 42 IC50 ug 117 Ki 23 IC50 % 113 Log IC50 4 IC50 um well-1 52 Log Ki 7 IC50 p.p.m. 51 Potency 11 IC50 ppm 36 log IC50 0 IC50 um-1 25 IC50 nm kg-1 25 IC50 milliequivalent 22 IC50 kj m-2 20 >5000 types Implemented using the Quantities, Dimension, Units, Types Ontology ( ~ 100 units

18 Chemistry within Open PHACTS The challenges associated with handling chemistry data require the support of a publicly accessible platform to integrate, standardise and host the data. ChemSpider, an online database from the Royal Society of Chemistry hosts the chemical compound collection underpinning Open PHACTS and is responsible for standardising the chemical compounds and providing both regular updates and ongoing data curation. To serve the Open PHACTS platform, a structure validation and standardisation platform (CVSP) has been developed to ensure chemical structures are normalised to rules derived from the FDA structure standardisation guidelines and modified based on input from the EFPIA members.

19 The many challenges of chemistry representation

20 Identities within Open PHACTS Open PHACTS integrates information from multiple different databases, many of which use unique identifiers. The Identity Mapping Service (IMS) ensures these identifiers are linked and available for use interchangeably throughout the Open PHACTS platform. To maintain vocabulary heterogeneity and provide interoperability, the ConceptWiki is used. The ConceptWiki is an open access system that accepts essentially unlimited numbers of synonyms, in multiple languages, and then maps all the terms correctly back to one unique concept identifier, alleviating vocabulary problems and identifier differences. Synonyms: Aspirin Dispril 2-Acetoxybenzoic acid Acetyl salicylic acid Salicylic acid, acetyl- DrugBank ID: APRD00264 ChEBI ID: CHEBI:15365 ChemSpider ID: 2157 FDA: Explorer IMS

21 Why Provenance Matters Using a community specification known as VoID (Vocabulary of Interlinked Datasets) Record version, author, derivations Builds trust with users know what you are querying (and why it might have changed) Provides mechanism to provide usage statistics back to providers, help them understand the value Easier to track errors and ensure quality Actively participating in community provenance programme (W3C)

22 What does Open PHACTS do? Currently integrated databases Number of Database triples (million) ACD Labs / ChemSpider ChEBI 0.91 ChEMBL_v ConceptWiki 3.74 DrugBank 0.52 Enzyme 0.07 Gene Ontology 0.85 SwissProt WikiPathways 0.14 TOTAL Open PHACTS draws together multiple sources of publiclyavailable pharmacological and chemical data, allowing public access to the information via the Open PHACTS Explorer, an intuitive interface.

23 Licensing: 3 public databases All are available as open RDF you can download right now. But: Drugbank OMIM Comparative Toxicogenomics Database

24 CUTTING THE GORDIAN KNOT What are the problems with licensing we had to address? To make the data and software generated by the project usable and reusable Multiplicity of unclear or non-standard licenses on original data sources Public can mean use but not redistribute, use in commercial environment, Legal position on use and reuse extremely unclear Different issues than just linking to data What is the legal status of integrated collections of the above, and of derived knowledge from such a collection? Appropriate software license selection Legal clarity for EFPIA and end users Approaches for commercial data integration, EFPIA in-house data AIM: to enable maximum possible dissemination and usability of the integrated data and architecture generated by the project - with approaches that will be applicable in other data integration projects

25 Data Licensing Solution Chose John Wilbanks as consultant A framework built around STANDARD well-understood Creative Commons licences and how they interoperate Deal with the problems by: Interoperable licences Appropriate terms Declare expectations to users and data publishers One size won t fit all requirements

26 Open PHACTS and the scientific community Associated partners Support, information Exchange of ideas, data, technology Opportunities to demo at ctions, mostommunity webinars Need MoU MoU +Annexe Associated partners Development partnerships Development partnerships Influence on API developments Opportunities to demo ideas & use cases to core team Need MoU and annexe Consortium Consortium 28 current members

27 Example applications Advanced analytics ChemBioNavigator TargetDossier PharmaTrek UTOPIA Navigating at the interface of chemical and biological data with sorting and plotting options Interconnecting Open PHACTS with multiple target centric services. Exploring target similarity using diverse criteria Interactive Polypharmacology space of experimental annotations Semantic enrichment of scientific PDFs Predictions GARFIELD etox collector Prediction of target pharmacology based on the Similar Ensemble Approach Automatic extraction of data for building predictive toxicology models in etox project

28 ChemBioNavigator Matthias Rarey et al PharmaTrek Jordi Mestres et al

29 Call for expressions of interest Open PHACTS ENSO proposal Open PHACTS intends to submit a proposal for IMI ENSO funding. We are currently drafting our ENSO proposal and invite all EFPIA companies with an interest in Open PHACTS to contact us to discuss opportunities for involvement. The Open PHACTS Foundation Open PHACTS has a successor organisation, the Open PHACTS Foundation. Please register your interest with us for further information on membership and other opportunities to get involved within Open PHACTS. For more information and/or to register interest us at pmu@openphacts.org

30 Acknowledgements Stefan Senger Gerhard Ecker The OpenPHACTS consortium

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32 SERVICES Application (Knowledge) Fact Visualisation e.g. Target Dossiers; SAR Visualisation Assertions e.g. Gene-to-Disease; Compound-to-Target; Compound-to-ADR Standards Ontology/taxonomy; Minimum information guide; Dictionaries; Interchange mapping Data Targets; Chemistry; Pharmacology; Literature; Patents After Barnes et al Nature Review Drug Discovery 2009 doi /nrd2944

33 Nanopublications Capturing scientific information in the Triple Store