An infrastructure project

Develop / apply a set of robust standards…

Implementing the standards in a semantic integration platform (“Open Pharmacological Space”)…

Delivering services to support on-going drug discovery programs in pharma and public domain

Mix ideal with the pragmatic.  Build open that can accommodate non-open components in the real world.

Major work streamsBuild: OPS service layer and resource integration “commons”

Drive: Development of exemplars & applications

Sustain: Community engagement and long-term sustainability

OPS Services• Integrate data on target expression, biological

pathways and pharmacology to identify the most productive points for therapeutic intervention

• Investigate the in vitro pharmacology and mode-of-action of novel targets to help develop screening assays for drug discovery

• Compare molecular interaction profiles to assess potential off-target effects and safety pharmacology

• Analyse chemical motifs against biological effects to deconvolute high content biology assays

Guiding principle is open access, open usage, open source

- Key to standards adoption -

Assertion & Meta Data MgmtTransform / TranslateIntegrator

OPS Service Layer

Corpus 1

‘Consumer’Firewall

SupplierFirewall

Db 2

Db 3

Db 4

Corpus 5

Std PublicVocabularies

TargetDossier

CompoundDossier

PharmacologicalNetworks

BusinessRules

Work Stream 1: Open Pharmacological Space (OPS) Service LayerStandardised 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

Work Stream 2: Exemplar Drug Discovery Informatics toolsDevelop exemplar services to test OPS Service Layer Target Dossier (Data Integration)Pharmacological Network Navigator (Data Visualisation)Compound Dossier (Data Analysis)

Open flavoursOPS Open - open access to allOPS Consortia - data sets licensed just to the consortia OPS Academia - fully open to academia. “My OPS”

Open Source Open Access Infrastructure. GUI and back-end platform, online at openphacts.org or download both + data for local setup. Open Services: for example, RSC services. Open Data + Private Data: licensing fun for all the family.Commercial providers: abstract service interface to swap in commercial and open source platforms

Developers(Builders)

End users(Drivers)

A use case driven approach

Prioritised research questions

Benchmarkpilots

Target validation work-bench: in silico

target validation studies

Fusion/aggregation of data from different

domains to improve predictions of drug-

transporter interactions

Combination of physicochemical data & data from

transporter interaction for

prediction of blood-brain barrier

permeation and tissue distribution

Prioritised data sources

N

N NH2

NH2

OMe

MeO

MeO

Developers(Builders)

End users(Drivers)

A use case driven approach

Prioritised research questions

Target dossiers about targets, incorporating related

information on sequences, structures, pathways, diseases and small

molecules

Chem/bio space navigator of sets of pharmacologically annotated small

molecules, by chemical substructures, pharmacophores,

biological activities

Polypharmacology browser map coverage of the chemo-biological space for polypharmacological profiling of small molecules

Prioritised data sources

Exemplars

Example research questions

• Give all compounds with IC50 < xxx for target Y in species W and Z plus assay data

• What substructures are associated with readout X (target, pathway, disease, …)

• Give all experimental and clinical data for compound X• Give all targets for compound X or a compound with a

similarity > y%

73 questions identified across consortium

Required cheminformatics functionality

Chemical substructure searching

Chemical similarity searching

Required bioinformatics functionality

Sequence and similarity searching

Bioprofile similarity searching

BiologyEntrezGeneHGNCUniprotInterproSCOPWikipathwaysOMIMIUPHAR

Selection of prioritised data sources

ChemistryChEMBLDrugBankChEBIPubChemChemSpiderHuman Metabolome DBWombat (commercial)OntologiesAmiGo (The Gene Ontology)KEGG (Kyoto Encyclopedia of Genes and Genomes)OBI (The Ontology for Biomedical Investigations)Bioassay Ontology EFO (Experimental Factor Ontology)

Prioritised research questions analysisPrevalent Concepts

CompoundBioassayTargetPathwayDiseasePrevalent data relationshipsCompound – targetCompound – bioassayBioassay – targetCompound – target – mode of actionTarget – target classificationTarget – pathwayTarget – diseasePathway – disease

• Produce a working “lash up” system • Constrained to technologies in consortium + a few

data sources• Focused on 2 prioritized research questions (Q15 and

Q30)• Q 15: All oxidoreductase inhibitors active

<100nM in both human and mouse• Q 30: For a given compound [clozapine], give

me the interaction profile with [human or mouse] targets

• Minimum requirements: two data sources (one targets, one compounds) and able to produce answers in “manual time”.

• Brenda, KEGG, PDSP, ChEMBL, ChEBI, ENZYME DB, Chem2Bio2RDF

Agile development: 6 month “lash up”

Uri mapping

ChEMBL ScaiView Index

ConceptWiki

ConceptWikiUI

BridgeDB

WikiPathways

PathPhysio

UtopiaDocs

LinkedLifeData

ConceptWiki

ChemSpider

LarKC

LinkedOpen

DrugData

Other mappings

Term mapping

User Interfacesoftware

Identity ResolutionService

Distributed system

LarKC in the core

SPARQL or LarKC plugin

IRS to disambiguate

Data sources

SPARQL

Outcomes of exercise:

• Team building• Performance /

scalability analysis

• Does it provide an adequate answer to the questions 15 and 30?

• Demo for users (drive group) to recalibrate build tasks in order to better respond to user requirements

Build a lash up

rdf mapping

id mapping

concept mapping

interface

data sources

triple store

chemical resolution

Chem2Bio2RDF

text mining

LSP4All (Lundbeck) Generic Interface search by enzyme familyQ15: All oxidoreductase inhibitors active <100nMolars in both human & mouse

Pharmacological dataExact and structure search

Navigate from compounds to targets

UTOPIA Documents (U Manchester)

PathVisio (Maastricht U)Biological dataGenes suggestion for selected protein

“Lash Up” Sanity CheckQ15: All oxidoreductase inhibitors active <100nM in both human and 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).

Demo: www.youtube.com/openPHACTS

Prototype Architecture

Onwards and Upwards

Connection between developers and usersSolidify interfaces for exemplar developersReview lash up for technology, content and exemplars

ArchitectureServices: e.g. entity identification and resolution and representing similarity, ORCID, DataCite

Models: RDF / Nanopublication model spec and guidelines

Tender documents for commercial storage providers

PrototypeMarch 2012: Internal Prototype DeliverySeptember 2012: Release 1st Prototype

Focus on different aspects of drug discovery, the technology used, data sharing, sustainability, licensing and practical applications.

1st Volendam (near Amsterdam) September 19-20, 2011 Joint with GEN2PHEN Solving Bottlenecks in Data Sharing in the Life Sciences

2nd Location TBD April 16-17, 2012

OPS Community Workshops

SummaryRobust standards and techniques

Solid integration between data sources via semantic technologies Development of high quality assertionsWorkflows and analysis pipelines across resources

A semantic integration hub (“Open Pharmacological Space”)Open, public domain infrastructure for drug discovery data integrationOpen web-services for drug discoverySecure access model to enable queries with proprietary data (pharma, SME, NGO and PPP)

Deliver services To support on-going drug discovery programs in pharma and public domainAlign development of standards, vocabs and data integration to selected drug discovery issues

Developers(Builders)

End users(Drivers)

Academia-Commercial Venture

FocusOne area - pharmacology“Production Level” softwareCurrency/Updates & Licensing keySemantic Pragmatics: everyday use by scientists not informaticians

FutureAn infrastructure that can be built upon, to provide a stable foundation for further pre-competitive informatics collaborationSustainability

The Open PHACTS project is funded by the IMI Programme.The Innovative Medicines Initiative (IMI) is a uniquepublic-private partnership designed by the EuropeanCommission and European Federation of PharmaceuticalIndustries and Associations (EFPIA). It is a pan-European collaboration that brings together largebiopharmaceutical companies, small- and medium sizedenterprises (SMEs), patient organisations,academia, hospitals and public authorities.

Starting date: 01/03/2011Duration: 36 monthsIMI funding: € 9.988.867Other contributions: € 2.265.938EFPIA in kind contribution: € 4.142.649Total project cost: € 16.397.454

CONTACTS

Project Coordinator: Bryn Williams-JonesPfizer / Connected DiscoveryEmail: bryn@connecteddiscovery.com

Managing entity of IMI beneficiariesProf Gerhard EckerProfessor of PharmacoinformaticsDepartment of Medicinal ChemistryUniversity of Wien, AustriaEmail: gerhard.f.ecker@univie.ac.at

EFPIA MEMBER COMPANIES• AstraZeneca AB, Sweden• Eli Lilly and Company Ltd, UK• GlaxoSmithKline Research & Development Ltd, UK• H. Lundbeck A/S, Denmark• Laboratorios del Dr. Esteve S.A, Spain• Merck, Germany• Novartis Pharma, AG, Switzerland• Pfizer Ltd, UK

UNIVERSITIES, RESEARCH ORGANISATIONS, PUBLIC BODIES & NON-PROFIT• Barcelona Mar Parc Health Consortium, Spain• Christian Association for Higher Education,Research and Patient Care, Netherlands• Leiden University Medical Centre(LUMC), Netherlands• Maastricht University, Netherlands• National Centre for Cancer Research (CNIO), Spain• Rheinische Friedrich-Wilhelms-Universität Bonn, Germany• Royal Society of Chemistry, UK• Technical University of Denmark, Denmark• University of Hamburg, Germany• University of Manchester, UK• University of Santiago de Compostela, Spain• University of Wien, Austria

SMEs• Academic Concept Knowledge Limited, UK• BioSolveIT GmbH, Germany

Project communication

pmu@openphacts.org

www.openphacts.org

@open_phacts