The information revolution has transformed many business sectors over the last decade and the pharmaceutical industry is no exception. Developments in scientific and information technologies have unleashed an avalanche of content on research scientists who are struggling to access and filter this in an efficient manner. Furthermore, this domain has traditionally suffered from a lack of standards in how entities, processes and experimental results are described, leading to difficulties in determining whether results from two different sources can be reliably compared. The need to transform the way the life-science industry uses information has led to new thinking about how companies should work beyond their firewalls. In this talk we will provide an overview of the traditional approaches major pharmaceutical companies have taken to knowledge management and describe the business reasons why pre-competitive, cross-industry and public-private partnerships have gained much traction in recent years. We will consider the scientific challenges concerning the integration of biomedical knowledge, highlighting the complexities in representing everyday scientific objects in computerised form. This leads us to discuss how the semantic web might lead us to a long-overdue solution. The talk will be illustrated by focusing on the EU-Open PHACTS initiative (openphacts.org), established to provide a unique public-private infrastructure for pharmaceutical discovery. The aims of this work will be described and how technologies such as just-in-time identity resolution, nanopublication and interactive visualisations are helping to build a powerful software platform designed to appeal to directly to scientific users across the public and private sectors.

1. Practical semantics in the pharmaceutical
industry - the Open PHACTS project
Antony Williams
On behalf of the Open PHACTS Team
(and with a focus on Chemistry!)

2. Fundamental issue:
There is a LOT of science online!
Chaotic, varying quality and very valuable!
Scientists want to find information quickly and
easily
Often they just “can‟t get there” (or don‟t even
know where “there” is)
And you have to manage it all (or not)

3. Pre-competitive Informatics:
Pharma are all accessing, processing, storing & re-processing external research data
Literature
PubChem
Genbank
Patents
Databases
Downloads
Data Integration Data Analysis
Firewalled Databases
Repeat @
each
company
x
Lowering industry firewalls: pre-competitive informatics in drug discovery
Nature Reviews Drug Discovery (2009) 8, 701-708 doi:10.1038/nrd2944

4. The Project
Innovative Medicines Initiative
• EC funded public-private
partnership for
pharmaceutical research
• Focus on key problems
– Efficacy, Safety, Educati
on &
Training, Knowledge
Management
The Open PHACTS Project
• Create a semantic integration hub (“Open
Pharmacological Space”)…
• Delivering services to support on-going drug
discovery programs in pharma and public domain
• Not just another project; Leading academics in
semantics, pharmacology and informatics, driven
by solid industry business requirements
• 23 academic partners, 8 pharmaceutical
companies, 3 biotechs INITIALLY
• Work split into clusters:
• Technical Build
• Scientific Drive
• Community & Sustainability

5. Major Work Streams
Build: OPS service layer and resource integration
Drive: Development of exemplar work packages & Applications
Sustain: Community engagement and long-term sustainability
Assertion & Meta Data Mgmt
Transform / Translate
Integrator
OPS Service Layer
Corpus 1
„Consumer‟
Firewall
Supplier
Firewall
Db 2
Db 3
Db 4
Corpus 5
Std Public
Vocabularies
Target
Dossier
Compound
Dossier
Pharmacological
Networks
Business
Rules
Work Stream 1: Open Pharmacological
Space (OPS)Service Layer
Standardised softwarelayerto 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 tools
Develop exemplar services to test OPS Service Layer
Target Dossier (Data Integration)
Pharmacological Network Navigator (Data Visualisation)
Compound Dossier (Data Analysis)

6. ChEMBL DrugBank
Gene
Ontology
Wikipathways
UniProt
ChemSpider
UMLS
ConceptWiki
ChEBI
TrialTrove
GVKBio
GeneGo
TR Integrity
“Find me compounds
that inhibit targets in
NFkB pathway assayed
in only functional assays
with a potency <1 μM”
“What is the
selectivity profile of
known p38 inhibitors?”
“Let me compare
MW, logP and PSA
for known
oxidoreductase
inhibitors”

7. Number sum Nr of 1 Question
15 12 9 All oxidoreductase inhibitors active <100nM in both human and mouse
18 14 8
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?
24 13 8
Given a target find me all actives against that target. Find/predict polypharmacology of actives.
Determine ADMET profile of actives.
32 13 8 For a given interaction profile, give me compounds similar to it.
37 13 8
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.
38 13 8
Retrieve all experimental and clinical data for a given list of compounds defined by their chemical
structure (with options to match stereochemistry or not).
41 13 8
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.
44 13 8 Give me all active compounds on a given target with the relevant assay data
46 13 8
Give me the compound(s) which hit most specifically the multiple targets in a given pathway
(disease)
59 14 8 Identify all known protein-protein interaction inhibitors
Business Question Driven Approach

9. Open PHACTS Scientific Services
Platform Explorer
Standards
Apps
API
“Provenance Everywhere”

10. Nanopub
Db
VoID
Data Cache
(Virtuoso Triple Store)
Semantic Workflow Engine
Linked Data API (RDF/XML, TTL, JSON)
Domain
Specific
Services
Identity
Resolution
Service
Chemistry
Registration
Normalisation
& Q/C
Identifier
Management
Service
Indexing
CorePlatform
P12374
EC2.43.4
CS4532
“Adenosine
receptor 2a”
VoID
Db
Nanopub
Db
VoID
Db
VoID
Nanopub
VoID
Public Content Commercial
Public
Ontologies
User
Annotations
Apps

12. RDF/VoID
RDF (Resource Description Framework)
VoID (Vocabulary of Interlinked Datasets)
– Metadata describing the RDF
– Describes how Datasets are linked using Linksets
• skos:exactMatch (Simple Knowledge Organisation System)
E.g. To link compounds in OPS with compounds in ChEBI.
• skos:closeMatch
E.g. To link Stereo Insensitive Parents to their Children within OPS.
• skos:relatedMatch
E.g. To link Parent compounds that contain others as Fragments.
• dul:expresses (DOLCE+DnS Ultralite) – describes what links the Datasets. We
use Cheminf to express the links E.g.
http://semanticscience.org/resource/CHEMINF_000059 represents an
InChIKey.
– Recommendations on how to create the VoID have been specified by Manchester
here: http://www.cs.man.ac.uk/~graya/ops/2012/ED-datadesc/

13. Chemistry
Registration
Normalisation
& Q/C
Chemistry Registration
• Old chemistry registration system uses standard
ChemSpider deposition system: includes low-
level structure validation and manual curation
service by RSC staff.
• New Registration System
• Utilizes ChemSpider Validation and
Standardization platform including collapsing
tautomers
• Utilizes FDA rule set as basis for
standardizations
• Generate Open PHACTS identifier (OPS ID)

14. STANDARD_TYPE UNIT_COUNT
---------------- -------
AC50 7
Activity 421
EC50 39
IC50 46
ID50 42
Ki 23
Log IC50 4
Log Ki 7
Potency 11
log IC50 0
STANDARD_TYPE STANDARD_UNITS COUNT(*)
------------------ ------------------ --------
IC50 nM 829448
IC50 ug.mL-1 41000
IC50 38521
IC50 ug/ml 2038
IC50 ug ml-1 509
IC50 mg kg-1 295
IC50 molar ratio 178
IC50 ug 117
IC50 % 113
IC50 uM well-1 52
~ 100 units
>5000 types
Implemented using the Quantities, Dimension, Units, Types
Ontology (http://www.qudt.org/)
Quantitative Data Challenges

15. Content Changes Regularly! POINT IN TIME
Source Initial Records Triples Properties
ChEMBL 1,149,792
~1,091,462 cmpds
~8845 targets
146,079,194 17 cmpds
13 targets
DrugBank 19,628
~14,000 drugs
~5000 targets
517,584 74
UniProt 536,789 156,569,764 78
ENZYME 6,187 73,838 2
ChEBI 35,584 905,189 2
GO/GOA 38,137 24,574,774 42
ChemSpider/ACD 1,194,437 161,336,857 22 ACD, 4 CS
ConceptWiki 2,828,966 3,739,884 1

16. Data Cache
(Virtuoso Triple Store)
Semantic Workflow Engine
Infrastructure
Hardware (development)
- 2 x Intel Xeon E5-2640 - 384 GB
DDR3 1333MHz RAM - 1.5 TB
SSD - 3TB 7200rpm
Triple Store
- Virtuoso 7 column store
- Shown to scale to > 100 billion
triples
Network
- AMX-IS
- Extensive memcache

17. Antony Williams vs Identifiers
Passport ID
Dad, Tony, others
SSN
Green Card
License
5 email addresses
ChemConnector
(blog, Twitter
account, Facebook, Fri
endfeed)
OpenID, ORCID
….

22. P12047
X31045
GB:29384
Let a Mapping Service take the strain….

23. PubChemDrugbankChemSpider
Imatinib
Mesylate
What Is Gleevec?

24. Strict Relaxed
Analysing Browsing
Dynamic Equality
LinkSet#1 {
chemspider:gleevec hasParent imatinib ...
drugbank:gleevec exactMatch imatinib ...
}
chemspider:gleevec drugbank:gleevec

25. ChemSpider Validation & Standardization Platform
Quality Assurance

26. Chemistry Validation and Standardization
Platform (CVSP)
at cvsp.chemspider.com
• Validation
• Standardization
• Parent generation
RDF Export
Data

27. CTAB
REGID1
DataSource
Synonym1
Synonym2
XRef1
etc
Deposited
SDF record Standardized
entity
OPS_ID1
Parents
Charge Parent (OPS_ID7)
Isotope Parent (OPS_ID5)
Stereo Parent (OPS_ID4)
Tautomer Parent
(OPS_ID6)
Super Parent (OPS_ID8)
Fragment (OPS_ID3)
Fragment (OPS_ID2)

28. For each Compound (CSID) parent generation is
attempted: “Tautomerism in large databases”, Sitzmann
and others, J.Comput Aided Mol Des (2010)
Parent Description
Charge-
Unsensitive
An attempt is made to neutralize ionized
acids and bases. Envisioned to be an
ongoing improvement while new cases
appear.
Isotope-
Unsensitive
Isotopes replaced by common weight
Stereo-Unsensitive Stereo is stripped
Tautomer-
Unsensitive
Tautomer canonicalization is attempting to
generate a “reasonable” tautomer
Super-Unsensitive This parent is all of the above

29. OPS1
DrugBank ID DB07241
OPS5OPS4
OPS3
OPS2
OPS6
ops:OPS1 skos:exactMatch
<http://www4.wiwiss.fu-
berlin.de/drugbank/resource/drugs/DB07241> .
ops:OPS2 skos:relatedMatch ops:OPS1 .
ops:OPS3 skos:relatedMatch ops:OPS1 .
ops:OPS3 skos:closeMatch ops:OPS4 .
ops:OPS3 skos:closeMatch ops:OPS5 .
ops:OPS4 skos:closeMatch ops:OPS6 .
ops:OPS5 skos:closeMatch ops:OPS6 .

31. A Precompetitive Knowledge Framework
Integration
Pharma Needs
Inputs
Sustainabilit
y
Stability
Security
Management
/ Governance
Data Mining
Services/Alg
orithms
Mapping &
Populating
Architecture
Interfaces
& Services
Content
Structured &
Unstructured
Vocabularies
& Identifiers
(URIs)
Community
KD
Innovation

32. The Ecosystem is ….
API
Approach
Community
Industry
Academia
Data
Provider
Software
Provider

33. Kick-Starting Sustainability
Collaboration
Grants
Industry
Open PHACTS APIUsers
Apps
API

34. explorer.openphacts.org

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

36. Front-endframework to visualizebiologicaldata
Target dossier (CNIO)

42. The Open PHACTS community ecosystem

43. Becoming part of the Open PHACTS Foundation
Members
membership offers early access to platform updates and releases
the opportunity to steer research and development directions
receive technical support
work with the ecosystem of developers and semantic data
integrators around Open PHACTS
tiered membership
familiar business and governance model
A UK-based not-for-profit
member owned company

44. What are the problems with licensing we had to address?
– To make data and software generated by the project usable/ 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
– Legal status of integrated collections of the above, and of derived
knowledge?
– Appropriate software license selection
– Legal clarity for EFPIA and end users
– Approaches for commercial data integration, EFPIA in-house data
AIM: enable maximum possible dissemination and usability of integrated
data and architecture with approaches that will be applicable in other data
integration projects
Licensing Challenges

45. 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
Data Licensing Solution

46. Open PHACTS 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
pmu@openphacts.org