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The Role of Ontology in the Pharmaceutical Industry of the Future Providence, RI - October 29, 2008

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Title: The Role of Ontology in the Pharmaceutical Industry of the Future Providence, RI - October 29, 2008


1
The Role of Ontology in the Pharmaceutical
Industry of the FutureProvidence, RI -
October 29, 2008
CHIs 4th Annual Bridging Pharma and
ITLeveraging Information Technology to Improve
Productivity
  • Werner CEUSTERS
  • Center of Excellence in Bioinformatics and Life
    Sciences
  • University at Buffalo, NY, USA
  • http//www.org.buffalo.edu/RTU

2
Short personal history
1977
2006
2004
1989
1992
2002
1998
1995
1993
3
Presentation overview
  • Europes response to the RD crisis in Pharma
  • The Innovative Medicines Initiative (IMI)
  • IMIs perspective on Knowledge Management
  • Ontology
  • Referent Tracking
  • Conclusion

4
1. Europes response to the RD crisis in pharma
5
Big Pharma besieged from all sides
  • Blockbusters are expiring, pipelines are
    emptying and watchdogs are growling.
  • The Guardian, Saturday August 30 2008.
  • In the longer term, the most successful
    developers will be those who radically change
    their entire approach to businessfrom RD to
    project management, manufacturing, and
    marketing.
  • Outlook 2008, Tufts Center for the Study of Drug
    Development.

6
Pharma stocks 2003 - 2008
Pharma Index 6 (only Schering
Plough is up) SP500, DOW, NASDAQ 18
(Sep 24, 2008)
7
Europes early awareness
  • Drug approvals
  • Between 1975 and 1994 of the 152 approvals, 45
    were of US origin and 40 of European.
  • By 2002, only 8 out of 29 were of European
    origin, as compared to 13 from the US and 7 from
    Japan.
  • Expenditures in pharma RD
  • In the early 1990's, Europe spent 50 more than
    the US.
  • By 2002, US investment was approximately 140 of
    that of Europe and this gap still widening.
  • Expectation
  • by 2012, 90 of all new medicinal products
    launched worldwide will come from the US.

8
its root cause analysis
  • the higher growth rate of the US public and
    private healthcare markets,
  • a European public market that favours cheaper
    generics at the expense of newer and more
    innovative products,
  • adverse operating conditions and lack of capital
    for the European small biotechnology industry,
  • a natural trend in the global pharmaceutical
    industry to relocate to larger markets, where
    innovation reaps greater rewards and where public
    research spending is highest.

9
and action !
  • InnoMED Innovative medicines for Europe
  • Objective
  • achieving accelerated development of, safe and
    more effective medicines
  • Activity
  • develop a Strategic Research Agenda (SRA) that
    will encompass the whole path from discovery of a
    new drug target to the validation and approval
    stages of a new drug compound.
  • Partnership
  • 16 large pharmaceutical companies
  • 14 universities
  • 8 SMEs

Time 2005-10 -
2009-01 Project Cost 18.53 million
euro Project Funding 12 million euro
10
InnoMED Achievements
  • Identification of bottlenecks
  • Strategic Research Agenda accepted by industry
    and academia
  • Convincing EU policy makers
  • Creation of a public/private joint undertaking
  • Launched March 3, 2008

11
The pharmaceutical RD process
Clinical development
Pharmaco- Vigilance
Discovery research
Preclinical development
Translational medicine
Validation of biomarkers
Identification of biomarkers
Predictive toxicology
Bottleneck areas
Risk assessment with regulatory authorities
Patient recruitment
Predictive pharmacology
Data generation
12
Reasons for failure
Kola I and Landis J (2004). A Survey of
Pharmaceutical Companies Comparing Reasons for
Attrition. Nature Reviews Drug Discovery 3
711715.
13
Addressing the bottlenecks
Innovative Medicines Initiative. Research Agenda
Creating Biomedical RD Leadership for Europe to
Benefit Patients and Society. 15 February 2008
(Version 2.0)
14
Innovative Medicines Initiative
15
(No Transcript)
16
IMI Priorities
17
Key dates
  • March 3, 2008 IMI launch
  • April 30, 2008 first call for proposals
  • 18 topics from
  • Safety
  • Education training
  • Efficacy
  • January 1, 2009
  • start of accepted RD projects
  • Launch of the Knowledge Management initiative.

Budget 122.7 million 172.5 million
295.2 million
18
2. The IMI perspective onKnowledge Management
19
Two levels of KM
  • Disease / patient oriented
  • concerns the physiology and pathophysiology
    related to disease stage or toxicological
    targets
  • Molecule oriented
  • involves lifecycle management of potential drug
    candidates from discovery, over non-clinical and
    clinical development to post-marketing
    surveillance.

20
Disease / patient oriented KM
  • Goal understanding the underlying process
    including the impact of pharmacogenomics in order
    to predict successfully the validity of a drug
    target and risk management for patient
    populations
  • Required KM-solution
  • systems biology models and tools
  • powerful computer models to capture and integrate
    information related to disease stages and related
    to molecules

21
Molecule life cycle oriented KM
  • Goal integrate all available knowledge at any
    given stage of the development process in order
    to make the best predictions possible for the
    chances of success of this molecule in the next
    stage
  • Required KM-solution
  • more elaborate drug databases with better models
    for tracking over time

22
Multiple disciplines
Pharmacology
Bio-Pharmaceutical RD
Translational Medicine
Biology
Medicine
Genomic Medicine
23
Division of labor two IMI teams
  • Translational KM team
  • Biobanks
  • Healthcare Information Technology and Electronic
    Health Records
  • Biomarker Databases and Data Integration and
    Analysis
  • KM Platform team
  • Technical infrastructure architecture and
    services
  • Data resources
  • Knowledge representations and models

24
Knowledge Management Platform
  • an integrative tool that
  • assures synergies with management and
    exploitation of research results by means of
  • extensive data sharing
  • in an open and consistent format that is suitable
    for
  • advanced data analysis in order to
  • obtain new biopharmaceutical insight.

25
KM platform functional architecture
26
Components of the IMI semantic technology
  • Ontology
  • (ideally) a representation of the generic parts
    of the first-order reality relevant for the
    application
  • Data model
  • implementation- independent description of the
    type of data (to be) collected about relevant
    particulars
  • Logical representation
  • implementation-specific description of the data
    model
  • Dictionary
  • list of terms useful to denote the entities about
    which data is collected in human readable format

27
3. Ontology
28
Ontology in philosophy
  • the study of what exists
  • Key questions
  • What exists ?
  • How do things that exist relate to each other ?
  • Some hypotheses
  • An external reality, time, space
  • Particulars, universals, objects, processes
  • Ideas, concepts
  • God
  • Ontologists from distinct schools differ in
    opinion about the existence of some of the above
  • Realism, nominalism, conceptualism, monism,

29
Ontology in software engineering
  • an explicit formal representation of entities
    that are assumed to exist in some area of
    interest
  • Come in various flavors
  • Reference ontologies
  • Application ontologies
  • Domain ontologies
  • Top-level ontologies

30
However
  • Although (almost) everybody (knowledgeable)
    agrees that
  • an ontology is a representation,
  • there is a huge variety in
  • what the representational units in an ontology
    stand for, if anything at all,
  • the degree to which the structure of the ontology
    corresponds with the structure of that part of
    reality it intends to represent.

31
Ontology dramatically hyped !
  • Every term collection with some sort of structure
    is now considered an ontology
  • e.g. The ontologies of interest to X include
  • Emtree from Elsevier,
  • the Gene Ontology,
  • the World Health Organization (WHO) dictionaries,
  • the MedDRA
  • Gary H. Merrill, "The Babylon Project Toward an
    Extensible Text-Mining Platform," IT
    Professional, vol. 5, no. 2, pp. 23-30, Mar/Apr,
    2003

32
Ontology dramatically hyped !
  • Every term collection with some sort of structure
    is now considered an ontology
  • e.g. The ontologies of interest to X include
  • Emtree from Elsevier,
  • the Gene Ontology,
  • the World Health Organization (WHO) dictionaries,
  • the MedDRA
  • Gary H. Merrill, "The Babylon Project Toward an
    Extensible Text-Mining Platform," IT
    Professional, vol. 5, no. 2, pp. 23-30, Mar/Apr,
    2003

None really are ontologies
33
e.g. MedDRA a first-generation term-thesaurus
  • MedDRA Medical Dictionary for Regulatory
    Activities

MedDRA is a registered trademark of the
International Federation of Pharmaceutical
Manufacturers and Associations (IFPMA)
34
The anti-ontological organization of MedDRA
  • Violates many principles for high quality
    terminology design and use
  • Mixing ontology with epistemology
  • HLT Gastrointestinal infections, site
    unspecified
  • HLT Headaches NEC
  • Obscure, non-documented classification criteria
  • LLT Retroauricular pain classified under PT
    Headache (v10, NCI Browser)
  • LLTs under PT denoting distinct generic entities
  • PT Nodal arrhythmia with LLTs Junctional
    bradycardia, Junctional tachycardia,
    Reciprocating tachycardia
  • Inadequate versioning and change management
  • No reasons for change, deletions of HLTs,
  • No definitions for terms.
  1. RL Richesson, KW Fung, JP Krischer. Heterogeneous
    but standard coding systems for adverse events
    Issues in achieving interoperability between
    apples and oranges. Contemporary Clinical Trials
    29 (2008) 635645.
  2. Bousquet C, Lagier G, Lillo-Le Louët A, Le Beller
    C, Venot A, Jaulent MC. Appraisal of the MedDRA
    conceptual structure for describing and grouping
    adverse drug reactions. Drug Saf.
    200528(1)19-34.

MedDRA is a registered trademark of the
International Federation of Pharmaceutical
Manufacturers and Associations (IFPMA)
35
In general, names and terms are inadequate
representational units in absence of documentation
  • JFK Enola Gay
  • Barry Smith George Bush

36
Hallelujah
  • many of the ways we're attempting to apply
    categorization to the electronic world are
    actually a bad fit, because we've adopted habits
    of mind that are left over from earlier
    strategies.
  • Clay Shirky. Ontology is Overrated Categories,
    Links, and Tags. In Clay Shirkys Writings about
    the Internet. 2005. http//shirky.com/writings/ont
    ology_overrated.html

37
Major problems
Solutions
  • A mismatch between what is - and has been - the
    case in reality, and representations thereof in
  • (generic) Knowledge repositories, and
  • (specific) Data and Information repositories.
  • An inadequate integration of a) and b).

P h i l o s o p h y H I T
Philosophical realism
Referent Tracking
38
Philosophical Realism
  • Basic assumptions
  • reality exists objectively in itself, i.e.
    independent of the perceptions or beliefs of
    cognitive beings
  • reality, including its structure, is accessible
    to us, and can be discovered
  • Various forms, e.g.
  • Naive realism
  • things really are as they seem
  • Scientific realism
  • things really are as science determines (or
    ultimately will determine) them to be
  • science discovers objective truths
  • mistakes can be made, but dont invalidate the
    enterprise.

39
Realism-based ontology
  • Three levels of reality
  • First-order reality what is on the side of the
    patient
  • disorders, anatomy, (patho)physiology,
  • Cognitive representations what the clinicians
    assume to observe and know in their mind
  • Representational artefacts for communication,
    documentation,
  • Terms, definitions, drawings, images,
  • Assumption The quality of an ontology is at
    least determined by the accuracy with which its
    structure mimics the pre-existing structure of
    reality.

Smith B, Kusnierczyk W, Schober D, Ceusters W.
Towards a Reference Terminology for Ontology
Research and Development in the Biomedical
Domain. Proceedings of KR-MED 2006, November 8,
2006, Baltimore MD, USA
40
Realism-based Ontology in Nature too
41
Three levels of reality
  • Both RU1B1 and RU1O1 are representational units
    referring to 1
  • RU1O1 is NOT a representation of RU1B1
  • RU1O1 is created through concretization of RU1B1
    in some medium.

42
Compare with Albertis grid
reality
Ontological theory
representation
43
The leading RBO Basic Formal Ontology
  • An ontology which is
  • Realist
  • Fallibilist
  • Perspectivalist
  • Adequatist

There is only one reality and its constituents
exist independently of our (linguistic,
conceptual, theoretical, cultural)
representations thereof,
theories and classifications can be subject to
revision,
there exists a plurality of alternative, equally
legitimate perspectives on that one reality
these alternative views are not reducible to any
single basic view.
44
The BFO view of the world
  • The world consists of
  • entities that are
  • Either particulars or universals
  • Either occurrents or continuants
  • Either dependent or independent and,
  • relationships between these entities of the form
  • ltparticular , universalgt e.g. is-instance-of,
  • ltparticular , particulargt e.g. is-part-of
  • ltuniversal , universalgt e.g. isa (is-subtype-of)

45
General principle about relationships
  • All universal level relationships are defined on
    the basis of particular level relationships
  • Examples of primitive relations
  • c part_of c1 at t - a primitive relation between
    two continuant instances and a time at which the
    one is part of the other
  • c derives_from c1 - a primitive relation
    involving two distinct material continuants c and
    c1

Smith B, Ceusters W, Klagges B, Koehler J, Kumar
A, Lomax J, Mungall C, Neuhaus F, Rector A, Rosse
C. Relations in biomedical ontologies, Genome
Biology 2005, 6R46.
46
Accepts that everything may change
  • changes in the underlying reality
  • Particulars come, change and go
  • changes in our (scientific) understanding
  • The plant Vulcan does not exist
  • reassessments of what is considered to be
    relevant for inclusion (notion of purpose).
  • encoding mistakes introduced during data entry or
    ontology development.

47
Key requirement for updating
  • Any change in an ontology or data repository
    should be associated with the reason for that
    change to be able to assess later what kind of
    mistake has been made !

Ceusters W, Smith B. A Realism-Based Approach to
the Evolution of Biomedical Ontologies.
Proceedings of AMIA 2006, Washington DC,
2006121-125.
48
Example a persons gender in the EHR
  • In John Smiths EHR
  • At t1 male at t2 female
  • What are the possibilities ?
  • Change in reality
  • transgender surgery
  • change in legal self-identification
  • Change in understanding it was female from the
    very beginning but interpreted wrongly
    (congenital malformation)
  • Correction of data entry mistake
  • (was understood as male, but wrongly transcribed)

49
A BFO view on what the IMI semantic technology
should be
Ontology
represents
  • IMI Ontology
  • A representation
  • of first-order reality
  • including the view on reality adhered to in the
    data model
  • of any participating system
  • of any participating organization.

50
Ontological interpretation of a data model
  • In the data model
  • Possible values for gender
  • Male, Female, Unknown, Changed
  • In the ontology (simplified reality)
  • every person has a gender,
  • unknown is not a type of gender, the value is
    used in case the actual gender is not known in
    the system
  • changed is either male ? female or female ?
    male

51
Three types of definitions
Definitions at the level of the
  • Dictionary
  • provide the conditions under which one is allowed
    in a community to use the term to denote some
    entity.
  • Ontology
  • provide the characteristics that distinguish
    entities of the type from entities of other
    types.
  • Data model
  • provide the characteristics that allow one to
    determine whether the entity is of a certain type.

52
4. Referent Tracking
53
For data, unfortunately, codes are not enough
54
How many fractures did this patient have ?
55
How many polyps did this patient have ?
56
In how many supermarkets occurred these accidents
?
57
Referent Tracking solves this problem
  • It is true that
  • (1) All Americans have one mother
  • (2) All Americans have one president
  • But
  • (1) all Americans have a distinct mother
  • (2) all Americans have a (numerically) identical
    president

58
Fundamental goal of Referent Tracking
  • explicit reference to the concrete individual
    entities relevant to the accurate description of
    each patients condition, therapies, outcomes,
    ...

Ceusters W, Smith B. Strategies for Referent
Tracking in Electronic Health Records. J Biomed
Inform. 2006 Jun39(3)362-78.
59
Method numbers instead of words
  • Introduce an Instance Unique Identifier (IUI) for
    each relevant particular (individual) entity

Ceusters W, Smith B. Strategies for Referent
Tracking in Electronic Health Records. J Biomed
Inform. 2006 Jun39(3)362-78.
60
Referent Tracking System Components
  • Referent Tracking Software
  • Manipulation of statements about facts and
    beliefs
  • Referent Tracking Datastore
  • IUI repository
  • A collection of globally unique singular
    identifiers denoting particulars
  • Referent Tracking Database
  • A collection of facts and beliefs about the
    particulars denoted in the IUI repository

Manzoor S, Ceusters W, Rudnicki R. Implementation
of a Referent Tracking System. International
Journal of Healthcare Information Systems and
Informatics 20072(4)41-58.
61
Understanding data and what data is about
62
Referent Tracking System Environment
63
Combining Referent Tracking with Ontology
RT-based Data model
64
Conclusion
  • Europe has set up IMI, a huge public/private
    partnership to solve knowledge management
    problems in the pharma industry.
  • Ontology is recognized to be a key component of
    the technology platform to be developed.
  • Unfortunately, ontology and semantics became
    fashionable words, and quality is hard to obtain.
  • We argue that only ontologies and data models
    based on realism can meet the challenge.
  • Will the US follow ?
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