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

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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

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Short personal history
1977
2006
2004
1989
1992
2002
1998
1995
1993
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Presentation overview

• Europes response to the RD crisis in Pharma
• The Innovative Medicines Initiative (IMI)
• IMIs perspective on Knowledge Management
• Ontology
• Referent Tracking
• Conclusion

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1. Europes response to the RD crisis in pharma
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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.

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Pharma stocks 2003 - 2008
Pharma Index 6 (only Schering
Plough is up) SP500, DOW, NASDAQ 18
(Sep 24, 2008)
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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.

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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.

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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
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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

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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
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Reasons for failure
Kola I and Landis J (2004). A Survey of
Pharmaceutical Companies Comparing Reasons for
Attrition. Nature Reviews Drug Discovery 3
711715.
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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)
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Innovative Medicines Initiative
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(No Transcript)
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IMI Priorities
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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
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2. The IMI perspective onKnowledge Management
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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.

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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

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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

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Multiple disciplines
Pharmacology
Bio-Pharmaceutical RD
Translational Medicine
Biology
Medicine
Genomic Medicine
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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

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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.

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KM platform functional architecture
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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

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3. Ontology
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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,

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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

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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.

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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

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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
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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)
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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)
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In general, names and terms are inadequate
representational units in absence of documentation

• JFK Enola Gay
• Barry Smith George Bush

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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

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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
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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.

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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
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Realism-based Ontology in Nature too
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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.

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Compare with Albertis grid
reality
Ontological theory
representation
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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.
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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)

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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.
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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.

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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.
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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)

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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.

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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

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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.

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4. Referent Tracking
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For data, unfortunately, codes are not enough
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How many fractures did this patient have ?
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How many polyps did this patient have ?
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In how many supermarkets occurred these accidents
?
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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

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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.
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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.
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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.
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Understanding data and what data is about
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Referent Tracking System Environment
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Combining Referent Tracking with Ontology
RT-based Data model
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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 ?