Research infrastructures to boost r d in the field of rare diseases
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Research Infrastructures to boost R&D in the field of rare Diseases. Ségolène Aymé INSERM, Paris, France Fundacion Ramon Areces 29 Oct 2014. International Rare Disease Research Consortium ( IRDiRC ). Cooperation at international level

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Research infrastructures to boost r d in the field of rare diseases

Research Infrastructures to boost R&D in the field of rare Diseases

SégolèneAymé

INSERM, Paris, France

Fundacion Ramon Areces

29 Oct 2014


International rare disease research consortium irdirc

International Rare Disease Research Consortium (IRDiRC)

Cooperation at international level

to stimulate, better coordinate and maximize output of rare disease research efforts around the world


Research infrastructures to boost r d in the field of rare diseases

  • Public healthcare and research system

Genomics

  • Industry & Manufactures

  • DIAGNOSIS

  • Technology

    • devices, instruments, bioinformatics, systems

Multiple Government Departments

Clinical expertise/experts

Interpretation and application

  • RARE DISEASE SECTOR

    • Clinical & Academic

    • Industry & Manufactures

    • Multiple Government Departments

    • Private Healthcare

Transcriptomics

Metabolomics

Training

Natural History

Phenomics

Policy

Phenomics

  • Public healthcare system

VOICE OF

DATA (EVIDENCE)

Genomics

Proteomics

Training

Position

statements

  • Public healthcare system

Policy

Clinical expertise/experts

  • Private Healthcare

Education

Education

Proteomics

Education

Metabolomics

Proteomics

  • Public healthcare system

  • Clinical and

  • disability services

Clinical expertise/experts

Multiple Government Departments

Interpretation and application

Training

THE CHALLENGE


Rare diseases peculiarities

Rare DiseasesPeculiarities

DISADVANTAGE

  • no or little evidence available

  • small populations , scattered

  • coding and classification poor

  • no jurisdiction , or country with sufficient data

  • require collective data and case finding for evidence

  • not all rare diseases are the same in terms of evidence: e.g. Cystic Fibrosis ≠ Progeria

  • orphan therapies fail the cost effectiveness threshold

ADVANTAGE

  • Clarity in the extreme

  • Phenotype:

    • genotype atomise disease;

    • permit re-aggregation based on pathways perturbed, not clinical presentation

  • New knowledge translation and the portal to Individualised medicine


Research infrastructures to boost r d in the field of rare diseases

Facioscapulohumeral dystrophy

Rett syndrome

Congenital myopathy

Malaria

Mesothelioma

Huntington disease

Hemophilia A

Noonan

Isolated Spina Bifida

Cutaneous lupus erythematosus

Hereditary breast & ovarian cancer syndrome

Systemic sclerosis

Familial long QT syndrome

Fetal cytomegalovirus syndrome

Partial chromosome Y deletion

Williams syndrome

Cystic fibrosis

Motor Neurone Disease

Retinoblastoma

Angelman Syndrome

Niemann-Pick disease

Nemalinemyopathy

Mucopolysaccharidosis 1-3

Duchenne muscular dystrophy

Hereditary spastic paraplegia

Young adult-onset Parkinsonism

Sickle cell anemia

Friedreich ataxia

Alport syndrome

Diffuse large B-cell lymphoma

Fragile X syndrome

Marfan syndrome

Myasthenia gravis

Tuberculosis

Turner Syndrome

Neurofibromatosis type 1

Charcot-Marie-Tooth disease

Phenylketonuria

Familial adenomatouspolyposis

70% of people living with a rare disease

75% of people living with a rare disease

80% of People living with a rare disease


Current status of research in the field of rare diseases based on orphanet data

Currentstatus of researchin the field of rare diseasesbased on Orphanet data


Research infrastructures to boost r d in the field of rare diseases

European rare diseases research landscape (36 countries)

5707 ongoing research projects in Orphanet

covering 2129 diseases, excluding clinical trials

(February 2014)


International rare diseases clinical trial landscape

International rare diseases clinical trial landscape

  • 2476 ongoing national or international clinical trials for 629 diseases in 29 countries

Percentage of clinical trials by category

(April 2014)


Number of genes tested in each country in europe by year

Number of genestested in each country in Europe by year

2010

2011

2012

2013


Possibility to diagnose rare diseases over 2 362 genes tested to date

Possibility to diagnose Rare Diseases:over 2 362genestested to date

Number of rare diseases tested by country

Number of genes tested by country

(April 2014)


Medicinal products on the european market in 2013

Medicinalproducts on the Europeanmarket in 2013

  • 68orphan medicinal products

  • 92 medicinal products without orphan designation with at least an indication for a rare disease or a group of rare disease

  • (January 2014)


Satisfaction for professionals frustration for patients anxiety for payors

Satisfaction for professionalsFrustration for patientsAnxiety for payors

  • Slow translation from bench to bedside

    • Limited access to innovations

  • Too few treatments compared to needs

    • Most patients feeling abandonned

  • High cost of diagnostic tests and drugs

    • Not affordable

  • Necessity to de-risk research

    • Cheaper R&D


How to speed up research and de risk it

How to speed-up research and de-risk it ?

  • Improve coordination and synergies of research at world level

    • To increase the research volume and the quantity of data

  • Support in-silico research

    • to make optimal use of available data

  • Find new business-model for R&D

    • To reduce the cost and et profide affordable treatments


To boost coordination at world level

To boost coordination at world level


Irdirc policy and guidelines principles applying to research activities

IRDiRCpolicy and guidelinesPrinciples applying to Research activities

Sharing and collaborative work in RD research

  • Sharing of data and resources

  • Rapid release of data

  • Interoperability and harmonization of data

  • Data in open access databases

    Scientific standards, requirements and regulations in RD research

  • Projects should adhere to IRDiRC standards

  • Develop ontologies, biomarkers and patient-centered outcome data

  • Cite use of databases and biobanks in publications


Irdirc policy and guidelines

IRDiRCpolicy and guidelines

Participation by patients and / or their representatives in research

  • Act in the best interest of patients

  • Involve patients in all aspects of research

  • Involve patients in design and governance of registries

  • Involve patients in the design, conduct and analysis of clinical trials

  • Acknowledge patients contribution in articles


Irdirc policy and guidelines principles applying to funding bodies

IRDiRCpolicy and guidelinesPrinciples applying to Funding Bodies

  • Promote the discovery of genes

  • Promote the development of therapies

  • Fund pre-clinical studies for proof of concept

  • Promote harmonization, interoperability, sharing, open access data

  • Promote coordination between human and animal models

  • Promote active exchanges between stakeholders through information dissemination of ongoing projects and events


Irdirc policy and guidelines endorsement of standards and tools

IRDiRC policy and guidelines Endorsement of standards and tools

  • Endorsement of standards and tools contributing to IRDiRCobjectives

    • Ontologies: HPO, ORDO…

    • Standards: BRIF…

    • Data sharing: PhenomeCentral, DECIPHER…

    • Ouctome measures: NINDS, PROMIS…


Irdirc recommended

IRDiRC Recommended

  • Label to be used in highlighting tools, standards and guidelines, which contributes directly to IRDiRC objectives

  • Application for ‘IRDiRC Recommended’ label is open to all, including non-IRDiRC members

  • ‘IRDiRC Recommended’ may be awarded to similar tools, standards and guidelines

  • Submission of 1-2 pages application

  • Evaluation of the application by a review panel

  • Approval/rejection of the application by the Executive Committee


Initiatives to speed up data s haring

Initiatives to Speed up Data Sharing


Rational

Rational

  • Research produces an enormous amount of data

  • If shared, will facilitate the development of diagnostics and treatments while ensuring efficient utilization of scarce resources

  • Resources include patient and family material (extracted DNA, cell lines, pathological samples), technical protocols, informatics infrastructure, and analysis tools

  • Datasets include phenotypes, genomic variants, other ‘omic’ data, natural histories, and clinical trial data…


Barriers to data sharing

Barriers to Data Sharing

  • Technical and Financial issues

    • Storingterabytes…Securing data

    • Providing the logistics for sharing data

    • Statistical and algorithmic issues to combine datasets

  • Ethical and Legal issues

    • Data across public and private networks

    • Pricacy protection at national level

  • Cultural issues

    • Reluctance to share data fromresearchers/ Institutions/Regulatory bodies


A c learinghouse of data standards is in development at irdirc

A ClearingHouseof Data Standardsis in development at IRDiRC

  • Five main fields of application

    • Standards in Genomics and other OMICS

    • Standards in Phenotyping

    • Standards in Outcome Measures for clinical trials

    • Standards in Human Data Registration

    • Open-access Data Repositories to store data

  • Alignment with other efforts to ensure interconnection and shareabilitybetween data

    • RD-Connect

    • PCORI, Comete

    • ELIXIR, BD2K, Data FAIRport


Open acess data repositories

Open Acess Data Repositories

  • PhenoTips and PhenomeCentral

  • Repository of data

  • Hub for data sharing

  • CareforRare, RDConnect

  • NIH undiagnosed patients

  • ClinVar and ICCG

  • Public archive of variants and assertions about significance

  • NCBI resource

  • Decipher Database of Chromosome imbalances and phenotypes

  • Using Ensembl resources

  • Sanger Institute

  • Wellcome Trust


Initiatives to speed up data mining

Initiatives to Speed up Data Mining


Rational1

Rational

Make the most of remarkableadvances in the molecular basis of humandiseases

  • dissectthe physiologicalpathways

  • improvediagnosis

  • developtreatments

    Make rare diseases visible in health information systems

  • to gain insight intothem

  • to access real life data alreadycollected

    Improvecoding of RD whichevercoding system used Cross-referencecodingsystems: Orpha nomenclature, ICD10, MeSH, SnoMed-CT, MedDRA


What is the problem computers are not smart enough

Whatis the problem ? Computers are not smart enough….

  • The following descriptions mean the same thing to you:

    • generalizedamyotrophy

    • generalizedmuscle atrophy

    • muscularatrophy, generalized

  • But your computer thinks they're completely unrelated


Research infrastructures to boost r d in the field of rare diseases

Phenomes: a continuum

  • Disease

  • Malformation syndrome

  • Morphologicalanomaly

  • Biologicalanomaly

  • Clinical syndrome

  • Particular clinical situation

  • No type: waiting to have a type attributed


Orphan diseasome

Orphan Diseasome

An Orphan Diseasomepermits investigators to explore the orphan disease (OD) or rare disease relationships based on shared genes and shared enriched features (e.g., Gene Ontology Biological Process, Cellular Component, Pathways, Mammalian Phenotype).

The red nodes represent the orphan diseases and the green ones the related genes. A disease is connected to a gene if and only if a mutation which is responsible of the disease has been identified on this gene.

http://research.cchmc.org/od/01/index.html


Umls unified medical language system

UMLS = Unified Medical Language System

  • ICD = International Classification of Diseases

    • Since 1863 by WHO

    • Used by most countries to code medical activity, mortality data

  • MeSH = Medical Subject Headings

    • controlled vocabulary thesaurus used for indexing articles for PubMed by National Library of Medicine (USA)

  • SnoMed CT = Systematized Nomenclature of Medicine--Clinical Terms

    • clinical terminology by the International Health Terminology Standards Development Organisation (IHTSDO) in Denmark

    • Used in the USA and a few other countries

  • MedDRA = Medical Dictionary for Regulatory Activities

    • medical terminology to classify adverse event information associated with the use of medical products

    • by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA)


Different resources different terminologies

Differentresources, different terminologies

(e)HR:

SNOMED CT

Others?

Free text

Mutation/patient registries,

databases:

HPO

LDDB

PhenoDB

Elements of morphology

Others? Free text?

Tools for diagnosis:

HPO

LDDB

Orphanet


Each terminology has a purpose driven approach

Eachterminology has a purpose–drivenapproach

  • Indexinghealthstatus of individual patients for health management (SnoMED)

  • Detailed, focus on manifestations and complaints

  • Adapted to clinical habits

  • Analyticalapproach

  • Indexinghealthstatus of individual patients for statisticalpurpose in public health (ICD)

    • More agregated, interpretedphenotypicfeatures

    • Agregated concepts

    • Unambiguous to avoidblanks


  • Purpose driven approach 2

    Purpose–drivenapproach (2)

    • Indexinghealthstatus of individual patients for clinicalresearchpurpose(HPO / PhenoDB / Elements of morphology)

      • Highlydetailed to fit with the research questions

      • Specific terminologies developed for disease-specific patient registries

    • Indexinghealthstatus of individual patients for retrievingpossible diagnoses (LDDB,POSSUM,Orphanet)

      • Agregated concepts

      • Requires a judgement of clinicians about phenomic expressions that are relevant

      • Unambiguous to avoidblanks


    How to make all these terminologies inter operable

    How to make all these terminologies inter-operable ?


    Convince the terminologies to converge in some way

    Convince the terminologies to converge in someway….

    • Sept 2012: start of mappings (Orphanet)

    • EUGT2 – EUCERD workshop (Paris, September 2012)

    • ICHPT workshop (ASHG, Boston, October 2013)

      • Selection of 2,300 coreterms

    LDDB

    Elements of Morphology

    POSSUM

    SNOMED CT (IHTSDO)

    ICD (WHO)

    DECIPHER

    PhenoDB

    Orphanet

    HPO


    Phenotype terminology project

    Phenotypeterminologyproject

    • Aims:

      • Mapcommonlyusedclinical terminologies (Orphanet, LDDB, HPO, Elements of morphology, PhenoDB, UMLS, SNOMED-CT, MESH, MedDRA):

        • automaticmap, expert validation, detection and correction of inconsistencies

      • Findcommontermsin the terminologies

      • Produce a coreterminology

        • Common denominatorallowing to share/exchange phenotypic data betweendatabases

        • Mapped to every single terminology


    M apping t erminologies

    MappingTerminologies

    • Orphanet: 1357 terms (Orphanet database, version 2008)

    • LDDB: 1348 dysmorphologicalterms (Installation CD)

    • Elements of Morphology: 423 terms (retrievedmanuallyfrom publication AJMG, January 2009)

    • HPO: 9895 terms (downloadbioportal, obo format, 30/08/12)

    • PhenoDB: 2846 terms (given in obo format, 02/05/2012)

    • UMLS: (version 2012AA) (integratingMeSH, MedDra, SNOMED CT)


    Tools

    Tools

    • OnaGUI (INSERM U729): ontologyalignmenttool

      • Workwith file in owl format

      • I-Subalgorithm: detectsyntaxicsimilarity

      • Graphical interface to check automaticmappings and manuallyaddones

  • Metamap (National Library of Medicine): a tool to map biomedical text to the UMLS Metathesaurus

  • Perl scripts: format conversion, launching Metamap, comparison of results…


  • Comparison of mappings and deduction

    Comparison of mappings and deduction

    • Perl script to compare all the mappings and infermappings of non-Orphanet terminologies

      Eg: Orphanet ID XX mapped to YY in HPO and ZZ in LDDB -> deduction: YY and ZZ shouldprobablymap

    • Retrieve HPO mappings versus UMLS, MeSH

    • First figures:


    Mapping of non orphanet terminologies

    Mapping of non-Orphanet terminologies

    • Automatic and inferedmappingswerechecked by experts

      • UsingOnaGUI for all, except UMLS

        • Automatic I-Sub: 7.0 + deduction

      • Metamap + deduction + HPO mappings

    • Figures:


    First list of common terms

    First list of commonterms

    • Present in at least 2 terminologies

    • Definition of rules for nomenclature

    • Addition of termspresent in eachterminology as synonyms


    Workshop on 21 22 october 2013 in boston success

    Workshop on 21-22 October 2013 in BostonSuccess!

    • Reviewed 2736 terms appearing 2 or more times in the 6 terminologies in 17 hours

    • 2302 terms chosen, including preferred term

    • Definitions are from Elements of Morphology if available, and HPO/Stedman’s Medical Dictionary, if not

    • List of terms, mapping to HPO, PhenoDB, Elements of Morphology will be available at http://ichpt.org by January 2015.

    • All tools will map to this terminology to allow interoperability among resources


    Adoption of a core set of 2 300 terms common to all terminologies

    Adoption of a core set of >2,300 terms common to all terminologies

    Workshop of validation, Boston

    21-22 October 2013

    • Workshop supported by HVP and EuroGenTest

    • Organized by AdaHamosh

    • Expert review of the initial proposal

    • Selection of 2,370 terms

    • Decision to propose them for adoption by all terminologies

    • Establishment of the International Consortium for Human Phenotype Terminologies – ICHPT

    • Publication on the IRDiRC website with definitions from

    • HPO

    • Elements of morphology

    Workshop on Terminologies for RD – Paris, 12 September 2012

    • Many terminologies in use to describe phenomes - No interoperability

    • Joint EuroGenTest and EUCERD workshop

    • Organized by SégolèneAymé

    • Agreement to define a core set of terms common to all terminologies and a methodology

    • Core set identified by cross referencing

    • HPO

    • PhenoDB

    • Orphanet

    • UMLS: MeSH, MedDRA, SnoMed CT

    • LDDB

    • Elements of morphology


    From a terminology to an ontology

    COMPUTERS ARE NOT SMART

    From a terminology to an ontology


    Why ontologies are needed

    Why ontologies are needed ?

    • Ontologies are representations of the knowledge in a waywhichisdirectlyunderstandable by computers

    • Ontologies allowreasoning

    • Ontologies define the objects AND the relationshipbetween the objects

      • Duchenne musculardystrophy (disease) Is a neuromusculardisease (group of diseases)

      • Schistosomias (disease) Is a cause ofanemia (manifestation)


    Standardization of phenotype ontologies

    Standardization of Phenotype Ontologies

    Workshop Sympathy, 19 Apr 2013, Dublin

    Organized by IRDiRC, supported by the University of Dublin, Forge and EuroGenTest

    Conclusion: Adopt HPO & ORDO & cross-reference with OMIM


    Standardisation of p henotype o ntologies

    Standardisation of PhenotypeOntologies

    Rare Diseases

    PhenotypicFeatures

    bioportal.bioontology.org/ontologies/ORDO

    bioportal.bioontology.org/ontologies/HP

    • Based on Orphanetmulti-hierarchical classification of RD

    • Genes– diseasesrelationships

    • Cross-references:

    • For RD nomenclature : OMIM, SNOMED CT, ICD10, MeSH, MedDRA, UMLS

    • For genes : OMIM, HGNC, UniProtKB, IUPHAR, ensembl, Reactome

    ICHPT

    (International Consortium for HumanPhenotype Terminologies)

    2,307 terms- coreterminology

    Mapped to:

    HPOElements of Morphology

    OrphanetLDDB

    SNOMED CT Pheno-DB (OMIM)

    MeSHUMLS

    Availablesoon for downloadat ichpt.org


    Please adopt disseminate hpo and ordo to speed up r d to the benefit of the patients

    Pleaseadopt/disseminateHPO and ORDOto speed up R&Dto the benefit of the patients


    They can help repurpose drugs

    COMPUTERS ARE VERY SMART

    Theycan help repurposedrugs


    Rational make optimal use of molecules already known

    Rational: Make optimal use of moleculesalreadyknown

    • Drug Repositioning orRepurposingisastrategyusedto generateneworadditional valueforadrug, bytargetingdiseasesotherthanthoseforwhichitwasoriginallyintended

      • Address unmet medical needs

      • Reduce time to market due to provided information on

      • Unbiased clinical safety and efficacy data

      • Add value to exiting porfolio

      • Increase drug pipeline

      • Decrease R&D failure risks

      • Decrease development costs

      • Creates new revenue potential


    Graph theory enables drug repurposing gramatica et al plos one vol 1 e84912 2014

    Graph Theory Enables Drug RepurposingGramatica et Al.: PLOS one, Vol 1 e84912, 2014

    • 23 Million articles from PubMed

    • Possible to link the gathered information on drugs, physiological pathways and resulting biological activities with the pathophysiological signs & symptoms of diseases

    • Possible to rank the matches in order to identify the most promising leads


    Graph theory enables drug repurposing gramatica et al plos one vol 1 e84912 20141

    Graph Theory Enables Drug RepurposingGramatica et Al.: PLOS one, Vol 1 e84912, 2014


    Graph theory enables drug repurposing gramatica et al plos one vol 1 e84912 20142

    Graph Theory Enables Drug RepurposingGramatica et Al.: PLOS one, Vol 1 e84912, 2014


    Conclusion

    Conclusion

    • Open access to dataWenowleave in an open world

      • It is an opportunity in research

      • Evidence that open-access to data isbeneficial, especially for the data producer !

      • Orphadataisaccessed by 3000 researchers/ month

    • Agreed standards to make data interoperable

    • Responsibility of Institutions and of individualresearchers


    Thank you for your invitation

    Thankyou for your invitation


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