a framework of knowledge integration and discovery for supporting pharmacogenomicstarget predication...
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A Framework of Knowledge Integration and
Discovery for Supporting PharmacogenomicsTarget Predication of Adverse Drug Events
A Case Study of Drug-Induced Long QT Syndrome
Guoqian Jiang, M.D., Ph.D.
Mayo Clinic2013 AMIA Summit on Translational BioinformaticsMarch 20, 2013
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Acknowledgements
Co-authorsChen Wang, PhDQian Zhu, PhDChristopher G. Chute, MD. Dr. PH
This work was supported in part by the Pharmacogenomic Research Network
(NIH/NIGMS - U19 GM61388 ) and
the SHARP Area 4: Secondary Use of EHRData (90TR000201)
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Introduction
Adverse drug events (ADEs) have been wellrecognized as a cause of patient morbidity andincreased health care costs in the UnitedStates.
The genetic component of ADEs is being considered as a significant
contributing factor for drug response
variability and drug toxicity, representing a major component of the
movement to pharmacogenomics andindividualized medicine
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Identifying pharmacogenomics evidence
Text mining of published literature MEDLINE abstracts
Human-based curation approachPharmGKB
Binary model Drug-gene Disease-gene
Ternary model Drugs vs. ADEs vs. Gene Targets Guiding pharmacogenomics knowledge integration
and discovery
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ADEpedia
A standardized knowledge base of ADEs fordrug safety surveillance
FDA Structured Product Labeling (SPL),FDA Adverse Event Reporting System(AERS) and
the Unified Medical Language System(UMLS)
http://adepedia.org
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Semantic MEDLINE
It is a recent development by the NationalLibrary of Medicine that is a semanticallyannotated literature corpus.
It identifies genes noted in biomedical text asassociated with a disease process or a drugand can potentially simplify secondary databasecuration.
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Objectives
To build a framework of knowledge integration anddiscovery that aims to support pharmacogenomics-target predication of ADEs.
For knowledge integration we leverage scalable semantic web technologies
For knowledge discovery we develop a network analysis-based knowledge
discovery approach
A case study of long QT syndrome induced by tricyclicantidepressive agents.
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Long QT Syndrome
It is a heart condition in which delayed repolarization of theheart following a heartbeat causes prolongation of the QTinterval, and increases the risk of torsades de pointes,ventricular fibrillation and sudden cardiac death.
Drug-induced QT prolongation is an increasing publichealth problem.
Many non-cardiac drugs such as tricyclic antidepressantshave also been reported to cause QT prolongation.
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System Architecture
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Severe ADE Extraction
Used a normalized dataset extracted from FDA-AERS database
Putative drug-ADE pairsOutcome codes
Validated using ADE dataset from SIDER andthe UMLS
Classified by the Common Terminology Criteriafor Adverse Event (CTCAE) grading system
grade >=3 is considered as Severe
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Severity Classification of ADEs
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SPARQL-Enabled Genetic AssociationExtraction
Long QT Syndrome|C0023976
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SPARQL Query Results
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A Network Analysis-based Approach
Used the Human Protein Reference Database (HPRD) protein-protein interaction (PPI) network
Assumption: closeness of interactions within PPI impliesrelevance between drug- and ADE- genes
calculated average distance of genes between two groups used a random permutation approach for statistical
significance
prioritized closely related genes Performed Gene Functional Classification and Enrichment analysis
an online bioinformatics application known as DAVIDdeveloped by the National Institute of Allergy and InfectiousDiseases (NIAID), NIH
Manually reviewed the relevant PubMed abstracts for validation
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Prioritized drug- and ADE- genes
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Gene Functional Classification
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Validation by a manual review
We retrieved the PubMed IDs linked with the drug genes (KCNK3,ACCN2, KCNJ6 and KCNH2), and manually reviewed all theoriginal abstracts.
Among 16 PubMed IDs retrieved, 12 abstracts are true positive(75%), i.e. correctly reflecting the association between a tricyclicantidepressant and a target gene.
Of the 12 abstracts, 8 abstracts (linked with KCNH2 ) mentioned ofthe target drug genes that are related to long QT syndromewhereas 4 abstracts (linked with ACCN2 and KCNJ6) did notmention of.
The results indicated that KCNH2 is a well-studied gene across thetarget drug and the target ADE while ACCN2 and KCNJ6 arepotential candidates for the pharmacogenomics-target predication.
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DiscussionKnowledge Integration
We integrated a semantically annotated literature corpusSemantic MEDLINE with a normalized ADEknowledgebase ADEpedia using a semantic web-baseddata integration approach.
Bio2RDF (http://bio2rdf.org/ ) Data2Semantics (http://aers.data2semantics.org/) Our ADEpedia project mainly focused on the
standardization of ADE knowledge using standard drug
and ADE terminologies (e.g., RxNorm, MedDRA,SNOMED CT and UMLS).
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DiscussionKnowledge Integration
We found that the normalization of the drugsand the ADEs using the UMLS is extremelyimportant for both data integration andaggregation.
For example, the UMLS enabled us to retrieveall the descendants of the drug classAntidepressive Agents, Tricyclic|C0003290,
which provided the aggregation power forcollecting genetic associations of the drug class.
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DiscussionKnowledge Discovery
As genes derived from text mining could be verygeneral and contain many false-positives, weproposed to apply network analysis to filter outless-relevant genes through additional
evidence.
We will explore other knowledge resources(e.g., Pathway interaction database) to improve
the performance of the knowledge discoverymodel.
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Questions & Discussion