a framework of knowledge integration and discovery for supporting pharmacogenomicstarget predication...

7/28/2019 A Framework of Knowledge Integration and Discovery for Supporting PharmacogenomicsTarget Predication of Adverse Drug Events

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2013 MFMER | slide-1

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.



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.



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

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