A Knowledge-Based System for
Intelligent Support in
Pharmacogenomics Evidence
Assessment: Ontology-Driven
Evidence Representation and
Retrieval
2017 Joint Summits on Translational Science | AMIA
Tuesday, March 28, 2017
S17: Papers – Clinical Integration
ChiaJu (Cheryl) Lee, PhD
Disclosure
> I disclose that I have no relevant financial relationships
with commercial interests.
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Background and Significance
Precision Medicine Takes Individual
Variation into Account
Harmful but effective
Harmful
Pharmacogenomics
(PGx)
• How genetic variants
affect a person’s
response to a drug
• Takes into account
individual variability in
genes
• For safe & effective
medication prescribing
Neither harmful nor effective
Safe and effective
Source: http://sites.google.com/site/genomicssok/home
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Background and Significance
Widespread Integration of PGx in Everyday
Clinical Practice Is Still Lacking
> Projects in support
of preemptive
genetic testing
–
–
–
–
PREDICT1
The 1200 Patients2
PG4KDS3
RIGHT Protocol4
Insufficient evidence to recommend clinical
validity and utility of a genetic testing5-8
The need for development of knowledge bases
PharmGKB9, ClinVar10, ClinicalTrials.gov
4
[1]PMID: 22588608, [2]PMID: 22929923, [3]PMID: 24619595, [4]PMID: 24388019
[5]PMID: 16013993, [6]PMID: 20981006, [7]PMID: 21884816, [8]PMID: 21619429
[9]PMID: 20350130, [10]PMID: 24234437
Research Hypothesis
Proposal: Knowledge-Based System (KBS)
for Providing Computable PGx Evidence
> With the rapid growth of genomic research, the availability of
evidence may not be a major concern
> Real challenge is how to make effective use of existing study
results to support timely decision making
> Hypothesis: a knowledge-based system to facilitate effective
and efficient evidence assessment of PGx adoption
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Methods
Improving the Systematic Review Pipeline
Natural language processing, machine learning, text mining11-13
Conduct a
literature search
Screen to identify
relevant studies
Extract essential
data from studies
Knowledge-based system (KBS)
Synthesize the
extracted data
6
Rate the quality
and strength of
evidence
[11]PMID: 20920176, [12]PMID: 25005128, [13]PMID: 26073888
Interpret the
results
Methods
Analogy between TINKERTOY® and
Knowledge-Based System
Constructors e.g., connector
Constructs e.g., rod
Source: http://www.knex.com/30-model-super-building-set
The more complex construction demands
more constructs and constructors
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Methods
Knowledge-Based System Is Capable of
Providing Reasoning Services
Constructs
i.e., classes,
properties,
instances
Knowledge
Representation
Language
formal
representation
Constructors
i.e., property
restrictions,
set operators
Ontology
(TBox)
Asserted
Individuals
(ABox)
reasoning
Knowledge Base
Knowledge-Based System
Expressivity in representation
vs.
Efficiency in reasoning
Applications
8
Retrieval
Reasoner
Classification
Draw Inferences
Methods
Strategy to Develop a KBS de novo
1. Conceptual modeling
–
Describe a domain through a conceptual model
2. Ontology development
–
Convert the model to ontology constructs
3. Ontology-based knowledge base development
–
Encode asserted individuals by combining ontology constructs with
constructors
4. Evaluation
–
–
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Correctness in representation
Efficiency in reasoning
Methods
Prototype PGx-KBS for Clopidogrel and
Warfarin
(2) Ontology development
(1) Conceptual modeling
(4) KBS evaluation
(3) KB development
A conceptual model composed of 9 information modules
Individual P
Publication
10
Individual S
Study Population
Study Design
Drug Therapy
Risk of Bias Assessment
Individual E
Comparison
Genetic Variation
Outcome
Effect Estimation
Results
A Piece of Computable Individual Evidence
7 constructs (3 classes, 4 properties), 2 constructors (some, and), 1 data value (i.e., 28)
Outcome measure was the percentage of time of international normalized ratio (INR) in
the therapeutic range up to the follow-up of 28 days14.
[14]PMID: 24251361
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Results
Evaluation of Prototype PGx-KBS for
Retrieval Effectiveness and Efficiency
73 computable Publications
82 computable Studies
445 computable Evidence
33 defined classes
33 IC
[15]PMID: 22929815, [16]PMID: 22591668, [17]PMID: 21816733
[18]PMID: 21693476, [19]PMID: 20845077, [20]PMID: 20620727
[21]PMID: 20351750, [22]PMID: 22203539, [23]PMID: 22757746
Singh15
Jang16
Bauer17
Zabalza18
# of MA included
9
6
4
4
# of E retrieved for all
included MAs
57
58
44
Computing time (sec)
23
21
100%
100%
SRs
Precision
11
Jin19
Hulot20
Sofi21
Holmes22
Yamaguchi23
3
3
2
1
1
31
19
22
23
31
16
21
16
18
17
18
11
9
100%
100%
100%
100%
100%
100%
100%
Contributions
Major Contributions
> Among the first to employ a KBS to improve efficiency,
precision and transparency in the systematic review process
– Ontology for formal representation of PGx knowledge and inclusion
criteria
– KB for accumulation of computable knowledge
– Reasoner for efficient retrieval
> Exploited the expressivity and reasoning ability of OWL 2 DL
– Advanced constructors of OWL 2 DL
> Benefit the evidence-based practice of genomics medicine
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Limitations & Future Work
Major Limitations and Future Work
> The whole process is not seamless in practice
– No efforts have been undertaken to develop plug-ins to automatically
export retrieval results from Protégé to existing statistical software
that supports meta-analysis.
> Scope is limited to clinical validity and utility of clopidogrel
and warfarin PGx
– Future Work: expand from germline to somatic/tumor PGx e.g.,
cancer PGx for targeted therapy selection
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Acknowledgements
Acknowledgements
>
>
>
>
>
Peter Tarczy-Hornoch, MD, FACMI
Beth Devine, Pharm.D., MBA, PhD
James Brinkley, MD, PhD, FACMI
John Horn, Pharm.D., FCCP
University of Washington Colleagues
– Precision Medicine Informatics Group
> This work was supported in part by U01 HG006507,
Biomedical and Health Informatics Program and
Pharmaceutical Outcomes Research & Policy Program
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Thank you! Questions? [email protected]
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