1. 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

2. Disclosure
I disclose that I have no relevant financial relationships with commercial interests. 2

3. Pharmacogenomics (PGx)
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: 3

4. The need for development of knowledge bases
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
[1]PMID: , [2]PMID: , [3]PMID: , [4]PMID:
[5]PMID: , [6]PMID: , [7]PMID: , [8]PMID:
[9]PMID: , [10]PMID: 4

5. 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 5

6. Improving the Systematic Review Pipeline
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
Synthesize the extracted data
Rate the quality and strength of evidence
Interpret the results
Knowledge-based system (KBS) 6
[11]PMID: , [12]PMID: , [13]PMID:

7. Analogy between TINKERTOY® and Knowledge-Based System
Methods
Analogy between TINKERTOY® and Knowledge-Based System
Constructs e.g., rod
Constructors e.g., connector
Source:
The more complex construction demands
more constructs and constructors 7

8. Knowledge-Based System Is Capable of Providing Reasoning Services
Methods
Knowledge-Based System Is Capable of Providing Reasoning Services
Constructs
i.e., classes,
properties,
instances
Knowledge-Based System
Knowledge Base
Ontology (TBox)
Knowledge Representation Language
Reasoner
formal
representation
reasoning
Asserted Individuals (ABox)
Constructors
i.e., property
restrictions,
set operators
Expressivity in representation
vs.
Efficiency in reasoning
Applications
Retrieval
Classification
Draw Inferences 8

9. Strategy to Develop a KBS de novo
Methods
Strategy to Develop a KBS de novo
Conceptual modeling
Describe a domain through a conceptual model
Ontology development
Convert the model to ontology constructs
Ontology-based knowledge base development
Encode asserted individuals by combining ontology constructs with constructors
Evaluation
Correctness in representation
Efficiency in reasoning 9

10. Prototype PGx-KBS for Clopidogrel and Warfarin
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
Individual S
Individual E
Publication
Study Population
Study Design
Drug Therapy
Risk of Bias Assessment
Comparison
Genetic Variation
Outcome
Effect Estimation 10

11. A Piece of Computable Individual Evidence
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 days [14]PMID: 11

12. 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: , [16]PMID: , [17]PMID:
[18]PMID: , [19]PMID: , [20]PMID:
[21]PMID: , [22]PMID: , [23]PMID:
SRs
Singh15
Jang16
Bauer17
Zabalza18
Jin19
Hulot20
Sofi21
Holmes22
Yamaguchi23
# of MA included 9 6 4 3 2 1
# of E retrieved for all
included MAs 57 58 44 31 19 22 23 16
Computing time (sec) 21 18 17 11
Precision
100% 11

13. 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 13

14. Major Limitations and Future Work
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 14

15. 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 15

16. Thank you! Questions? cherylee@uw.edu16