A Knowledge-based System for Intelligent Support in Pharmacogenomics Evidence Assessment: Ontology-driven Evidence Representation, Retrieval, Classification and Interpretation
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Pharmacogenomics is the study of how genetic variants affect a person’s response to a drug. With great advances to date, pharmacogenomics holds promise as one of the approaches to precision medicine. Yet, the use of pharmacogenomics in routine clinical care is minimal, partly due to the misperception that there is insufficient evidence to determine the value of pharmacogenomics and the lack of efficient and effective use of already existing evidence. Enormous efforts have been directed to develop pharmacogenomics knowledge bases; however, none of them fulfills the functionality of providing effective and efficient evidence assessment that supports decisions on adoption of pharmacogenomics in clinical care. In this context, my overall hypothesis was that a knowledge-based system that fulfills three critical features, including clinically relevant evidence, providing an evidence-based approach, and using semantically computable formalism, could facilitate effective and efficient evidence assessment to support decisions on adoption of pharmacogenomics in clinical care. My overarching research question has been: How can we exploit state-of-the-art knowledge representation and reasoning in developing a knowledge-based system with the intended features and applications as specified above. The first aim of this research was to develop a conceptual model to address the information needs and heterogeneity problem for the domain of pharmacogenomics evidence assessment. Faceted analysis and fine-grained characterization of clinically relevant evidence acquired from empirical pharmacogenomics studies were deployed to identify 3 information entities, 9 information components, 30 concepts, 49 relations and approximately 250 terms as building blocks of the conceptual model. These building blocks were then organized into a model, which features a layered and modular structure so that heterogeneous information content of pharmacogenomics evidence could be expressed to reflect its intended meaning. The developed conceptual model was validated against a general ontology of clinical research (OCRe) to show its strength in modeling pharmacogenomics publications, studies and evidence in an extensible and easy-to-understand way. The second aim of this research was to exploit OWL 2 DL to build a knowledge-based system that enables formal representation and automatic retrieval of pharmacogenomics evidence for systematic review with meta-analysis. The conceptual model developed in Aim 1 was encoded into an OWL 2 DL ontology using Protégé. The constructed ontology provides approximately 400 formalized vocabularies, which were used in turn to formally represent 73 individual publications, 82 individual studies and 445 individual pieces of evidence, and thereafter formed a knowledge base. After a series of subsumption checking and instance checking using HermiT reasoner, the implemented knowledge-based system was verified as consistent and correct. The third aim of this research was to use the implemented knowledge-based system to provide four applications in pharmacogenomics evidence assessment. The first application focused on the ontology-driven evidence retrieval for meta-analysis. A total of 33 meta-analyses selected from 9 existing systematic reviews were used as test cases. The results showed that the ontology-based approach achieved a 100% precision of evidence retrieval in a very short time, ranging from 9 to 23 seconds. The second application addressed the evidence assessment of the clinical validity of CYP2C19 loss-of-function variants in predicting efficacy of clopidogrel therapy. The third application addressed the evidence assessment of the comparative effectiveness of genotype-guided versus non-genotype-guided warfarin therapy. These two applications focused on ontology-driven evidence classification to provide useful information to assist in the planning, execution, and reporting of a multitude of meta-analyses. The fourth application focused on ontology-driven interpretation of a multitude of synthesized evidence that was enabled by formal representation of synthesized evidence and typology of clinical significance in the context of assessing clinical validity and clinical utility of pharmacogenomics. In conclusion, the major contributions of this research include: deriving an extensible conceptual model that expresses heterogeneous information content, constructing an ontology that exploits the advanced features of OWL 2 DL, and implementing a knowledge-based system that supports ontology-driven evidence retrieval, classification and interpretation. Future research would focus on (1) enhancing the system’s applicability in pharmacogenomics evidence assessment by representing evidence of other sub-domains of pharmacogenomics such as cancer drugs, and (2) expanding the system’s capability beyond pharmacogenomics evidence assessment by representing individuals’ genomic profiles and providing evidence-based interpretation based on their individual genomic profiles. With the enhanced applicability, the pharmacogenomics knowledge-based system might improve pharmacogenomics evidence assessment as well as evidence-based interpretation of pharmacogenomics at the point of care, and ultimately increase the adoption of pharmacogenomics in routine clinical care.