Potential drug-drug interactions (PDDIs) represent a significant causality for adverse drug events. Unfortunately, current resources for providers are incomplete and inaccurate. We propose a new PDDI knowledge representation paradigm that we hypothesize will yield more clinically relevant evidence than is currently possible. Starting from our extensive body of preliminary work, we will build a framework that implements the new paradigm using statins and psychotropics (antidepressants and antipsychotics). We expect that the framework will be generalizable to PDDIs involving other drugs, including those predicted using methods from pharmacology and bioinformatics. We will advance three research aims while building the exemplar framework. The first research aim is to derive a new meta-data standard for representing PDDI knowledge that satisfies the information needs of pharmacist working in different care settings. An information needs inquiry will result in clinical scenarios that will then inform, and later validate, the new standard. We will design the standard so that it reflects the best thinking of Semantic Web community and will have a high likelihood of widespread adoption. We will then combine the new standard with semantic annotation and best practices for publishing Linked Data to create a Semantic Web knowledge base of statin and psychotropic PDDIs. The second research aim is to compare PDDI evidence on the Semantic Web with existing PDDI knowledge resources for completeness, accuracy and currency. We will validate a mechanism for linking statin and psychotropic PDDI assertions to relevant evidence on the Semantic Web. Because pharmacogenomics can impact many PDDIs, we will also link to an interoperable representation of this evidence. Two pharmacists will then compare the coverage and quality of the PDDI evidence on the Semantic Web with three existing resources using a new PDDI evidence scoring tool. The third research aim is to investigate a process for filling in gaps in clinically useful PDDI knowledge that cannot be filled with available evidence. We will utilize a consensus-based approach to select high priority PDDIs and evaluate their clinical relevance by retrospective cohort studies. We will extend the Linked Data PDDI knowledge base with the results of these studies, and make the knowledge base publicly available via a pilot web portal. The proposed work will contribute to public health by making more effective use of PDDI evidence, filling in important gaps in drug safety knowledge, and spurring innovations in drug information retrieval.

Public Health Relevance

We propose a new knowledge representation paradigm for potential drug-drug interactions that we hypothesize will yield more clinically relevant evidence than is currently possible. The proposed work will contribute to public health by making more effective use of drug-drug interaction evidence, filling in important gaps in drug safety knowledge, and spurring innovations in drug information retrieval.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
1R01LM011838-01
Application #
8614005
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2014-02-15
Project End
2018-01-31
Budget Start
2014-02-15
Budget End
2015-01-31
Support Year
1
Fiscal Year
2014
Total Cost
$400,000
Indirect Cost
$115,678
Name
University of Pittsburgh
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Knowledge Base workgroup of the Observational Health Data Sciences and Informatics (OHDSI) collaborative (2017) Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data. J Biomed Semantics 8:11
Ie, Kenya; Chou, Eric; Boyce, Richard D et al. (2017) Potentially Harmful Medication Use and Decline in Health-Related Quality of Life among Community-Dwelling Older Adults. Drugs Real World Outcomes 4:257-264
Romagnoli, Katrina M; Nelson, Scott D; Hines, Lisa et al. (2017) Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews. BMC Med Inform Decis Mak 17:21
Freimuth, R R; Formea, C M; Hoffman, J M et al. (2017) Implementing Genomic Clinical Decision Support for Drug-Based Precision Medicine. CPT Pharmacometrics Syst Pharmacol 6:153-155
Romagnoli, Katrina M; Boyce, Richard D; Empey, Philip E et al. (2017) Design and evaluation of a pharmacogenomics information resource for pharmacists. J Am Med Inform Assoc 24:822-831
Utecht, Joseph; Brochhausen, Mathias; Judkins, John et al. (2017) Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation. Stud Health Technol Inform 245:960-964
Voss, E A; Boyce, R D; Ryan, P B et al. (2017) Accuracy of an automated knowledge base for identifying drug adverse reactions. J Biomed Inform 66:72-81
Huser, Vojtech; DeFalco, Frank J; Schuemie, Martijn et al. (2016) Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets. EGEMS (Wash DC) 4:1239
Boyce, Richard D; Handler, Steven M; Karp, Jordan F et al. (2016) Preparing Nursing Home Data from Multiple Sites for Clinical Research - A Case Study Using Observational Health Data Sciences and Informatics. EGEMS (Wash DC) 4:1252
Hochheiser, Harry; Ning, Yifan; Hernandez, Andres et al. (2016) Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study. JMIR Res Protoc 5:e40

Showing the most recent 10 out of 19 publications