Addressing Gaps in Clinically Useful Evidence on Drug-drug Interactions – overview of aims презентация

of aims

Richard Boyce, PhD
University of Pittsburgh

Department of Biomedical Informatics
NLM Training Conference
June 18th 2014

information retrieval

National Library of Medicine
(1R01LM011838-01)

effect of a drug (object drug) occurs as a result of coadministration of another drug (precipitant drug) [1]
Potential drug-drug interaction (PDDI):
two drugs known to interact are prescribed whether or not harm ensues [1]

Hines LE, Malone DC, Murphy JE. Recommendations for Generating, Evaluating, and Implementing Drug-Drug Interaction Evidence. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. 2012;32(4):304–313.

of preventable drug-related harm [2,3]

Studies of drug-drug interactions
Harm 1.9 to 5 million inpatients per year
Cause 2,600 to 220,000 emergency department visits per year

2. Magro L, Moretti U, Leone R. Epidemiology and characteristics of adverse drug reactions caused by drug-drug interactions. Expert Opin Drug Saf. 2012 Jan;11(1):83-94. doi: 10.1517/14740338.2012.631910. Epub 2011 Oct 25. Review. PubMed PMID: 22022824.
3. http://www.cdc.gov/nchs/fastats/ervisits.htm, http://www.cdc.gov/nchs/fastats/hospital.htm Last Accessed 12/06/2013

exist
their potential to cause harm

This leads to
confusion and frustration for clinicians
greater risks of harm to patients

25% of 59 contraindicated drug pairs found in black box warnings [28]

18 (28%) of 64 pharmacy information and clinical decisions support systems correctly identified 13 clinically significant DDIs [29]

Four sources agreed on only 2.2% of 406 PDDIs considered to be “major” by at least one source [30]

literature

Rarely reported in

Rarely reported in

Reported in

Rarely reported in

Drug Compendia synthesize PDDI evidence into knowledge but
May fail to include important PDDIs
Often disagree about PDDI evidence and seriousness ranking
May include numerous PDDIs with little evidence for liability reasons

Source for

Source for

representation paradigm

This paradigm should do for PDDIs what the Pharmacogenomics Knowledge Base (PharmGKB) and Pharmacogenomics Research Network (PGRN) have done for clinical pharmacogenomics

single point of entry to nearly all relevant pharmacogenomics research
A network of researchers and stakeholders
A growing set of clinical pharmacogenomics guidelines

Future DIKB
A single point of entry to nearly all relevant DDI research and case reports
A network of researchers and stakeholders
A growing set of clinical guidelines for PDDI exposure

meta-data standard that can meet the information needs of drug compendia editors and pharmacist working in different care settings
the best thinking of drug information system designers and the biomedical ontology community
extends existing national drug terminology efforts
will have a high likelihood of widespread adoption

Pre-market studies

Post-market studies

Clinical experience

A framework for representing PDDI assertions and evidence as interoperable Linked Data available for community annotation

Semantic annotation pipeline

information system designers and the biomedical ontology community
a new OBO ontology for PDDIs and evidence
grounded competency questions
qualitative analysis of interviews with clinical pharmacists, drug compendia editors, and the results of a systematic search of the literature
…extends existing national drug terminology efforts
interoperability with RxNorm and the NDF-RT
…will have a high likelihood of widespread adoption
stakeholders from FDA, NLM, W3C, Pharma, and Cochrane Collaboration

synthesis process to enhance drug product label PDDI information
implement a pipeline for extracting PDDI mentions from product labeling and integrating them with other public sources
annotations can be “curated” by a distributed group of drug experts and non-experts
dynamically enhance product label content

A framework for representing PDDI assertions and evidence as interoperable Linked Data available for community annotation

Data driven:
Synthesis of public PDDI sources
Expert:
Web-based scientific discourse

Knowledge curation

Aim 1

Aim 2

product labeling

PDDI Extraction algorithm

Lovastatin product label

Human curation

Semantic tags

Linking to other relevant sources

expand or update the information in this section…

section…

mapping across sources [1]
Identification and merging of PDDI public information sources [2]
Advancing PDDI evidence reviews
A “Micropublication” model for drug-drug interaction evidence [3]

1. Hassanzadeh O, Zhu Q, Freimuth R, Boyce R. Extending the "Web of Drug Identity" with Knowledge Extracted from United States Product Labels. AMIA Summits Transl Sci Proc. 2013 Mar 18;2013:64-68. PubMed PMID: 24303301; PMCID: PMC3814463
2. Ayvaz S., Zhu Q., Hochheiser H., Brochhausen M., Horn, J., Dumontier, M., Samwald M., Boyce, RD. “Drug-Drug Interaction Data Source Survey and Linking.” Abstract and Poster presentation to appear in AMIA Summits Transl Sci Proc. 2014.
3. Schneider, J., Collins, C., Hines, L., Horn, JR, Boyce, R. “Modeling Arguments in Scientific Papers.” at the 12th Annual ArgDiaP Conference: From Real Data to Argument Mining. Warsaw, Poland, May 23-24 2014. http://jodischneider.com/pubs/argdiap2014.pdf

to 4:15 PM
Lister Hill Auditorium, National Library of Medicine

Topics include:
extracting indication and drug interaction data from structured product labels using natural language processing
Linked Data and structured product labels

http://goo.gl/3rZH9N

for PDDI information retrieval supporting drug information experts
Develop a high performance PDDI information retrieval algorithm

Develop and iteratively refine multiple initial prototypes based on feedback from end users

Report on a single end-user validated design implemented for public demonstration

PDDI assertions and evidence as interoperable Linked Data available for community annotation

Semantic annotation pipeline

Reduced risk of a PDDI medication error!

More efficient synthesis of PDDI evidence, easier identification of gaps

Expected benefits:
More complete and accurate PDDI evidence
Better informed pharmacists and other clinicians
More effective PDDI alerting and decisions support systems

Data driven:
Synthesis of public PDDI sources
Expert:
Web-based scientific discourse

Knowledge curation

Dynamic enhancements

John Horn (UW Seattle), Dan Malone (U of A), Lisa Hines (U of A), William Hogan (UAMS), Mathias Brochhausen (UAMS)
Programmer: Yifan Ning
Students and Research assistants: Katrina Romagnoli, Andres Hernandez Camacho, Jeremy Jao, Serkan Ayvaz (Kent State), Majid Rastegar-Mojarad (Mayo)
Advisors: Rebecca Crowley, Steven Handler, Chip Reynolds, Jordan Karp, Wendy Chapman (U of Utah), Tim Clark and Paulo Ciccarese (Harvard), Robert Freimuth (Mayo, PGRN), Qian Zhu (U of Maryland)
Additional stakeholders: FDA, Cochrane, W3C Health Care and Life Sciences Interest Group, ASHP, IBM Research

K07AG033174)
Agency for Healthcare Research and Quality (K12HS019461 and R01HS018721)
NIH/NCATS (KL2TR000146)
NIH/NIGMS (U19 GM61388; the Pharmacogenomic Research Network)