Презентация на тему Addressing Gaps in Clinically Useful Evidence on Drug-drug Interactions – overview of aims

Презентация на тему Addressing Gaps in Clinically Useful Evidence on Drug-drug Interactions – overview of aims, предмет презентации: Красота и здоровье. Этот материал содержит 28 слайдов. Красочные слайды и илюстрации помогут Вам заинтересовать свою аудиторию. Для просмотра воспользуйтесь проигрывателем, если материал оказался полезным для Вас - поделитесь им с друзьями с помощью социальных кнопок и добавьте наш сайт презентаций ThePresentation.ru в закладки!

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


Richard Boyce, PhD
University of Pittsburgh

Department of Biomedical Informatics
NLM Training Conference
June 18th 2014


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My Lab - Translational Informatics Applied to Drug Safety (TRIADS)




http://www.dbmi.pitt.edu/content/triads


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The focus of todays “Show Case”

Improving drug-drug interaction knowledge representation and information retrieval

National Library of Medicine
(1R01LM011838-01)



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What is a drug-drug interaction

Drug-drug interaction:
a clinically meaningful alteration of the 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.


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The clinical importance of PDDIs

Exposure to PDDIs is a significant source 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


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Knowledge is important

Borrowed from Phil Hansten and John Horn


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Key point

Many drug information systems disagree about PDDIs
the specific ones that exist
their potential to cause harm

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



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The danger of incomplete drug-drug interaction knowledge


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Evidence of drug compendia problems

Three PDDI information sources agreed upon only 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]


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Evidence from the drug compendium perspective

Pre-market studies

Post-market studies

Product labeling

Reported in

Clinical experience

Scientific 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


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There is a need for a new PDDI knowledge representation paradigm

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


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PharmGKB as inspiration for a new drug interaction knowledge base (DIKB)

PharmGKB
A 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


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Informatics foundations for a new DIKB: Aim 1

Derive a new PDDI 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


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Aim 1 – Highlights of the approach

…the best thinking of drug 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






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http://goo.gl/232LS2


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Informatics foundations for a new DIKB: Aim 2

Apply a novel evidence 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


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Aim 2 - a step toward the next generation of drug product labeling

PDDI Extraction algorithm

Lovastatin product label





Human curation

Semantic tags

Linking to other relevant sources



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Take the drug interactions section of a drug product label…


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Make it simple for the reader to see claims that could expand or update the information in this section…



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Example: an interaction affecting venlafaxine that may not be in this section…


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Aim 2 – Highlights of the approach







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Aim 2 – Highlights of the approach continued






PDDI information interlinking
Drug name 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


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SPL/DailyMed Jamboree Workshop

Using DailyMed Drug Product Label Data

September 18, 9:30 AM 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


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Informatics foundations for a new DIKB: Aim 3

Pilot test new methods 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


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Informatics foundations for a new DIKB

Product labeling

Scientific literature

A framework for representing 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


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Acknowledgements - People

Co-investigators: Harry Hochheiser, Phil Empey, Carol Collins (UW Seattle), 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




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Acknowledgements - Funding

The American taxpayers via:
NLM (1R01LM011838-01 and T15 LM007059-24)
NIH/NIA (K01AG044433-01, K07AG033174)
Agency for Healthcare Research and Quality (K12HS019461 and R01HS018721)
NIH/NCATS (KL2TR000146)
NIH/NIGMS (U19 GM61388; the Pharmacogenomic Research Network)



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Discussion



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