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Evidence and knowledge on drug-drug interactions to support drug compendia editors--obi-dev-call--2016 05 09

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A talk to developers of The Ontology for Biomedical Investigations (OBI), in the lead-up to the OBI-ECO workshop in Baltimore May 11th and 12th.

Focus is the evidence modeling underway for the "Addressing gaps in clinically useful evidence on drug-drug interactions" R01 project led by Richard Boyce.

See also https://github.com/dbmi-pitt/DIKB-Micropublication

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Evidence and knowledge on drug-drug interactions to support drug compendia editors--obi-dev-call--2016 05 09

  1. 1. Evidence and knowledge on drug-drug interactions to support drug compendia editors Jodi Schneider 1 OBI-DEV call 2016-05-09
  2. 2. “Addressing gaps in clinically useful evidence on drug-drug interactions” 4-year project, U.S. National Library of Medicine R01 grant (PI, Richard Boyce; R01LM011838) o Evidence panel of domain experts: Carol Collins, Amy Grizzle, Lisa Hines, John R Horn, Phil Empey, Dan Malone o Informaticists: Jodi Schneider, Harry Hochheiser, Katrina Romagnoli, Samuel Rosko o Ontologists: Mathias Brochhausen, Bill Hogan o Programmers: Yifan Ning, Wen Zhang, Louisa Zhang 2
  3. 3. Problem o Thousands of preventable medication errors occur each year. o Clinicians rely on information in drug compendia (Physician’s Desk Reference, Medscape, Micromedex, Epocrates, …). 3
  4. 4. Problem o Drug compendia have information quality problems: • differ significantly in their coverage, accuracy, and agreement • often fail to provide essential management recommendations about prescription drugs o Drug compendia synthesize drug interaction evidence into knowledge claims but: • Disagree on whether specific evidence items can support or refute particular knowledge claims • May fail to include important evidence 4
  5. 5. Goals o Long-term, provide drug compendia editors with better information and better tools, to create the information clinicians use. o Work on semantic representation and annotation: • Representing knowledge claims about medication safety • Representing their supporting evidence • Acquiring knowledge claims & evidence 5
  6. 6. DOMAIN-SPECIFIC ARGUMENTATION 6
  7. 7. Drug Interaction Probability Score 1. Are there previous credible reports in humans? • If there are case reports or prospective studies that clearly provide evidence supporting the interaction, answer YES. For case reports, at least one case should have a “possible” DIPS rating (score of 2 or higher). • If a study appropriately designed to test for the interaction shows no evidence of an interaction, answer NO. … 5. Did the interaction remit upon de-challenge of the precipitant drug with no change in the object drug? (if no de-challenge, use Unknown or NA and skip Question 6) • Stopping the precipitant drug should bring about resolution of the interaction, even if the object drug is continued without change. … • If dechallenge of the precipitant drug without a change in object drug did not result in remission of the interaction, answer NO. • If no dechallenge occurred, the doses of both drugs were altered, or no information on dechallenge is provided, answer NA. [Horn et al. 2007] 7
  8. 8. [Boyce, DIKB, 2006-present] 8
  9. 9. [Boyce, DIKB, 2006-present] 9
  10. 10. DESIGNING AN EVIDENCE BASE 10
  11. 11. Multiple layers of evidence Medication Safety Studies Layer Clinical Studies and Experiments Scientific Evidence Layer 11
  12. 12. [Brochhausen, Schneider, Malone, Empey, Hogan and Boyce “Towards a foundational representation of potential drug-drug interaction knowledge.” First International Workshop on Drug Interaction Knowledge Representation (DIKR-2014) at ICBO.] 12
  13. 13. SCIENTIFIC EVIDENCE LAYER 13
  14. 14. Scientific Evidence Layer: Micropublications [Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications.] 14
  15. 15. Scientific Evidence Layer: Micropublications [Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications] 15
  16. 16. MODELING NARRATIVE DOCUMENTS AS EVIDENCE 16
  17. 17. 17
  18. 18. 18
  19. 19. MP:Claim 19
  20. 20. 20
  21. 21. Building up an MP graph 21
  22. 22. Building up an MP graph 22
  23. 23. Building up an MP graph 23
  24. 24. Building up an MP graph 24
  25. 25. Building up an MP graph 25
  26. 26. Building up an MP graph 26
  27. 27. Building up an MP graph 27
  28. 28. Building up an MP graph 28
  29. 29. Building up an MP graph 29
  30. 30. Work to date o 410 assertions and 519 evidence items transformed from prior work. o 609 evidence items (pharmacokinetic potential drug-drug interactions) annotated by hand from 27 FDA-approved drug labels. o 230 assertions of drug-drug interactions annotated by hand from 158 non-regulatory documents, including full text research articles. 30
  31. 31. DIRECTIONS & FUTURE WORK 31
  32. 32. We are developing a search/retrieval portal It will: o Integrate across multiple types of source materials (FDA drug labels, scientific literature, …) o Systematize search: Enable ALL drug compendium editors to access the same info o Provide direct access to source materials • E.g. quotes in context 32
  33. 33. 33
  34. 34. Evaluation plan for the search/retrieval portal o 20-person user study o Measures of • Completeness of information • Level of agreement • Time required • Perceived ease of use 34
  35. 35. Generate multiple KBs from the same EB 35
  36. 36. Evidence Base "erythromycin inhibits CYP3A4." "CYP3A4 catalyzes a Phase I or Phase II enzymatic reaction involving simvastatin." OWL inference "erythromycin ‘inhibits-catalyzes metabolism of’ simvastatin." Knowledge Base } Statement A "Statement A is about {erythromycin}." "Statement A is about {simvastatin}." "Statement A is about {CYP3A4}." "Statement A is a PDDI statement." transformation Each OWL-inferred assertion in the evidence base generates a new individual in the knowledge base.
  37. 37. Thanks to collaborators & funders o Training grant T15LM007059 from the National Library of Medicine and the National Institute of Dental and Craniofacial Research o The entire “Addressing gaps in clinically useful evidence on drug-drug interactions” team from U.S. National Library of Medicine R01 grant (PI, Richard Boyce; R01LM011838) and other collaborators 37
  38. 38. Jodi Schneider, Mathias Brochhausen, Samuel Rosko, Paolo Ciccarese, William R. Hogan, Daniel Malone, Yifan Ning, Tim Clark and Richard D. Boyce. “Formalizing knowledge and evidence about potential drug-drug interactions.” International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I 2015) at ISWC 2015 Bethlehem, Pennsylvania, USA. Jodi Schneider, Paolo Ciccarese, Tim Clark and Richard D. Boyce. “Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.” 4th Workshop on Linked Science 2014—Making Sense Out of Data (LISC2014) at ISWC 2014 Riva de Garda, Italy. Mathias Brochhausen, Jodi Schneider, Daniel Malone, Philip E. Empey, William R. Hogan and Richard D. Boyce “Towards a foundational representation of potential drug-drug interaction knowledge.” First International Workshop on Drug Interaction Knowledge Representation (DIKR-2014) at the International Conference on Biomedical Ontologies (ICBO 2014) Houston, Texas, USA. Richard D. Boyce, John Horn, Oktie Hassanzadeh, Anita de Waard, Jodi Schneider, Joanne S. Luciano, Majid Rastegar-Mojarad, Maria Liakata, “Dynamic Enhancement of Drug Product Labels to Support Drug Safety, Efficacy, and Effectiveness.” Journal of Biomedical Semantics. 4(5), 2013. doi:10.1186/2041-1480-4-5 38

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