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Biomedical Informatics1
Initial progress on the
journey toward an open
source potential drug-
drug interaction
knowledge b...
Biomedical Informatics2
Disclosures
• Neither myself or my spouse have any
relevant financial relationships with
commercia...
Biomedical Informatics3
Key point from my last VistA
Community talk in January…
• Many drug information systems disagree
a...
Biomedical Informatics4
The danger of incomplete drug-
drug interaction knowledge
Evidence of drug compendia problems
• Three PDDI information sources agreed upon only
25% of 59 contraindicated drug pairs...
Biomedical Informatics6
Problems with drug compendia extend
to non-commercial sources
“our investigation of NDF-RT and Dru...
Is this true of the translated French
PDDI dataset?
• Free online: http://ansm.sante.fr/Dossiers/Interactions-
medicamente...
Examples interactions
DIGOXIN
RxNorm: 3407
ATC: C01AA05
CLARITHROMYCIN
RxNorm: 21212
ATC:
J01FA09 A02BD04
A02BD05 A02BD06
...
Biomedical Informatics9
Extending our overlap comparison to the
translated French PDDI dataset
• Methods
– Map the drug me...
The examined datasets
Source Description Mapped/
Original
Category Data owner/
Maintainer
Frequen
cy of
updates
Credibleme...
Biomedical Informatics11
PDDI comparison caveats
• Comparisons were with versions of the other
database that were current ...
Results – Simple overlap
Credible
Meds
Credible
Meds
NDF-RT
16
(0.6%, 19.5%)
NDF-RT
ONC High
Priority
8
(0.7%, 9.8%)
225
(...
Thinking through these results
• WorldVista DrugBank : ~16%
– Similar to NDF-RT : ~11%
• DrugBank  WorldVista ~12%
– Int...
Information needs
Drug Information
• Pharmacology
• Mechanism of action
• Formulation
• Timing
• …
Evidence
• Study design...
What information in the French PDDI data?
Data element CredibleMeds ONC High
Priority
ONC Non-
interruptive
Drug-
Bank
Wor...
Biomedical Informatics16
What about agreement across sources?
1) NDF-RT (2,598), WorldVista (16,202), DrugBank
(12,113)
Ov...
Biomedical Informatics17
Thoughts about the overlap analysis…
• Not surprisingly – pairwise overlap is fairly
poor
– Does ...
Biomedical Informatics18
Take home point
• The WorldVista translation of the French
PDDI dataset is very promising as a “k...
Biomedical Informatics19
Contributions my lab plans to make toward
converting the French PDDI dataset to a
public drug-dru...
Biomedical Informatics20
Better structuring of the information to
meet clinical information needs
• drugs involved,
• seri...
Biomedical Informatics21
Example
• Warfarin – NSAIDs
– Current example
• http://goo.gl/1W0k6A
• https://www.dikb.org/Merge...
Data element Value
clinical
consequences
 Increase of the oral anticoagulant’s risk of hemorrhage, especially upper
gastr...
Biomedical Informatics23
PDDI Minimum Information Task Force
• Formed to create broad consensus on
the definitions and con...
Biomedical Informatics24
Meet the PDDI Minimum Information Task
Force:
• volunteer-based – currently ~40 participants
– Wo...
Task force objective and deliverables
• Objective: Develop a minimal information model for
drug interaction evidence and k...
Biomedical Informatics26
Possibly one of the most important
deliverables….
• Create a foundation for further
collaborative...
Progress so far…
• Selected most of the PDDIs to focus on
– https://goo.gl/rYpmjt
– Decision trees developed for:
• Warfar...
Biomedical Informatics28
Contributions my lab plans to make toward
converting the French PDDI dataset to a
public drug-dru...
Data element Value
clinical
consequences
 Increase of the oral anticoagulant’s risk of hemorrhage, especially upper
gastr...
Example of the need to connect evidence
ATORVASTATIN
RxNorm: 83367
ATC: C10AA05
CLARITHROMYCIN
RxNorm: 21212
ATC:
J01FA09 ...
Biomedical Informatics31
Questions about clarithromycin –
atorvastatin PDDI
• What is the mechanism – pharmacokinetic or
p...
Primary
data
Authors using annotation toolsNew evidence
items
Existing evidence
items
Argument
graphs
Product label,
Journ...
Evidence annotation example
Evidence annotation example…
Biomedical Informatics35
Progress so far…
• Annotated pharmacokinetic interactions for
65 drugs
– The evidence board for m...
Biomedical Informatics36
Progress so far…
• A proposed standard approach for
assessing the existence of an interaction
– B...
PDDI evidence assessment
Sufficient
Evidence?
• Conflicting evidence
• Magnitude of effect
• Biological plausibility
Clini...
The DRug Interaction eVidence Evaluation
(DRIVE) Instrument (being tested)
Category Evidence
Sufficient
evidence that a
dr...
Biomedical Informatics39
Evaluating DRIVE
• Phase 1: “usability” evaluation
– Completed
• Phase 2a: interrater reliability...
Biomedical Informatics40
Conclusions
• While challenging, progress is being made
toward a high quality, open source, PDDI
...
Biomedical Informatics41
Learn more!
• http://dikb.org
• Contact me rdb20@pitt.edu
Biomedical Informatics42
Acknowledgements - Funding
• The American taxpayers via:
– NLM (R01LM011838 and T15 LM007059-24)
...
Biomedical Informatics43
Acknowledgements - People
• Co-investigators: Harry Hochheiser, Phil Empey, Carol Collins
(UW Sea...
Biomedical Informatics44
Discussion
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Initial progress on the journey toward an open source potential drug-drug interaction knowledge base

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Presentation given at the 33rd VistA Community Meeting - George Mason University focusing on progress towards and open source potential drug interaction knowledge base

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Initial progress on the journey toward an open source potential drug-drug interaction knowledge base

  1. 1. Biomedical Informatics1 Initial progress on the journey toward an open source potential drug- drug interaction knowledge base Richard D. Boyce, PhD University of Pittsburgh 33rd VistA Community Meeting May 24th 2016
  2. 2. Biomedical Informatics2 Disclosures • Neither myself or my spouse have any relevant financial relationships with commercial interests
  3. 3. Biomedical Informatics3 Key point from my last VistA Community talk in January… • Many drug information systems disagree about potential drug-drug interactions (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
  4. 4. Biomedical Informatics4 The danger of incomplete drug- drug interaction knowledge
  5. 5. Evidence of drug compendia problems • Three PDDI information sources agreed upon only 25% of 59 contraindicated drug pairs found in black box warnings • 18 (28%) of 64 pharmacy information and clinical decisions support systems correctly identified 13 clinically significant DDIs • Four sources agreed on only 2.2% of 406 PDDIs considered to be “major” by at least one source Wang LM, Wong M, Lightwood JM, Cheng CM. Black box warning contraindicated comedications: concordance among three major drug interaction screening programs. Ann Pharmacother. 2010;44(1):28–34. doi:10.1345/aph.1M475. Saverno KR, Hines LE, Warholak TL, et al. Ability of pharmacy clinical decision-support software to alert users about clinically important drug-drug interactions. J Am Med Inform Assoc. 2011;18(1):32–37. doi:10.1136/jamia.2010.007609. Abarca J, Malone DC, Armstrong EP, et al. Concordance of severity ratings provided in four drug interaction compendia. J Am Pharm Assoc (2003). 2004;44(2):136–141.
  6. 6. Biomedical Informatics6 Problems with drug compendia extend to non-commercial sources “our investigation of NDF-RT and DrugBank as sources of DDIs for our API provides a mixed picture. Not only do they both provide incomplete coverage of the reference set (about 60% each), but their overlap is also limited (42%)”. Peters, Lee B., Nathan Bahr, and Olivier Bodenreider. "Evaluating drug-drug interaction information in NDF-RT and DrugBank." Journal of biomedical semantics 6.1 (2015): 19.
  7. 7. Is this true of the translated French PDDI dataset? • Free online: http://ansm.sante.fr/Dossiers/Interactions- medicamenteuses/Interactions-medicamenteuses/%28offset%29/0 • Potentially interacting drugs and drug groups – Ingredients X drug groups : ~3,000 – Expands to ~ 116,000 ingredient pairs
  8. 8. Examples interactions DIGOXIN RxNorm: 3407 ATC: C01AA05 CLARITHROMYCIN RxNorm: 21212 ATC: J01FA09 A02BD04 A02BD05 A02BD06 A02BD07 Increase of the digoxinemia due to increase of its absorption Precaution for use Clinical monitoring and possibly monitoring of the digoxinemia during the treatment with the clarithromycin and after it is stopped. DIGOXIN RxNorm: 3407 ATC: C01AA05 RITONAVIR BOOSTED PROTEASE INHIBITORS CLASS CODE: J05AE-002 Increase of the digoxinemia, more marked for IV route, due to increase of the absorption of the digoxin or decrease of its renal clearance. Precaution for use Clinical monitoring and, if there is reason for them, EKG and monitoring of the digoxinemia, with possible adjustment of the dosage of digoxin.
  9. 9. Biomedical Informatics9 Extending our overlap comparison to the translated French PDDI dataset • Methods – Map the drug mentions in the French PDDI to DrugBank • using an RxNorm to DrugBank mapping table – Compared the drug pairs with other sources • Focused on clinically oriented datasets • Pair-wise matching at the drug ingredient level • Comparison of the information items provided – Code: https://github.com/dbmi-pitt/public-PDDI-analysis/
  10. 10. The examined datasets Source Description Mapped/ Original Category Data owner/ Maintainer Frequen cy of updates Crediblemeds .org A list of clinically important drug- drug interactions 82/83 Clinically- oriented Crediblemeds. org As needed VA NDF- RT PDDIs used until 2014 by the Veteran’s Administration health care system 2,598/5,265 Clinically- oriented Veterans Health Administratio n No future updates. Discontin ued ONC High Priority A consensus list of PDDIs that are recommended by the Office of the National Coordinator as high priority for inclusion in alerting systems 1,150/1,150 Clinically- oriented ONC One-time ONC Non- interruptive A consensus list of PDDIs that are recommended by the Office of the National Coordinator for use in non-interruptive alerts 2,101/2,101 Clinically- oriented ONC One-time DrugBank Comprehensive drug information resource 12,113 Bioinformati cs- Pharmacovig ilance DrugBank.ca Roughly bi-annual WorldVista Comprehensive list of clinically important drug-drug interactions 16,202/44,758 Clinically- oriented WordVista Twice per year
  11. 11. Biomedical Informatics11 PDDI comparison caveats • Comparisons were with versions of the other database that were current in late 2014 – might not reflect the current state (though the ONC data is static and DrugBank and NDF-RT should not have changed much since then) • Analysis was done using our existing pipeline for expediency [1]. – comparing the PDDIs with the NDF-RT dataset using RxNorm directly was not feasible due to time restrictions 1. Ayvaz S, Horn J, Hassanzadeh O, Zhu Q, Stan J, Tatonetti NP, Vilar S, Brochhausen M, Samwald M, Rastegar-Mojarad M, Dumontier M, Boyce RD, Toward a complete dataset of drug-drug interaction information from publicly available sources, Journal of Biomedical Informatics. 55 (2015), 206-217. DOI:10.1016/j.jbi.2015.04.006. http://www.sciencedirect.com/science/article/pii/S1532046415000738# PMCID: PMC4464899
  12. 12. Results – Simple overlap Credible Meds Credible Meds NDF-RT 16 (0.6%, 19.5%) NDF-RT ONC High Priority 8 (0.7%, 9.8%) 225 (19.6%, 8.7%) ONC High Priority ONC Non- interruptive 4 (0.2%, 4.9%) 27 (1.3%, 1.0%) 2 (0.1%, 0.2%) ONC Non- interruptive DRUGBANK 57 (0.5%, 69.5%) 1296 (10.7%, 49.9%) 319 (2.6%, 27.7%) 180 (1.5%, 8.6%) DRUG BANK WordVista 16 (0.1%,19.5%) 1024 (6.3%,39.4%) 282 (1.7%,24.5%) 326 (2.0%,15.5%) 1918 (11.8%,15.8%) https://raw.githubusercontent.com/dbmi-pitt/public-PDDI-analysis/master/analysis-results/WorldVista- analysis-02152016/log-output-OR-analysis.txt
  13. 13. Thinking through these results • WorldVista DrugBank : ~16% – Similar to NDF-RT : ~11% • DrugBank  WorldVista ~12% – Interactions in DrugBank are not listed at the class level • WorldVista NDF-RT : ~40% – Comparable to DrugBank : ~50% • WorldVista  ONC High Priority : ~25% – Comparable to DrugBank : ~28% – Comparable to NDF-RT : ~20%
  14. 14. Information needs Drug Information • Pharmacology • Mechanism of action • Formulation • Timing • … Evidence • Study design • Reporting information (e.g., funding agency) • Causality assessment (case reports) • … Clinical Information • Seriousness • Severity • Time of onset • Manageability • … Consequences • Adverse effect(s) • Reversibility • Frequency • Modifying and mitigating factors • … Recommendation • Monitor, change drugs, modify strength, adjust timing, etc • Strength of recommendation
  15. 15. What information in the French PDDI data? Data element CredibleMeds ONC High Priority ONC Non- interruptive Drug- Bank WorldVista clinical consequences x* x* x* Frequency of harm and exposure Contextual information/modifyi ng factors Drugs involved (terminology code) x x Evidence mechanism x* x† x‡ Recommended actions x* x* Seriousness rating x x x * Provided as unstructured text. †Available on the public website but not explicitly in the downloadable data. ‡ Only computable for CYP3A4 inhibition.
  16. 16. Biomedical Informatics16 What about agreement across sources? 1) NDF-RT (2,598), WorldVista (16,202), DrugBank (12,113) Overlap: 327 -- https://goo.gl/9JpNhZ 2) ONC High Priority (1,150), CredibleMeds (82), WorldVista (16,202) Overlap: 2 -- https://goo.gl/21aiKz
  17. 17. Biomedical Informatics17 Thoughts about the overlap analysis… • Not surprisingly – pairwise overlap is fairly poor – Does not really say anything about the quality of the French dataset – disagreement is common • Positives – The French dataset is comparable in terms of overlap to DrugBank and NDF-RT – More frequently updated – Provides more information that can be structured to aid with decision support
  18. 18. Biomedical Informatics18 Take home point • The WorldVista translation of the French PDDI dataset is very promising as a “kernel” for a public PDDI knowledge base – To my knowledge, the only truly clinically- oriented dataset that is actively maintained • But, more work to be done… – Better structuring of the information to meet clinical information needs – Connections to evidence
  19. 19. Biomedical Informatics19 Contributions my lab plans to make toward converting the French PDDI dataset to a public drug-drug interaction knowledge base • Better structuring of the information to meet clinical information needs • Connections to evidence
  20. 20. Biomedical Informatics20 Better structuring of the information to meet clinical information needs • drugs involved, • seriousness, • clinical consequences, • mechanism of the interaction, • contextual information/modifying factors, • recommended action(s), and • evidence
  21. 21. Biomedical Informatics21 Example • Warfarin – NSAIDs – Current example • http://goo.gl/1W0k6A • https://www.dikb.org/Merged-PDDI – Decision tree • See PDF
  22. 22. Data element Value clinical consequences  Increase of the oral anticoagulant’s risk of hemorrhage, especially upper gastrointestinal bleeding (UGIB) Frequency of harm and exposure  INFORMATION NEEDED Contextual information/modif ying factors  Mitigating: topical diclofenac [1], patient also taking on proton pump inhibitor or misoprostol  Predisposing: history of UGIB or peptic ulcer, > 65 years old, systemic corticosteroids, aldosterone antagonist [2], high dose or multiple NSAIDs Drugs involved (terminology code)  NSAIDS: http://goo.gl/E9yNiY  Oral Anticoagulants: http://goo.gl/BdMvZt Evidence 1. In one study a topical gel (16 g/day) produced about 6% of the absorption seen with systemic administration of 150 mg/day. A higher than recommended dose of topical gel (48 g/day) produced 20% of a systemic dose of diclofenac. 2. Both corticosteroids and aldosterone antagonists have been shown to substantially increase the risk of UGIB in patients on NSAIDs, with relative risks of 12.8 and 11 respectively compared to a risk of 4.3 with NSAIDs alone (Masclee et al. Gastroenterology 2014;147:784-92.) mechanism  Non-steroidal anti-inflammatory drugs (NSAIDs) have antiplatelet effects which increase the bleeding risk when combined with oral anticoagulants such as warfarin. The antiplatelet effect of NSAIDs lasts only as long as the NSAID is present in the circulation, unlike aspirin’s antiplatelet effect, which lasts for up to 2 weeks after aspirin is discontinued. NSAIDs also can cause peptic ulcers and most of the evidence for increased bleeding risk with NSAIDs plus warfarin is due to upper gastrointestinal bleeding (UGIB). Recommended actions  With only mitigating factors present: Assess risk and take action if necessary  With one or more predisposing factors present: Use only if benefit outweighs risk Seriousness rating  If the NSAID is topical diclofenac then Clinically inconsequential, otherwise Interruptive
  23. 23. Biomedical Informatics23 PDDI Minimum Information Task Force • Formed to create broad consensus on the definitions and content of information to be structured – https://goo.gl/MDq2Ye
  24. 24. Biomedical Informatics24 Meet the PDDI Minimum Information Task Force: • volunteer-based – currently ~40 participants – WorldVista, W3C, AMIA Pharmacoinformatics, ISPE, and academics • broad stakeholder involvement – NLM, ASHP, industry, academic institutions • Open public participation – formed within the Health Care and Life Sciences Interest Group that operates publicly through the World Wide Web Consortium (W3C)
  25. 25. Task force objective and deliverables • Objective: Develop a minimal information model for drug interaction evidence and knowledge as part of an HIT standard like HL7 • Deliverables: using an interesting and non-trivial set of potential drug-drug interactions: – Data Model: A data model (schema) for potential drug interaction knowledge and evidence – Vocabulary: A precise vocabulary describing/defining the data model – Serializations: one or more serialization formats of the abstract data model, such as Structured Product Labeling (HL7 CDA), JSON/JSON-LD) – Demonstration of how the minimum information model can support medication reconciliation
  26. 26. Biomedical Informatics26 Possibly one of the most important deliverables…. • Create a foundation for further collaborative work by disseminating results through an interest group note, a website, and an online discussion forum – https://forums.dikb.org
  27. 27. Progress so far… • Selected most of the PDDIs to focus on – https://goo.gl/rYpmjt – Decision trees developed for: • Warfarin - NSAIDs • KCL – K-sparing diuretcs • Beta-blocker – Epinephrine • Agreement on the scope of knowledge representation • Definitions for major categories in process • Initial format of draft W3C Interest Group Note – http://goo.gl/DkKSwj
  28. 28. Biomedical Informatics28 Contributions my lab plans to make toward converting the French PDDI dataset to a public drug-drug interaction knowledge base • Better structuring of the information to meet clinical information needs • Connections to evidence
  29. 29. Data element Value clinical consequences  Increase of the oral anticoagulant’s risk of hemorrhage, especially upper gastrointestinal bleeding (UGIB) Frequency of harm and exposure  INFORMATION NEEDED Contextual information/modif ying factors  Mitigating: topical diclofenac [1], patient also taking on proton pump inhibitor or misoprostol  Predisposing: history of UGIB or peptic ulcer, > 65 years old, systemic corticosteroids, aldosterone antagonist [2], high dose or multiple NSAIDs Drugs involved (terminology code)  NSAIDS: http://goo.gl/E9yNiY  Oral Anticoagulants: http://goo.gl/BdMvZt Evidence 1. In one study a topical gel (16 g/day) produced about 6% of the absorption seen with systemic administration of 150 mg/day. A higher than recommended dose of topical gel (48 g/day) produced 20% of a systemic dose of diclofenac. 2. Both corticosteroids and aldosterone antagonists have been shown to substantially increase the risk of UGIB in patients on NSAIDs, with relative risks of 12.8 and 11 respectively compared to a risk of 4.3 with NSAIDs alone (Masclee et al. Gastroenterology 2014;147:784-92.) mechanism  Non-steroidal anti-inflammatory drugs (NSAIDs) have antiplatelet effects which increase the bleeding risk when combined with oral anticoagulants such as warfarin. The antiplatelet effect of NSAIDs lasts only as long as the NSAID is present in the circulation, unlike aspirin’s antiplatelet effect, which lasts for up to 2 weeks after aspirin is discontinued. NSAIDs also can cause peptic ulcers and most of the evidence for increased bleeding risk with NSAIDs plus warfarin is due to upper gastrointestinal bleeding (UGIB). Recommended actions  With only mitigating factors present: Assess risk and take action if necessary  With one or more predisposing factors present: Use only if benefit outweighs risk Seriousness rating  If the NSAID is topical diclofenac then Clinically inconsequential, otherwise
  30. 30. Example of the need to connect evidence ATORVASTATIN RxNorm: 83367 ATC: C10AA05 CLARITHROMYCIN RxNorm: 21212 ATC: J01FA09 A02BD06 A02BD07 A02BD05 A02BD04 Increased risk of undesirable effects (concentration- dependant) of the rhabodmyolysis type due to decrease of the hepatic metabolism of the cholesterol- lowering drug Precaution for use Administer weaker doses of cholesterol- lowering agent. If the therapeutic objective is not reached, use another statin not affected by this type of interaction DrugBank: “The macrolide, clarithromycin, may increase the toxicity of the statin, atorvastatin.” WorldVista:
  31. 31. Biomedical Informatics31 Questions about clarithromycin – atorvastatin PDDI • What is the mechanism – pharmacokinetic or pharmacodynamic? • What is the expected magnitude of pharmacokinetic effect? • What potential frequency of the adverse event (rhabdomyolysis) relative to other statins w/ and w/out clarithromycin? • What are factors that increase or decrease those risks?
  32. 32. Primary data Authors using annotation toolsNew evidence items Existing evidence items Argument graphs Product label, Journal article, Other… Claim Support Reference • “drug X interacts with drug Y • drug X inhibits enzyme Q • Data • Materials • Methods • Literature • Product label • Other… Authors using annotation tools
  33. 33. Evidence annotation example
  34. 34. Evidence annotation example…
  35. 35. Biomedical Informatics35 Progress so far… • Annotated pharmacokinetic interactions for 65 drugs – The evidence board for my R01 project Addressing gaps in clinically useful evidence on drug-drug interactions" (R01LM011838) – All drug product labeling completed • Plan to release this fall – ~300 full text articles screened • Complete annotation beginning this summer • Plan to release next Spring
  36. 36. Biomedical Informatics36 Progress so far… • A proposed standard approach for assessing the existence of an interaction – Based on two AHRQ-funded conference series have brought together a wide spectrum of stakeholders Scheife RT, Hines LE, Boyce RD, Chung SP, Momper JD, Sommer CD, Abernethy DR, Horn JR, Sklar SJ, Wong SK, Jones G, Brown ML, Grizzle AJ, Comes S, Wilkins TL, Borst C, Wittie MA, Malone DC. Consensus Recommendations for Systematic Evaluation of Drug-Drug Interaction Evidence for Clinical Decision Support. Drug Saf. 2015 Feb. 38(2):197-206 http://link.springer.com/article/10.1007%2Fs40264-014-0262-8. PubMed PMID: 25556085.
  37. 37. PDDI evidence assessment Sufficient Evidence? • Conflicting evidence • Magnitude of effect • Biological plausibility Clinically Relevant? • Clinical Consequences • Frequency • Modifying factors • Seriousness How to Present DDI Information? • Seriousness • Recommended actions • Strength of evidence • Strength of recommendations
  38. 38. The DRug Interaction eVidence Evaluation (DRIVE) Instrument (being tested) Category Evidence Sufficient evidence that a drug interaction exists and can be evaluated for clinical relevance One or more of the following:  Well-designed and executed, prospective controlled studies  Well-designed and executed, observational studies  Case reports or series demonstrating probable or highly probable causality of an interaction (Drug Interaction Probability Score of 5-10)  Reasonable extrapolation on the basis of studies of drugs with similar pharmacologic properties  Reasonable extrapolation on the basis of studies with in vitro substrate data  Reasonable extrapolation on the basis of human genetic polymorphism studies Insufficient evidence that a drug interaction exists One or more of the following, without supporting evidence from the “sufficient” category:  Extrapolation on the basis of studies with in vitro inhibitor or inducer data  Case reports or series demonstrating only possible or doubtful causality of an interaction (Drug Interaction Probability Score of <5)  Studies of poor design or execution  Hypothesis-generating research methods  Animal data  Unsubstantiated statements in product labeling and regulatory documents  “Data on file” from product sponsors/manufacturers
  39. 39. Biomedical Informatics39 Evaluating DRIVE • Phase 1: “usability” evaluation – Completed • Phase 2a: interrater reliability – In progress! – 15 participants enrolled • Phase 2b: empirical evaluation of in vitro data – In process
  40. 40. Biomedical Informatics40 Conclusions • While challenging, progress is being made toward a high quality, open source, PDDI knowledge base – But it will require sustained involvement by a broad group of stakeholders
  41. 41. Biomedical Informatics41 Learn more! • http://dikb.org • Contact me rdb20@pitt.edu
  42. 42. Biomedical Informatics42 Acknowledgements - Funding • The American taxpayers via: – NLM (R01LM011838 and T15 LM007059-24) – NIH/NIA (K01AG044433, K07AG033174) – Agency for Healthcare Research and Quality (K12HS019461 and R01HS018721) – NIH/NCATS (KL2TR000146) – NIH/NIGMS (U19 GM61388; the Pharmacogenomic Research Network)
  43. 43. Biomedical Informatics43 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) • Programmers, staff, postdocs: Yifan Ning, Wen Zhang, Katrina Romagnoli, Jodi Schneider (U of Pitt), Amy Grizzle (U of Arizona), Scott Nelson (Vanderbilt) • Students and Research assistants: Sam Rosko, Steven DeMarco (U of Pitt), Nikolas Milosevec (U of Manchester) • Advisors: Rebecca Crowley, Michel Dumontier (Stanford, W3C), Matthias Samwald (Medical U of Vienna), Tim Clark and Paulo Ciccarese (Harvard), Robert Freimuth (Mayo, PGRN) • Additional stakeholders: FDA, Cochrane, W3C Health Care and Life Sciences Interest Group, ASHP, IBM Research, OHDSI
  44. 44. Biomedical Informatics44 Discussion

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