Addressing gaps in clinically useful evidence on potential drug-drug interactions


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Potential drug-drug interactions (PDDIs) are a significant public health concern. Unfortunately, the fragmented, incomplete, and dynamic nature of evidence on PDDIs makes designing effect clinical decisions support tools very challenging. In this talk, I present a conceptual model of how evidence issues affect patient safety with respect to PDDIs. I then propose a new paradigm for representing PDDI knowledge that I hypothesize will result in more clinically useful evidence than is currently possible. Finally, I place several of my recent research projects in the context of the new paradigm and make some final suggestions for future work. Throughout the talk I try to highlight the various roles that natural language processing, Semantic Web technologies, and pharmacoepidemiology have to play in improving medication safety for patients exposed to PDDIs.

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  • I am very privileged to have been invited to speak at your biomedical data and language processing seminar. I would like to thank Dr. Prasad for inviting me and all those who have helped arrange my visit. The title of my talk is…. I chose to speak on this topic because biomedical data and language processing are critical for this task. Also, Masters student Majid Rastegar-Mojarad, Dr. Prasad, and I have been collaborating on research with this focus.
  • I have three goals for this talk… The first is to describe PDDIs and present a conceptual model that explains why they present a major clinical decision support challenge to clinicians and drug information systems The second is to…. The third is to…. To achieve these goals, I have the divided the talk into three parts This talk will go just a bit over 50 minutes so I would like to encourage anyone who would have to leave early to ask questions as I go through the talk…
  • The term PDDI is used to make a distinction between two very different situations…. A DDI is… A PDDI is…. Up until recently, there has been little distinction between these two scenarios in the literature, but the research community and drug information systems have agreed that this should be used from here on out…
  • One of the things about PDDIs that makes them hard to study from an epidemiologic perspective is that there are many reasons why a PDDI might not result in harm….drugs are designed to be safe, random effects like patient non-compliance… Well designed prospective and retrospective studies have found evidence of the role of PDDIs in causing patient harm… Gurwitz et al, in their cohort study of ADEs among older Americans receiving ambulatory care, found that 13.3% of preventable errors leading to an ADE involved the co-prescription of drugs for which a “...well established, clinically important interaction” was known. 2 Nearly 7% (23/338) of the ADEs experienced by residents of two academic NHs over a nine-month period were attributable to PDDIs. 3 Sixteen cohort and case-control studies reported an elevated risk of hospitalization in patients who were exposed to PDDIs. 4
  • First there are many defenses in place that help reduce risk….. None-the-less, Indeed, health care providers often have inadequate knowledge of what drug interactions can occur, patient specific factors that can increase the risk of harm from an interaction, and how to properly manage an interaction when patient exposure cannot be avoided
  • A hypothetical illustration of how incomplete PDDI knowledge could lead to a harmful medication error. A clinician considers risperidone for treating an HIV patient taking ritonavir. Only one of two different drug information systems lists the PDDI. How system PDDI knowledge might influence probable clinician actions and patient outcomes.
  • There is plenty of evidence on how drug information systems disagree about PDDIs Contraindications are generally considered to be cases where two drugs should almost never be prescribed together…yet…
  • Understanding the potential clinical relevance of a PDDI in a given patient requires multiple types of evidence. 7,20–22 Eric van Roon et al. proposed a core PDDI information model using the definition that clinically-useful PDDI information is that which helps discern if some action should be taken with respect to a PDDI. 23 The model includes four elements shown in Figure 3. Collecting evidence related to each information item on 244 PDDIs enabled them to determine that 12% would require no action by physicians.
  • In this conceptual model, PDDI evidence is perhaps a very critical element…for the next few slides, I am going to unpack what I mean by PDDI evidence and then I will present a more detailed conceptual model
  • Explain the drug development process, note mandatory testing for PK interactions and the effect on product labeling, confusion on what to do with predicted interactions, the difficulty of getting primary study data for older drugs, cite FDA guidance, IOM report to the FDA, etc… Cerivistatin and gemfibrozil --- a ddi resulting in serious rhabdomyolysis
  • Mention Vioxx (rofecoxib, 2004) pain in arthritis) recall – patients with potential heart conditions excluded from pre-market studies. Poor response to post-market evidence of association with heart attach and stroke. Postmarket studies include those done in response to drug-AE signal detection, followups on mechanistic studies, mandated phase IV studies, and studies initiated by interested pharmacoepidemiologists Make sure to emphasize that the scientific literature includes conference abstracts that are almost never indexed in PubMed
  • Mention DIPS, role of case reports in identifying new DDIs
  • Mention alert burden and evidence for negative effects of over-alerting. Note your own experience with shut off systems.
  • Be sure to review the references and give a specific example
  • Be sure to review the references and give a specific example
  • Highlight the key points of the video
  • The data model for the SW are triples consisting of subject, predicate, and object. This model if very general and could be implemented in a number of different ways. The SW uses RDF to encode this model in such a way that the subject, predicates, and objects are all specified by URIs. This means that any part of the triple could be from a different data source than any other part of the triple.
  • Annotations in the data model are a set of RDF resources that connect some target to a set of resources that are in some way about it.
  • Highlight links by URIs of the objects, the complementary information
  • Emphasize the synthesis and expected benefits
  • Discuss the shortcomings of Structured Product Labels published by FDA
  • Discuss why we need linkage to external resources This can be using an example use case that relies on existence of links and so LinkedSPLs makes it possible (if not shown already in the discussion of the shortcomings of existing SPLs) Examples from paper: For example, RxNorm provides normalized names for the drug products and Unified Medical Language System mappings from the drug product and its active ingredients to concepts in numerous other vocabularies. DrugBank contains information on the specific biochemical targets that a drug entity may influence, major enzymatic pathways, and potential drug-drug interactions. While information on the latter two items may be present in the SPLs, it is hidden in the unstructured text. Similarly, ChEBI provides a rigorous classification of drug entities using a formal ontology maintained by members of the OBO. Both resources provide links to other important drug taxonomies (such as the ATC system) as well as resources that provide further information on the genes that encode drug targets, metabolism and transport of the drug, and diseases that the drug may help treat.
  • To make this interesting to the audience, discuss the use of SAPIENTA to automatically identify claims regarding “conclusions” from clinical effectiveness studies
  • escitalopram-tapentadol – SSRI syndrome escitalopram-metoclopramide – escitalopram a weak inhibitor of CYP2D6 which is important for metoclopramide clearance NDF-RT referred to digoxing as digitalis
  • Be sure to point out the use of preceding/post/exact text and explain why
  • Addressing gaps in clinically useful evidence on potential drug-drug interactions

    1. 1. Biomedical Informatics1Addressing gaps inclinically useful evidenceon drug-druginteractionsMay 2nd2013BioDLP Seminar at theUniversity of Wisconsin - MilwaukeeRichard Boyce, University of PittsburghDepartment of Biomedical Informatics
    2. 2. Biomedical Informatics2Goals for this talk• Describe potential drug-drug interactions(PDDIs)– the significant challenges facing clinicians andmaintainers of drug information systems.• Present a new PDDI knowledgerepresentation paradigm– that I hypothesize will yield more clinicallyrelevant evidence than is currently possible• Discuss my BioDLP research– Within the context of the new paradigm
    3. 3. Biomedical Informatics3Part I – PDDIs and challenges forPDDI knowledge representation
    4. 4. Biomedical Informatics4What is a PDDI?• Drug-drug interaction:– a clinically meaningful alteration of theeffect of a drug (object drug) occurs as aresult of coadministration of anotherdrug (precipitant drug) [10]• Potential drug-drug interaction(PDDI):– two drugs known to interact areprescribed whether or not harm ensues[10]• Pharmacokinetic or
    5. 5. Biomedical Informatics5The clinical importance of PDDIs• PDDIs are a significant source ofpreventable drug-related harm– 13.3% of preventable errors leading toan ADE [1]– 7% (23/338) of the ADEs attributable toPDDIs [2]– 16 cohort and case-control studiesreported an elevated risk ofhospitalization in elderly patients whowere exposed to PDDIs [3]
    6. 6. Biomedical Informatics6Knowledge is important• Failure to properly manage a PDDI is amedical error• The IOM has noted that a lack of drugknowledge is one of the most frequentproximal causes such errors [4]
    7. 7. Biomedical Informatics7The danger of incomplete drug-drug interaction knowledge
    8. 8. Biomedical Informatics8Key point• Many drug information systemsdisagree 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
    9. 9. Biomedical Informatics9Evidence of drug compendia problems• Three PDDI information sources agreed upononly 25% of 59 contraindicated drug pairsfound in black box warnings [5]• 18 (28%) of 64 pharmacy information andclinical decisions support systems correctlyidentified 13 clinically significant DDIs [6]• Four sources agreed on only 2.2% of 406PDDIs considered to be “major” by at least onesource [7]
    10. 10. Biomedical Informatics10Why do compendia disagree?• Four types of information to decide if a PDDIwarrants clinical action. [21]• Collecting evidence related to each informationitem on 244 PDDIs enabled them to determinethat 12% would require no action by physicians[8]
    11. 11. A conceptual model – 30,000 feetviewLimits the effectiveness ofPDDI alerting andCPOE systemsDrug Compendia synthesize PDDIevidence into knowledge but•May fail to include important PDDIs•Often disagree about PDDI evidenceand seriousness ranking•May include numerous PDDIs withlittle evidence for liability reasonsPDDI adverse eventIncreases the risk ofPDDI evidenceScattered across numerous sources
    12. 12. Biomedical Informatics12PDDI evidence – pre-market studiesPre-market studies establish PDDIfeasibility but:•usually do not indicate ADEseriousness, incidence, or risk•Focus on generally younger andhealthier populations•Do not exist for many older drugsProduct labelingReported inScientific literatureRarely reported inSee references 31 and 32
    13. 13. Biomedical Informatics13PDDI evidence – post-market studiesPost-market studies can provideevidence of PDDI risk and incidenceif well-designed but:•rarely are randomized studies due toethical considerations•older drugs less likely to be studiedProduct labelingScientific literatureReported inRarely reported in
    14. 14. Biomedical Informatics14PDDI evidence – Clinical experienceProduct labelingClinical experience can provide firstwarning of a PDDIs and offersunique insight on PDDI severity:•are often case reports of lowevidential quality•there is no general way to collect andshare these insightsRarely reported inRarely reported inScientific literature
    15. 15. Evidence from the drug compendiumperspectivePre-market studies Post-market studiesProduct labelingReported inClinical experienceScientific literatureRarely reported inRarely reported inReported inRarely reported inDrug Compendia synthesize PDDIevidence into knowledge but•May fail to include important PDDIs•Often disagree about PDDI evidenceand seriousness ranking•May include numerous PDDIs withlittle evidence for liability reasonsSource forSource for
    16. 16. Biomedical Informatics16Effects on the clinician and patientPDDI alerting andCPOE systemsDrug Compendia synthesize PDDIevidence into knowledge but•May fail to include important PDDIs•Often disagree about PDDI evidenceand seriousness ranking•May include numerous PDDIs withlittle evidence for liability reasonsPDDI adverse eventIncreases the risk ofLimits the effectiveness of
    17. 17. Biomedical Informatics17PDDI over-alerting• Systems that provide PDDI alerts atthe point of care often alert to PDDIsthat have little potential clinicalsignificance– frustrating clinicians“Drug safety alerts are overridden by cliniciansin 49% to 96% of cases” [11]– can lead to inappropriate responses“An increased number of non-critical alerts…was the only variable associated with aninappropriate provider response” [12]
    18. 18. Biomedical Informatics18Summary of challenges for PDDIknowledge representation• PDDI evidence is distributed, dynamic, andof varying quality• There are significant gaps in PDDI evidencemaking it hard to assess– what is the potential harmful effect?– who is the PDDI most likely to affect?– when is a patient most at risk?• Alerting has to become moreintelligent!
    19. 19. Biomedical Informatics19Part II – a new PDDI knowledgerepresentation paradigm
    20. 20. The new paradigmProduct labelingScientific literatureA framework for representing PDDIassertions as interoperable LinkedDataPharmacoepidemiologystudiesSemanticannotationHigh priorityPDDIs forresearchSemantic annotationReduced riskof a PDDImedicationerror!Clinical experienceBetter synthesis of PDDI evidence,easier identification of gapsExpected benefits:•More complete and accurate PDDI evidence•Better informed pharmacists and otherclinicians•More effective PDDI alerting and decisionssupport systems
    21. 21. Biomedical Informatics21Elements of the new paradigm• Linked Data [13]– a Semantic Web technology that makesdistributed knowledge sourcesinteroperable, with interconnectionsproviding rich context that would beunavailable from any single database• Semantic annotation [14]– a technology that enhances digitalinformation artifacts by linking them toprovenance and expert commentary• Pharmacoepidemiology [15]– an approach to studying of the use andeffects of drugs in large numbers of people
    22. 22. Biomedical Informatics22Linked Data• What is it?– 3 minute jargon free introduction:•• My research has shown Linked Data tobe a potentially effective means oflinking clinical drug information [9]– Several high quality resources– More complete information
    23. 23. Biomedical Informatics23predicateResource Description Framework (RDF)• Data model – triples• Syntax – RDF– The subject, predicate, and objects arespecified by URIs<http://.../AnneHathaway> <http://.../Married> <http://../Shakespeare><http://.../Shakespeare> <http://.../Wrote> <http://../Hamlet>subject objectAnnHathawayShakespeareHamletmarriedwrote
    24. 24. Biomedical Informatics24Semantic Annotation
    25. 25. Semantic Annotation of PDDIs
    26. 26. Combining Linked Data and Semantic Annotation
    27. 27. Biomedical Informatics27A structured assessment scores evidence andpotential severity [21]Pharmacoepidemiology – filling in the gaps
    28. 28. Recap of the new paradigmProduct labelingScientific literatureA framework for representing PDDIassertions as interoperable LinkedDataPharmacoepidemiologystudiesSemanticannotationHigh priorityPDDIs forresearchSemantic annotationReduced riskof a PDDImedicationerror!Clinical experienceBetter synthesis of PDDI evidence,easier identification of gapsExpected benefits:•More complete and accurate PDDI evidence•Better informed pharmacists and otherclinicians•More effective PDDI alerting and decisionssupport systems
    29. 29. Biomedical Informatics29Anticipated benefits of the new paradigm• A computable representation of PDDI safetyconcerns that is linked to:– evidence– expert input, and– pharmacoepidemiologic study results• More complete, timely, and accurate PDDIevidence– easier integration for drug compendia and CPOEdevelopers• Better informed clinicians and patients
    30. 30. Biomedical Informatics30Part II – A brief review of myresearch within the context of thisparadigm
    31. 31. Overview of my recent PDDI studiesProduct labelingScientific literatureA framework for representing PDDIassertions as interoperable LinkedDataPharmacoepidemiology studiesSemanticannotationHigh priority PDDIsfor researchSemantic annotationClinical experienceBetter synthesis of PDDI evidence, easieridentification of gapsA, BCE GA. Boyce et al. Am J Geriatr Pharmacother. 2012. Apr;10(2):139-50. [22]B. Boyce et al. Annals of Pharmacotherapy. 2012. Oct;46(10):1287-98 [23]C. Boyce et al. Proceedings of the 2012. Workshop on BioNLP. 206-213 [16]D. & E. Boyce et al. Proceedings of the 2013. AMIA Summit onTranslational Bioinformatics. 28-32 (D), 64-68 (E). [18,19]F. Boyce et al. J Biomed Semantics. 2013. Jan 26;4(1):5. [9]G. Boyce et al. Poster at Aging Institute Research Day. 2013. [20]FD
    32. 32. Linked Data – linking product labels to the“Web of Drug Identity”Product labelingA framework for representing PDDIassertions as interoperable LinkedDataC. Boyce et al. Proceedings of the 2012. Workshop on BioNLP. 206-213 [16]E. Boyce et al. Proceedings of the 2013. AMIA Summit on Translational Bioinformatics. 64-68 [19]F. Boyce et al. J Biomed Semantics. 2013. Jan 26;4(1):5. [9]EHypothesis: A Linked Data knowledge base of drugproduct labels with accurate links to other relevantsources of drug information will provide a dynamicplatform for drug information NLP that provides real valueto clinical and translational researchers.Better synthesis of PDDI evidence, easieridentification of gapsFC
    33. 33. Biomedical Informatics33Structured Product Labels (SPLs)• All package inserts for currently marketeddrugs are available as SPLs [27-29]
    34. 34. Biomedical Informatics34More about SPLs
    35. 35. Biomedical Informatics35Key point• LinkedSPLs [26] is a Linked Data versionof SPLs– >36,000 FDA-approved prescription andover-the-counter drugs present inDailyMed– simplifies access to SPL content– interoperable with other important drugterminologies and resources– Enables queries across drug informationresources…
    36. 36. Biomedical Informatics36Example cross-resource queries• What are the known targets of all activeingredients that are classified asantidepressants?• Is there a pharmacogenomics concern forany of the drugs associated withHyperkalemia• Show the evidence support for allpharmacokinetic PDDIs affectingbuproprion that are supported by arandomized study
    37. 37. Biomedical Informatics37LinkedSPLs – A research program
    38. 38. Biomedical Informatics38LinkedSPLs – A research programYour annotationswould go here!
    39. 39. Biomedical Informatics39An Example - extracting PDDIs from productlabels Product labelingCC. Boyce et al. Proceedings of the 2012. Workshop on BioNLP. 206-213 [16]Recently published NLP algorithm specifically designed to extract PDDIsfrom drug product labels• A drug package insert PK PDDI corpus [24]:• 592 PK PDDIs,• 3,351 active ingredients,• 234 drug product mentions,• 201 metabolite mentions.• SVM performed best• F = 0.859 for pharmacokinetic PDDI identification• F = 0.949 for modality assignment• Syntactic information helped with sentences containing bothinteracting and non-interacting pairs
    40. 40. Biomedical Informatics40Application of the NLP algorithm• Extract PDDIs and integrate into linked SPLsPDDI ExtractionalgorithmLovastatinproduct label
    41. 41. Biomedical Informatics41Linkage to external sources• There are many sources of drug informationthat are complementary to each other.– DrugBank: contains drug targets, pathways,interactions– RxNorm: provides UMLS mappings– VA NDF-RT: PDDIs and drug classification– ChEBI: provides rigorous classification of drugs
    42. 42. Biomedical Informatics42Two linking studies• Active ingredients in the structured portion of SPLsto DrugBank [19]• Three different approaches• One fully unsupervised• PDDIs (VA NDF-RT) to the Drug Interactions sectionof 26 psychotropics [9]• What benefits for this linkage?• Collaboration with Majid Rastegar-Mojarad
    43. 43. Biomedical Informatics43Linking Active ingredients in SPLsto DrugBank• Three different linking approaches to linkto DrugBank1. Structure string (InChI)2. Ontology label matching (ChEBI)3. Unsupervised linkage point discovery(Automated) [30]
    44. 44. Biomedical Informatics44Linkage to DrugBank – Results• 1,246 active ingredients (53%) could be mapped toDrugBank by at least one method• 1,096 unmapped ingredients• The three approaches complement each otherInChIidentifierChEBIidentifierInChI +ChEBIAutomaticInChI identifier 424 261 424 395ChEBI identifier --- 707 707 650InChI + ChEBI -- -- 831 791Automatic -- -- -- 1162
    45. 45. Biomedical Informatics45• The automatic approach performs very well– A greater number of accurate links discoveredwith less effort• A significant number remain unmapped:– Some salt or racemic forms of mapped ingredients(e.g., alpha tocopherol acetate D)– Elements (e.g., gold, iodine), and variety of naturalorganic compounds including pollens (N~200)• Not all ingredients are included in DrugBank– other resources may be required to obtaincomplete mappings for active ingredients.Linking methods conclusions
    46. 46. Biomedical Informatics46Linking from VA NDF-RT to LinkedSPLs• How often would it provide morecomplete information?VA NDF-RT inBioportalOctober 2012SPARQL: Get all DDIsfor antidepressantsFilter out DDIspreviously identifiedin antidepressantproduct labelsTabulate potentiallynovel PDDIs
    47. 47. Biomedical Informatics47PDDI studies comparing information sourcesProduct label PDDIs for 20 drugs manually identified [22]• ~70 interactions• Pharmacokinetic and pharmacodynamicWe filtered NDF-RT PDDIs• String matching and an expanded version of the PDDI table• ~2,500 drug-drug and drug-class pairsFace validity but future work needed for• validate the accuracy of this approach• create a more scalable approachFilter out DDIspreviously identifiedin antidepressantproduct labels
    48. 48. Biomedical Informatics48Linking from VA NDF-RT - results• At least one potentially novel interaction was linkedto a product label for products containing each ofthe 20 antidepressants– tranylcypromine (33), nefazodone (31), fluoxetine (28)• Several cases where all of the PDDIs werepotentially novel– e.g., trazodone, venlafaxine, trimipramine• Pharmacist review– Several true positives• e.g., escitalopram-tapentadol, escitalopram-metoclopramide– Some false positives• e.g., nefazodone-digoxin (digitalis)
    49. 49. Biomedical Informatics49Proof of concept mashup
    50. 50. Biomedical Informatics50The complete proof of concept•– 29 psychotropic drug products– Created using four Semantic Web nodes1.
    51. 51. Biomedical Informatics51Concluding points• The paradigm provides a frameworkfor tying together– NLP for extracting PDDIs– NLP for linking evidence it to PDDIs– Aggregating existing PDDI resources in asingle framework• Research prioritization• Community engagement
    52. 52. Biomedical Informatics52Acknowledgements• Agency for Healthcare Research and Quality (K12HS019461).• NIH/NCATS (KL2TR000146),• NIH/NIGMS (U19 GM61388; the Pharmacogenomic ResearchNetwork)• NIH/NLM (T15 LM007059-24)• The Drug Interaction Knowledge Base team– John Horn Pharm.D, Carol Collins MD, Greg Gardner, RobGuzman• W3C LODD Task Force and the Clinical Genomics WorkingGroup
    53. 53. Biomedical Informatics53References1. Gurwitz JH, Field TS, Harrold LR, et al. Incidence and preventabilityof adverse drug events among older persons in the ambulatorysetting. JAMA. 2003;289(9):1107–11162. Gurwitz JH, Field TS, Judge J, et al. The incidence of adverse drugevents in two large academic long-term care facilities. Am. J. Med.2005;118(3):251–2583. Hines LE, Murphy JE. Potentially harmful drug-drug interactions inthe elderly: a review. Am J Geriatr Pharmacother. 2011;9(6):364–377.4. Committee on Identifying and Preventing Medication Errors, PhilipAspden, Julie Wolcott, J. Lyle Bootman, Linda R. Cronenwett,Editors. Preventing Medication Errors: Quality Chasm Series.Washington, D.C.: The National Academies Press; 2007.5. Wang LM, Wong M, Lightwood JM, Cheng CM. Black box warningcontraindicated comedications: concordance among three majordrug interaction screening programs. Ann Pharmacother.2010;44(1):28–346. Saverno KR, Hines LE, Warholak TL, et al. Ability of pharmacyclinical decision-support software to alert users about clinicallyimportant drug-drug interactions. J Am Med Inform Assoc.
    54. 54. Biomedical Informatics54References cont.7. Abarca J, Malone DC, Armstrong EP, et al. Concordance of severityratings provided in four drug interaction compendia. J Am PharmAssoc (2003). 2004;44(2):136–141.8. Van Roon EN, Flikweert S, Le Comte M, et al. Clinical relevance ofdrug-drug interactions : a structured assessment procedure.  Drug Saf.2005;28(12):1131–1139.9. Boyce R, Horn J, Hassanzadeh O, et al. Dynamic Enhancement ofDrug Product Labels to Support Drug Safety, Efficacy, andEffectiveness. Journal of Biomedical Semantics. Journal of BiomedicalSemantics. 2013. Jan 26;4(1):5.10. Hines LE, Malone DC, Murphy JE. Recommendations forGenerating, Evaluating, and Implementing Drug-Drug InteractionEvidence. Pharmacotherapy: The Journal of Human Pharmacology andDrug Therapy. 2012;32(4):304–313.11. Van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safetyalerts in computerized physician order entry. J Am Med Inform Assoc.2006;13(2):138–147.
    55. 55. Biomedical Informatics55References cont.12. Miller AM, Boro MS, Korman NE, Davoren JB. Provider andpharmacist responses to warfarin drug-drug interaction alerts: a studyof healthcare downstream of CPOE alerts. J Am Med Inform Assoc.2011;18 Suppl 1:i45–50. PMCID: PMC324116513. Marshall MS, Boyce RD, Deus H, Zhao J, Willighagen E, SamwaldM, Pichler E, Hajagos J, Prud’hommeaux E, and Stephens, S. Emergingpractices for mapping life sciences data to RDF - a case series. Journalof Web Semantics. Special Issue: Reasoning with Context in theSemantic Web. Volume 14, July 2012, Pages 2–13.14. Open Annotation Collaboration. Strom BL, Kimmel SE eds. Textbook of Pharmacoepidemiology. 1sted. Wiley; 200716. Boyce R, Gardner G, Harkema H. Using Natural LanguageProcessing to Extract Drug-Drug Interaction Information from PackageInserts. Proceedings of the 2012 Workshop on BioNLP. Montreal,Quebec, Canada. June 2012:206-213.
    56. 56. Biomedical Informatics56References cont.17. Rasteger-Mojarad, M., Boyce RD., Prasad, R. UWM-TRIADS:Classifying Drug-Drug Interactions with Two-Stage SVM and Post-Processing. Proceedings of the 2013 International Workshop onSemantic Evaluation (SemEval), Task 9 - Extraction of Drug-drugInteractions from BioMedical Texts. Atlanta Georgia, June 2013. (InPress).18. Boyce, RD., Freimuth, RR., Romagnoli, KM., Pummer, T.,Hochheiser, H., Empey, PE. Toward semantic modeling ofpharmacogenomic knowledge for clinical and translational decisionsupport. Proceedings of the 2013 AMIA Summit on TranslationalBioinformatics. San Francisco, March 2013:28-32.19. Hassanzadeh, O., Zhu, Qian., Freimuth, RR., Boyce R. Extending the“Web of Drug Identity” with Knowledge Extracted from United StatesProduct Labels. Proceedings of the 2013 AMIA Summit onTranslational Bioinformatics. San Francisco, March 2013:64-68.20. Boyce RD., Handler SM., Karp JF., Perera, S., Hanlon JT. Prevalenceof Potential Drug-Drug Interactions Affecting Antidepressant in USNursing Home Residents. Poster presentation at the 2013 AgingInstitute Research Day. University of Pittsburgh. Pittsburgh PA. April,2013
    57. 57. Biomedical Informatics57References cont.21. E. N. van Roon, S. Flikweert, M. le Comte, P. N. Langendijk, W. J.Kwee-Zuiderwijk, P. Smits, and J. R. Brouwers. Clinical relevance ofdrug-drug interactions : a structured assessment procedure. Drug Saf,28(12):1131-1139, 2005.22. Boyce RD, Handler SM, Karp JF, Hanlon JT. Age-related changes inantidepressant pharmacokinetics and potential drug-drug interactions:a comparison of evidence-based literature and package insertinformation. Am J Geriatr Pharmacother. 2012 Apr;10(2):139-50. Epub2012 Jan 27. PMID 22285509. PMCID: PMC338453823. Boyce RD, Collins C, Clayton M, Kloke J, Horn J. InhibitoryMetabolic Drug Interactions with Newer Psychotropic Drugs: Inclusionin Package Inserts and Influences of Concurrence in Drug InteractionScreening Software. Annals of Pharmacotherapy. 2012Oct;46(10):1287-98. Epub 2012 Oct 2. DOI 10.1345/aph.1R150. PMID2303265524.
    58. 58. Biomedical Informatics58References cont.27. O. Hassanzadeh et al. “Discovering Linkage Points over Web Data”.To Appear in PVLDB, Vol 6. Issue 6, August 201331. FDA. Guidance for Industry Drug Interaction Studies — StudyDesign, Data Analysis, Implications for Dosing, and LabelingRecommendations. Silver Spring, MD: Federal Drug Administration;2012. Available at: Accessed January 7, 2013.32. Platt R, Wilson M, Chan KA, Benner JS, Marchibroda J, McClellan M.The new Sentinel Network--improving the evidence of medical-productsafety. N Engl J Med. 2009 Aug 13;361(7):645-7.
    59. 59. Biomedical Informatics59Backup Slides
    60. 60. Semantic annotation and Linked DataProduct labelingA framework for representing PDDIassertions as interoperable LinkedDataSemantic annotationD. Boyce et al. Proceedings of the 2013. AMIA Summit on Translational Bioinformatics. 28-32 [18]DD – Semantic annotation of pharmacogenomics statements indrug product labeling• First semantically annotated corpus of clinical pharmacogenomicsstatements present in drug product labeling• Potential impact• pharmacokinetic / pharmacodynamic• Patient specific risk factors• concomitant medications• medical conditions• Recommendations• dosage, drug administration, alternatives, monitoring, and tests• First pharmacogenomics dataset to use the W3C Open DataAnnotation standard [25]
    61. 61. Biomedical Informatics61Pharmacogenomics example - CodeinePredicate Objectdrug CODEINEbiomarker CYP2D6variant Ultra-rapidmetabolizerPharmacokineticeffectMetabolism-increasePharmacodynamiceffectDrug-toxicity-risk-increasePredicate ObjecthasSource URL to product labelExact-text “Nursing mothers…”Preceding-text…Post-text …ex:body-1 ex:target-1ex:annotation-1about“Nursing mothers who are ultra-rapid metabolizers mayalso experience overdose symptoms such as extremesleepiness, confusion, or shallow breathing.”
    62. 62. Biomedical Informatics62Risk factorspatient characteristics X potential adverse eventpatient characteristics X DDI mechanismdrug characteristicsroute of administration, dose, timing, sequence
    63. 63. Biomedical Informatics63Incidenceprevalence of co-prescriptionprevalence of AEincidence of AE in exposed and non-exposed
    64. 64. Biomedical Informatics64Seriousness of the AEClassified by specific clinical outcome...but, can any seriousness ranking be generallyaccepted?no effect death?
    65. 65. Biomedical Informatics65
    66. 66. Biomedical Informatics66Linkage to DrugBank – Approach 11. FDA UNII table provides structure string:2. NCI Resolver provides InChIKey:3. DrugBank record with the above InChIKey providesidentifier:Results:429 out of 2,264 ingredients are linked, out of which 424 arevalid“N7U69T4SZR”Starting with UNII….2-METHYL-4-(4-METHYL-1-PIPERAZINYL)-10H-THIENO(2,3-B)(1,5)BENZODIAZEPINEKVWDHTXUZHCGIO-UHFFFAOYSA-NDB00334Idea: Using NCI Resolver & InChIKey
    67. 67. Biomedical Informatics67Linkage to DrugBank – Approach 2“OLANZAPINE”1. ChEBI preferred name from NCBO Bioportal:2. ChEBI identifier from NCBO Bioportal:3. DrugBank record with the above ChEBI identifier providesidentifier:Results:718 out of 2,264 ingredients are linked, out of which 707 arevalid“OLANZAPINE”7735DB00334Idea: Using ChEBI identifier & NCBO PortalStarting with name….
    68. 68. Biomedical Informatics68Linkage to DrugBank – Approach 3Starting with all data in the FDA UNII table and DrugBank….1. Index all FDA UNII table and DrugBank XML attributes2. Search for linkage points and score similarity:UNII -> Substance Name  DrugBank -> brands -> brand: 0.94UNII -> Preferred Substance Name  DrugBank -> name : 0.91UNII -> Substance Name  DrugBank -> synonyms -> synonym : 0.83…3. Prune list of linkage points based on cardinality, coverage, and average score4. Establish links between FDA UNII table and DrugBank using the linkage pointsUNII “OLANZAPINE”   DrugBank “Zyprexa” : 1.0…Results: 1,179 out of 2,264 ingredients are linked, out of which 1,169 are valid“N7U69T4SZR”UNII“OLANZAPINE”Preferred Substance Name“2-METHYL-4….”Molecular Formula“ZYPREXA”synonymIdea:Automatic discovery oflinkage points
    69. 69. Biomedical Informatics69Linkage Point Discovery Framework• A generic framework for unsupervised discoveryof linkage pointsDetails can be found at:O. Hassanzadeh et al. “Discovering Linkage Points over Web Data”. To Appear inPVLDB, Vol 6. Issue 6, August 2013