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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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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