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Text Mining for Drug–Drug Interaction

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Biomedical Literature Mining

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1159))

Abstract

In order to understand the mechanisms of drug–drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases.

To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis.

The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions.

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References

  1. Second Annual Adverse Drug/Biologic Reaction Report (1987) US Food and Drug Administration

    Google Scholar 

  2. Becker ML et al (2007) Hospitalisations and emergency department visits due to drug–drug interactions: a literature review. Pharmacoepidemiol Drug Saf 16:641–651

    Article  PubMed  Google Scholar 

  3. Chou TC (2006) Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev 58(3):621–681

    Article  PubMed  CAS  Google Scholar 

  4. Magro L, Moretti U, Leone R (2012) Epidemiology and characteristics of adverse drug reactions caused by drug–drug interactions. Expert Opin Drug Saf 11(1):83–94

    Article  PubMed  CAS  Google Scholar 

  5. Juurlink DN et al (2003) Drug–drug interactions among elderly patients hospitalized for drug toxicity. JAMA 289(13): 1652–1658

    Article  PubMed  CAS  Google Scholar 

  6. Merle L et al (2005) Predicting and preventing adverse drug reactions in the very old. Drugs Aging 22(5):375–392

    Article  PubMed  Google Scholar 

  7. Johansson I, Ingelman-Sundberg M (2011) Genetic polymorphism and toxicology: with emphasis on cytochrome p450. Toxicol Sci 120(1):1–13

    PubMed  CAS  Google Scholar 

  8. Ajayi FO, Sun H, Perry J (2000) Adverse drug reactions: a review of relevant factors. J Clin Pharmacol 40(10):1093–1101

    PubMed  CAS  Google Scholar 

  9. DiMasi JA, Grabowski HG (2007) The cost of biopharmaceutical R&D: is biotech different? Manage Decis Econ 28:469–479

    Article  Google Scholar 

  10. Pang KS, Rodrigues AD, Peter RM (2010) Enzyme- and transporter-based drug–drug interactions, vol 746. Springer, New York

    Book  Google Scholar 

  11. The European Medicines Agency (2012) Guideline on the investigation of drug interactions. The European Medicines Agency, London

    Google Scholar 

  12. Jia J et al (2009) Mechanisms of drug combinations: interaction and network perspectives. Nat Rev Drug Discov 8(2):111–128

    PubMed  CAS  Google Scholar 

  13. Rowland M, Tozer TN (1995) Clinical pharmacokinetics: concepts and applications. Lippincott Williams & Wilkins, London

    Google Scholar 

  14. Wienkers LC, Heath TG (2005) Predicting in vivo drug interactions from in vitro drug discovery data. Nat Rev Drug Discov 4(10): 825–833

    Article  PubMed  CAS  Google Scholar 

  15. Rostami-Hodjegan A, Tucker G (2004) ‘In silico’ simulations to assess the ‘in vivo’ consequences of ‘in vitro’ metabolic drug–drug interactions. Drug Discov Today 1(4): 441–448

    Article  CAS  Google Scholar 

  16. Huang SM et al (2007) Drug interaction studies: study design, data analysis, and implications for dosing and labeling. Clin Pharmacol Ther 81(2):298–304

    Article  PubMed  CAS  Google Scholar 

  17. Li L, Yu M, Chin R, Lucksiri A, Flockhart D, Hall S (2007) Drug–drug interaction prediction: a Bayesian meta-analysis approach. Stat Med 26(20):3700–3721

    Article  PubMed  Google Scholar 

  18. Yu M et al (2008) A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug–drug interaction prediction. J Biopharm Stat 18(6): 1063–1083

    Article  PubMed Central  PubMed  Google Scholar 

  19. Zhou J et al (2009) A new probabilistic rule for drug–drug interaction prediction. J Pharmacokinet Pharmacodyn 36:1–18

    Article  PubMed Central  PubMed  Google Scholar 

  20. Zhou J, Qin Z, Kim S, Wang Z, Hall DS, Li L (2009) Drug–drug interaction prediction assessment. J Pharmacokinet Pharmacodyn 19:641–657

    Google Scholar 

  21. Wang Z, Kim S, Quinney SK, Zhou J, Li L (2010) Non-compartment model/compartment model transformation. BMC System Biol 4(1):S8

    Google Scholar 

  22. Li L (2007) Discussion on parameter estimation for differential equations: a generalized smoothing approach. J Royal Stat Soc B 69: 787–788

    Article  Google Scholar 

  23. Chien JY, Lucksiri A, Ernest CS, Gorski JC, Wrighton SA, Hall SD (2006) Stochastic prediction of CYP3A-mediated inhibition of midazolam clearance by ketoconazole. Drug Metab Dispos 34(7):1208–1219

    Article  PubMed  CAS  Google Scholar 

  24. Quinney SK, Zhang X, Lucksiri A, Gorski JC, Li L et al (2010) Physiologically based pharmacokinetic model of mechanism-based inhibition of CYP3A by clarithromycin. Drug Metab Dispos 38(2):241–248

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  25. Hachad H, Ragueneau-Majlessi I, Levy RH (2010) A useful tool for drug interaction evaluation: the University of Washington Metabolism and Transport Drug Interaction Database. Hum Genomics 5(1):61–72

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  26. Hewett M et al (2002) PharmGKB: the pharmacogenetics knowledge base. Nucleic Acids Res 30(1):163–165

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  27. Knox C et al (2011) DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res 39(Database issue): D1035–D1041

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  28. Gottlieb A et al (2012) INDI: a computational framework for inferring drug interactions and their associated recommendations. Mol Syst Biol 8:592

    Article  PubMed Central  PubMed  Google Scholar 

  29. Nadkarni PM, Ohno-Machado L, Chapman WW (2011) Natural language processing: an introduction. J Am Med Inform Assoc 18: 544–551

    Article  PubMed Central  PubMed  Google Scholar 

  30. Zweigenbaum P et al (2007) Frontiers of biomedical text mining: current progress. Brief Bioinform 8(5):358–375

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  31. Kim JD et al (2003) GENIA corpus–semantically annotated corpus for bio-text mining. Bioinformatics 19(Suppl 1):i180–i182

    Article  PubMed  Google Scholar 

  32. Wilbur WJ, Rzhetsky A, Shatkay H (2006) New directions in biomedical text annotation: definitions, guidelines and corpus construction. BMC Bioinformatics 7:356

    Article  PubMed Central  PubMed  Google Scholar 

  33. Muller HM, Kenny EE, Sternberg PW (2004) Textpresso: an ontology-based information retrieval and extraction system for biological literature. PLoS Biol 2(11):e309

    Article  PubMed Central  PubMed  Google Scholar 

  34. Feldman R et al (2002) Mining biomedical literature using information extraction. Curr Drug Discov 2:19–23

    Google Scholar 

  35. Fundel K, Küffner R, Zimmer R (2007) RelEx: relation extraction using dependency parse trees. Bioinformatics 23:365–371

    Article  PubMed  CAS  Google Scholar 

  36. Qian L, Zhou G (2012) Tree kernel-based protein–protein interaction extraction from biomedical literature. J Biomed Inform 45(3): 535–543

    Article  PubMed  CAS  Google Scholar 

  37. Airola A et al (2008) All-paths graph kernel for protein–protein interaction extraction with evaluation of cross-corpus learning. BMC Bioinformatics 9(Suppl 11):S2

    Article  PubMed Central  PubMed  Google Scholar 

  38. Pyysalo S et al (2008) Comparative analysis of five protein–protein interaction corpora. BMC Bioinformatics 9(Suppl 3):S6

    Article  PubMed Central  PubMed  Google Scholar 

  39. Tikk D et al (2010) A comprehensive benchmark of kernel methods to extract protein–protein interactions from literature. PLoS Comput Biol 6:e1000837

    Article  PubMed Central  PubMed  Google Scholar 

  40. Chen Y, Liu F, Manderick B (2009) Normalizing interactor proteins and extracting interaction protein pairs using support vector machines. In: BioCreative II. 5 Workshop 2009 on Digital Annotations

    Google Scholar 

  41. Zhou D, He Y (2008) Extracting interactions between proteins from the literature. J Biomed Inform 41(2):393–407

    Article  PubMed  CAS  Google Scholar 

  42. Krallinger M, Leitner F, Valencia A (2009) The BioCreative II.5 challenge overview. In: Proceedings of the BioCreative II. 5 Workshop 2009 on Digital Annotations

    Google Scholar 

  43. Segura-Bedmar I, Martinez P, de Pablo-Sanchez C (2011) A linguistic rule-based approach to extract drug–drug interactions from pharmacological documents. BMC Bioinformatics 12(Suppl 2):S1

    Article  PubMed Central  PubMed  Google Scholar 

  44. Segura-Bedmar I, Martinez P, Sanchez-Cisneros D (2011) The 1st DDIExtraction-2011 challenge task: extraction of drug–drug interactions from biomedical texts. In: Proceedings of the 1st challenge task on drug–drug interaction extraction 2011, Spain

    Google Scholar 

  45. Percha B, Garten Y, Altman RB (2012) Discovery and explanation of drug–drug interactions via text mining. Pac Symp Biocomput 410–421

    Google Scholar 

  46. Tari L et al (2010) Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism. Bioinformatics 26(18):i547–i553

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  47. Segel IH (1975) Enzyme kinetics: behavior and analysis of rapid equilibrium and steady state enzyme systems. Wiley, New York

    Google Scholar 

  48. Consortium IT et al (2010) Membrane transporters in drug development. Nat Rev Drug Discov 9(3):215–236

    Google Scholar 

  49. Rostami-Hodjegan A, Tucker G (2004) In silico simulations to assess the in vivo consequences of in vitro metabolic drug–drug interactions. Drug Disc Today Technol 1:441–448

    Article  CAS  Google Scholar 

  50. Lam YW, Alfaro CL, Ereshefsky L, Miller M (2003) Pharmacokinetic and pharmacodynamic interactions of oral midazolam with ketoconazole, fluoxetine, fluvoxamine, and nefazodone. J Clin Pharmacol 43(11):1274–1282

    Article  PubMed  CAS  Google Scholar 

  51. Gibaldi M, Perrier D (1982) Pharmacokinetics, 2nd edn. Marcel Dekker, New York

    Google Scholar 

  52. Vazquez M et al (2011) Text mining for drugs and chemical compounds: methods, tools and applications. Mol Inform 30:506–519

    Article  CAS  Google Scholar 

  53. Spasic I et al (2005) Text mining and ontologies in biomedicine: making sense of raw text. Brief Bioinform 6(3):239–251

    Article  PubMed  CAS  Google Scholar 

  54. Kim JD, Ohta T, Tsujii J (2008) Corpus annotation for mining biomedical events from literature. BMC Bioinformatics 9:10

    Article  PubMed Central  PubMed  Google Scholar 

  55. Brunton LL, Chabner BA, Knollmann BC (2011) Goodman & Gilman’s the pharmacological basis of therapeutics, 12th edn. McGraw-Hill, New York

    Google Scholar 

  56. Witte R, Kappler T, Baker CJO (2007) Ontology design for biomedical text mining, in semantic Web: revolutionizing knowledge discovery in the life sciences. Springer, USA, pp 281–313

    Google Scholar 

  57. Giacomini KM et al (2010) Membrane transporters in drug development. Nat Rev Drug Discov 9(3):215–236

    PubMed  CAS  Google Scholar 

  58. Guengerich FP (2008) Cytochrome p450 and chemical toxicology. Chem Res Toxicol 21(1): 70–83

    Article  PubMed  CAS  Google Scholar 

  59. Rubin DL, Noy NF, Musen MA (2007) Protege: a tool for managing and using terminology in radiology applications. J Digit Imaging 20(Suppl 1):34–46

    Article  PubMed Central  PubMed  Google Scholar 

  60. Wang Z et al (2009) Literature mining on pharmacokinetics numerical data: a feasibility study. J Biomed Inform 42(4):726–735

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  61. Krippendorff K (2004) Content analysis: an introduction to its methodology. SAGE, Thousand Oaks, CA

    Google Scholar 

  62. de Marneffe M-C, MacCartney B, Manning CD (2006) Generating typed dependency parses from phrase structure parses. In LREC

    Google Scholar 

  63. Karnik S et al (2011) Extraction of drug–drug interactions using all paths graph kernel. In: The 1st challenge task on drug–drug interaction extraction, Huelva, Spain

    Google Scholar 

  64. van Deemter K, Kibble R (2000) On coreferring: coreference in muc and related annotation schemes. Comput Linguist 26(4): 629–637

    Article  Google Scholar 

  65. Hobbs J (1986) Resolving pronoun references. Readings in natural language processing. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 339–352

    Google Scholar 

  66. Grosz BJ, Weinstein S, Joshi AK (1995) Centering: a framework for modeling the local coherence of discourse. Comput Linguist 21(2):203–225

    Google Scholar 

  67. Yoshikawa K et al (2011) Coreference based event-argument relation extraction on biomedical text. J Biomed Semantics 2(Suppl 5):S6

    Article  PubMed Central  PubMed  Google Scholar 

  68. Brennan SE, Friedman MW, Pollard CJ (1987) A centering approach to pronouns. In: Proceedings of the 25th annual meeting on Association for Computational Linguistics, Morristown, NJ, USA

    Google Scholar 

  69. Elango P (2005) Coreference resolution: a survey. University of Wisconsin, Madison, WI

    Google Scholar 

  70. Lee H et al (2013) Deterministic coreference resolution based on entity-centric, precision-ranked rules. Comput Linguist 34(4):885–916

    Article  Google Scholar 

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Acknowledgment

This work is supported by the US National Institutes of Health grant R01 GM74217 (Lang Li).

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Authors and Affiliations

  1. Center for Computational Biology and Bioinformatics, School of Informatics, Indiana University, 410 W. 10th Street, Suite 5000, Indianapolis, IN, 46202, USA

    Heng-Yi Wu, Chien-Wei Chiang & Lang Li

Authors
  1. Heng-Yi Wu
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  2. Chien-Wei Chiang
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  3. Lang Li
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Corresponding author

Correspondence to Lang Li .

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Editors and Affiliations

  1. GlaxoSmithKline, King of Prussia, Pennsylvania, USA

    Vinod D. Kumar

  2. GlaxoSmithKline, Hitchin, Hertfordshire, United Kingdom

    Hannah Jane Tipney

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Wu, HY., Chiang, CW., Li, L. (2014). Text Mining for Drug–Drug Interaction. In: Kumar, V., Tipney, H. (eds) Biomedical Literature Mining. Methods in Molecular Biology, vol 1159. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0709-0_4

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  • DOI: https://doi.org/10.1007/978-1-4939-0709-0_4

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0708-3

  • Online ISBN: 978-1-4939-0709-0

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