Advertisement

Drug Safety

, Volume 41, Issue 11, pp 1059–1072 | Cite as

Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

  • Mary K. La
  • Alexander Sedykh
  • Denis Fourches
  • Eugene Muratov
  • Alexander TropshaEmail author
Original Research Article

Abstract

Introduction

Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug–target–effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug–target–effect paradigm, can be integrated to facilitate the inference of relationships between these entities.

Objective

This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature.

Materials and Methods

Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C–T–E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson’s paradigm to form C–T–E triangles. Missing C–E edges were then inferred as C–E relationships.

Results

Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C–E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale.

Conclusions

The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C–E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C–E inferences, this workflow may provide an effective computational method for the early detection of potential drug candidate ADEs that can be followed by targeted experimental investigations.

Notes

Acknowledgements

The authors would like to thank Mr Alexander Gartland for fruitful discussions that helped to improve the quality of the manuscript.

The MedDRA® trademark is owned by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA) on behalf of the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH).

Compliance with Ethical Standards

Funding

This study was supported in part by the National Institutes of Health (Grant 1U01CA207160).

Conflict of interest

Mary La, Alexander Sedykh, Denis Fourches, Eugene Muratov, and Alexander Tropsha declare no conflict of interests that are directly relevant to the content of this study.

Supplementary material

40264_2018_688_MOESM1_ESM.pdf (601 kb)
Supplementary material 1 (PDF 601 kb)

References

  1. 1.
    Aronson JK. Distinguishing hazards and harms, adverse drug effects and adverse drug reactions: implications for drug development, clinical trials, pharmacovigilance, biomarkers, and monitoring. Drug Saf. 2013;36:147–53.CrossRefPubMedGoogle Scholar
  2. 2.
    Shepherd G, Mohorn P, Yacoub K, May DW. Adverse drug reaction deaths reported in United States vital statistics, 1999–2006. Ann Pharmacother. 2012;46:169–75.CrossRefPubMedGoogle Scholar
  3. 3.
    Suh DC, Woodall BS, Shin SK, Hermes-De Santis ER. Clinical and economic impact of adverse drug reactions in hospitalized patients. Ann Pharmacother. 2000;34:1373–9.CrossRefPubMedGoogle Scholar
  4. 4.
    Agency for Healthcare Research and Quality. Reducing and preventing adverse drug events to decrease hospital costs. AHRQ Archive. https://archive.ahrq.gov/research/findings/factsheets/errors-safety/aderia/ade.html. Accessed 2 Apr 2014.
  5. 5.
    Ninan B, Wertheimer A. Withdrawing drugs in the U.S. versus other countries. Innov Pharm. 2012;3(3):Article 87. https://pubs.lib.umn.edu/index.php/innovations/article/view/269/263. Accessed 19 Apr 2018.
  6. 6.
    Swanson DR. Migraine and magnesium: eleven neglected connections. Perspect Biol Med. 1988;31:526–57.CrossRefPubMedGoogle Scholar
  7. 7.
    Ramadan NM, Halvorson H, Vande-Linde A, Levine SR, Helpern JA, Welch KM. Low brain magnesium in migraine. Headache. 1989;29:416–9.CrossRefPubMedGoogle Scholar
  8. 8.
    US Food and Drug Administration. openFDA datasets: FAERS. https://open.fda.gov/data/faers/. Accessed 19 Apr 2018.
  9. 9.
    Harpaz R, Haerian K, Chase HS, Friedman C. Statistical mining of potential drug interaction adverse effects in FDA’s spontaneous reporting system. AMIA Annu Symp Proc AMIA Symp. 2010;2010:281–5.PubMedGoogle Scholar
  10. 10.
    Schuemie MJ, Coloma PM, Straatman H, Herings RMC, Trifirò G, Matthews JN, et al. Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods. Med Care. 2012;50:890–7.CrossRefPubMedGoogle Scholar
  11. 11.
    Bai JPF, Abernethy DR. Systems pharmacology to predict drug toxicity: integration across levels of biological organization. Annu Rev Pharmacol Toxicol. 2013;53:451–73.CrossRefPubMedGoogle Scholar
  12. 12.
    Yang L, Agarwal P. Systematic drug repositioning based on clinical side-effects. PLoS ONE. 2011;6:e28025.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Yamanishi Y, Pauwels E, Kotera M. Drug side-effect prediction based on the integration of chemical and biological spaces. J Chem Inf Model. 2012;52:3284–92.CrossRefPubMedGoogle Scholar
  14. 14.
    Cheng F, Li W, Wang X, Zhou Y, Wu Z, Shen J, et al. Adverse drug events: database construction and in silico prediction. J Chem Inf Model. 2013;53:744–52.CrossRefPubMedGoogle Scholar
  15. 15.
    Cami A, Arnold A, Manzi S, Reis B. Predicting adverse drug events using pharmacological network models. Sci Transl Med. 2011;3:114ra127.CrossRefPubMedGoogle Scholar
  16. 16.
    Chen B, Ding Y, Wild DJ. Assessing drug target association using semantic linked data. PLoS Comput Biol. 2012;8:e1002574.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Campillos M, Kuhn M, Gavin A-C, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science. 2008;321:263–6.CrossRefPubMedGoogle Scholar
  18. 18.
    Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature. 2012;486:361–7.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Oprea TI, Nielsen SK, Ursu O, Yang JJ, Taboureau O, Mathias SL, et al. Associating drugs, targets and clinical outcomes into an integrated network affords a new platform for computer-aided drug repurposing. Mol Inform. 2011;30:100–11.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Davis AP, Wiegers TC, Roberts PM, King BL, Lay JM, Lennon-Hopkins K, et al. A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions. Database. 2013;2013:bat080.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    King BL, Davis AP, Rosenstein MC, Wiegers TC, Mattingly CJ. Ranking transitive chemical-disease inferences using local network topology in the comparative toxicogenomics database. PLoS One. 2012;7:e46524.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Simon Z, Peragovics A, Vigh-Smeller M, Csukly G, Tombor L, Yang Z, et al. Drug effect prediction by polypharmacology-based interaction profiling. J Chem Inf Model. 2012;52:134–45.CrossRefPubMedGoogle Scholar
  23. 23.
    Wang Y, Chen S, Deng N, Wang Y. Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS One. 2013;8:e78518.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Wallach I, Jaitly N, Lilien R. A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways. PLoS One. 2010;5:e12063.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Mathur S, Dinakarpandian D. Drug repositioning using disease associated biological processes and network analysis of drug targets. AMIA Annu Symp Proc. 2011;2011:305–11.PubMedCentralPubMedGoogle Scholar
  26. 26.
    Kim Kjærulff S, Wich L, Kringelum J, Jacobsen UP, Kouskoumvekaki I, Audouze K, et al. ChemProt-2.0: visual navigation in a disease chemical biology database. Nucleic Acids Res. 2013;41:D464–9.CrossRefPubMedGoogle Scholar
  27. 27.
    Jacob L, Vert J-P. Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinforma Oxf Engl. 2008;24:2149–56.CrossRefGoogle Scholar
  28. 28.
    Lo HZ, Ding W, Nazeri Z. Mining adverse drug reactions from electronic health records. In: 2013 IEEE 13th Int Conf Data Min Workshop. 2013. p. 1137–40.Google Scholar
  29. 29.
    Jensen K, Soguero-Ruiz C, Mikalsen KO, Lindsetmo R-O, Kouskoumvekaki I, Girolami M, et al. Analysis of free text in electronic health records for identification of cancer patient trajectories. Sci Rep. 2017;7:46226.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Yadav P, Steinbach M, Kumar V, Simon G. Mining electronic health records (EHRs): a survey. ACM Comput Surv. 2018;50:85:1–85:40.CrossRefGoogle Scholar
  31. 31.
    Huang R, Southall N, Wang Y, Yasgar A, Shinn P, Jadhav A, et al. The NCGC Pharmaceutical Collection: a comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics. Sci Transl Med. 2011;3:80ps16. https://tripod.nih.gov/npc/. Accessed 23 May 2018.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model. 2010;50:1189–204.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Fourches D, Muratov E, Tropsha A. Curation of chemogenomics data. Nat Chem Biol. 2015;11:535.CrossRefPubMedGoogle Scholar
  34. 34.
    Fourches D, Muratov E, Tropsha A. Trust, but verify II: a practical guide to chemogenomics data curation. J Chem Inf Model. 2016;56:1243–52.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Kuz’min VE, Artemenko AG, Muratov EN. Hierarchical QSAR technology based on the Simplex representation of molecular structure. J Comput Aided Mol Des. 2008;22:403–21.CrossRefPubMedGoogle Scholar
  36. 36.
    Yang H, Qin C, Li YH, Tao L, Zhou J, Yu CY, et al. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res. 2016;44:D1069–74.CrossRefPubMedGoogle Scholar
  37. 37.
    UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:D204–12.CrossRefGoogle Scholar
  38. 38.
    Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010;6:343.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    National Library of Medicine. Unified Medical Language System (UMLS). 2013. https://www.nlm.nih.gov/research/umls/. Accessed 2 Mar 2015.
  40. 40.
    Kuhn M, Szklarczyk D, Franceschini A, von Mering C, Jensen LJ, Bork P. STITCH 3: zooming in on protein–chemical interactions. Nucleic Acids Res. 2012;40:D876–80.CrossRefGoogle Scholar
  41. 41.
    Davis AP, Murphy CG, Johnson R, Lay JM, Lennon-Hopkins K, Saraceni-Richards C, et al. The comparative toxicogenomics database: update 2013. Nucleic Acids Res. 2013;41:D1104–14.CrossRefPubMedGoogle Scholar
  42. 42.
    Baker NC, Hemminger BM. Mining connections between chemicals, proteins, and diseases extracted from Medline annotations. J Biomed Inform. 2010;43:510–9.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    MedDRA. 2013. https://www.meddra.org/. Accessed 19 Apr 2018.
  44. 44.
    Wohlgemuth G, Haldiya PK, Willighagen E, Kind T, Fiehn O. The Chemical Translation Service—a web-based tool to improve standardization of metabolomic reports. Bioinformatics. 2010;26:2647–8.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Cover TM, Thomas JA. Relative entropy and mutual information. In: Elements of information theory. 2nd ed. New York: Wiley Interscience; 2006. p. 19–20.Google Scholar
  46. 46.
    Wysowski DK, Swartz L. Adverse drug event surveillance and drug withdrawals in the United States, 1969-2002: the importance of reporting suspected reactions. Arch Intern Med. 2005;165:1363–9.CrossRefPubMedGoogle Scholar
  47. 47.
    Trifirò G, Pariente A, Coloma PM, Kors JA, Polimeni G, Miremont-Salamé G, et al. Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor? Pharmacoepidemiol Drug Saf. 2009;18:1176–84.CrossRefPubMedGoogle Scholar
  48. 48.
    Houwerzijl EJ, Blom NR, van der Want JJL, Esselink MT, Koornstra JJ, Smit JW, et al. Ultrastructural study shows morphologic features of apoptosis and para-apoptosis in megakaryocytes from patients with idiopathic thrombocytopenic purpura. Blood. 2004;103:500–6.CrossRefPubMedGoogle Scholar
  49. 49.
    Mitra D, Kim J, MacLow C, Karsan A, Laurence J. Role of caspases 1 and 3 and Bcl-2-related molecules in endothelial cell apoptosis associated with thrombotic microangiopathies. Am J Hematol. 1998;59:279–87.CrossRefPubMedGoogle Scholar
  50. 50.
    Taler M, Gil-Ad I, Lomnitski L, Korov I, Baharav E, Bar M, et al. Immunomodulatory effect of selective serotonin reuptake inhibitors (SSRIs) on human T lymphocyte function and gene expression. Eur Neuropsychopharmacol. 2007;17:774–80.CrossRefPubMedGoogle Scholar
  51. 51.
    Metjian A, Abrams CS. New advances in the treatment of adult chronic immune thrombocytopenic purpura: role of thrombopoietin receptor-stimulating agents. Biol Targets Ther. 2009;3:499–513.Google Scholar
  52. 52.
    Schipperus M, Fijnheer R. New therapeutic options for immune thrombocytopenia. Neth J Med. 2011;69:480–5.PubMedGoogle Scholar
  53. 53.
    Metcalfe PD, Leslie JA, Campbell MT, Meldrum DR, Hile KL, Meldrum KK. Testosterone exacerbates obstructive renal injury by stimulating TNF-alpha production and increasing proapoptotic and profibrotic signaling. Am J Physiol Endocrinol Metab. 2008;294:E435–43.CrossRefPubMedGoogle Scholar
  54. 54.
    Ridker PM, Rifai N, Pfeffer M, Sacks F, Lepage S, Braunwald E. Elevation of tumor necrosis factor-alpha and increased risk of recurrent coronary events after myocardial infarction. Circulation. 2000;101:2149–53.CrossRefPubMedGoogle Scholar
  55. 55.
    Li D, Zhao L, Liu M, Du X, Ding W, Zhang J, et al. Kinetics of tumor necrosis factor alpha in plasma and the cardioprotective effect of a monoclonal antibody to tumor necrosis factor alpha in acute myocardial infarction. Am Heart J. 1999;137:1145–52.CrossRefPubMedGoogle Scholar
  56. 56.
    Giannakopoulou M, Bozas E, Philippidis H, Stylianopoulou F. Protooncogene c-fos involvement in the molecular mechanism of rat brain sexual differentiation. Neuroendocrinology. 2001;73:387–96.CrossRefPubMedGoogle Scholar
  57. 57.
    Zhang S, Zhang M, Goldstein S, Li Y, Ge J, He B, et al. The effect of c-fos on acute myocardial infarction and the significance of metoprolol intervention in a rat model. Cell Biochem Biophys. 2013;65:249–55.CrossRefPubMedGoogle Scholar
  58. 58.
    US Food and Drug Administration, Center for Drug Evaluation and Research. FDA Drug Safety Communication: FDA cautions about using testosterone products for low testosterone due to aging; requires labeling change to inform of possible increased risk of heart attack and stroke with use. 2015. https://www.fda.gov/Drugs/DrugSafety/ucm436259.htm. Accessed 19 Apr 2018.
  59. 59.
    Vigen R, O’Donnell CI, Barón AE, Grunwald GK, Maddox TM, Bradley SM, et al. Association of testosterone therapy with mortality, myocardial infarction, and stroke in men with low testosterone levels. JAMA. 2013;310:1829–36.CrossRefPubMedGoogle Scholar
  60. 60.
    Finkle WD, Greenland S, Ridgeway GK, Adams JL, Frasco MA, Cook MB, et al. Increased risk of non-fatal myocardial infarction following testosterone therapy prescription in men. PLoS One. 2014;9:e85805.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Morgentaler A, Zitzmann M, Traish AM, Fox AW, Jones TH, Maggi M, et al. Fundamental concepts regarding testosterone deficiency and treatment: international expert consensus resolutions. Mayo Clin Proc. 2016;91:881–96.CrossRefPubMedGoogle Scholar
  62. 62.
    Ali WHAB. Ciprofloxacin-associated posterior reversible encephalopathy. BMJ Case Rep. 2013;2013.  https://doi.org/10.1136/bcr-2013-008636.Google Scholar
  63. 63.
    Patel AS, Supan EM, Ali SN. Toxic epidermal necrolysis associated with rifaximin. Am J Health Syst Pharm. 2013;70:874–6.CrossRefPubMedGoogle Scholar
  64. 64.
    Sharma D, Ivanovski S, Slevin M, Hamlet S, Pop TS, Brinzaniuc K, et al. Bisphosphonate-related osteonecrosis of jaw (BRONJ): diagnostic criteria and possible pathogenic mechanisms of an unexpected anti-angiogenic side effect. Vasc Cell. 2013;5:1.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Smalheiser NR, Swanson DR. Using ARROWSMITH: a computer-assisted approach to formulating and assessing scientific hypotheses. Comput Methods Programs Biomed. 1998;57:149–53.CrossRefPubMedGoogle Scholar
  66. 66.
    Shang N, Xu H, Rindflesch TC, Cohen T. Identifying plausible adverse drug reactions using knowledge extracted from the literature. J Biomed Inform. 2014;52:293–310.CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Hristovski D, Rindflesch T, Peterlin B. Using literature-based discovery to identify novel therapeutic approaches. Cardiovasc Hematol Agents Med Chem. 2013;11:14–24.CrossRefPubMedGoogle Scholar
  68. 68.
    Yetisgen-Yildiz M, Pratt W. A new evaluation methodology for literature-based discovery systems. J Biomed Inform. 2009;42:633–43.CrossRefPubMedGoogle Scholar
  69. 69.
    Preiss J, Stevenson M, Gaizauskas R. Exploring relation types for literature-based discovery. J Am Med Inform Assoc. 2015;22:987–92.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Tari L, Vo N, Liang S, Patel J, Baral C, Cai J. Identifying novel drug indications through automated reasoning. PLoS One. 2012;7:e40946.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Andronis C, Sharma A, Virvilis V, Deftereos S, Persidis A. Literature mining, ontologies and information visualization for drug repurposing. Brief Bioinform. 2011;12:357–68.CrossRefPubMedGoogle Scholar
  72. 72.
    Doulaverakis C, Nikolaidis G, Kleontas A, Kompatsiaris I. Panacea, a semantic-enabled drug recommendations discovery framework. J Biomed Semant. 2014;5:13.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Practice Advancement and Clinical EducationUNC Eshelman School of PharmacyChapel HillUSA
  2. 2.Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal ChemistryUNC Eshelman School of PharmacyChapel HillUSA
  3. 3.Sciome LLCResearch Triangle ParkUSA
  4. 4.Department of ChemistryNorth Carolina State UniversityRaleighUSA

Personalised recommendations