Advertisement

Journal of Medical Systems

, 40:185 | Cite as

Using Literature-Based Discovery to Explain Adverse Drug Effects

  • Dimitar Hristovski
  • Andrej Kastrin
  • Dejan Dinevski
  • Anita Burgun
  • Lovro Žiberna
  • Thomas C. Rindflesch
Education & Training
Part of the following topical collections:
  1. Emerging Technologies for Connected Health

Abstract

We report on our research in using literature-based discovery (LBD) to provide pharmacological and/or pharmacogenomic explanations for reported adverse drug effects. The goal of LBD is to generate novel and potentially useful hypotheses by analyzing the scientific literature and optionally some additional resources. Our assumption is that drugs have effects on some genes or proteins and that these genes or proteins are associated with the observed adverse effects. Therefore, by using LBD we try to find genes or proteins that link the drugs with the reported adverse effects. These genes or proteins can be used to provide insight into the processes causing the adverse effects. Initial results show that our method has the potential to assist in explaining reported adverse drug effects.

Keywords

Literature-based discovery Text mining Pharmacovigilance Adverse drug effects Adverse drug reactions Pharmacogenomics 

Notes

Acknowledgments

This work was supported in part by the Intramural Research Program of the U.S. National Institutes of Health, National Library of Medicine. Authors would like to thank Celine Narjoz and Marie-Anne Loriot for suggesting the additional adverse drug reactions, which we used in this study. We are also grateful for the contribution of the medical students (Faculty of Medicine, University of Maribor) in the evaluation of the extracted relations.

References

  1. 1.
    Sakaeda, T., Tamon, A., Kadoyama, K., and Okuno, Y., Data mining of the public version of the FDA adverse event reporting system. Int. J. Med. Sci. 10:796–803, 2013. doi: 10.7150/ijms.6048.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Avillach, P., Dufour, J.-C., Diallo, G., et al., Design and validation of an automated method to detect known adverse drug reactions in MEDLINE: a contribution from the EU-ADR project. J. Am. Med. Inform. Assoc. 20:446–52, 2013. doi: 10.1136/amiajnl-2012-001083.CrossRefPubMedGoogle Scholar
  3. 3.
    Warrer, P., Hansen, E. H., Juhl-Jensen, L., and Aagaard, L., Using text-mining techniques in electronic patient records to identify ADRs from medicine use. Br. J. Clin. Pharmacol. 73:674–84, 2012. doi: 10.1111/j.1365-2125.2011.04153.x.CrossRefPubMedGoogle Scholar
  4. 4.
    Li, Y., Ryan, P. B., Wei, Y., and Friedman, C., A method to combine signals from spontaneous reporting systems and observational healthcare data to detect adverse drug reactions. Drug Saf. 38:895–908, 2015. doi: 10.1007/s40264-015-0314-8.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Benton, A., Ungar, L., Hill, S., et al., Identifying potential adverse effects using the web: a new approach to medical hypothesis generation. J. Biomed. Inform. 44:989–96, 2011. doi: 10.1016/j.jbi.2011.07.005.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Freifeld, C. C., Brownstein, J. S., Menone, C. M., et al., Digital drug safety surveillance: monitoring pharmaceutical products in twitter. Drug Saf. 37:343–350, 2014. doi: 10.1007/s40264-014-0155-x.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Swanson, D. R., Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspect. Biol. Med. 30:7–18, 1986.CrossRefPubMedGoogle Scholar
  8. 8.
    Hristovski, D., Rindflesch, T., and Peterlin, B., Using literature-based discovery to identify novel therapeutic approaches. Cardiovasc. Hematol. Agents Med. Chem. 11:14–24, 2013.CrossRefPubMedGoogle Scholar
  9. 9.
    Hristovski, D., Kastrin, A., Peterlin, B., and Rindflesch, T. C., Combining Semantic Relations and DNA Microarray Data for Novel Hypotheses Generation. Link Lit. Inf. Knowl. Biol. 6004(Str):53–61, 2010. doi: 10.1007/978-3-642-13131-8.CrossRefGoogle Scholar
  10. 10.
    Hristovski, D., Dinevski, D., Kastrin, A., and Rindflesch, T. C., Biomedical question answering using semantic relations. BMC Bioinform. 16:6, 2015. doi: 10.1186/s12859-014-0365-3.CrossRefGoogle Scholar
  11. 11.
    Hristovski D., SemBT. http://sembt.mf.uni-lj.si. 2009.
  12. 12.
    Rindflesch, T. C., and Fiszman, M., The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J. Biomed. Inform. 36:462–77, 2003. doi: 10.1016/j.jbi.2003.11.003.CrossRefPubMedGoogle Scholar
  13. 13.
    Liverani, E., Leonardi, F., Castellani, L., et al., Asymptomatic and persistent elevation of pancreatic enzymes in an ulcerative colitis patient. Case Rep. Gastrointest. Med. 2013:415619, 2013. doi: 10.1155/2013/415619.PubMedPubMedCentralGoogle Scholar
  14. 14.
    Ventrucci, M., Pezzilli, R., Naldoni, P., et al., Serum pancreatic enzyme behavior during the course of acute pancreatitis. Pancreas 2:506–9, 1987.CrossRefPubMedGoogle Scholar
  15. 15.
    Schmitz-Moormann, P., Comparative radiological and morphological study of the human pancreas. IV. acute necrotizing pancreatitis in man. Pathol. Res. Pract. 171:325–35, 1981. doi: 10.1016/S0344-0338(81)80105-7.CrossRefPubMedGoogle Scholar
  16. 16.
    Magos, L., Cikrt, M., and Snowden, R., The dependence of biliary methylmercury secretion on liver GSH and ligandin. Biochem. Pharmacol. 34:301–5, 1985.CrossRefPubMedGoogle Scholar
  17. 17.
    Schoenberg, M. H., Büchler, M., Pietrzyk, C., et al., Lipid peroxidation and glutathione metabolism in chronic pancreatitis. Pancreas 10:36–43, 1995.CrossRefPubMedGoogle Scholar
  18. 18.
    Akai, S., Hosomi, H., Minami, K., et al., Knock down of gamma-glutamylcysteine synthetase in rat causes acetaminophen-induced hepatotoxicity. J. Biol. Chem. 282:23996–4003, 2007. doi: 10.1074/jbc.M702819200.CrossRefPubMedGoogle Scholar
  19. 19.
    Kuhn, J. G., Pharmacology of irinotecan. Oncology (Williston Park) 12:39–42, 1998.Google Scholar
  20. 20.
    Xu, J.-M., Wang, Y., Ge, F.-J., et al., Severe irinotecan-induced toxicity in a patient with UGT1A1 28 and UGT1A1 6 polymorphisms. World J. Gastroenterol. 19:3899–903, 2013. doi: 10.3748/wjg.v19.i24.3899.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Stock, J., Statin-associated muscle symptoms EAS Consensus Panel paper focuses on this neglected patient group. Atherosclerosis 242:346–50, 2015. doi: 10.1016/j.atherosclerosis.2015.06.049.CrossRefPubMedGoogle Scholar
  22. 22.
    Niemi, M., Transporter pharmacogenetics and statin toxicity. Clin. Pharmacol. Ther. 87:130–3, 2010. doi: 10.1038/clpt.2009.197.CrossRefPubMedGoogle Scholar
  23. 23.
    Schröder, J. P., Mau, W., Schumacher, S., and Zierz, S., Abnormal regulation of carnitine palmitoyltransferase in monozygotic twins as the cause of rhabdomyolysis. Dtsch. Med. Wochenschr. 115:337–9, 1990. doi: 10.1055/s-2008-1065012.CrossRefPubMedGoogle Scholar
  24. 24.
    Roglans, N., Sanguino, E., Peris, C., et al., Atorvastatin treatment induced peroxisome proliferator-activated receptor alpha expression and decreased plasma nonesterified fatty acids and liver triglyceride in fructose-fed rats. J. Pharmacol. Exp. Ther. 302:232–9, 2002.CrossRefPubMedGoogle Scholar
  25. 25.
    Keverline, J. P., Recurrent rhabdomyolysis associated with influenza-like illness in a weight-lifter. J. Sports Med. Phys. Fitness 38:177–9, 1998.PubMedGoogle Scholar
  26. 26.
    Yang, S.-H., Choi, J.-S., and Choi, D.-H., Effects of HMG-CoA reductase inhibitors on the pharmacokinetics of losartan and its main metabolite EXP-3174 in rats: possible role of CYP3A4 and P-gp inhibition by HMG-CoA reductase inhibitors. Pharmacology 88:1–9, 2011. doi: 10.1159/000328773.CrossRefPubMedGoogle Scholar
  27. 27.
    Dopazo, C., Bilbao, I., Lázaro, J. L., et al., Severe rhabdomyolysis and acute renal failure secondary to concomitant use of simvastatin with rapamycin plus tacrolimus in liver transplant patient. Transplant. Proc. 41:1021–4, 2009. doi: 10.1016/j.transproceed.2009.02.019.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Dimitar Hristovski
    • 1
  • Andrej Kastrin
    • 2
  • Dejan Dinevski
    • 3
  • Anita Burgun
    • 4
  • Lovro Žiberna
    • 5
  • Thomas C. Rindflesch
    • 6
  1. 1.Institute for Biostatistics and Medical Informatics, Faculty of MedicineUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Faculty of Information StudiesNovo mestoLjubljanaSlovenia
  3. 3.Faculty of MedicineUniversity of MariborMariborSlovenia
  4. 4.INSERM UMRS 1138 Eq 22, Paris Descartes University, Georges Pompidou European Hospital, APHPParisFrance
  5. 5.Institute of Pharmacology and Experimental Toxicology, Faculty of MedicineUniversity of LjubljanaLjubljanaSlovenia
  6. 6.National Library of MedicineNIHBethesdaUSA

Personalised recommendations