Linking MedDRA®-Coded Clinical Phenotypes to Biological Mechanisms by the Ontology of Adverse Events: A Pilot Study on Tyrosine Kinase Inhibitors
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A translational bioinformatics challenge exists in connecting population and individual clinical phenotypes in various formats to biological mechanisms. The Medical Dictionary for Regulatory Activities (MedDRA®) is the default dictionary for adverse event (AE) reporting in the US Food and Drug Administration Adverse Event Reporting System (FAERS). The ontology of adverse events (OAE) represents AEs as pathological processes occurring after drug exposures.
The aim of this work was to establish a semantic framework to link biological mechanisms to phenotypes of AEs by combining OAE with MedDRA® in FAERS data analysis. We investigated the AEs associated with tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs) targeting tyrosine kinases. The five selected TKIs/mAbs (i.e., dasatinib, imatinib, lapatinib, cetuximab, and trastuzumab) are known to induce impaired ventricular function (non-QT) cardiotoxicity.
Statistical analysis of FAERS data identified 1053 distinct MedDRA® terms significantly associated with TKIs/mAbs, where 884 did not have corresponding OAE terms. We manually annotated these terms, added them to OAE by the standard OAE development strategy, and mapped them to MedDRA®. The data integration to provide insights into molecular mechanisms of drug-associated AEs was performed by including linkages in OAE for all related AE terms to MedDRA® and the existing ontologies, including the human phenotype ontology (HP), Uber anatomy ontology (UBERON), and gene ontology (GO). Sixteen AEs were shared by all five TKIs/mAbs, and each of 17 cardiotoxicity AEs was associated with at least one TKI/mAb. As an example, we analyzed “cardiac failure” using the relations established in OAE with other ontologies and demonstrated that one of the biological processes associated with cardiac failure maps to the genes associated with heart contraction.
By expanding the existing OAE ontological design, our TKI use case demonstrated that the combination of OAE and MedDRA® provides a semantic framework to link clinical phenotypes of adverse drug events to biological mechanisms.
KeywordsLapatinib Dasatinib Proportional Reporting Ratio Human Phenotype Mammalian Phenotype
The authors thank Zuoshuang Xiang for his programming assistance in building OAE.
Compliance with Ethical Standards
This work was supported by the Oak Ridge Institute for Science and Education (ORISE) (Sirarat Sarntivijai), the Undergraduate Research Opportunity Program at the University of Michigan (Shelley Zhang, Desikan Jagannathan, Yongqun He), the Food and Drug Administration (FDA) Commissioner’s Fellowship Program (Shadia Zaman), National Institute of Environmental Health Sciences (NIEHS) Grant Number P30ES017885-01A1 (Gilbert Omenn), National Institutes of Health (NIH) Grant Numbers U54 DA021529 and UL1 TR000433-09 (Brian Athey), and National Institute of Allergy and Infectious Disease (NIAID) Grant Number R01 AI081062 (Yongqun He).
Conflict of interest
Sirarat Sarntivijai, Shelley Zhang, Desikan Jagannathan, Shadia Zaman, Keith Burkhart, Gilbert Omenn, Yongqun He, Brian Athey, and Darrell Abernethy have no conflicts of interest that are directly relevant to the content of this study.
- 2.Brown EG, Wood L. Coding of data—MedDRA and other medical technologies. In: Rondel RK, Varley SA, Webb CF, editors, Clinical data management. Chichester: Wiley; 1999. doi: 10.1002/0470846364.ch10.
- 5.World Health Organization. International statistical classification of diseases and related health problems. Geneva: World Health Organization; 2004.Google Scholar
- 6.Colevas A, Setser A. The NCI Common Terminology Criteria for Adverse Events (CTCAE) v 3.0 is the new standard for oncology clinical trials. J Clin Oncol. 2004;22(14S):6098.Google Scholar
- 7.National Cancer Institute. Common Terminology Criteria for Adverse Events (CTCAE): version 4.0. Available on line: http://evs.nci.nih.gov/ftp1/CTCAE/CTCAE_4.03_2010-06-14_QuickReference_8.5x11.pdf. 2012.
- 9.He Y, Xiang Z, Sarntivijai S, Toldo L, Ceusters W. AEO: a realism-based biomedical ontology for the representation of adverse events. In: International Conference on Biomedical Ontology; 2011.Google Scholar
- 11.Smith B, Kumar A, Bittner T. Basic formal ontology for bioinformatics. J Inf Syst 2005:1–16. https://www.researchgate.net/profile/Barry_Smith4/publication/240744641_Basic_Formal_Ontology_for_Bioinformatics/links/0c96052e01c2e8c835000000.pdf. Accessed 15 Feb 2016.
- 15.Scheuermann RH, Ceusters W, Smith B. Toward an ontological treatment of disease and diagnosis. In: Proceedings of the 2009 AMIA Summit on Translational Bioinformatics, 2009. p. 116–20.Google Scholar
- 24.US National Library of Medicine. MedlinePlus [Internet] 2005 [cited 2014]. Available from: https://www.nlm.nih.gov/medlineplus/.
- 28.McGuinness DL, Van Harmelen F. OWL web ontology language overview. W3C recommendation. 2004;10(2004-03):10.Google Scholar
- 29.Xiang Z, Mungall C, Ruttenberg A, He Y, editors. Ontobee: a linked data server and browser for ontology terms. In: International Conference on Biomedical Ontology; 2011.Google Scholar
- 30.Arp R, Smith B. Function, role, and disposition in basic formal ontology. Nat Precedings. 2008;1941(1):1–4.Google Scholar
- 33.Gkoutos GV, Mungall C, Dolken S, Ashburner M, Lewis S, Hancock J, et al., editors. Entity/quality-based logical definitions for the human skeletal phenome using PATO. Engineering in Medicine and Biology Society, 2009 EMBC 2009 Annual International Conference of the Institute of Electrical and Electronics Engineers: Institute of Electrical and Electronics Engineers; 2009.Google Scholar
- 34.Ceusters W. An Information artifact ontology perspective on data collections and associated representational artifacts. Stud Health Technol Inf. 2012;180:68–72.Google Scholar
- 37.He Y, Xiang Z. Databases and in silico tools for vaccine design. In Silico Models Drug Discov: Springer; 2013. p. 115–27.Google Scholar
- 38.Chibucos MC, Mungall CJ, Balakrishnan R, Christie KR, Huntley RP, White O, et al. Standardized description of scientific evidence using the evidence ontology (ECO). Database (Oxford). 2014. doi: 10.1093/database/bau075. http://m.database.oxfordjournals.org/content/2014/bau075.long. Accessed 15 Feb 2016.