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http://dx.doi.org/10.5487/TR.2019.35.4.319

Future Directions of Pharmacovigilance Studies Using Electronic Medical Recording and Human Genetic Databases  

Choi, Young Hee (College of Pharmacy, Dongguk University)
Han, Chang Yeob (Department of Pharmacology, School of Medicine, Wonkwang University)
Kim, Kwi Suk (Department of Pharmacy, Seoul National University Hospital)
Kim, Sang Geon (Department of Pharmacy, Seoul National University Hospital)
Publication Information
Toxicological Research / v.35, no.4, 2019 , pp. 319-330 More about this Journal
Abstract
Adverse drug reactions (ADRs) constitute key factors in determining successful medication therapy in clinical situations. Integrative analysis of electronic medical record (EMR) data and use of proper analytical tools are requisite to conduct retrospective surveillance of clinical decisions on medications. Thus, we suggest that electronic medical recording and human genetic databases are considered together in future directions of pharmacovigilance. We analyzed EMR-based ADR studies indexed on PubMed during the period from 2005 to 2017 and retrospectively acquired 1161 (29.6%) articles describing drug-induced adverse reactions (e.g., liver, kidney, nervous system, immune system, and inflammatory responses). Of them, only 102 (8.79%) articles contained useful information to detect or predict ADRs in the context of clinical medication alerts. Since insufficiency of EMR datasets and their improper analyses may provide false warnings on clinical decision, efforts should be made to overcome possible problems on data-mining, analysis, statistics, and standardization. Thus, we address the characteristics and limitations on retrospective EMR database studies in hospital settings. Since gene expression and genetic variations among individuals impact ADRs, pharmacokinetics, and pharmacodynamics, appropriate paths for pharmacovigilance may be optimized using suitable databases available in public domain (e.g., genome-wide association studies (GWAS), non-coding RNAs, microRNAs, proteomics, and genetic variations), novel targets, and biomarkers. These efforts with new validated biomarker analyses would be of help to repurpose clinical and translational research infrastructure and ultimately future personalized therapy considering ADRs.
Keywords
Adverse drug reactions; Electronic medical record; Retrospective surveillance;
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1 Wilkins, M.R. (2002) What do we want from proteomics in the detection and avoidance of adverse drug reactions. Toxicol. Lett., 127, 245-249.   DOI
2 Amadoz, A., Sebastian-Leon, P., Vidal, E., Salavert, F. and Dopazo, J. (2015) Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity. Sci. Rep., 5, 18494.   DOI
3 Frueh, F.W. (2010) Real-world clinical effectiveness, regulatory transparency and payer coverage: three ingredients for translating pharmacogenomics into clinical practice. Pharmacogenomics, 11, 657-660.   DOI
4 Ventola, C.L. (2011) Pharmacogenomics in clinical practice: reality and expectations. P T, 36, 412-450.
5 Madian, A.G., Wheeler, H.E., Jones, R.B. and Dolan, M.E. (2012) Relating human genetic variation to variation in drug responses. Trends Genet., 28, 487-495.   DOI
6 Ventola, C.L. (2013) Role of pharmacogenomic biomarkers in predicting and improving drug response: part 1: the clinical significance of pharmacogenetic variants. P T, 38, 545-560.
7 Trent, R.J. (2010) Pathology practice and pharmacogenomics. Pharmacogenomics, 11, 105-111.   DOI
8 Ma, J.D., Lee, K.C. and Kuo, G.M. (2012) Clinical application of pharmacogenomics. J. Pharm. Pract., 25, 417-427.   DOI
9 Squassina, A., Manchia, M., Manolopoulos, V.G., Artac, M., Lappa-Manakou, C., Karkabouna, S., Mitropoulos, K., Del Zompo, M. and Patrinos, G.P. (2010) Realities and expectations of pharmacogenomics and personalized medicine: impact of translating genetic knowledge into clinical practice. Pharmacogenomics, 11, 1149-1167.   DOI
10 Wang, S.D. (2013) Opportunities and challenges of clinical research in the bigdataera: from RCT to BCT. J. Thorac. Dis., 5, 721-723.   DOI
11 Liu, M., Melton, B.L., Ator, G. and Waitman, L.R. (2017) Integrating medication alert data into a clinical data repository to enable retrospective study of drug interaction alerts in clinical practice. AMIA Jt. Summits Transl. Sci. Proc., 2017, 213-220.
12 Jaspers, M.W., Smeulers, M., Vermeulen, H. and Peute, L.W. (2011) Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J. Am. Med. Inform. Assoc., 18, 327-334.   DOI
13 Murdoch, T. and Detsky, A. (2013) The inevitable application of big data to healthcare. JAMA, 309, 1351-1352.   DOI
14 Springate, D.A., Kontopantelis, E., Ashcroft, D.M., Olier, I., Rosa Parisi, R., Chamapiwa, E. and Reeves, D. (2014) Clinical codes: an online clinical codes repository toimprove the validity and reproducibility of researchusing electronic medical records. PLoS ONE, 9, e99825.   DOI
15 Cheetham, T.C., Lee, J., Hunt, C.M., Niu, F., Reisinger, S., Murray, R., Powell, G. and Papay, J. (2014) An automated causality assessment algorithm to detect drug-induced liver injury in electronic medical record data. Pharmacoepidemiol. Drug Saf., 23, 601-608.   DOI
16 Bjornsson, E.S. (2016) Hepatotoxicity by drugs: the most common implicated agents. Int. J. Mol. Sci., 17, 224.   DOI
17 Pichler, W.J. (2003) Delayed drug hypersensitivity reactions. Ann. Intern. Med., 139, 683-693.   DOI
18 Vogenberg, F.R., Isaacson Barash, C. and Pursel, M. (2010) Personalized medicine: part 1: evolution and development into theranostics. P T, 35, 560-576.
19 Chan, S.L., Jin, S., Loh, M. and Brunham, L.R. (2015) Progress in understanding the genomic basis for adverse drug reactions: a comprehensive review and focus on the role of ethnicity. Pharmacogenomics, 16, 1161-1178.   DOI
20 Nelson, M.R., Bacanu, S.A., Mosteller, M., Li, L., Bowman, C.E., Roses, A.D., Lai, E.H. and Ehm, M.G. (2009) Genome-wide approaches to identify pharmacogenetic contributions to adverse drug reactions. Pharmacogenomics J., 9, 23-33.   DOI
21 Ling, H., Fabbri, M. and Calin, G.A. (2013) MicroRNAs and other non-coding RNAs as targets for anticancer drug development. Nat. Rev. Drug Discov., 12, 847-865.   DOI
22 Matsui, M. and Corey, D.R. (2017) Non-coding RNAs as drug targets. Nat. Rev. Drug Discov., 16, 167-179.   DOI
23 Fu, X.D. (2014) Non-coding RNA: a new frontier in regulatory biology. Natl. Sci. Rev., 1, 190-204.   DOI
24 Maurano, M.T., Humbert, R., Rynes, E., Thurman, R.E., Haugen, E., Wang, H., Reynolds, A.P., Sandstrom, R., Qu, H., Brody, J., Shafer, A., Neri, F., Lee, K., Kutyavin, T., Stehling-Sun, S., Johnson, A.K., Canfield, T.K., Giste, E., Diegel, M., Bates, D., Hansen, R.S., Neph, S., Sabo, P.J., Heimfeld, S., Raubitschek, A., Ziegler, S., Cotsapas, C., Sotoodehnia, N., Glass, I., Sunyaev, S.R., Kaul, R. and Stamatoyannopoulos, J.A. (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science, 337, 1190-1195.   DOI
25 Esteller, M. (2011) Non-coding RNAs in human disease. Nat. Rev. Genet., 12, 861-874.   DOI
26 Croce, C.M. (2009) Causes and consequences of microRNA dysregulation in cancer. Nat. Rev. Genet., 10, 704-714.   DOI
27 Kohn, L.T., Corrgan, J.M. and Donaldson, M.S. (2000) To Error in Human; Building a Safer Health System, National Academy Press, Washington, DC.
28 Kullak-Ublick, G.A., Andrade, R.J., Merz, M., End, P. and Benesic, A. (2017) Drug-induced liver injury: recent advances in diagnosis and risk assessment. Gut, 66, 1154-1164.   DOI
29 Pirmohamed, M., James, S. and Meakin, S. (2004) Adverse drug reaction as cause of admission to hospital: prospective analysis of 18820 patients. BMJ, 329, 15-19.   DOI
30 Lazarous, J., Pomeranz, B. and Corey, P.N. (1998) Incidence of adverse drug reactions in hospitalized patients. A meta-analysis of prospective studies. JAMA, 279, 1200-1205.   DOI
31 Shin, J., Hunt, D.M., Suzuki, A., Papay, J.I., Beach, K.J. and Cheetham, T.C. (2013) Characterizing phenotypes and outcomes of drug-associated liver injury using electronic medical record data. Pharmacoepidemiol. Drug Saf., 22, 190-198.   DOI
32 Lin, K.J. and Schneeweiss, S. (2016) Considerations for the analysis of longitudinal electronic health records linked to claims data to study the effectiveness and safety of drugs. Clin. Pharmacol. Ther., 100, 147-157.   DOI
33 Andreea, F. and Marius, B. (2009) Adverse drug reactions in clinical practice: a causality assessment of a case of drug-induced pancreatitis. J. Gastrointest. Liver Dis., 18, 353-358.
34 Pan, Y.Z., Gao, W. and Yu, A.M. (2009) MicroRNAs regulate CYP3A4 expression via direct and indirect targeting. Drug Metab. Dispos., 37, 2112-2117.   DOI
35 Nakajima, M. and Yokoi, T. (2011) MicroRNAs from biology to future pharmacotherapy: regulation of cytochrome P450s and nuclear receptors. Pharmacol. Ther., 131, 330-337.   DOI
36 Yu, A.M. (2009) Role of microRNAs in the regulation of drug metabolism and disposition. Exp. Opin. Drug Metab. Toxicol., 5, 1513-1528.   DOI
37 Zanger, U.M. and Schwab, M. (2013) Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol. Ther., 138, 103-141.   DOI
38 Lu, Y. and Cederbaum, A.I. (2008) CYP2E1 and oxidative liver injury by alcohol. Free Radic. Biol. Med., 44, 723-738.   DOI
39 Mohri, T., Nakajima, M., Fukami, T., Takamiya, M., Aoki, Y. and Yokoi, T. (2010) Human CYP2E1 is regulated by miR-378. Biochem. Pharmacol., 79, 1045-1052.   DOI
40 Song, K.H., Li, T., Owsley, E. and Chiang, J.Y. (2010) A putative role of micro RNA in regulation of cholesterol 7alpha-hydroxylase expression in human hepatocytes. J. Lipid Res., 51, 2223-2233.   DOI
41 Pandit, A., Sachdeva, T. and Bafna, P. (2012) Drug-induced hepatotoxicity: a review. J. Appl. Pharm. Sci., 02, 233-243.
42 Jha, A.K., Kuperman, G.J., Teich, J.M., Leape, L., SHea, B., Rittenberg, E., Burdick, E., Seger, D.L., Vander Vliet, M., Bates, D.W. (1998) Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J. Am. Med. Inform. Assoc., 5, 305-314.   DOI
43 Tisdale, J.E. and Miller, D.A. (2005) Drug-Induced Diseases: Prevention, Detection and Management, American Society of Health-System Pharmacist Press, Bethesda, Maryland, pp. 1004-1007.
44 Komagata, S., Nakajima, M., Takagi, S., Mohri, T., Taniya, T. and Yokoi, T. (2009) Human CYP24 catalyzing the inactivation of calcitriol is post-transcriptionally regulated by miR-125b. Mol. Pharmacol., 76, 702-709.   DOI
45 Lin, H., Ewing, L.E., Koturbash, I., Gurley, B.J. and Miousse, I.R. (2017) MicroRNAs as biomarkers for liver injury: current knowledge, challenges and future prospects. Food Chem. Toxicol., 110, 229-239.   DOI
46 Marrone, A.K., Beland, F.A. and Pogribny, I.P. (2015) The role for microRNAs in drug toxicity and in safety assessment. Exp. Opin. Drug Metab. Toxicol., 11, 601-611.   DOI
47 Lee, Y.H., Kang, U.G. and Park, R.W. (2008) Development of adverse drug event surveillance system using BI technology. Int. J. Contents, 9, 106-113.
48 Miller, T.P., Ki, Y., Getz, K.D., Dudley, J., Burrows, E., Pennington, J., Ibrahimova, A., Fisher, B.T., Baqatell, R., Seif, A.E., Grundmeier, R. and Aplenc, R. (2017) Using electronic medical record data to report laboratory adverse events. Br. J. Haematol., 177, 283-286.   DOI
49 Mauben, M., Madigan, D. and Gerritis, C.M. (2007) The role of data mining in pharmacovigilance. Exp. Opin. Drug Saf., 14, 929-948.
50 Phansalkar, S., Hoffman, J.M., Nebeker, J.R. and Hurdle, J.F. (2007) Pharmacists versus nonpharmacists in adverse drug event detection: a meta-analysis and systematic review. Am. J. Health Syst. Pharm., 64, 842-849.   DOI
51 Phansalkar, S., Hoffman, J.M., Hurdle, J.F. and Patel, V.L. (2009) Understanding pharmacist decision making for adverse drug event (ADE) detection. J. Eval. Clin. Pract., 15, 266-275.   DOI
52 Sato, S., Ichihara, A., Jinnin, M., Izuno, Y., Fukushima, S. and Ihn, H. (2015) Serum miR-124 up-regulation as a disease marker of toxic epidermal necrolysis. Eur. J. Dermatol., 25, 457-462.   DOI
53 Tojios, S. and Fontana, R.J. (2011) Mechanisms of drug-induced liver injury: from bedside to bench. Nat. Rev. Gastroenterol. Hepatol., 8, 202-211.   DOI
54 Manuel, D.G., Rosella, L.C. and Stukel, T.A. (2010) Importance of accurately identifying disease in studies using electronic health records. BMJ, 341, c4226.   DOI
55 Pratt, N., Cham, E.W., Choi, N.K., Kimura, M., Kimura, T., Kubota, K., Lai, E.C., Man, L.L., Ooba, N., Park, B.J., Sato, T., Shin, J.Y., Wong, I.C., Kao Yang, Y.H. and Roughead, E.E. (2015) Prescription sequence symmetry analysis: assessing risk, temporality, and consistency for adverse drug reactions across datasets in five countries. Pharmacoepidemiol. Drug Saf., 24, 858-864.   DOI
56 Antoine, D.J., Dear, J.W., Lewis, P.S., Platt, V., Coyle, J., Masson, M., Thanacoody, R.H., Gray, A.J., Webb, D.J., Moggs, J.G., Bateman, D.N., Goldring, C.E. and Park, B.K. (2013) Mechanistic biomarkers provide early and sensitive detection of acetaminophen-induced acute liver injury at first presentation to hospital. Hepatology, 58, 777-787.   DOI
57 Howell, L.S., Ireland, L., Park, B.K. and Goldring, C.E. (2018) MiR-122 and other microRNAs as potential circulating biomarkers of drug-induced liver injury. Exp. Rev. Mol. Diagn., 18, 47-54.   DOI
58 Zhou, H., Gao, M. and Skolnick, J. (2015) Comprehensive prediction of drug-protein interactions and side effects for the human proteome. Sci. Rep., 5, 11090.   DOI
59 Ichihara, A., Wang, Z., Jinnin, M., Izuno, Y., Shimozono, N., Yamane, K., Fujisawa, A., Moriya, C., Fukushima, S., Inoue, Y., Shimozono, N., Yamane, K., Fujisawa, A., Moriya, C., Fukushima, S., Inoue, Y. and Ihn, H. (2014) Upregulation of miR-18a-5p contributes to epidermal necrolysis in severe drug eruptions. J. Allergy Clin. Immunol., 133, 1065-1074.   DOI
60 Williams, S.A., Murthy, A.C., DeLisle, R.K., Hyde, C., Malarstig, A., Ostroff, R., Weiss, S.J., Segal, M.R. and Ganz, P. (2018) Improving assessment of drug safety through proteomics: early detection and mechanistic characterization of the unforeseen harmful effects of torcetrapib. Circulation, 137, 999-1010.   DOI
61 Huang, L.C., Wu, X. and Chen, J.Y. (2013) Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures. Proteomics, 13, 313-324.   DOI
62 Ge, F. and He, Q.Y. (2009) Genomic and proteomic approaches for predicting toxicity and adverse drug reactions. Exp. Opin. Drug Metab. Toxicol., 5, 29-37.   DOI
63 Koutkias, V.G. and Jaulent, M.-C. (2015) Computational approaches for pharmacovigilance signaldetection: toward integrated and semantically-enriched frameworks. Drug Saf., 38, 219-232.   DOI
64 Scholl, J.H.G., van Hunsel, F.P.A.M., Hak, E. and van Puijenbroek, E.P. (2017) A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands. Pharmacoepidemiol. Drug Saf., 27, 199-205.   DOI
65 Chen, X., Wang, Y., Wang, P., Lian, B., Li, C., Wamg, J., Li, X. and Jiang, W. (2015) Systematic analysis of the associations between adverse drug reactions and pathways. Biomed. Res. Int., 2015, 670949.
66 Kohonen, P., Parkkinen, J.A., Willinghaegen, E.L., Ceder, R., Wennerbern, K., Kaski, S. and Grafstrom, R.C. (2017) A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury. Nat. Commun., 8, 15932.   DOI
67 Xu, Q., Higgins, T. and Cembrowski, G.S. (2015) Limiting the testing of AST: a diagnostically nonspecific enzyme. Am. J. Clin. Pathol., 144, 423-426.   DOI
68 Gervasini, G., Benitez, J. and Carrillo, J.A. (2010) Pharmacogenetic testing and therapeutic drug monitoring are complementary tools for optimal individualization of drug therapy. Eur. J. Clin. Pharmacol., 66, 755-774.   DOI
69 Link, E., Parish, S., Armitage, J., Bowman, L., Heath, S., Matsuda, F., Gut, I., Lathrop, M. and Collins, R. (2008) SLCO1B1 variants and statin-induced myopathy--a genomewide study. N. Engl. J. Med., 359, 789-799.   DOI
70 Carr, D.F. and Pirmohamed, M. (2017) Biomarkers of adverse drug reactions. Exp. Biol. Med. (Maywood), 243, 291-299.   DOI
71 Alfirevic, A., Neely, D., Armitage, J., Chinoy, H., Cooper, R.G., Laaksonen, R., Carr, D.F., Bloch, K.M., Fahy, J., Hanson, A., Yue, Q.Y., Wadelius, M., Maitland-van Der Zee, A.H., Voora, D., Psaty, B.M., Paimer, C.N. and Pirmohamed, M. (2014) Phenotype standardization for statin-induced myotoxicity. Clin. Pharmacol. Ther., 96, 470-476.   DOI
72 Duma, R.J. and Siegel, A.L. (1965) Serum creatinine phohphokinase in acute myocardical infarction: diagnostic value. Arch. Intern. Med., 115, 443-451.   DOI
73 Kindermann, W. (2016) Creatine kinase levels after exercise. Dtsch. Arztebl. Int., 113, 344.
74 Crews, K.R., Hicks, J.K., Pui, C.H., Relling, M.V. and Evans, W.E. (2012) Pharmacogenomics and individualized medicine: translating science into practice. Clin. Pharmacol. Ther., 92, 467-475.
75 Flockhart, D.A., O'Kane, D., Williams, M.S., Watson, M.S., Flockhart, D.A., Gage, B., Gandolfi, R., King, R., Lyon, E., Nussbaum, R., O'Kane, D., Schulman, K., Veenstra, D., Williams, M.S., Watson, M.S.; ACMG Working Group on Pharmacogenetic Testing of CYP2C9, VKORC1 Alleles for Warfarin Use (2008) Pharmacogenetic testing of CYP2C9 and VKORC1 alleles for warfarin. Genet. Med., 10, 139-150.   DOI
76 Scott, S.A. (2011) Personalizing medicine with clinical pharmacogenetics. Genet. Med., 13, 987-995.   DOI
77 Taneja, I., Reddy, B. and Damhorst, G. (2017) Combining biomarkers with EMR data to identify patients in different phases of sepsis. Sci. Rep., 7, 10800.   DOI
78 Jiang, X.Y., Zhang, Q., Chen, P., Li, S.Y., Zhang, N.N., Chen, X.D., Wang, G.C., Wang, H.B., Zhuang, M.Q. and Lu, M. (2012) CYP7A1 polymorphism influences the LDL cholesterol-lowering response to atorvastatin. J. Clin. Pharm. Ther., 37, 719-723.   DOI
79 Zhu, M., Qiu, S., Zhang, X., Wang, Y., Souraka, T.D.M., Wen, X., Liang, C. and Tu, J. (2018) The associations between CYP24A1 polymorphisms and cancer susceptibility: a meta-analysis and trial sequential analysis. Pathol. Res. Pract., 214, 53-63.   DOI
80 Wilke, R.A., Lin, D.W., Roden, D.M., Watkins, P.B., Flockhart, D., Zineh, I., Giacomini, K.M. and Krauss, R.M. (2007) Identifying genetic risk factors for serious adverse drug reactions:current progress and challenges. Nat. Rev. Drug Discov., 6, 904-916.   DOI
81 Redwood, A.J., Pavlos, R.K., White, K.D. and Phillips, E.J. (2018) Human leukocyte antigens: key regulators of T-cell mediated drug hypersensitivity. HLA, 91, 3-16.   DOI
82 Wang, C.-W., Chung, W.-H. and Hung, S.-I. (2017) Genetics of adverse drug reactions. eLS, 1-10.
83 Lea, J.D., Clarke, J.I., McGuire, N. and Antoine, D.J. (2016) Redox-dependent HMGB1 isoforms as pivotal co-ordinators of drug-induced liver injury: mechanistic biomarkers and therapeutic targets. Antioxid. Redox. Signal., 24, 652-665.   DOI