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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2015

Open Access 01-12-2015 | Research article

GraphSAW: A web-based system for graphical analysis of drug interactions and side effects using pharmaceutical and molecular data

Authors: Alban Shoshi, Tobias Hoppe, Benjamin Kormeier, Venus Ogultarhan, Ralf Hofestädt

Published in: BMC Medical Informatics and Decision Making | Issue 1/2015

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Abstract

Background

Adverse drug reactions are one of the most common causes of death in industrialized Western countries. Nowadays, empirical data from clinical studies for the approval and monitoring of drugs and molecular databases is available.

Methods

The integration of database information is a promising method for providing well-based knowledge to avoid adverse drug reactions. This paper presents our web-based decision support system GraphSAW which analyzes and evaluates drug interactions and side effects based on data from two commercial and two freely available molecular databases. The system is able to analyze single and combined drug-drug interactions, drug-molecule interactions as well as single and cumulative side effects. In addition, it allows exploring associative networks of drugs, molecules, metabolic pathways, and diseases in an intuitive way. The molecular medication analysis includes the capabilities of the upper features.

Results

A statistical evaluation of the integrated data and top 20 drugs concerning drug interactions and side effects is performed. The results of the data analysis give an overview of all theoretically possible drug interactions and side effects. The evaluation shows a mismatch between pharmaceutical and molecular databases. The concordance of drug interactions was about 12% and 9% of drug side effects. An application case with prescribed data of 11 patients is presented in order to demonstrate the functionality of the system under real conditions. For each patient at least two interactions occured in every medication and about 8% of total diseases were possibly induced by drug therapy.

Conclusions

GraphSAW (http://​tunicata.​techfak.​uni-bielefeld.​de/​graphsaw/​) is meant to be a web-based system for health professionals and researchers. GraphSAW provides comprehensive drug-related knowledge and an improved medication analysis which may support efforts to reduce the risk of medication errors and numerous drastic side effects.
Literature
1.
Barker KN, Flynn EA, Pepper GA, Bates DW, Mikeal RL. Medication errors observed in 36 health care facilities. Arch Intern Med. 2002;162:1897–903.CrossRefPubMed
2.
Brennan TA, Leape LL, Laird NM, Herbert L, Localio AR, Lawthers AG, et al. Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I N Engl J Med. 1991;324:370–6.PubMed
3.
Bates DW, Boyle DL, Vander Vliet MB, Schneider J, Leape L. Relationship between medication errors and adverse drug events. J Gen Intern Med. 1995;10:199–205.CrossRefPubMed
4.
Lesar TS, Briceland L, Stein DS. Factors related to errors in medication prescribing. JAMA. 1997;277:312–7.CrossRefPubMed
5.
Ebbesen J, Buajordet I, Erikssen J, Brørs O, Hilberg T, Svaar H, et al. Drug-related deaths in a department of internal medicine. Arch Intern Med. 2001;161:2317–23.CrossRefPubMed
6.
Schnurrer JU, Frölich JC. Incidence and prevention of lethal undesirable drug effects. Internist. 2003;44:889–95.CrossRefPubMed
7.
Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma'Luf N, et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc. 1999;6:313–21.CrossRefPubMedPubMedCentral
8.
Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;163:1409–16.CrossRefPubMed
9.
10.
11.
12.
13.
14.
Von Eichborn J, Murgueitio MS, Dunkel M, Koerner S, Bourne PE, Preissner R. PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res. 2011;39:D1060–6.CrossRef
15.
Kuhn M, von Mering C, Campillos M, Jensen LJ, Bork P. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. 2008;36:D684–8.CrossRefPubMed
16.
Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010;38:D355–60.CrossRefPubMed
17.
18.
Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Res. 2012;40:D109–14.CrossRefPubMed
19.
20.
Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 2011;39:1035–41.CrossRef
21.
WHO Collaborating Centre for Drug Statistics Methodology: Guidelines for ATC classification and DDD assignment 2013. Oslo; 2012
22.
Brown EG, Wood L, Wood S. The Medical Dictionary for Regulatory Activities (MedDRA). Drug Saf. 1999;20:109–17.CrossRefPubMed
23.
Bolton E, Wang Y, Thiessen PA, Bryant SH. PubChem: Integrated Platform of Small Molecules and Biological Activities. In: Annual Reports in Computational Chemistry, vol. 4. Oxford: Elsevier; 2008. p. 217–40.
24.
25.
26.
27.
Töpel T, Kormeier B, Klassen A, Hofestädt R: BioDWH: a data warehouse kit for life science data integration. J Integr Bioinform. 2008, 5.
28.
GEK - Gmünder ErsatzKasse. GEK-Arzneimittel-Report 2009. Bremen, Schwäbisch Gmünd: GEK - Gmünder ErsatzKasse; 2009.
Metadata
Title
GraphSAW: A web-based system for graphical analysis of drug interactions and side effects using pharmaceutical and molecular data
Authors
Alban Shoshi
Tobias Hoppe
Benjamin Kormeier
Venus Ogultarhan
Ralf Hofestädt
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2015
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-015-0139-5

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