Bioimpacts. 2020;10(2): 97-104.
doi: 10.34172/bi.2020.12
PMID: 32363153
PMCID: PMC7186546
  Abstract View: 181
  PDF Download: 110
  Full Text View: 57

Original Research

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

Reza Ferdousi 1,2 ORCID logo, Ali Akbar Jamali 3 ORCID logo, Reza Safdari 4 * ORCID logo

1 Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
2 Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
3 Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
4 Department of Health Care Management, Tehran University of Medical Sciences, Tehran, Iran
*Corresponding author: Reza Safdari, Email: rsafdari@tums.ac.ir


Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved.
Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements.
Results: The analyses of over 45 000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2 000 000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200 000 potential DDIs.
Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.
Keywords: Drug-drug interaction, Market Basket Analysis, Rule discovery, Biological targets
First Name
Last Name
Email Address
Security code

Abstract View: 181

Your browser does not support the canvas element.

PDF Download: 110

Your browser does not support the canvas element.

Full Text View: 57

Your browser does not support the canvas element.

Submitted: 21 May 2019
Revision: 17 Oct 2019
Accepted: 22 Oct 2019
ePublished: 02 Nov 2019
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - Firefox Plugin)