Medical and Dental Consultantsí Association of Nigeria
Home - About us - Editorial board - Search - Ahead of print - Current issue - Archives - Submit article - Instructions - Subscribe - Advertise - Contacts - Login 
  Users Online: 842   Home Print this page Email this page Small font sizeDefault font sizeIncrease font size
 

  Table of Contents 
ORIGINAL ARTICLE
Year : 2022  |  Volume : 25  |  Issue : 12  |  Page : 2005-2009

Determination and comparison of potential drug–drug interactions using three different databases in northern cyprus community pharmacies


Department of Clinical Pharmacy, Near East University, Faculty of Pharmacy, Nicosia, North Cyprus, Mersin, Turkey

Date of Submission06-Jul-2022
Date of Acceptance13-Oct-2022
Date of Web Publication19-Dec-2022

Correspondence Address:
Dr. N Birand
Department of Clinical Pharmacy, Near East University, Faculty of Pharmacy, Nicosia, North Cyprus, Mersin 10
Turkey
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/njcp.njcp_448_22

Rights and Permissions
   Abstract 


Background: Drug interactions are common drug-related problems that can lead to adverse drug reactions and hospitalization. Aims: The objectives of the study were to determine the potential drug–drug interactions (pDDIs) in Northern Cyprus community pharmacies and to compare three electronic databases regarding the frequency, mechanism, and severity of drug–drug interactions. Material and Methods: A retrospective observational study was conducted between July 1 and September 30, 2021, in Northern Cyprus community pharmacies using the Drugs.com, Lexicomp, and Medscape databases. The Mann–Whitney U-test was used to determine the difference between the values of the databases. Pearson's correlation was used to determine the association between DDIs and polypharmacy. Results: A total of 558 (52.1%) of 1072 prescriptions were included in the study. Drugs.com, Lexicomp, and Medscape databases detected 185, 176, and 213 potential drug–drug interactions in patients' prescriptions, respectively. There was a statistically significant difference in moderate drug interactions between the Medscape and Lexicomp databases (p = 0.02). Pearson's correlation showed a weak association (Medscape: r = 0.296, Lexicomp: r = 0.341, Drugs.com: r = 0.289, P = 0.0001) between pDDIs and polypharmacy. The assessment of agreement on severity of pDDIs characterized by Drugs.com and Lexicomp databases using the Kappa index was moderate agreement (0.509, P = 0.0001), while Drugs.com and Medscape databases using the Kappa index were moderate agreement (0.442, P = 0.0001), and Lexicomp and Medscape databases using the Kappa index were fair agreement (0.365, P = 0.0001). Conclusions: This study showed that Medscape detected more potential DDIs than Drugs.com and Lexicomp. Therefore, we propose that more than one database should be used to evaluate and identify pDDIs in pharmacy.

Keywords: Community pharmacy, drug–drug interactions, Northern Cyprus, pharmacists


How to cite this article:
Alkhalid Z N, Birand N. Determination and comparison of potential drug–drug interactions using three different databases in northern cyprus community pharmacies. Niger J Clin Pract 2022;25:2005-9

How to cite this URL:
Alkhalid Z N, Birand N. Determination and comparison of potential drug–drug interactions using three different databases in northern cyprus community pharmacies. Niger J Clin Pract [serial online] 2022 [cited 2023 Jan 28];25:2005-9. Available from: https://www.njcponline.com/text.asp?2022/25/12/2005/364197




   Introduction Top


Drug interactions are a common problem that occur when two or more medications are taken simultaneously during drug therapy and can lead to clinically significant, sometimes serious, events.[1],[2] Physicians and pharmacists have the knowledge and ability to assess drug–drug interactions because of their training. Therefore, they can reduce the potential side effects and switch medications by monitoring drug–drug interactions for patients. Electronic databases are widely used in hospitals and community pharmacies for DDI assessment.[3] In a study by Sancar M et al., it was found that 39.2% of 1000 prescriptions in public pharmacies in Istanbul, Turkey, had at least one potential DDI.[4] In previous studies conducted in Northern Cyprus, Laban et al. reviewed cancer while the Drugs.com database detected 394 DDIs and the Lexicomp database detected only 313 DDIs.[5] A study by Gökçekuş et al. showed that inappropriate combinations of drugs or foods were the second most common drug-related problem. However, the authors did not classify the drug interactions according to the mechanism or severity of interactions.[6] There is no previous study on the classification of drug–drug interactions in community pharmacies in Northern Cyprus. Therefore, this study was conducted to identify potential drug–drug interactions in Northern Cyprus community pharmacies, comparing three electronic databases in terms of frequency, mechanism, and severity of drug–drug interactions.


   Material and Methods Top


Study design and setting

A retrospective observational study was conducted in Northern Cyprus community pharmacies between July 1, 2021 and September 30, 2021 using Drugs.com, Lexicomp, and Medscape databases.

There are six districts in Northern Cyprus. These districts are Nicosia, Famagusta, Girne, Güzelyurt, İskele, and Lefke. The current list of all community pharmacies in the six districts of Northern Cyprus was downloaded from the Northern Cyprus Pharmacy Association website. Then, one community pharmacy from each district was randomly selected and contacted by a pharmacist. Pharmacies that agreed to participate in the study were asked to present three-month old prescriptions. Data collection was conducted for one month. The most frequently used databases (Lexicomp, Medscape, and Drugs.com) in Northern Cyprus were used to check the patients' prescriptions for drug–drug interactions. All patients who were prescribed two or more medications were included in the study. Drug–herbal or food interactions were excluded from this study.

Data collection and analysis

Data were reviewed using the Lexi-interact tool from Lexicomp (Copyright 2018, Wolters Kluwer Clinical Drug Information, Inc), Medscape, and the Drugs.com databases.[7],[8],[9] Mechanisms of DDIs were categorized as pharmacodynamic, pharmacokinetic, and unknown. According to Lexicomp, interactions are classified into five categories: A, B, C, D, and X. According to Medscape, interactions are divided into four categories: Minor, Monitor, Serious-Use Alternative, and Contraindicated. According to Drugs.com, interactions are classified into four: Minor, Moderate, Major, and Unknown.

Ethical consideration

The study was approved by the Near East Institutional Review Board (IRB) of Near East University with approval number YDU/2021/96-1421.

Statically analysis

The study data were analyzed using Microsoft Excel 2016 and Statistical Package for the Social Sciences (SPSS), software version 21.0. According to the frequency of the databases, the data on mechanism and severity of drug–drug interactions were expressed in frequency and percentage. The Mann–Whitney U-test was used to determine the difference between the values of the databases. Pearson's correlation was used to determine the association between DDIs and polypharmacy. The kappa index was used to evaluate the agreement between the DDIs identified by the three drug interaction assessors. The kappa value ranges from 0 to 1. A value less than 0.00 is interpreted as no agreement, 0.01–0.20 as slight agreement, 0.21-0.40 as fair agreement, 0.41–0.60 as moderate agreement, 0.61–0.80 as substantial agreement, and 0.81–1.00 as almost perfect agreement. A P value of less than 0.05 (p < 0.05) was considered statistically significant.


   Results Top


A total of 558 (52.1%) of 1072 prescriptions were included in the study, whereas 514 (47.9%) prescriptions containing a drug or cosmetic product were excluded. This study showed that drug–drug interactions were detected in patients' prescriptions [Figure 1] and [Table 1]. Drugs.com has shown that 382 prescriptions (68.5%) had no interactions, while 176 prescriptions (31.5%) had drug interactions. Lexicomp has shown that 409 prescriptions (73.3%) had no interactions, while 149 prescriptions (26.7%) had drug interactions. Medscape has shown that 375 prescriptions (67.2%) had no interactions, while 183 prescriptions (32.8%) had drug interactions.
Figure 1: Study sample size

Click here to view
Table 1: Information of prescriptions according to six community pharmacies

Click here to view


Drugs.com showed that 382 prescriptions (68.5%) had no interactions, while 176 prescriptions (31.5%) had interactions. Lexicomp showed that 409 prescriptions (73.3%) had no interactions, while 149 prescriptions (26.7%) had interactions. Medscape showed that 375 prescriptions (67.2%) had no interactions, while 183 prescriptions (32.8%) had interactions.

According to Drugs.com, 58 (31.4%) pharmacokinetic interactions, 104 (56.2%) pharmacodynamic interactions, and 23 (12.4%) unknown mechanisms of drug–drug interactions were identified. There were 25 (13.5%) major, 103 (55.7%) moderate, and 57 (30.8%) minor drug–drug interactions identified [Table 2] and [Table 3]. In the Lexicomp database, 63 (35.8%) pharmacokinetic interactions, 111 (63.1%) pharmacodynamic interactions, and 2 (1.1%) unknown mechanisms of drug–drug interactions were detected. Based on the severity of interactions, there were 41 (23.3%) category B, 89 (50.6%) category C, 23 (13%) category D, and 23 (13%) category X interactions [Table 2] and [Table 3]. According to the Medscape database, 80 (37.6%) pharmacokinetic interactions, 120 (56.3%) pharmacodynamic interactions, and 13 (6.1%) unknown mechanisms of drug–drug interactions were identified. There were 0 (0%) contraindicated drug interactions, 16 (7.5%) Serious-Use Alternative, 136 (63.8%) Monitor Closely, and 61 (28.6%) Minor severity of drug interactions [Table 2] and [Table 3].
Table 2: Mechanism of drug-drug interactions according to all databases

Click here to view
Table 3: Severity of drug–drug interactions according to all databases

Click here to view


There were no statistically significant differences in pharmacokinetic interactions and pharmacodynamic interactions among all databases. For unknown interactions, there were statistically significant differences between the Drugs.com and Lexicomp databases and between Medscape and Lexicomp (p = 0.0001 and P = 0.01, respectively). For unknown interactions, there was no statistically significant difference between Medscape and Drugs.com (p = 0.107) [Table 4].
Table 4: Comparison of severity and mechanism of drug-drug interactions according to all databases

Click here to view


There was no statistically significant difference among all databases in the major drug interaction category. There were no statistically significant differences between the Drugs.com and Medscape databases and between Drugs.com and Lexicomp in moderate drug interactions. There was a statistically significant difference in moderate drug interactions between the Medscape and Lexicomp databases (p = 0.02). There was no statistically significant difference in minor drug interactions between all databases. There was no statistically significant difference in the number of DDI between all databases [Table 4]. Pearson's correlation showed a weak association (Medscape: r = 0.296, Lexicomp: r = 0.341, Drugs.com: r = 0.289, P = 0.0001) between potential DDIs and polypharmacy. The assessment of agreement on severity of pDDIs characterized by Drugs.com and Lexicomp databases using the Kappa index was moderate agreement (0.509, P = 0.0001), Drugs.com and Medscape databases using the Kappa index were moderate agreement (0.442, P = 0.0001), and Lexicomp and Medscape databases using the Kappa index were fair agreement (0.365, P = 0.0001).


   Discussion Top


This study examined the frequency, mechanism, and severity of DDIs in patients' prescriptions and compared the databases with three different drug interaction checkers. Community pharmacists can detect and prevent drug interactions by checking the prescriptions of every patient who comes to the community pharmacy. With the electronic databases available to health care professionals in the pharmacy, this task can be accomplished in a very short time.

A study conducted in the oncology department of a university Hospital showed that there were more pharmacodynamic drug interaction mechanisms compared to pharmacokinetic drug interactions as detected by Drugs.com, Medscape, and Lexicomp databases.[5] In our study, pharmacodynamic interaction was higher than pharmacokinetic interaction in all databases, so our results are consistent with previous studies based on drug interaction mechanism. Therefore, health professionals should consider the risk of this pharmacodynamic interaction when prescribing medications to patients.

A study conducted at a South Indian teaching hospital showed that 91% of 204 prescriptions contained 856 drug interactions and most of the drug interactions were moderate.[10] A study by Chatsisvili et al. showed that over 85% of DDI were moderate interactions.[11] In this study, the most common severity of drug interactions was moderate in all databases. Our results on the severity of drug interactions were similar to previous studies. Therefore, patients should be monitored regularly by pharmacists until the end of therapy, and they should inform the physician if they observe any drug-related problems.

Shetty et al. reported that about 3% of drug interactions belong to category X.[12] Lexicomp is the only database that acknowledged this category of severity of drug interactions. In the present study, 13% of category X interactions was identified. The frequency of category X interaction identified in this study was higher compared to previous studies. Due to the high frequency of category X, pharmacists in Northern Cyprus should regularly monitor drug interactions for each patient's medications.

As shown in the results of the present study, pharmacists should be able to prevent potential drug interactions before patients take their medications due to the high frequency of drug interactions. According to our results, the most important drug interactions were clopidogrel and omeprazole or more than one NSAID such as naproxen and diclofenac. Moderate interactions included pantoprazole and levothyroxine or ramipril and furosemide. Minor interactions included, for example, clarithromycin and omeprazole or paracetamol (acetaminophen) and metoclopramide.

Limitations of the study

Only six community pharmacies in the six districts in Northern Cyprus were included in this study. The number of prescriptions evaluated was not very high; hence, the findings cannot be generalized. We therefore recommend that more pharmacies be included in future studies.

This study showed that Medscape detected more potential DDIs than Drugs.com and Lexicomp. The Lexicomp database provided more detailed information on drug interactions although it detected fewer potential DDIs than the other databases in this study. We recommend that pharmacists use more than one database to screen for drug interactions when evaluating their patients' medications.

Acknowledgements

We would like to thank the pharmacists at the six pharmacies for their willingness to participate in this study.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
European Medicines Agency. Guideline on the Investigation of Drug Interactions. CPMP/EWP/560/95/Rev. 1 Corr. 2. 2012.  Back to cited text no. 1
    
2.
Leone R, Magro L, Moretti U, Cutroneo P, Moschini M, Motola D, et al. Identifying adverse drug reactions associated with drug-drug interactions. Drug Saf 2010;33:667-75.  Back to cited text no. 2
    
3.
Avery AJ, Rodgers S, Cantrill JA, Armstrong S, Cresswell K, Eden M, et al. A pharmacist-led information technology intervention for medication errors (PINCER): A multicentre, cluster randomised, controlled trial and cost-effectiveness analysis. Lancet 2012;379:1310-9.  Back to cited text no. 3
    
4.
Sancar M, Kaşik A, Okuyan B, Batuhan S, Izzettin FV. Determination of potential drug–drug interactions using various software programs in a community pharmacy setting. Turk J Pharm Sci 2019;16:14-9.  Back to cited text no. 4
    
5.
Laban A, Birand N, Chukwunyere U, Abdi A, Basgut B. Evaluation of drug-drug interactions in cancer patients treated at a university hospital in North Cyprus using two interaction databases. Niger J Clin Pract 2021;24:1067-71.  Back to cited text no. 5
[PUBMED]  [Full text]  
6.
Gökçekuş L, Mestrovic A, Basgut B. Pharmacist intervention in drug-related problems for patients with cardiovascular diseases in selected community pharmacies in Northern Cyprus. Trop J Pharm Res 2016;15:2275-81.  Back to cited text no. 6
    
7.
Wolters Kluwer. Lexicomp. Database. Available from: https://www.wolterskluwercdi.com/lexicomp-online/. [Last accessed on 2022 Jul 05].  Back to cited text no. 7
    
8.
Medscape. Drug interaction checker. Available from: https://reference.medscape.com/drug-interactionchecker. [Last accessed on 2022 Jul 05].  Back to cited text no. 8
    
9.
Drugs.com. Drug interaction checker. Available from: https://www.drugs.com/interaction/list/. [Last accessed on 2022 Jul 05].  Back to cited text no. 9
    
10.
Kulkarni V, Bora SS, Sirisha S, Saji M, Sundaran S. A study on drug–drug interactions through prescription analysis in a South Indian teaching hospital. Ther Adv Drug Saf 2013;4:141-6.  Back to cited text no. 10
    
11.
Chatsisvili A, Sapounidis I, Pavlidou G, Zoumpouridou E, Karakousis VA, Spanakis M, et al. Potential drug–drug interactions in prescriptions dispensed in community pharmacies in Greece. Pharm World Sci 2010;32:187-93.  Back to cited text no. 11
    
12.
Shetty V, Chowta MN, Chowta KN, Shenoy A, Kamath A, Kamath P. Evaluation of potential drug-drug interactions with medications prescribed to geriatric patients in a tertiary care hospital. J Aging Res 2018;2018:5728957.  Back to cited text no. 12
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

Top
  
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
    Abstract
   Introduction
   Material and Methods
   Results
   Discussion
    References
    Article Figures
    Article Tables

 Article Access Statistics
    Viewed260    
    Printed10    
    Emailed0    
    PDF Downloaded29    
    Comments [Add]    

Recommend this journal