Background: The prevalence of potential drug-drug interactions (pDDIs) is indicative of the prevalence of actual drug-drug interactions and prescription quality. However, they are significantly understudied in Greece.
Objective: The objective of the study was to determine the prevalence of pDDIs among outpatients and identify factors associated with their occurrence.
Methods: Anonymous e-prescription data between 2012 and 2017 were obtained from community pharmacies in Thessaloniki, Greece. Patients taking more than one medication for at least three months were included. pDDIs were identified and categorized depending on their clinical significance using Drug Interactions Checker. Crude and adjusted odds ratios (ORs) with accompanying 95% confidence intervals (CIs) of risk factors of pDDIs occurrence were identified using multivariable logistic regression.
Results: During the study period, 6,000 anonymous e-prescriptions (1,000 per year) satisfying the inclusion criteria were collected. The overall prevalence of major pDDIs was 17.4% (63.0% for moderate pDDIs). The most common major pDDIs were between amlodipine and simvastatin (22.8% of major interactions), followed by clopidogrel and omeprazole (6.4% of major interactions). Polypharmacy (≥5 concomitantly received medications) was associated with an increased risk of major pDDIs (adjusted OR, 5.72; 95% CI, 4.87-6.72); no associations were observed regarding age, sex, and number of prescribing physicians.
Conclusion: The prevalence of pDDIs in this study was higher than previously reported in other European countries, with polypharmacy being a potential risk factor. Those results argue for a need for improvement in the area of prescribing in Greece.
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http://dx.doi.org/10.2174/1574886316666210816115811 | DOI Listing |
Clin Transl Sci
February 2025
Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, Florida, USA.
Tramadol, the 41st most prescribed drug in the United States in 2021 is a prodrug activated by CYP2D6, which is highly polymorphic. Previous studies showed enzyme-inhibitor affinity varied between different CYP2D6 allelic variants with dextromethorphan and atomoxetine metabolism. However, no study has compared tramadol metabolism in different CYP2D6 alleles with different CYP2D6 inhibitors.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Industrial and Molecular Pharmaceutics, Purdue University, West Lafayette, Indiana 47907, United States.
Drug-drug interaction can lead to diminished therapeutic effects or increased toxicity, posing significant risks, especially in polypharmacy, and cytochrome P450 plays an indispensable role in this interaction. Cytochrome P450, responsible for the metabolism and detoxification of most drugs, metabolizes about 90% of Food and Drug Administration-approved drugs, making early detection of potential drug-drug interactions. Over the years, in-silico modeling has become a valuable tool for predicting drug-drug interactions.
View Article and Find Full Text PDFJ Am Geriatr Soc
January 2025
School of Pharmacy, University of Washington, Seattle, Washington, USA.
Improving the quality of medication use and medication safety are important priorities for healthcare providers who care for older adults. The objective of this article was to identify four exemplary articles with this focus in 2023. We selected high-quality studies that advanced this field of research.
View Article and Find Full Text PDFComput Biol Med
January 2025
College of Electronic Information, Xijing University, Xi'an, China. Electronic address:
Accurate and efficient drug-drug interaction extraction (DDIE) from the medical corpus is essential for pharmacovigilance, drug therapy and drug development. To solve the problems of unbalance dataset and lack of accurate manual annotations in DDIE, a cross-attention guided Siamese quantum BiGRU (CA-SQBG) is constructed to improve feature representation learning ability for DDIE. It mainly consists of two quantum BiGRUs (QBiGRUs) and a cross-attention, where two QBiGRUs are Siamese implemented in a variational quantum environment to learn the contextual semantic feature representation of drug pairs, cross-attention is employed to learn mutual information from the Siamese QBiGRUs, which in turn allows the two modules to extract DDI more collaboratively.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
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