Background And Aims: Multiple sclerosis (MS) is the most common neuroimmunological disease of the central nervous system in young adults. Despite recommended contraception, unplanned pregnancies can occur in women of childbearing age with MS. MS- and comorbidities-related multimedication in these patients represents a potential risk. We aimed to raise awareness regarding the frequency of polypharmacy and drug-drug interactions (DDIs) in female MS patients of childbearing age.
Methods: Sociodemographic, clinical and pharmaceutical data were collected through patient records, clinical investigations and structured patient interviews of 131 women with MS. The clinical decision support software MediQ was used to identify potential DDIs. A medication and DDI profile of the study population was created by statistical analysis of the recorded data.
Results: Of the 131 female MS patients, 41.2% were affected by polypharmacy (concurrent use of ⩾5 drugs). Polypharmacy was associated with higher age, higher degree of disability, chronic progressive MS disease course and comorbidities. With an average intake of 4.2 drugs per patient, a total of 1033 potential DDIs were identified. Clinically relevant DDIs were significantly more frequent in patients with polypharmacy than in patients without polypharmacy (31.5% 5.2%; Fisher's exact test: < 0.001).
Conclusion: For the first time, a comprehensive range of potential DDIs in women of childbearing age with MS is presented. Polypharmacy is associated with the occurrence of clinically relevant DDIs. This shows the need for effective and regular screening for such interactions in order to prevent avoidable adverse effects.
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http://dx.doi.org/10.1177/1756286420969501 | DOI Listing |
Interdiscip Sci
January 2025
School of Computer Science, Qufu Normal University, Rizhao, 276826, China.
Combination therapy, which synergistically enhances treatment efficacy and inhibits disease progression through the combined effects of multiple drugs, has emerged as a mainstream approach for treating complex diseases and alleviating symptoms. However, drug-drug interactions (DDIs) can sometimes lead to adverse reactions, potentially endangering lives. Therefore, developing efficient and accurate DDI prediction methods is crucial for elucidating drug mechanisms and preventing side effects.
View Article and Find Full Text PDFClin 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.
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