Purpose: This study aims to evaluate the associations between switching from warfarin to non-vitamin K oral anticoagulants (NOACs), exposure to potential drug-drug interactions (DDIs), and major bleeding events in working-age adults with atrial fibrillation (AF).
Methods: We conducted a retrospective cohort study using the claims database of commercially insured working-age adults with AF from 2010 to 2015. Switchers were defined as patients who switched from warfarin to NOAC; non-switchers were defined as those who remained on warfarin. We developed novel methods to calculate the number and proportion of days with potential DDIs with NOAC/warfarin. Multivariate logistic regressions were utilized to evaluate the associations between switching to NOACs, exposure to potential DDIs, and major bleeding events.
Results: Among a total of 4126 patients with AF, we found a significantly lower number of potential DDIs and the average proportion of days with potential DDIs in switchers than non-switchers. The number of potential DDIs (AOR 1.14, 95% CI 1.02-1.27) and the HAS-BLED score (AOR 1.64, 95% CI 1.48-1.82) were significantly and positively associated with the likelihood of a major bleeding event. The proportion of days with potential DDIs was also significantly and positively associated with risk for bleeding (AOR 1.42, 95% CI 1.03, 1.96). We did not find significant associations between switching to NOACs and major bleeding events.
Conclusions: The number and duration of potential DDIs and patients' comorbidity burden are important factors to consider in the management of bleeding risk in working-age AF adults who take oral anticoagulants.
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http://dx.doi.org/10.1007/s10557-018-6825-7 | DOI Listing |
J Glob Antimicrob Resist
December 2024
Research Center of Clinical Pharmacy, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. Electronic address:
Background: Nirmatrelvir-ritonavir is effective in the treatment of SARS-CoV-2 infection. It can cause drug‒drug interactions (DDIs), even several days after withdrawal, due to irreversible inhibition of the cytochrome enzyme.
Methods: Hospitalized patients diagnosed with COVID-19 infection and treated with nirmatrelvir-ritonavir were retrospectively included according to preset criteria.
Sci Rep
December 2024
School of Public Health, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China.
The study aims to address the critical issue of toxic side effects resulting from drug combinations, which can significantly increase health risks, clinical complications, and lead to drug being withdrawn from the market. A model named TSEDDI (toxic side effects of drug-drug interaction) has been developed to improve the identification of drug pairs that may induce toxicity or adverse reactions. By utilizing drug chemical structures and diverse proteins, we employ a convolutional neural network (CNN) to extract features from molecular images, enzyme proteins, transporter proteins, and target proteins.
View Article and Find Full Text PDFDrugs R D
December 2024
Galapagos SASU, Romainville, France.
Background And Objective: This study provides a physiologically based pharmacokinetic (PBPK) model-based analysis of the potential drug-drug interaction (DDI) between cyclosporin A (CsA), a breast cancer resistance protein transporter (BCRP) inhibitor, and methotrexate (MTX), a putative BCRP substrate.
Methods: PBPK models for CsA and MTX were built using open-source tools and published data for both model building and for model verification and validation. The MTX and CsA PBPK models were evaluated for their application in simulating BCRP-related DDIs.
CPT Pharmacometrics Syst Pharmacol
December 2024
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA.
Ritonavir (RTV) is a potent CYP3A inhibitor that is widely used as a pharmacokinetic (PK) enhancer to increase exposure to select protease inhibitors. However, as a strong and complex perpetrator of CYP3A interactions, RTV can also enhance the exposure of other co-administered CYP3A substrates, potentially causing toxicity. Therefore, the prediction of drug-drug interactions (DDIs) and estimation of dosing requirements for concomitantly administered drugs is imperative.
View Article and Find Full Text PDFIdentifying potential drug-drug interactions (DDIs) before clinical use is essential for patient safety yet remains a significant challenge in drug development. We presented DDI-GPT, a deep learning framework that predicts DDIs by combining knowledge graphs (KGs) and pre-trained large language models (LLMs), enabling early detection of potential drug interactions. We demonstrated that DDI-GPT outperforms current state-of-the-art methods by capturing contextual dependencies between biomedical entities to infer potential DDIs.
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