Drug-drug interactions with aprepitant in antiemetic prophylaxis for chemotherapy.

Neth J Med

Department of Medical Oncology, Radboudumc, Nijmegen, the Netherlands.

Published: April 2018

In the current guidelines to prevent hemotherapyinduced nausea and vomiting, multiple antiemetic drugs are administered simultaneously. In patients who receive highly emetogenic chemotherapy, aprepitant, an NK1-receptor antagonist, is combined with ondansetron and dexamethasone. Aprepitant can influence the pharmacokinetics of other drugs, as it is an inhibitor and inducer of CYP3A4. Some anticancer drugs and other co-medication frequently used in cancer patients are CYP3A4 or CYP29C substrates. We give an overview of the metabolism and current data on clinically relevant drug-drug interactions with aprepitant during chemotherapy. Physicians should be aware of the potential risk of drug-drug interactions with aprepitant, especially in regimens with curative intent. More research should be performed on drug-drug interactions with aprepitant and their clinical consequences to make evidence-based recommendations.

Download full-text PDF

Source

Publication Analysis

Top Keywords

drug-drug interactions
16
interactions aprepitant
16
aprepitant
6
drug-drug
4
aprepitant antiemetic
4
antiemetic prophylaxis
4
prophylaxis chemotherapy
4
chemotherapy current
4
current guidelines
4
guidelines prevent
4

Similar Publications

 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 PDF

In vitro comparative analysis of metabolic capabilities and inhibitory profiles of selected CYP2D6 alleles on tramadol metabolism.

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 PDF

Deep Learning of CYP450 Binding of Small Molecules by Quantum Information.

J 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 PDF

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 PDF

CA-SQBG: Cross-attention guided Siamese quantum BiGRU for drug-drug interaction extraction.

Comput 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 PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!