The corticosteroid dexamethasone, which is used to treat numerous health conditions, remains the first-line treatment for patients hospitalized with COVID-19 requiring oxygen. Current British National Formulary prescribing advice warns of a "severe theoretical" or "severe anecdotal" risk of drug-drug interactions between dexamethasone and 138 different medications. In humans, dexamethasone is eliminated via the cytochrome P450 monooxygenase system, particularly CYP3A4. It is also described as a human cytochrome P450-inducing agent. To establish factors that affect concomitant therapy and dexamethasone efficacy in the treatment of COVID-19, we used a unique mouse model humanized for cytochrome P450s and the transcription factors that regulate their expression, the pregnane X receptor, and the constitutive androstane receptor. We found that induction of CYP3A4 with the anticancer drug dabrafenib or the herbal medicine St John's wort profoundly reduced dexamethasone exposure. These data suggest that comedications that induce cytochrome P450 expression can have a marked effect on dexamethasone exposure and, potentially, clinical efficacy. We also observed that rather than increasing CYP3A4 expression, dexamethasone at doses equivalent to or higher than those used in the treatment of COVID-19 reduced CYP3A4 expression and increased exposure to dabrafenib. These data indicate the need for a clinical trial to establish the risk of overexposure to comedications during dexamethasone treatment, including the treatment of COVID-19. SIGNIFICANCE STATEMENT: Current prescribing advice identifies a potential theoretical risk of severe side effects when dexamethasone, one of the most widely used drugs in clinical practice, is coadministered with many other drugs; it is, however, difficult to define the magnitude of this risk for specific drug combinations. We describe the use of cytochrome P450-humanized 8HUM mice to predict drug-drug interactions in patients on polypharmacy, a means of generating data that could better inform clinicians regarding foreseeable drug-drug interactions involving dexamethasone.
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http://dx.doi.org/10.1016/j.jpet.2024.100053 | DOI Listing |
Cureus
February 2025
Pharmacy, Mie University Hospital, Tsu, JPN.
Background The increasing prevalence of polypharmacy has raised concerns about drug-drug interactions (DDIs) and their impact on patient safety. Database-based DDI detection often suffers from insufficient patient background information and missing data, limiting the accuracy and applicability of DDI assessments. A novel model is needed to overcome these limitations and provide a more comprehensive evaluation of DDIs to enhance patient safety in the context of multiple medication use.
View Article and Find Full Text PDFBiopharm Drug Dispos
March 2025
Department of Information Technology, Panimalar Engineering College, Chennai, India.
Drug-drug interactions (DDIs) are an important concern in the clinical practice and drug development process as these may lead to serious adverse effects on patient safety. Thorough DDI prediction is important for effective medication management and reduced risk factors. This work presents a new technique, namely MV2SAPCNNO: MobileNetV2 with simplicial attention network-based parallel convolutional neural network and narwhal optimiser, for improving the precision of DDI prediction.
View Article and Find Full Text PDFJ Chromatogr B Analyt Technol Biomed Life Sci
February 2025
Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
Selpercatinib (RETEVMO®) is a selective anticancer agent recently approved for thyroid and non-small cell lung cancer. Reliable analytical methods are essential for investigating its potential drug interactions. In this study, the fluorescence properties of selpercatinib were exploited for the first time to develop a sensitive high-performance liquid chromatography with fluorescence detection (HPLC-FLD) method to quantify selpercatinib in human and rat liver microsomes and rat plasma.
View Article and Find Full Text PDFEur J Med Chem
March 2025
Department of Biomedical and Chemical Engineering, Liaoning Institute of Science and Technolgy, Benxi, 117004, China. Electronic address:
Polo like kinase 1 (PLK1) is a serine/threonine kinase that plays an important role in multiple phases of the cell cycle, inhibiting its activity has been considered an effective treatment for acute myeloid leukemia (AML). Here, we reported a series of highly potent PLK1 inhibitors. Among them, compound WD6 was identified as the most promising PLK1 inhibitor, with an IC value of 0.
View Article and Find Full Text PDFArtificial intelligence techniques play a pivotal role in the accurate identification of drug-drug interaction (DDI) events, thereby informing clinical decisions and treatment regimens. While existing DDI prediction models have made significant progress by leveraging sequence features such as chemical substructures, targets, and enzymes, they often face limitations in integrating and effectively utilizing multi-modal drug representations. To address these limitations, this study proposes a novel multi-modal feature fusion model for DDI event prediction: MMDDI-SSE.
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