Identification of reliable markers to predict drug-related adverse events (DRAEs) is an important goal of the pharmaceutical industry and others within the healthcare community. We have used genetic polymorphisms, including the most frequent source of variation (single nucleotide polymorphisms, SNPs) in the human genome, in pharmacogenetic approaches designed to predict DRAEs. Three studies exemplify the principles of using polymorphisms to identify associations in progressively larger genomic regions: polymorphic repeats within the UDP-glucuronysltransferase I (UGT1A1) gene in patients experiencing hyperbilirubinemia after administration of tranilast, an experimental drug to prevent re-stenosis following coronary revascularization; high linkage disequilibrium within the Apolipoprotein E (ApoE) gene in patients with Alzheimer Disease (AD); and the polymorphic variant HLA-B57 in patients with hypersensitivity reaction after administration of abacavir, a nucleoside reverse transcriptase inhibitor for the treatment of HIV. Together, these studies demonstrate in a stepwise manner the feasibility of using pharmacogenetic approaches to predict DRAEs.
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http://dx.doi.org/10.1080/01926230490424743 | DOI Listing |
Reg Anesth Pain Med
January 2025
Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
Background/importance: Opioids continue to play a key role in managing acute postoperative pain, but their use contributes to adverse outcomes. Buprenorphine may offer effective analgesia with a superior safety profile.
Objective: To compare the efficacy and safety of buprenorphine with other opioids for acute postoperative pain management in adults.
JMIR Form Res
December 2024
School of Media and Journalism, Kent State University, Kent, OH, United States.
Background: The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics.
View Article and Find Full Text PDFJ Manag Care Spec Pharm
January 2025
Department of Internal Medicine, UT Health McGovern Medical School, Houston, TX.
The majority of a health plan's performance and designated Star Rating is related to medication-related behavior, eg, medication adherence, medication review, and reconciliation, that are intricately related to adverse drug events (ADEs). Altered pharmacodynamics and pharmacokinetics owing to aging make older adults more vulnerable to ADEs like falls, fractures, hospitalizations, and mortality. Prevention of avoidable risk factors such as medication burden can help maintain quality of life.
View Article and Find Full Text PDFZhonghua Yan Ke Za Zhi
January 2025
Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology & Visual Sciences, Beijing100730, China.
Immune checkpoint inhibitors (ICI), as a new type of targeted therapeutic drugs, have demonstrated durable efficacy in cancer treatment. However, some patients receiving ICI treatment may be affected by immune-related adverse events (irAE). Compared with irAE in skin tissues, gastrointestinal system, etc.
View Article and Find Full Text PDFSci 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.
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