Older people are increasingly susceptible to adverse drug reactions (ADRs) or therapeutic failure. This could be mediated by considerable polypharmacy, which increases the possibility of drug-drug and drug-gene interactions. Precision medicine, based on individual genetic variations, enables the screening of patients at risk for ADRs and the implementation of personalized treatment regimens. It combines genetic and genomic data with environmental and clinical factors in order to tailor prevention and disease-management strategies, including pharmacotherapy. The identification of genetic factors that influence drug absorption, distribution, metabolism, excretion, and action at the drug target level allows individualized therapy. Positive pharmacogenomic findings have been reported for the majority of cardiovascular drugs (CVD), suggesting that pre-emptive testing can improve efficacy and minimize the toxicity risk. Gene variants related to drug metabolism and transport variability or pharmacodynamics of major CVD have been translated into dosing recommendations. Pharmacogenetics consortia have issued guidelines for oral anticoagulants, antiplatelet agents, statins, and some beta-blockers. Since the majority of pharmacogenetics recommendations are based on the assessment of single drug-gene interactions, it is imperative to develop tools for the prediction of multiple drug-drug-gene interactions, which are common in the elderly with comorbidity. The availability of genomic testing has grown, but its clinical application is still insufficient.
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http://dx.doi.org/10.3325/cmj.2020.61.147 | DOI Listing |
Bioinformatics
January 2025
College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
Motivation: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph motifs of (disease, drug, gene) triplets. Among them, the triangle is a steady and important motif structure in the network, and other various motifs different from the triangle also indicate rich semantic relationships.
View Article and Find Full Text PDFJ Clin Exp Dent
December 2024
DDS. Titular Professor. Universidad de Antioquia U de A, Medellín, Colombia. Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Medellín, Colombia.
Background: The RTK-VEGF4 receptor family, which includes VEGFR-1, VEGFR-2, and VEGFR-3, plays a crucial role in tissue regeneration by promoting angiogenesis, the formation of new blood vessels, and recruiting stem cells and immune cells. Machine learning, particularly graph neural networks (GNNs), has shown high accuracy in predicting these interactions. This study aims to predict drug-gene interactions of the RTK-VEGF4 receptor family in periodontal regeneration using graph neural networks.
View Article and Find Full Text PDFNan Fang Yi Ke Da Xue Xue Bao
January 2025
Provincial School of Clinical Medicine, Fujian Medical University; Department of Respiratory and Critical Care Medicine, Fujian Provincial Hospital of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China.
Objectives: To identify the key genes and immunological pathways shared by type 2 diabetes mellitus (T2DM) and chronic obstructive pulmonary disease (COPD) and explore the potential therapeutic targets of T2DM complicated by COPD.
Methods: GEO database was used for analyzing the gene expression profiles in T2DM and COPD to identify the common differentially expressed genes (DEGs) in the two diseases. A protein-protein interaction network was constructed to identify the candidate hub genes, which were validated in datasets and disease sets to obtain the target genes.
Sci Rep
January 2025
Department of Dermatology, Suining Central Hospital, No. 127, Western Desheng Road, Suining, 629000, People's Republic of China.
Vitiligo is a complex autoimmune skin disorder characterized by depigmentation and immune dysregulation. To elucidate the role of ferroptosis-related genes (FRGs) in vitiligo, we conducted a comprehensive analysis of gene expression data from the GSE53146 and GSE65127 datasets obtained from the GEO database. We identified 31 differentially expressed FRGs (DE-FRGs), with 21 genes upregulated and 10 downregulated.
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