Acute pancreatitis (AP), induced by tetracycline, a widely used antibiotic, poses significant clinical and toxicological challenges, yet its molecular mechanisms remain unclear. This study aims to promote drug toxicology strategies for the effective investigation of the putative toxicity and potential molecular mechanisms of antibiotic drugs through the study of tetracycline in AP. Using the SwissTargetPrediction, SEA Search, Super-PRED, GeneCards, Drugbank, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD), we identified 259 potential targets associated with tetracycline exposure and AP. Further refinement via the STRING database and Cytoscape (version 3.10.1) software highlighted 22 core targets, including TP53, TNF, and AKT1. Functional enrichment via the Database for Annotation, Visualization, and Integrated Discovery (DAVID) identified pathways through Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, highlighting PI3K-Akt, MAPK, HIF-1, and AGE-RAGE as critical mediators in tetracycline-induced AP. Molecular docking confirmed the strong binding between tetracycline and the core targets. Overall, these findings suggest that tetracycline may affect the occurrence and progression of pancreas-related inflammation by regulating pancreatic cell apoptosis and proliferation, activating inflammatory signaling pathways, and regulating lipid metabolic pathways. This study provides a theoretical basis for understanding the molecular mechanism of tetracycline-induced AP and lays the foundation for the prevention and treatment of digestive system diseases associated with excessive exposure to tetracycline antibiotics and certain tetracyclines. In addition, our network toxicology approach has accelerated the elucidation of toxic pathways in antibiotic drugs that lack specific characteristics.
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http://dx.doi.org/10.3390/toxics12120929 | DOI Listing |
J Mol Model
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Department of Biochemistry, Faculty of Basic Medical Science, Olabisi Onabanjo University, Sagamu Campus, Ago Iwoye, Ogun State, Nigeria.
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Laboratory of Toxicology, Department of Pharmacological and Biomolecular Science, University of Milan, Via Balzaretti 9, 20133 Milan, Italy.
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Key Lab of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China. Electronic address:
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Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, 222005, China. Electronic address:
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View Article and Find Full Text PDFJ Comput Chem
January 2025
College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China.
In the realm of artificial intelligence-driven drug discovery (AIDD), accurately predicting the influence of molecular structures on their properties is a critical research focus. While deep learning models based on graph neural networks (GNNs) have made significant advancements in this area, prior studies have primarily concentrated on molecule-level representations, often neglecting the impact of functional group structures and the potential relationships between fragments on molecular property predictions. To address this gap, we introduce the multi-scale feature attention graph neural network (MfGNN), which enhances traditional atom-based molecular graph representations by incorporating fragment-level representations derived from chemically synthesizable BRICS fragments.
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