抗菌剂
医学
传统医学
抗感染药
生物技术
兽医学
抗生素
抗生素耐药性
动物健康
兽药
抗菌药物
食品污染物
生物
兽医公共卫生
作者
Tolis Panayi,Andria Kotsoni,Rotem Badash,Annita Achilleos,Konstantinos Voskarides,Lefteris Zacharia,Manos C. Vlasiou
出处
期刊:
[Elsevier BV]
日期:2026-03-01
卷期号:1 (1): 100002-100002
标识
DOI:10.1016/j.tvjpt.2026.100002
摘要
Foodborne pathogens like Escherichia coli and Staphylococcus spp. continue to be major contributors to antimicrobial resistance (AMR) in livestock systems and pose significant therapeutic challenges in veterinary medicine. Therefore, the search for safe, naturally derived antimicrobial candidates is a critical priority within a One Health framework. This study presents early pharmacological evaluations of two zinc–polyphenol complexes: zinc–tocopherol and zinc–resveratrol, and characterised using spectroscopic, computational, antimicrobial, and toxicological methods. Complex formation was confirmed through FT-IR and elemental analysis. Molecular docking and molecular dynamics simulations revealed favourable, stable interactions with key bacterial targets (FabI, GyrB, and FtsZ), supporting their predicted inhibitory potential. Both complexes demonstrated antimicrobial activity against E. coli and Staphylococcus epidermidis , with zinc–resveratrol exhibiting greater potency. Fluorescence spectroscopy indicated strong interactions with bovine serum albumin, suggesting potential for systemic bioavailability. Toxicity screening in PC-12 cells and zebrafish larvae revealed concentration-dependent effects, with zinc–resveratrol showing higher toxicity at elevated doses, yet both complexes showed acceptable safety margins at lower concentrations. Overall, these findings highlight zinc–polyphenol complexes as promising natural antimicrobial candidates with potential relevance for veterinary pharmacology. Their multimodal mechanisms, moderate bioactivity, and early safety profiles merit further investigation, including pharmacokinetic assessment and testing against veterinary pathogens of clinical importance.
科研通智能强力驱动
Strongly Powered by AbleSci AI