药物重新定位
重新调整用途
药物数据库
药品
药物发现
以兹提米比
药理学
化学
流出
格罗尔
药物开发
对接(动物)
工作流程
计算生物学
抗菌活性
抗菌剂
生物制药
加替沙星
公共化学
抗生素
抗菌剂
作者
Dongdong Zhang,Feng-Biao Guo,Haotian Li
摘要
Background and Purpose Drug repurposing (DR) presents a compelling alternative to traditional drug discovery, offering lower risk and cost by applying approved drugs to new indications. Computational methods play a vital role in early‐stage drug repurposing and the development of robust computational workflows can accelerate antibiotic discovery. Experimental Approach We established a novel computational workflow comprising three key steps: target screening (based on CEG 2.0 database), drug screening (utilizing DrugBank database, antiBac‐Pred database, molecular docking and molecular dynamics [MD] simulation) and in vitro antibacterial experiments. Key Results Our workflow identified numerous commercially available drugs predicted to target the bacterial chaperone GroEL. Antibacterial assays revealed that both daprodustat and ezetimibe exhibited efficacy against Staphylococcus aureus and Escherichia coli Δ tolC . Notably, the efflux pump inhibitor PA β N enhanced the antibacterial efficacy of daprodustat against both S. aureus and E. coli , while potentiating the antibacterial potency of ezetimibe specifically against S. aureus . MD simulations confirmed stable binding of both drugs to S. aureus or E. coli GroEL, aligning with the antibacterial results. Conclusion and Implications This study validated our computational workflow for repurposing non‐antibacterial drugs as antibacterial agents, demonstrating that cost‐effective, computer‐aided drug repurposing is a feasible strategy for identifying new therapeutic approaches to diseases, such as cancer, diabetes and COVID‐19. Furthermore, the synergistic effect of daprodustat combined with an efflux pump inhibitor (e.g. PA β N) represents a promising therapeutic approach against both Gram‐positive and Gram‐negative bacterial pathogens.
科研通智能强力驱动
Strongly Powered by AbleSci AI